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https://paperswithcode.com/paper/neural-network-based-equations-for-predicting
|
1806.01052
| null | null |
Neural Network-Based Equations for Predicting PGA and PGV in Texas, Oklahoma, and Kansas
|
Parts of Texas, Oklahoma, and Kansas have experienced increased rates of
seismicity in recent years, providing new datasets of earthquake recordings to
develop ground motion prediction models for this particular region of the
Central and Eastern North America (CENA). This paper outlines a framework for
using Artificial Neural Networks (ANNs) to develop attenuation models from the
ground motion recordings in this region. While attenuation models exist for the
CENA, concerns over the increased rate of seismicity in this region necessitate
investigation of ground motions prediction models particular to these states.
To do so, an ANN-based framework is proposed to predict peak ground
acceleration (PGA) and peak ground velocity (PGV) given magnitude, earthquake
source-to-site distance, and shear wave velocity. In this framework,
approximately 4,500 ground motions with magnitude greater than 3.0 recorded in
these three states (Texas, Oklahoma, and Kansas) since 2005 are considered.
Results from this study suggest that existing ground motion prediction models
developed for CENA do not accurately predict the ground motion intensity
measures for earthquakes in this region, especially for those with low
source-to-site distances or on very soft soil conditions. The proposed ANN
models provide much more accurate prediction of the ground motion intensity
measures at all distances and magnitudes. The proposed ANN models are also
converted to relatively simple mathematical equations so that engineers can
easily use them to predict the ground motion intensity measures for future
events. Finally, through a sensitivity analysis, the contributions of the
predictive parameters to the prediction of the considered intensity measures
are investigated.
| null |
http://arxiv.org/abs/1806.01052v1
|
http://arxiv.org/pdf/1806.01052v1.pdf
| null |
[
"Farid Khosravikia",
"Yasaman Zeinali",
"Zoltan Nagy",
"Patricia Clayton",
"Ellen M. Rathje"
] |
[
"motion prediction",
"Prediction"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/deep-face-recognition-a-survey
|
1804.06655
| null | null |
Deep Face Recognition: A Survey
|
Deep learning applies multiple processing layers to learn representations of data with multiple levels of feature extraction. This emerging technique has reshaped the research landscape of face recognition (FR) since 2014, launched by the breakthroughs of DeepFace and DeepID. Since then, deep learning technique, characterized by the hierarchical architecture to stitch together pixels into invariant face representation, has dramatically improved the state-of-the-art performance and fostered successful real-world applications. In this survey, we provide a comprehensive review of the recent developments on deep FR, covering broad topics on algorithm designs, databases, protocols, and application scenes. First, we summarize different network architectures and loss functions proposed in the rapid evolution of the deep FR methods. Second, the related face processing methods are categorized into two classes: "one-to-many augmentation" and "many-to-one normalization". Then, we summarize and compare the commonly used databases for both model training and evaluation. Third, we review miscellaneous scenes in deep FR, such as cross-factor, heterogenous, multiple-media and industrial scenes. Finally, the technical challenges and several promising directions are highlighted.
|
Deep learning applies multiple processing layers to learn representations of data with multiple levels of feature extraction.
|
https://arxiv.org/abs/1804.06655v9
|
https://arxiv.org/pdf/1804.06655v9.pdf
| null |
[
"Mei Wang",
"Weihong Deng"
] |
[
"Face Recognition",
"Face Verification",
"Miscellaneous",
"Survey"
] | 2018-04-18T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/topic-modelling-of-empirical-text-corpora
|
1806.01045
| null | null |
Topic Modelling of Empirical Text Corpora: Validity, Reliability, and Reproducibility in Comparison to Semantic Maps
|
Using the 6,638 case descriptions of societal impact submitted for evaluation
in the Research Excellence Framework (REF 2014), we replicate the topic model
(Latent Dirichlet Allocation or LDA) made in this context and compare the
results with factor-analytic results using a traditional word-document matrix
(Principal Component Analysis or PCA). Removing a small fraction of documents
from the sample, for example, has on average a much larger impact on LDA than
on PCA-based models to the extent that the largest distortion in the case of
PCA has less effect than the smallest distortion of LDA-based models. In terms
of semantic coherence, however, LDA models outperform PCA-based models. The
topic models inform us about the statistical properties of the document sets
under study, but the results are statistical and should not be used for a
semantic interpretation - for example, in grant selections and micro-decision
making, or scholarly work-without follow-up using domain-specific semantic
maps.
| null |
http://arxiv.org/abs/1806.01045v1
|
http://arxiv.org/pdf/1806.01045v1.pdf
| null |
[
"Tobias Hecking",
"Loet Leydesdorff"
] |
[
"Decision Making",
"Topic Models"
] | 2018-06-04T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Linear discriminant analysis** (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics, pattern recognition, and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later classification.\r\n\r\nExtracted from [Wikipedia](https://en.wikipedia.org/wiki/Linear_discriminant_analysis)\r\n\r\n**Source**:\r\n\r\nPaper: [Linear Discriminant Analysis: A Detailed Tutorial](https://dx.doi.org/10.3233/AIC-170729)\r\n\r\nPublic version: [Linear Discriminant Analysis: A Detailed Tutorial](https://usir.salford.ac.uk/id/eprint/52074/)",
"full_name": "Linear Discriminant Analysis",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Dimensionality Reduction** methods transform data from a high-dimensional space into a low-dimensional space so that the low-dimensional space retains the most important properties of the original data. Below you can find a continuously updating list of dimensionality reduction methods.",
"name": "Dimensionality Reduction",
"parent": null
},
"name": "LDA",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/a-desirability-based-axiomatisation-for
|
1806.01044
| null | null |
A Desirability-Based Axiomatisation for Coherent Choice Functions
|
Choice functions constitute a simple, direct and very general mathematical
framework for modelling choice under uncertainty. In particular, they are able
to represent the set-valued choices that typically arise from applying decision
rules to imprecise-probabilistic uncertainty models. We provide them with a
clear interpretation in terms of attitudes towards gambling, borrowing ideas
from the theory of sets of desirable gambles, and we use this interpretation to
derive a set of basic axioms. We show that these axioms lead to a full-fledged
theory of coherent choice functions, which includes a representation in terms
of sets of desirable gambles, and a conservative inference method.
| null |
http://arxiv.org/abs/1806.01044v1
|
http://arxiv.org/pdf/1806.01044v1.pdf
| null |
[
"Jasper De Bock",
"Gert de Cooman"
] |
[] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-25d-cascaded-convolutional-neural-network
|
1806.01018
| null | null |
A 2.5D Cascaded Convolutional Neural Network with Temporal Information for Automatic Mitotic Cell Detection in 4D Microscopic Images
|
In recent years, intravital skin imaging has been increasingly used in
mammalian skin research to investigate cell behaviors. A fundamental step of
the investigation is mitotic cell (cell division) detection. Because of the
complex backgrounds (normal cells), the majority of the existing methods cause
several false positives. In this paper, we proposed a 2.5D cascaded end-to-end
convolutional neural network (CasDetNet) with temporal information to
accurately detect automatic mitotic cell in 4D microscopic images with few
training data. The CasDetNet consists of two 2.5D networks. The first one is
used for detecting candidate cells with only volume information and the second
one, containing temporal information, for reducing false positive and adding
mitotic cells that were missed in the first step. The experimental results show
that our CasDetNet can achieve higher precision and recall compared to other
state-of-the-art methods.
| null |
http://arxiv.org/abs/1806.01018v2
|
http://arxiv.org/pdf/1806.01018v2.pdf
| null |
[
"Titinunt Kitrungrotsakul",
"Xian-Hau Han",
"Yutaro Iwamoto",
"Satoko Takemoto",
"Hideo Yokota",
"Sari Ipponjima",
"Tomomi Nemoto",
"Xiong Wei",
"Yen-Wei Chen"
] |
[
"Cell Detection"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/synthetic-data-generation-for-end-to-end
|
1806.01013
| null | null |
Synthetic data generation for end-to-end thermal infrared tracking
|
The usage of both off-the-shelf and end-to-end trained deep networks have
significantly improved performance of visual tracking on RGB videos. However,
the lack of large labeled datasets hampers the usage of convolutional neural
networks for tracking in thermal infrared (TIR) images. Therefore, most state
of the art methods on tracking for TIR data are still based on handcrafted
features. To address this problem, we propose to use image-to-image translation
models. These models allow us to translate the abundantly available labeled RGB
data to synthetic TIR data. We explore both the usage of paired and unpaired
image translation models for this purpose. These methods provide us with a
large labeled dataset of synthetic TIR sequences, on which we can train
end-to-end optimal features for tracking. To the best of our knowledge we are
the first to train end-to-end features for TIR tracking. We perform extensive
experiments on VOT-TIR2017 dataset. We show that a network trained on a large
dataset of synthetic TIR data obtains better performance than one trained on
the available real TIR data. Combining both data sources leads to further
improvement. In addition, when we combine the network with motion features we
outperform the state of the art with a relative gain of over 10%, clearly
showing the efficiency of using synthetic data to train end-to-end TIR
trackers.
| null |
http://arxiv.org/abs/1806.01013v2
|
http://arxiv.org/pdf/1806.01013v2.pdf
| null |
[
"Lichao Zhang",
"Abel Gonzalez-Garcia",
"Joost Van de Weijer",
"Martin Danelljan",
"Fahad Shahbaz Khan"
] |
[
"Image-to-Image Translation",
"Synthetic Data Generation",
"Translation",
"Visual Tracking"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/exemplar-guided-unsupervised-image-to-image
|
1805.11145
| null |
S1lTg3RqYQ
|
Exemplar Guided Unsupervised Image-to-Image Translation with Semantic Consistency
|
Image-to-image translation has recently received significant attention due to
advances in deep learning. Most works focus on learning either a one-to-one
mapping in an unsupervised way or a many-to-many mapping in a supervised way.
However, a more practical setting is many-to-many mapping in an unsupervised
way, which is harder due to the lack of supervision and the complex inner- and
cross-domain variations. To alleviate these issues, we propose the Exemplar
Guided & Semantically Consistent Image-to-image Translation (EGSC-IT) network
which conditions the translation process on an exemplar image in the target
domain. We assume that an image comprises of a content component which is
shared across domains, and a style component specific to each domain. Under the
guidance of an exemplar from the target domain we apply Adaptive Instance
Normalization to the shared content component, which allows us to transfer the
style information of the target domain to the source domain. To avoid semantic
inconsistencies during translation that naturally appear due to the large
inner- and cross-domain variations, we introduce the concept of feature masks
that provide coarse semantic guidance without requiring the use of any semantic
labels. Experimental results on various datasets show that EGSC-IT does not
only translate the source image to diverse instances in the target domain, but
also preserves the semantic consistency during the process.
| null |
http://arxiv.org/abs/1805.11145v4
|
http://arxiv.org/pdf/1805.11145v4.pdf
|
ICLR 2019 5
|
[
"Liqian Ma",
"Xu Jia",
"Stamatios Georgoulis",
"Tinne Tuytelaars",
"Luc van Gool"
] |
[
"Image-to-Image Translation",
"Translation",
"Unsupervised Image-To-Image Translation"
] | 2018-05-28T00:00:00 |
https://openreview.net/forum?id=S1lTg3RqYQ
|
https://openreview.net/pdf?id=S1lTg3RqYQ
|
exemplar-guided-unsupervised-image-to-image-1
| null |
[] |
https://paperswithcode.com/paper/meta-learner-with-linear-nulling
|
1806.01010
| null | null |
Meta-Learner with Linear Nulling
|
We propose a meta-learning algorithm utilizing a linear transformer that
carries out null-space projection of neural network outputs. The main idea is
to construct an alternative classification space such that the error signals
during few-shot learning are quickly zero-forced on that space so that reliable
classification on low data is possible. The final decision on a query is
obtained utilizing a null-space-projected distance measure between the network
output and reference vectors, both of which have been trained in the initial
learning phase. Among the known methods with a given model size, our
meta-learner achieves the best or near-best image classification accuracies
with Omniglot and miniImageNet datasets.
| null |
http://arxiv.org/abs/1806.01010v3
|
http://arxiv.org/pdf/1806.01010v3.pdf
| null |
[
"Sung Whan Yoon",
"Jun Seo",
"Jaekyun Moon"
] |
[
"Classification",
"Few-Shot Learning",
"General Classification",
"image-classification",
"Image Classification",
"Meta-Learning"
] | 2018-06-04T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "A **Linear Layer** is a projection $\\mathbf{XW + b}$.",
"full_name": "Linear Layer",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Linear Layer",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "**Absolute Position Encodings** are a type of position embeddings for [[Transformer](https://paperswithcode.com/method/transformer)-based models] where positional encodings are added to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension $d\\_{model}$ as the embeddings, so that the two can be summed. In the original implementation, sine and cosine functions of different frequencies are used:\r\n\r\n$$ \\text{PE}\\left(pos, 2i\\right) = \\sin\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\n$$ \\text{PE}\\left(pos, 2i+1\\right) = \\cos\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\nwhere $pos$ is the position and $i$ is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from $2\\pi$ to $10000 \\dot 2\\pi$. This function was chosen because the authors hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset $k$, $\\text{PE}\\_{pos+k}$ can be represented as a linear function of $\\text{PE}\\_{pos}$.\r\n\r\nImage Source: [D2L.ai](https://d2l.ai/chapter_attention-mechanisms/self-attention-and-positional-encoding.html)",
"full_name": "Absolute Position Encodings",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Position Embeddings",
"parent": null
},
"name": "Absolute Position Encodings",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": null,
"description": "**Position-Wise Feed-Forward Layer** is a type of [feedforward layer](https://www.paperswithcode.com/method/category/feedforwad-networks) consisting of two [dense layers](https://www.paperswithcode.com/method/dense-connections) that applies to the last dimension, which means the same dense layers are used for each position item in the sequence, so called position-wise.",
"full_name": "Position-Wise Feed-Forward Layer",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Position-Wise Feed-Forward Layer",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/resnet.py#L118",
"description": "**Residual Connections** are a type of skip-connection that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. \r\n\r\nFormally, denoting the desired underlying mapping as $\\mathcal{H}({x})$, we let the stacked nonlinear layers fit another mapping of $\\mathcal{F}({x}):=\\mathcal{H}({x})-{x}$. The original mapping is recast into $\\mathcal{F}({x})+{x}$.\r\n\r\nThe intuition is that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers.",
"full_name": "Residual Connection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connections** allow layers to skip layers and connect to layers further up the network, allowing for information to flow more easily up the network. Below you can find a continuously updating list of skip connection methods.",
"name": "Skip Connections",
"parent": null
},
"name": "Residual Connection",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": null,
"description": "**Byte Pair Encoding**, or **BPE**, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations).\r\n\r\n[Lei Mao](https://leimao.github.io/blog/Byte-Pair-Encoding/) has a detailed blog post that explains how this works.",
"full_name": "Byte Pair Encoding",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Subword Segmentation",
"parent": null
},
"name": "BPE",
"source_title": "Neural Machine Translation of Rare Words with Subword Units",
"source_url": "http://arxiv.org/abs/1508.07909v5"
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "**Label Smoothing** is a regularization technique that introduces noise for the labels. This accounts for the fact that datasets may have mistakes in them, so maximizing the likelihood of $\\log{p}\\left(y\\mid{x}\\right)$ directly can be harmful. Assume for a small constant $\\epsilon$, the training set label $y$ is correct with probability $1-\\epsilon$ and incorrect otherwise. Label Smoothing regularizes a model based on a [softmax](https://paperswithcode.com/method/softmax) with $k$ output values by replacing the hard $0$ and $1$ classification targets with targets of $\\frac{\\epsilon}{k}$ and $1-\\frac{k-1}{k}\\epsilon$ respectively.\r\n\r\nSource: Deep Learning, Goodfellow et al\r\n\r\nImage Source: [When Does Label Smoothing Help?](https://arxiv.org/abs/1906.02629)",
"full_name": "Label Smoothing",
"introduced_year": 1985,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Label Smoothing",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!\r\n\r\n\r\nHow Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!",
"full_name": "*Communicated@Fast*How Do I Communicate to Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/b7bda236d18815052378c88081f64935427d7716/torch/optim/adam.py#L6",
"description": "**Adam** is an adaptive learning rate optimization algorithm that utilises both momentum and scaling, combining the benefits of [RMSProp](https://paperswithcode.com/method/rmsprop) and [SGD w/th Momentum](https://paperswithcode.com/method/sgd-with-momentum). The optimizer is designed to be appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. \r\n\r\nThe weight updates are performed as:\r\n\r\n$$ w_{t} = w_{t-1} - \\eta\\frac{\\hat{m}\\_{t}}{\\sqrt{\\hat{v}\\_{t}} + \\epsilon} $$\r\n\r\nwith\r\n\r\n$$ \\hat{m}\\_{t} = \\frac{m_{t}}{1-\\beta^{t}_{1}} $$\r\n\r\n$$ \\hat{v}\\_{t} = \\frac{v_{t}}{1-\\beta^{t}_{2}} $$\r\n\r\n$$ m_{t} = \\beta_{1}m_{t-1} + (1-\\beta_{1})g_{t} $$\r\n\r\n$$ v_{t} = \\beta_{2}v_{t-1} + (1-\\beta_{2})g_{t}^{2} $$\r\n\r\n\r\n$ \\eta $ is the step size/learning rate, around 1e-3 in the original paper. $ \\epsilon $ is a small number, typically 1e-8 or 1e-10, to prevent dividing by zero. $ \\beta_{1} $ and $ \\beta_{2} $ are forgetting parameters, with typical values 0.9 and 0.999, respectively.",
"full_name": "Adam",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Stochastic Optimization** methods are used to optimize neural networks. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. Below you can find a continuously updating list of stochastic optimization algorithms.",
"name": "Stochastic Optimization",
"parent": "Optimization"
},
"name": "Adam",
"source_title": "Adam: A Method for Stochastic Optimization",
"source_url": "http://arxiv.org/abs/1412.6980v9"
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": "https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/fec78a687210851f055f792d45300d27cc60ae41/transformer/SubLayers.py#L9",
"description": "**Multi-head Attention** is a module for attention mechanisms which runs through an attention mechanism several times in parallel. The independent attention outputs are then concatenated and linearly transformed into the expected dimension. Intuitively, multiple attention heads allows for attending to parts of the sequence differently (e.g. longer-term dependencies versus shorter-term dependencies). \r\n\r\n$$ \\text{MultiHead}\\left(\\textbf{Q}, \\textbf{K}, \\textbf{V}\\right) = \\left[\\text{head}\\_{1},\\dots,\\text{head}\\_{h}\\right]\\textbf{W}_{0}$$\r\n\r\n$$\\text{where} \\text{ head}\\_{i} = \\text{Attention} \\left(\\textbf{Q}\\textbf{W}\\_{i}^{Q}, \\textbf{K}\\textbf{W}\\_{i}^{K}, \\textbf{V}\\textbf{W}\\_{i}^{V} \\right) $$\r\n\r\nAbove $\\textbf{W}$ are all learnable parameter matrices.\r\n\r\nNote that [scaled dot-product attention](https://paperswithcode.com/method/scaled) is most commonly used in this module, although in principle it can be swapped out for other types of attention mechanism.\r\n\r\nSource: [Lilian Weng](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html#a-family-of-attention-mechanisms)",
"full_name": "Multi-Head Attention",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Attention Modules** refer to modules that incorporate attention mechanisms. For example, multi-head attention is a module that incorporates multiple attention heads. Below you can find a continuously updating list of attention modules.",
"name": "Attention Modules",
"parent": "Attention"
},
"name": "Multi-Head Attention",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": "https://github.com/CyberZHG/torch-layer-normalization/blob/89f405b60f53f85da6f03fe685c190ef394ce50c/torch_layer_normalization/layer_normalization.py#L8",
"description": "Unlike [batch normalization](https://paperswithcode.com/method/batch-normalization), **Layer Normalization** directly estimates the normalization statistics from the summed inputs to the neurons within a hidden layer so the normalization does not introduce any new dependencies between training cases. It works well for [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and improves both the training time and the generalization performance of several existing RNN models. More recently, it has been used with [Transformer](https://paperswithcode.com/methods/category/transformers) models.\r\n\r\nWe compute the layer normalization statistics over all the hidden units in the same layer as follows:\r\n\r\n$$ \\mu^{l} = \\frac{1}{H}\\sum^{H}\\_{i=1}a\\_{i}^{l} $$\r\n\r\n$$ \\sigma^{l} = \\sqrt{\\frac{1}{H}\\sum^{H}\\_{i=1}\\left(a\\_{i}^{l}-\\mu^{l}\\right)^{2}} $$\r\n\r\nwhere $H$ denotes the number of hidden units in a layer. Under layer normalization, all the hidden units in a layer share the same normalization terms $\\mu$ and $\\sigma$, but different training cases have different normalization terms. Unlike batch normalization, layer normalization does not impose any constraint on the size of the mini-batch and it can be used in the pure online regime with batch size 1.",
"full_name": "Layer Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Layer Normalization",
"source_title": "Layer Normalization",
"source_url": "http://arxiv.org/abs/1607.06450v1"
},
{
"code_snippet_url": "",
"description": "",
"full_name": "Attention Is All You Need",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "If you're looking to get in touch with American Airlines fast, ☎️+1-801-(855)-(5905)or +1-804-853-9001✅ there are\r\nseveral efficient ways to reach their customer service team. The quickest method is to dial ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. American’s phone service ensures that you can speak with a live\r\nrepresentative promptly to resolve any issues or queries regarding your booking, reservation,\r\nor any changes, such as name corrections or ticket cancellations.",
"name": "Attention Mechanisms",
"parent": "Attention"
},
"name": "Attention",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": "https://github.com/tunz/transformer-pytorch/blob/e7266679f0b32fd99135ea617213f986ceede056/model/transformer.py#L201",
"description": "A **Transformer** is a model architecture that eschews recurrence and instead relies entirely on an [attention mechanism](https://paperswithcode.com/methods/category/attention-mechanisms-1) to draw global dependencies between input and output. Before Transformers, the dominant sequence transduction models were based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The Transformer also employs an encoder and decoder, but removing recurrence in favor of [attention mechanisms](https://paperswithcode.com/methods/category/attention-mechanisms-1) allows for significantly more parallelization than methods like [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and [CNNs](https://paperswithcode.com/methods/category/convolutional-neural-networks).",
"full_name": "Transformer",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Transformer",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
}
] |
https://paperswithcode.com/paper/adversarial-learning-of-structure-aware-fully
|
1711.00253
| null | null |
Adversarial Learning of Structure-Aware Fully Convolutional Networks for Landmark Localization
|
Landmark/pose estimation in single monocular images have received much effort
in computer vision due to its important applications. It remains a challenging
task when input images severe occlusions caused by, e.g., adverse camera views.
Under such circumstances, biologically implausible pose predictions may be
produced. In contrast, human vision is able to predict poses by exploiting
geometric constraints of landmark point inter-connectivity. To address the
problem, by incorporating priors about the structure of pose components, we
propose a novel structure-aware fully convolutional network to implicitly take
such priors into account during training of the deep network. Explicit learning
of such constraints is typically challenging. Instead, inspired by how human
identifies implausible poses, we design discriminators to distinguish the real
poses from the fake ones (such as biologically implausible ones). If the pose
generator G generates results that the discriminator fails to distinguish from
real ones, the network successfully learns the priors. Training of the network
follows the strategy of conditional Generative Adversarial Networks (GANs). The
effectiveness of the proposed network is evaluated on three pose-related tasks:
2D single human pose estimation, 2D facial landmark estimation and 3D single
human pose estimation. The proposed approach significantly outperforms the
state-of-the-art methods and almost always generates plausible pose
predictions, demonstrating the usefulness of implicit learning of structures
using GANs.
| null |
http://arxiv.org/abs/1711.00253v5
|
http://arxiv.org/pdf/1711.00253v5.pdf
| null |
[
"Yu Chen",
"Chunhua Shen",
"Hao Chen",
"Xiu-Shen Wei",
"Lingqiao Liu",
"Jian Yang"
] |
[
"Pose Estimation"
] | 2017-11-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/distributed-learning-from-interactions-in
|
1806.01003
| null | null |
Distributed Learning from Interactions in Social Networks
|
We consider a network scenario in which agents can evaluate each other
according to a score graph that models some interactions. The goal is to design
a distributed protocol, run by the agents, that allows them to learn their
unknown state among a finite set of possible values. We propose a Bayesian
framework in which scores and states are associated to probabilistic events
with unknown parameters and hyperparameters, respectively. We show that each
agent can learn its state by means of a local Bayesian classifier and a
(centralized) Maximum-Likelihood (ML) estimator of parameter-hyperparameter
that combines plain ML and Empirical Bayes approaches. By using tools from
graphical models, which allow us to gain insight on conditional dependencies of
scores and states, we provide a relaxed probabilistic model that ultimately
leads to a parameter-hyperparameter estimator amenable to distributed
computation. To highlight the appropriateness of the proposed relaxation, we
demonstrate the distributed estimators on a social interaction set-up for user
profiling.
| null |
http://arxiv.org/abs/1806.01003v1
|
http://arxiv.org/pdf/1806.01003v1.pdf
| null |
[
"Francesco Sasso",
"Angelo Coluccia",
"Giuseppe Notarstefano"
] |
[] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/conditional-probability-models-for-deep-image
|
1801.04260
| null | null |
Conditional Probability Models for Deep Image Compression
|
Deep Neural Networks trained as image auto-encoders have recently emerged as a promising direction for advancing the state-of-the-art in image compression. The key challenge in learning such networks is twofold: To deal with quantization, and to control the trade-off between reconstruction error (distortion) and entropy (rate) of the latent image representation. In this paper, we focus on the latter challenge and propose a new technique to navigate the rate-distortion trade-off for an image compression auto-encoder. The main idea is to directly model the entropy of the latent representation by using a context model: A 3D-CNN which learns a conditional probability model of the latent distribution of the auto-encoder. During training, the auto-encoder makes use of the context model to estimate the entropy of its representation, and the context model is concurrently updated to learn the dependencies between the symbols in the latent representation. Our experiments show that this approach, when measured in MS-SSIM, yields a state-of-the-art image compression system based on a simple convolutional auto-encoder.
|
During training, the auto-encoder makes use of the context model to estimate the entropy of its representation, and the context model is concurrently updated to learn the dependencies between the symbols in the latent representation.
|
https://arxiv.org/abs/1801.04260v4
|
https://arxiv.org/pdf/1801.04260v4.pdf
|
CVPR 2018 6
|
[
"Fabian Mentzer",
"Eirikur Agustsson",
"Michael Tschannen",
"Radu Timofte",
"Luc van Gool"
] |
[
"Image Compression",
"MS-SSIM",
"Navigate",
"Quantization",
"SSIM"
] | 2018-01-12T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Mentzer_Conditional_Probability_Models_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Mentzer_Conditional_Probability_Models_CVPR_2018_paper.pdf
|
conditional-probability-models-for-deep-image-1
| null |
[] |
https://paperswithcode.com/paper/syllable-based-sequence-to-sequence-speech
|
1804.10752
| null | null |
Syllable-Based Sequence-to-Sequence Speech Recognition with the Transformer in Mandarin Chinese
|
Sequence-to-sequence attention-based models have recently shown very
promising results on automatic speech recognition (ASR) tasks, which integrate
an acoustic, pronunciation and language model into a single neural network. In
these models, the Transformer, a new sequence-to-sequence attention-based model
relying entirely on self-attention without using RNNs or convolutions, achieves
a new single-model state-of-the-art BLEU on neural machine translation (NMT)
tasks. Since the outstanding performance of the Transformer, we extend it to
speech and concentrate on it as the basic architecture of sequence-to-sequence
attention-based model on Mandarin Chinese ASR tasks. Furthermore, we
investigate a comparison between syllable based model and context-independent
phoneme (CI-phoneme) based model with the Transformer in Mandarin Chinese.
Additionally, a greedy cascading decoder with the Transformer is proposed for
mapping CI-phoneme sequences and syllable sequences into word sequences.
Experiments on HKUST datasets demonstrate that syllable based model with the
Transformer performs better than CI-phoneme based counterpart, and achieves a
character error rate (CER) of \emph{$28.77\%$}, which is competitive to the
state-of-the-art CER of $28.0\%$ by the joint CTC-attention based
encoder-decoder network.
|
Furthermore, we investigate a comparison between syllable based model and context-independent phoneme (CI-phoneme) based model with the Transformer in Mandarin Chinese.
|
http://arxiv.org/abs/1804.10752v2
|
http://arxiv.org/pdf/1804.10752v2.pdf
| null |
[
"Shiyu Zhou",
"Linhao Dong",
"Shuang Xu",
"Bo Xu"
] |
[
"Automatic Speech Recognition",
"Automatic Speech Recognition (ASR)",
"Decoder",
"Language Modeling",
"Language Modelling",
"Machine Translation",
"NMT",
"Sequence-To-Sequence Speech Recognition",
"speech-recognition",
"Speech Recognition",
"Translation"
] | 2018-04-28T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "A **Linear Layer** is a projection $\\mathbf{XW + b}$.",
"full_name": "Linear Layer",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Linear Layer",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "**Absolute Position Encodings** are a type of position embeddings for [[Transformer](https://paperswithcode.com/method/transformer)-based models] where positional encodings are added to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension $d\\_{model}$ as the embeddings, so that the two can be summed. In the original implementation, sine and cosine functions of different frequencies are used:\r\n\r\n$$ \\text{PE}\\left(pos, 2i\\right) = \\sin\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\n$$ \\text{PE}\\left(pos, 2i+1\\right) = \\cos\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\nwhere $pos$ is the position and $i$ is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from $2\\pi$ to $10000 \\dot 2\\pi$. This function was chosen because the authors hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset $k$, $\\text{PE}\\_{pos+k}$ can be represented as a linear function of $\\text{PE}\\_{pos}$.\r\n\r\nImage Source: [D2L.ai](https://d2l.ai/chapter_attention-mechanisms/self-attention-and-positional-encoding.html)",
"full_name": "Absolute Position Encodings",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Position Embeddings",
"parent": null
},
"name": "Absolute Position Encodings",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": null,
"description": "**Position-Wise Feed-Forward Layer** is a type of [feedforward layer](https://www.paperswithcode.com/method/category/feedforwad-networks) consisting of two [dense layers](https://www.paperswithcode.com/method/dense-connections) that applies to the last dimension, which means the same dense layers are used for each position item in the sequence, so called position-wise.",
"full_name": "Position-Wise Feed-Forward Layer",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Position-Wise Feed-Forward Layer",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/resnet.py#L118",
"description": "**Residual Connections** are a type of skip-connection that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. \r\n\r\nFormally, denoting the desired underlying mapping as $\\mathcal{H}({x})$, we let the stacked nonlinear layers fit another mapping of $\\mathcal{F}({x}):=\\mathcal{H}({x})-{x}$. The original mapping is recast into $\\mathcal{F}({x})+{x}$.\r\n\r\nThe intuition is that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers.",
"full_name": "Residual Connection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connections** allow layers to skip layers and connect to layers further up the network, allowing for information to flow more easily up the network. Below you can find a continuously updating list of skip connection methods.",
"name": "Skip Connections",
"parent": null
},
"name": "Residual Connection",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": null,
"description": "**Byte Pair Encoding**, or **BPE**, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations).\r\n\r\n[Lei Mao](https://leimao.github.io/blog/Byte-Pair-Encoding/) has a detailed blog post that explains how this works.",
"full_name": "Byte Pair Encoding",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Subword Segmentation",
"parent": null
},
"name": "BPE",
"source_title": "Neural Machine Translation of Rare Words with Subword Units",
"source_url": "http://arxiv.org/abs/1508.07909v5"
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "**Label Smoothing** is a regularization technique that introduces noise for the labels. This accounts for the fact that datasets may have mistakes in them, so maximizing the likelihood of $\\log{p}\\left(y\\mid{x}\\right)$ directly can be harmful. Assume for a small constant $\\epsilon$, the training set label $y$ is correct with probability $1-\\epsilon$ and incorrect otherwise. Label Smoothing regularizes a model based on a [softmax](https://paperswithcode.com/method/softmax) with $k$ output values by replacing the hard $0$ and $1$ classification targets with targets of $\\frac{\\epsilon}{k}$ and $1-\\frac{k-1}{k}\\epsilon$ respectively.\r\n\r\nSource: Deep Learning, Goodfellow et al\r\n\r\nImage Source: [When Does Label Smoothing Help?](https://arxiv.org/abs/1906.02629)",
"full_name": "Label Smoothing",
"introduced_year": 1985,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Label Smoothing",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!\r\n\r\n\r\nHow Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!",
"full_name": "*Communicated@Fast*How Do I Communicate to Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/b7bda236d18815052378c88081f64935427d7716/torch/optim/adam.py#L6",
"description": "**Adam** is an adaptive learning rate optimization algorithm that utilises both momentum and scaling, combining the benefits of [RMSProp](https://paperswithcode.com/method/rmsprop) and [SGD w/th Momentum](https://paperswithcode.com/method/sgd-with-momentum). The optimizer is designed to be appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. \r\n\r\nThe weight updates are performed as:\r\n\r\n$$ w_{t} = w_{t-1} - \\eta\\frac{\\hat{m}\\_{t}}{\\sqrt{\\hat{v}\\_{t}} + \\epsilon} $$\r\n\r\nwith\r\n\r\n$$ \\hat{m}\\_{t} = \\frac{m_{t}}{1-\\beta^{t}_{1}} $$\r\n\r\n$$ \\hat{v}\\_{t} = \\frac{v_{t}}{1-\\beta^{t}_{2}} $$\r\n\r\n$$ m_{t} = \\beta_{1}m_{t-1} + (1-\\beta_{1})g_{t} $$\r\n\r\n$$ v_{t} = \\beta_{2}v_{t-1} + (1-\\beta_{2})g_{t}^{2} $$\r\n\r\n\r\n$ \\eta $ is the step size/learning rate, around 1e-3 in the original paper. $ \\epsilon $ is a small number, typically 1e-8 or 1e-10, to prevent dividing by zero. $ \\beta_{1} $ and $ \\beta_{2} $ are forgetting parameters, with typical values 0.9 and 0.999, respectively.",
"full_name": "Adam",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Stochastic Optimization** methods are used to optimize neural networks. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. Below you can find a continuously updating list of stochastic optimization algorithms.",
"name": "Stochastic Optimization",
"parent": "Optimization"
},
"name": "Adam",
"source_title": "Adam: A Method for Stochastic Optimization",
"source_url": "http://arxiv.org/abs/1412.6980v9"
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": "https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/fec78a687210851f055f792d45300d27cc60ae41/transformer/SubLayers.py#L9",
"description": "**Multi-head Attention** is a module for attention mechanisms which runs through an attention mechanism several times in parallel. The independent attention outputs are then concatenated and linearly transformed into the expected dimension. Intuitively, multiple attention heads allows for attending to parts of the sequence differently (e.g. longer-term dependencies versus shorter-term dependencies). \r\n\r\n$$ \\text{MultiHead}\\left(\\textbf{Q}, \\textbf{K}, \\textbf{V}\\right) = \\left[\\text{head}\\_{1},\\dots,\\text{head}\\_{h}\\right]\\textbf{W}_{0}$$\r\n\r\n$$\\text{where} \\text{ head}\\_{i} = \\text{Attention} \\left(\\textbf{Q}\\textbf{W}\\_{i}^{Q}, \\textbf{K}\\textbf{W}\\_{i}^{K}, \\textbf{V}\\textbf{W}\\_{i}^{V} \\right) $$\r\n\r\nAbove $\\textbf{W}$ are all learnable parameter matrices.\r\n\r\nNote that [scaled dot-product attention](https://paperswithcode.com/method/scaled) is most commonly used in this module, although in principle it can be swapped out for other types of attention mechanism.\r\n\r\nSource: [Lilian Weng](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html#a-family-of-attention-mechanisms)",
"full_name": "Multi-Head Attention",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Attention Modules** refer to modules that incorporate attention mechanisms. For example, multi-head attention is a module that incorporates multiple attention heads. Below you can find a continuously updating list of attention modules.",
"name": "Attention Modules",
"parent": "Attention"
},
"name": "Multi-Head Attention",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": "https://github.com/CyberZHG/torch-layer-normalization/blob/89f405b60f53f85da6f03fe685c190ef394ce50c/torch_layer_normalization/layer_normalization.py#L8",
"description": "Unlike [batch normalization](https://paperswithcode.com/method/batch-normalization), **Layer Normalization** directly estimates the normalization statistics from the summed inputs to the neurons within a hidden layer so the normalization does not introduce any new dependencies between training cases. It works well for [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and improves both the training time and the generalization performance of several existing RNN models. More recently, it has been used with [Transformer](https://paperswithcode.com/methods/category/transformers) models.\r\n\r\nWe compute the layer normalization statistics over all the hidden units in the same layer as follows:\r\n\r\n$$ \\mu^{l} = \\frac{1}{H}\\sum^{H}\\_{i=1}a\\_{i}^{l} $$\r\n\r\n$$ \\sigma^{l} = \\sqrt{\\frac{1}{H}\\sum^{H}\\_{i=1}\\left(a\\_{i}^{l}-\\mu^{l}\\right)^{2}} $$\r\n\r\nwhere $H$ denotes the number of hidden units in a layer. Under layer normalization, all the hidden units in a layer share the same normalization terms $\\mu$ and $\\sigma$, but different training cases have different normalization terms. Unlike batch normalization, layer normalization does not impose any constraint on the size of the mini-batch and it can be used in the pure online regime with batch size 1.",
"full_name": "Layer Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Layer Normalization",
"source_title": "Layer Normalization",
"source_url": "http://arxiv.org/abs/1607.06450v1"
},
{
"code_snippet_url": "",
"description": "",
"full_name": "Attention Is All You Need",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "If you're looking to get in touch with American Airlines fast, ☎️+1-801-(855)-(5905)or +1-804-853-9001✅ there are\r\nseveral efficient ways to reach their customer service team. The quickest method is to dial ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. American’s phone service ensures that you can speak with a live\r\nrepresentative promptly to resolve any issues or queries regarding your booking, reservation,\r\nor any changes, such as name corrections or ticket cancellations.",
"name": "Attention Mechanisms",
"parent": "Attention"
},
"name": "Attention",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": "https://github.com/tunz/transformer-pytorch/blob/e7266679f0b32fd99135ea617213f986ceede056/model/transformer.py#L201",
"description": "A **Transformer** is a model architecture that eschews recurrence and instead relies entirely on an [attention mechanism](https://paperswithcode.com/methods/category/attention-mechanisms-1) to draw global dependencies between input and output. Before Transformers, the dominant sequence transduction models were based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The Transformer also employs an encoder and decoder, but removing recurrence in favor of [attention mechanisms](https://paperswithcode.com/methods/category/attention-mechanisms-1) allows for significantly more parallelization than methods like [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and [CNNs](https://paperswithcode.com/methods/category/convolutional-neural-networks).",
"full_name": "Transformer",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Transformer",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
}
] |
https://paperswithcode.com/paper/automatic-goal-generation-for-reinforcement
|
1705.06366
| null |
SyhRVm-Rb
|
Automatic Goal Generation for Reinforcement Learning Agents
|
Reinforcement learning is a powerful technique to train an agent to perform a
task. However, an agent that is trained using reinforcement learning is only
capable of achieving the single task that is specified via its reward function.
Such an approach does not scale well to settings in which an agent needs to
perform a diverse set of tasks, such as navigating to varying positions in a
room or moving objects to varying locations. Instead, we propose a method that
allows an agent to automatically discover the range of tasks that it is capable
of performing. We use a generator network to propose tasks for the agent to try
to achieve, specified as goal states. The generator network is optimized using
adversarial training to produce tasks that are always at the appropriate level
of difficulty for the agent. Our method thus automatically produces a
curriculum of tasks for the agent to learn. We show that, by using this
framework, an agent can efficiently and automatically learn to perform a wide
set of tasks without requiring any prior knowledge of its environment. Our
method can also learn to achieve tasks with sparse rewards, which traditionally
pose significant challenges.
|
Instead, we propose a method that allows an agent to automatically discover the range of tasks that it is capable of performing.
|
http://arxiv.org/abs/1705.06366v5
|
http://arxiv.org/pdf/1705.06366v5.pdf
|
ICML 2018 7
|
[
"Carlos Florensa",
"David Held",
"Xinyang Geng",
"Pieter Abbeel"
] |
[
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2017-05-17T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2287
|
http://proceedings.mlr.press/v80/florensa18a/florensa18a.pdf
|
automatic-goal-generation-for-reinforcement-1
| null |
[] |
https://paperswithcode.com/paper/asymptotic-optimality-of-adaptive-importance
|
1806.00989
| null | null |
Asymptotic optimality of adaptive importance sampling
|
Adaptive importance sampling (AIS) uses past samples to update the
\textit{sampling policy} $q_t$ at each stage $t$. Each stage $t$ is formed with
two steps : (i) to explore the space with $n_t$ points according to $q_t$ and
(ii) to exploit the current amount of information to update the sampling
policy. The very fundamental question raised in this paper concerns the
behavior of empirical sums based on AIS. Without making any assumption on the
allocation policy $n_t$, the theory developed involves no restriction on the
split of computational resources between the explore (i) and the exploit (ii)
step. It is shown that AIS is asymptotically optimal : the asymptotic behavior
of AIS is the same as some "oracle" strategy that knows the targeted sampling
policy from the beginning. From a practical perspective, weighted AIS is
introduced, a new method that allows to forget poor samples from early stages.
|
Each stage $t$ is formed with two steps : (i) to explore the space with $n_t$ points according to $q_t$ and (ii) to exploit the current amount of information to update the sampling policy.
|
http://arxiv.org/abs/1806.00989v2
|
http://arxiv.org/pdf/1806.00989v2.pdf
|
NeurIPS 2018 12
|
[
"Bernard Delyon",
"François Portier"
] |
[] | 2018-06-04T00:00:00 |
http://papers.nips.cc/paper/7576-asymptotic-optimality-of-adaptive-importance-sampling
|
http://papers.nips.cc/paper/7576-asymptotic-optimality-of-adaptive-importance-sampling.pdf
|
asymptotic-optimality-of-adaptive-importance-1
| null |
[] |
https://paperswithcode.com/paper/dnn-hmm-based-speaker-adaptive-emotion
|
1806.00984
| null | null |
DNN-HMM based Speaker Adaptive Emotion Recognition using Proposed Epoch and MFCC Features
|
Speech is produced when time varying vocal tract system is excited with time
varying excitation source. Therefore, the information present in a speech such
as message, emotion, language, speaker is due to the combined effect of both
excitation source and vocal tract system. However, there is very less
utilization of excitation source features to recognize emotion. In our earlier
work, we have proposed a novel method to extract glottal closure instants
(GCIs) known as epochs. In this paper, we have explored epoch features namely
instantaneous pitch, phase and strength of epochs for discriminating emotions.
We have combined the excitation source features and the well known
Male-frequency cepstral coefficient (MFCC) features to develop an emotion
recognition system with improved performance. DNN-HMM speaker adaptive models
have been developed using MFCC, epoch and combined features. IEMOCAP emotional
database has been used to evaluate the models. The average accuracy for emotion
recognition system when using MFCC and epoch features separately is 59.25% and
54.52% respectively. The recognition performance improves to 64.2% when MFCC
and epoch features are combined.
| null |
http://arxiv.org/abs/1806.00984v1
|
http://arxiv.org/pdf/1806.00984v1.pdf
| null |
[
"Md. Shah Fahad",
"Jainath Yadav",
"Gyadhar Pradhan",
"Akshay Deepak"
] |
[
"Emotion Recognition"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/automatic-clustering-of-a-network-protocol
|
1806.00981
| null | null |
Automatic Clustering of a Network Protocol with Weakly-Supervised Clustering
|
Abstraction is a fundamental part when learning behavioral models of systems.
Usually the process of abstraction is manually defined by domain experts. This
paper presents a method to perform automatic abstraction for network protocols.
In particular a weakly supervised clustering algorithm is used to build an
abstraction with a small vocabulary size for the widely used TLS protocol. To
show the effectiveness of the proposed method we compare the resultant abstract
messages to a manually constructed (reference) abstraction. With a small amount
of side-information in the form of a few labeled examples this method finds an
abstraction that matches the reference abstraction perfectly.
| null |
http://arxiv.org/abs/1806.00981v1
|
http://arxiv.org/pdf/1806.00981v1.pdf
| null |
[
"Tobias Schrank",
"Franz Pernkopf"
] |
[
"Clustering"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/similarity-encoding-for-learning-with-dirty
|
1806.00979
| null | null |
Similarity encoding for learning with dirty categorical variables
|
For statistical learning, categorical variables in a table are usually
considered as discrete entities and encoded separately to feature vectors,
e.g., with one-hot encoding. "Dirty" non-curated data gives rise to categorical
variables with a very high cardinality but redundancy: several categories
reflect the same entity. In databases, this issue is typically solved with a
deduplication step. We show that a simple approach that exposes the redundancy
to the learning algorithm brings significant gains. We study a generalization
of one-hot encoding, similarity encoding, that builds feature vectors from
similarities across categories. We perform a thorough empirical validation on
non-curated tables, a problem seldom studied in machine learning. Results on
seven real-world datasets show that similarity encoding brings significant
gains in prediction in comparison with known encoding methods for categories or
strings, notably one-hot encoding and bag of character n-grams. We draw
practical recommendations for encoding dirty categories: 3-gram similarity
appears to be a good choice to capture morphological resemblance. For very
high-cardinality, dimensionality reduction significantly reduces the
computational cost with little loss in performance: random projections or
choosing a subset of prototype categories still outperforms classic encoding
approaches.
|
We show that a simple approach that exposes the redundancy to the learning algorithm brings significant gains.
|
http://arxiv.org/abs/1806.00979v1
|
http://arxiv.org/pdf/1806.00979v1.pdf
| null |
[
"Patricio Cerda",
"Gaël Varoquaux",
"Balázs Kégl"
] |
[
"Dimensionality Reduction"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/sequential-test-for-the-lowest-mean-from
|
1806.00973
| null | null |
Sequential Test for the Lowest Mean: From Thompson to Murphy Sampling
|
Learning the minimum/maximum mean among a finite set of distributions is a
fundamental sub-task in planning, game tree search and reinforcement learning.
We formalize this learning task as the problem of sequentially testing how the
minimum mean among a finite set of distributions compares to a given threshold.
We develop refined non-asymptotic lower bounds, which show that optimality
mandates very different sampling behavior for a low vs high true minimum. We
show that Thompson Sampling and the intuitive Lower Confidence Bounds policy
each nail only one of these cases. We develop a novel approach that we call
Murphy Sampling. Even though it entertains exclusively low true minima, we
prove that MS is optimal for both possibilities. We then design advanced
self-normalized deviation inequalities, fueling more aggressive stopping rules.
We complement our theoretical guarantees by experiments showing that MS works
best in practice.
| null |
http://arxiv.org/abs/1806.00973v1
|
http://arxiv.org/pdf/1806.00973v1.pdf
|
NeurIPS 2018 12
|
[
"Emilie Kaufmann",
"Wouter Koolen",
"Aurelien Garivier"
] |
[
"Reinforcement Learning",
"Reinforcement Learning (RL)",
"Thompson Sampling"
] | 2018-06-04T00:00:00 |
http://papers.nips.cc/paper/7870-sequential-test-for-the-lowest-mean-from-thompson-to-murphy-sampling
|
http://papers.nips.cc/paper/7870-sequential-test-for-the-lowest-mean-from-thompson-to-murphy-sampling.pdf
|
sequential-test-for-the-lowest-mean-from-1
| null |
[] |
https://paperswithcode.com/paper/training-deep-learning-based-image-denoisers
|
1806.00961
| null | null |
Training deep learning based image denoisers from undersampled measurements without ground truth and without image prior
|
Compressive sensing is a method to recover the original image from
undersampled measurements. In order to overcome the ill-posedness of this
inverse problem, image priors are used such as sparsity in the wavelet domain,
minimum total-variation, or self-similarity. Recently, deep learning based
compressive image recovery methods have been proposed and have yielded
state-of-the-art performances. They used deep learning based data-driven
approaches instead of hand-crafted image priors to solve the ill-posed inverse
problem with undersampled data. Ironically, training deep neural networks for
them requires "clean" ground truth images, but obtaining the best quality
images from undersampled data requires well-trained deep neural networks. To
resolve this dilemma, we propose novel methods based on two well-grounded
theories: denoiser-approximate message passing and Stein's unbiased risk
estimator. Our proposed methods were able to train deep learning based image
denoisers from undersampled measurements without ground truth images and
without image priors, and to recover images with state-of-the-art qualities
from undersampled data. We evaluated our methods for various compressive
sensing recovery problems with Gaussian random, coded diffraction pattern, and
compressive sensing MRI measurement matrices. Our methods yielded
state-of-the-art performances for all cases without ground truth images and
without image priors. They also yielded comparable performances to the methods
with ground truth data.
| null |
http://arxiv.org/abs/1806.00961v2
|
http://arxiv.org/pdf/1806.00961v2.pdf
|
CVPR 2019 6
|
[
"Magauiya Zhussip",
"Shakarim Soltanayev",
"Se Young Chun"
] |
[
"Compressive Sensing",
"Deep Learning"
] | 2018-06-04T00:00:00 |
http://openaccess.thecvf.com/content_CVPR_2019/html/Zhussip_Training_Deep_Learning_Based_Image_Denoisers_From_Undersampled_Measurements_Without_CVPR_2019_paper.html
|
http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhussip_Training_Deep_Learning_Based_Image_Denoisers_From_Undersampled_Measurements_Without_CVPR_2019_paper.pdf
|
training-deep-learning-based-image-denoisers-1
| null |
[] |
https://paperswithcode.com/paper/the-capacity-constrained-facility-location
|
1806.00960
| null | null |
The Capacity Constrained Facility Location problem
|
We initiate the study of the capacity constrained facility location problem
from a mechanism design perspective. The capacity constrained setting leads to
a new strategic environment where a facility serves a subset of the population,
which is endogenously determined by the ex-post Nash equilibrium of an induced
subgame and is not directly controlled by the mechanism designer. Our focus is
on mechanisms that are ex-post dominant-strategy incentive compatible (DIC) at
the reporting stage. We provide a complete characterization of DIC mechanisms
via the family of Generalized Median Mechanisms (GMMs). In general, the social
welfare optimal mechanism is not DIC. Adopting the worst-case approximation
measure, we attain tight lower bounds on the approximation ratio of any DIC
mechanism. The well-known median mechanism is shown to be optimal among the
family of DIC mechanisms for certain capacity ranges. Surprisingly, the
framework we introduce provides a new characterization for the family of GMMs,
and is responsive to gaps in the current social choice literature highlighted
by Border and Jordan (1983) and Barbar{\`a}, Mass{\'o} and Serizawa (1998).
| null |
http://arxiv.org/abs/1806.00960v2
|
http://arxiv.org/pdf/1806.00960v2.pdf
| null |
[
"Haris Aziz",
"Hau Chan",
"Barton E. Lee",
"David C. Parkes"
] |
[] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/private-pac-learning-implies-finite
|
1806.00949
| null | null |
Private PAC learning implies finite Littlestone dimension
|
We show that every approximately differentially private learning algorithm
(possibly improper) for a class $H$ with Littlestone dimension~$d$ requires
$\Omega\bigl(\log^*(d)\bigr)$ examples. As a corollary it follows that the
class of thresholds over $\mathbb{N}$ can not be learned in a private manner;
this resolves open question due to [Bun et al., 2015, Feldman and Xiao, 2015].
We leave as an open question whether every class with a finite Littlestone
dimension can be learned by an approximately differentially private algorithm.
| null |
http://arxiv.org/abs/1806.00949v3
|
http://arxiv.org/pdf/1806.00949v3.pdf
| null |
[
"Noga Alon",
"Roi Livni",
"Maryanthe Malliaris",
"Shay Moran"
] |
[
"Open-Ended Question Answering",
"PAC learning"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/robustness-may-be-at-odds-with-accuracy
|
1805.12152
| null |
SyxAb30cY7
|
Robustness May Be at Odds with Accuracy
|
We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. We demonstrate that this trade-off between the standard accuracy of a model and its robustness to adversarial perturbations provably exists in a fairly simple and natural setting. These findings also corroborate a similar phenomenon observed empirically in more complex settings. Further, we argue that this phenomenon is a consequence of robust classifiers learning fundamentally different feature representations than standard classifiers. These differences, in particular, seem to result in unexpected benefits: the representations learned by robust models tend to align better with salient data characteristics and human perception.
|
We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization.
|
https://arxiv.org/abs/1805.12152v5
|
https://arxiv.org/pdf/1805.12152v5.pdf
|
ICLR 2019 5
|
[
"Dimitris Tsipras",
"Shibani Santurkar",
"Logan Engstrom",
"Alexander Turner",
"Aleksander Madry"
] |
[
"Adversarial Robustness"
] | 2018-05-30T00:00:00 |
https://openreview.net/forum?id=SyxAb30cY7
|
https://openreview.net/pdf?id=SyxAb30cY7
|
robustness-may-be-at-odds-with-accuracy-1
| null |
[] |
https://paperswithcode.com/paper/program-synthesis-from-visual-specification
|
1806.00938
| null | null |
Program Synthesis from Visual Specification
|
Program synthesis is the process of automatically translating a specification
into computer code. Traditional synthesis settings require a formal, precise
specification. Motivated by computer education applications where a student
learns to code simple turtle-style drawing programs, we study a novel synthesis
setting where only a noisy user-intention drawing is specified. This allows
students to sketch their intended output, optionally together with their own
incomplete program, to automatically produce a completed program. We formulate
this synthesis problem as search in the space of programs, with the score of a
state being the Hausdorff distance between the program output and the user
drawing. We compare several search algorithms on a corpus consisting of real
user drawings and the corresponding programs, and demonstrate that our
algorithms can synthesize programs optimally satisfying the specification.
| null |
http://arxiv.org/abs/1806.00938v1
|
http://arxiv.org/pdf/1806.00938v1.pdf
| null |
[
"Evan Hernandez",
"Ara Vartanian",
"Xiaojin Zhu"
] |
[
"Program Synthesis"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-possibility-distribution-based-multi
|
1806.01650
| null | null |
A Possibility Distribution Based Multi-Criteria Decision Algorithm for Resilient Supplier Selection Problems
|
Thus far, limited research has been performed on resilient supplier selection
- a problem that requires simultaneous consideration of a set of numerical and
linguistic evaluation criteria, which are substantially different from
traditional supplier selection problem. Essentially, resilient supplier
selection entails key sourcing decision for an organization to gain competitive
advantage. In the presence of multiple conflicting evaluation criteria,
contradicting decision makers, and imprecise decision relevant information
(DRI), this problem becomes even more difficult to solve with the classical
optimization approaches. However, prior research focusing on MCDA based
supplier selection problem has been lacking in the ability to provide a
seamless integration of numerical and linguistic evaluation criteria along with
the consideration of multiple decision makers. To address these challenges, we
present a comprehensive decision-making framework for ranking a set of
suppliers from resiliency perspective. The proposed algorithm is capable of
leveraging imprecise and aggregated DRI obtained from crisp numerical
assessments and reliability adjusted linguistic appraisals from a group of
decision makers. We adapt two popular tools - Single Valued Neutrosophic Sets
(SVNS) and Interval-valued fuzzy sets (IVFS), and for the first time extend
them to incorporate both crisp and linguistic evaluations in a group decision
making platform to obtain aggregated SVNS and IVFS decision matrix. This
information is then used to rank the resilient suppliers by using TOPSIS
method. We present a case study to illustrate the mechanism of the proposed
algorithm.
| null |
http://arxiv.org/abs/1806.01650v2
|
http://arxiv.org/pdf/1806.01650v2.pdf
| null |
[
"Dizuo Jiang",
"Md Mahmudul Hassan",
"Tasnim Ibn Faiz",
"Md. Noor-E-Alam"
] |
[
"Decision Making"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/an-information-theoretic-analysis-for
|
1805.11845
| null | null |
An Information-Theoretic Analysis for Thompson Sampling with Many Actions
|
Information-theoretic Bayesian regret bounds of Russo and Van Roy capture the dependence of regret on prior uncertainty. However, this dependence is through entropy, which can become arbitrarily large as the number of actions increases. We establish new bounds that depend instead on a notion of rate-distortion. Among other things, this allows us to recover through information-theoretic arguments a near-optimal bound for the linear bandit. We also offer a bound for the logistic bandit that dramatically improves on the best previously available, though this bound depends on an information-theoretic statistic that we have only been able to quantify via computation.
| null |
https://arxiv.org/abs/1805.11845v4
|
https://arxiv.org/pdf/1805.11845v4.pdf
|
NeurIPS 2018 12
|
[
"Shi Dong",
"Benjamin Van Roy"
] |
[
"Thompson Sampling"
] | 2018-05-30T00:00:00 |
http://papers.nips.cc/paper/7670-an-information-theoretic-analysis-for-thompson-sampling-with-many-actions
|
http://papers.nips.cc/paper/7670-an-information-theoretic-analysis-for-thompson-sampling-with-many-actions.pdf
|
an-information-theoretic-analysis-for-1
| null |
[] |
https://paperswithcode.com/paper/recursive-optimization-of-convex-risk
|
1804.00636
| null | null |
Recursive Optimization of Convex Risk Measures: Mean-Semideviation Models
|
We develop recursive, data-driven, stochastic subgradient methods for
optimizing a new, versatile, and application-driven class of convex risk
measures, termed here as mean-semideviations, strictly generalizing the
well-known and popular mean-upper-semideviation. We introduce the MESSAGEp
algorithm, which is an efficient compositional subgradient procedure for
iteratively solving convex mean-semideviation risk-averse problems to
optimality. We analyze the asymptotic behavior of the MESSAGEp algorithm under
a flexible and structure-exploiting set of problem assumptions. In particular:
1) Under appropriate stepsize rules, we establish pathwise convergence of the
MESSAGEp algorithm in a strong technical sense, confirming its asymptotic
consistency. 2) Assuming a strongly convex cost, we show that, for fixed
semideviation order $p>1$ and for $\epsilon\in\left[0,1\right)$, the MESSAGEp
algorithm achieves a squared-${\cal L}_{2}$ solution suboptimality rate of the
order of ${\cal O}(n^{-\left(1-\epsilon\right)/2})$ iterations, where, for
$\epsilon>0$, pathwise convergence is simultaneously guaranteed. This result
establishes a rate of order arbitrarily close to ${\cal O}(n^{-1/2})$, while
ensuring strongly stable pathwise operation. For $p\equiv1$, the rate order
improves to ${\cal O}(n^{-2/3})$, which also suffices for pathwise convergence,
and matches previous results. 3) Likewise, in the general case of a convex
cost, we show that, for any $\epsilon\in\left[0,1\right)$, the MESSAGEp
algorithm with iterate smoothing achieves an ${\cal L}_{1}$ objective
suboptimality rate of the order of ${\cal
O}(n^{-\left(1-\epsilon\right)/\left(4\bf{1}_{\left\{ p>1\right\} }+4\right)})$
iterations. This result provides maximal rates of ${\cal O}(n^{-1/4})$, if
$p\equiv1$, and ${\cal O}(n^{-1/8})$, if $p>1$, matching the state of the art,
as well.
| null |
http://arxiv.org/abs/1804.00636v5
|
http://arxiv.org/pdf/1804.00636v5.pdf
| null |
[
"Dionysios S. Kalogerias",
"Warren B. Powell"
] |
[] | 2018-04-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/holographic-neural-architectures
|
1806.00931
| null | null |
Holographic Neural Architectures
|
Representation learning is at the heart of what makes deep learning
effective. In this work, we introduce a new framework for representation
learning that we call "Holographic Neural Architectures" (HNAs). In the same
way that an observer can experience the 3D structure of a holographed object by
looking at its hologram from several angles, HNAs derive Holographic
Representations from the training set. These representations can then be
explored by moving along a continuous bounded single dimension. We show that
HNAs can be used to make generative networks, state-of-the-art regression
models and that they are inherently highly resistant to noise. Finally, we
argue that because of their denoising abilities and their capacity to
generalize well from very few examples, models based upon HNAs are particularly
well suited for biological applications where training examples are rare or
noisy.
| null |
http://arxiv.org/abs/1806.00931v1
|
http://arxiv.org/pdf/1806.00931v1.pdf
| null |
[
"Tariq Daouda",
"Jeremie Zumer",
"Claude Perreault",
"Sébastien Lemieux"
] |
[
"Denoising",
"Representation Learning"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/nrtr-a-no-recurrence-sequence-to-sequence
|
1806.00926
| null | null |
NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition
|
Scene text recognition has attracted a great many researches due to its importance to various applications. Existing methods mainly adopt recurrence or convolution based networks. Though have obtained good performance, these methods still suffer from two limitations: slow training speed due to the internal recurrence of RNNs, and high complexity due to stacked convolutional layers for long-term feature extraction. This paper, for the first time, proposes a no-recurrence sequence-to-sequence text recognizer, named NRTR, that dispenses with recurrences and convolutions entirely. NRTR follows the encoder-decoder paradigm, where the encoder uses stacked self-attention to extract image features, and the decoder applies stacked self-attention to recognize texts based on encoder output. NRTR relies solely on self-attention mechanism thus could be trained with more parallelization and less complexity. Considering scene image has large variation in text and background, we further design a modality-transform block to effectively transform 2D input images to 1D sequences, combined with the encoder to extract more discriminative features. NRTR achieves state-of-the-art or highly competitive performance on both regular and irregular benchmarks, while requires only a small fraction of training time compared to the best model from the literature (at least 8 times faster).
|
Considering scene image has large variation in text and background, we further design a modality-transform block to effectively transform 2D input images to 1D sequences, combined with the encoder to extract more discriminative features.
|
https://arxiv.org/abs/1806.00926v2
|
https://arxiv.org/pdf/1806.00926v2.pdf
| null |
[
"Fenfen Sheng",
"Zhineng Chen",
"Bo Xu"
] |
[
"Decoder",
"Optical Character Recognition (OCR)",
"Scene Text Recognition"
] | 2018-06-04T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/lorenzopapa5/SPEED",
"description": "The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key components in autonomous and robotic systems. \r\nApproaches based on the state of the art vision transformer architectures are extremely deep and complex not suitable for real-time inference operations on edge and autonomous systems equipped with low resources (i.e. robot indoor navigation and surveillance). This paper presents SPEED, a Separable Pyramidal pooling EncodEr-Decoder architecture designed to achieve real-time frequency performances on multiple hardware platforms. The proposed model is a fast-throughput deep architecture for MDE able to obtain depth estimations with high accuracy from low resolution images using minimum hardware resources (i.e. edge devices). Our encoder-decoder model exploits two depthwise separable pyramidal pooling layers, which allow to increase the inference frequency while reducing the overall computational complexity. The proposed method performs better than other fast-throughput architectures in terms of both accuracy and frame rates, achieving real-time performances over cloud CPU, TPU and the NVIDIA Jetson TX1 on two indoor benchmarks: the NYU Depth v2 and the DIML Kinect v2 datasets.",
"full_name": "SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings",
"introduced_year": 2000,
"main_collection": null,
"name": "SPEED",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/understanding-humans-in-crowded-scenes-deep
|
1804.03287
| null | null |
Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing
|
Despite the noticeable progress in perceptual tasks like detection, instance
segmentation and human parsing, computers still perform unsatisfactorily on
visually understanding humans in crowded scenes, such as group behavior
analysis, person re-identification and autonomous driving, etc. To this end,
models need to comprehensively perceive the semantic information and the
differences between instances in a multi-human image, which is recently defined
as the multi-human parsing task. In this paper, we present a new large-scale
database "Multi-Human Parsing (MHP)" for algorithm development and evaluation,
and advances the state-of-the-art in understanding humans in crowded scenes.
MHP contains 25,403 elaborately annotated images with 58 fine-grained semantic
category labels, involving 2-26 persons per image and captured in real-world
scenes from various viewpoints, poses, occlusion, interactions and background.
We further propose a novel deep Nested Adversarial Network (NAN) model for
multi-human parsing. NAN consists of three Generative Adversarial Network
(GAN)-like sub-nets, respectively performing semantic saliency prediction,
instance-agnostic parsing and instance-aware clustering. These sub-nets form a
nested structure and are carefully designed to learn jointly in an end-to-end
way. NAN consistently outperforms existing state-of-the-art solutions on our
MHP and several other datasets, and serves as a strong baseline to drive the
future research for multi-human parsing.
|
Despite the noticeable progress in perceptual tasks like detection, instance segmentation and human parsing, computers still perform unsatisfactorily on visually understanding humans in crowded scenes, such as group behavior analysis, person re-identification and autonomous driving, etc.
|
http://arxiv.org/abs/1804.03287v3
|
http://arxiv.org/pdf/1804.03287v3.pdf
| null |
[
"Jian Zhao",
"Jianshu Li",
"Yu Cheng",
"Li Zhou",
"Terence Sim",
"Shuicheng Yan",
"Jiashi Feng"
] |
[
"Autonomous Driving",
"Clustering",
"Generative Adversarial Network",
"Human Parsing",
"Instance Segmentation",
"Multi-Human Parsing",
"Person Re-Identification",
"Saliency Prediction",
"Semantic Segmentation"
] | 2018-04-10T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/automatic-catheter-detection-in-pediatric-x
|
1806.00921
| null | null |
Automatic catheter detection in pediatric X-ray images using a scale-recurrent network and synthetic data
|
Catheters are commonly inserted life supporting devices. X-ray images are
used to assess the position of a catheter immediately after placement as
serious complications can arise from malpositioned catheters. Previous computer
vision approaches to detect catheters on X-ray images either relied on
low-level cues that are not sufficiently robust or only capable of processing a
limited number or type of catheters. With the resurgence of deep learning,
supervised training approaches are begining to showing promising results.
However, dense annotation maps are required, and the work of a human annotator
is hard to scale. In this work, we proposed a simple way of synthesizing
catheters on X-ray images and a scale recurrent network for catheter detection.
By training on adult chest X-rays, the proposed network exhibits promising
detection results on pediatric chest/abdomen X-rays in terms of both precision
and recall.
|
In this work, we proposed a simple way of synthesizing catheters on X-ray images and a scale recurrent network for catheter detection.
|
http://arxiv.org/abs/1806.00921v1
|
http://arxiv.org/pdf/1806.00921v1.pdf
| null |
[
"Xin Yi",
"Scott Adams",
"Paul Babyn",
"Abdul Elnajmi"
] |
[
"Position"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/drcd-a-chinese-machine-reading-comprehension
|
1806.00920
| null | null |
DRCD: a Chinese Machine Reading Comprehension Dataset
|
In this paper, we introduce DRCD (Delta Reading Comprehension Dataset), an open domain traditional Chinese machine reading comprehension (MRC) dataset. This dataset aimed to be a standard Chinese machine reading comprehension dataset, which can be a source dataset in transfer learning. The dataset contains 10,014 paragraphs from 2,108 Wikipedia articles and 30,000+ questions generated by annotators. We build a baseline model that achieves an F1 score of 89.59%. F1 score of Human performance is 93.30%.
|
In this paper, we introduce DRCD (Delta Reading Comprehension Dataset), an open domain traditional Chinese machine reading comprehension (MRC) dataset.
|
https://arxiv.org/abs/1806.00920v3
|
https://arxiv.org/pdf/1806.00920v3.pdf
| null |
[
"Chih Chieh Shao",
"Trois Liu",
"Yuting Lai",
"Yiying Tseng",
"Sam Tsai"
] |
[
"Articles",
"Machine Reading Comprehension",
"Reading Comprehension",
"Transfer Learning"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/adversarial-confidence-and-smoothness
|
1806.00919
| null | null |
Adversarial confidence and smoothness regularizations for scalable unsupervised discriminative learning
|
In this paper, we consider a generic probabilistic discriminative learner
from the functional viewpoint and argue that, to make it learn well, it is
necessary to constrain its hypothesis space to a set of non-trivial piecewise
constant functions. To achieve this goal, we present a scalable unsupervised
regularization framework. On the theoretical front, we prove that this
framework is conducive to a factually confident and smooth discriminative model
and connect it to an adversarial Taboo game, spectral clustering and virtual
adversarial training. Experimentally, we take deep neural networks as our
learners and demonstrate that, when trained under our framework in the
unsupervised setting, they not only achieve state-of-the-art clustering results
but also generalize well on both synthetic and real data.
| null |
http://arxiv.org/abs/1806.00919v1
|
http://arxiv.org/pdf/1806.00919v1.pdf
| null |
[
"Yi-Qing Wang"
] |
[
"Clustering"
] | 2018-06-04T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "Spectral clustering has attracted increasing attention due to\r\nthe promising ability in dealing with nonlinearly separable datasets [15], [16]. In spectral clustering, the spectrum of the graph Laplacian is used to reveal the cluster structure. The spectral clustering algorithm mainly consists of two steps: 1) constructs the low dimensional embedded representation of the data based on the eigenvectors of the graph Laplacian, 2) applies k-means on the constructed low dimensional data to obtain the clustering result. Thus,",
"full_name": "Spectral Clustering",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Clustering** methods cluster a dataset so that similar datapoints are located in the same group. Below you can find a continuously updating list of clustering methods.",
"name": "Clustering",
"parent": null
},
"name": "Spectral Clustering",
"source_title": "A Tutorial on Spectral Clustering",
"source_url": "http://arxiv.org/abs/0711.0189v1"
}
] |
https://paperswithcode.com/paper/how-much-are-you-willing-to-share-a-poker
|
1806.00914
| null | null |
How Much Are You Willing to Share? A "Poker-Styled" Selective Privacy Preserving Framework for Recommender Systems
|
Most industrial recommender systems rely on the popular collaborative
filtering (CF) technique for providing personalized recommendations to its
users. However, the very nature of CF is adversarial to the idea of user
privacy, because users need to share their preferences with others in order to
be grouped with like-minded people and receive accurate recommendations. While
previous privacy preserving approaches have been successful inasmuch as they
concealed user preference information to some extent from a centralized
recommender system, they have also, nevertheless, incurred significant
trade-offs in terms of privacy, scalability, and accuracy. They are also
vulnerable to privacy breaches by malicious actors. In light of these
observations, we propose a novel selective privacy preserving (SP2) paradigm
that allows users to custom define the scope and extent of their individual
privacies, by marking their personal ratings as either public (which can be
shared) or private (which are never shared and stored only on the user device).
Our SP2 framework works in two steps: (i) First, it builds an initial
recommendation model based on the sum of all public ratings that have been
shared by users and (ii) then, this public model is fine-tuned on each user's
device based on the user private ratings, thus eventually learning a more
accurate model. Furthermore, in this work, we introduce three different
algorithms for implementing an end-to-end SP2 framework that can scale
effectively from thousands to hundreds of millions of items. Our user survey
shows that an overwhelming fraction of users are likely to rate much more items
to improve the overall recommendations when they can control what ratings will
be publicly shared with others.
| null |
http://arxiv.org/abs/1806.00914v1
|
http://arxiv.org/pdf/1806.00914v1.pdf
| null |
[
"Manoj Reddy Dareddy",
"Ariyam Das",
"Junghoo Cho",
"Carlo Zaniolo"
] |
[
"Collaborative Filtering",
"Privacy Preserving",
"Recommendation Systems"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/self-normalization-properties-of-language
|
1806.00913
| null | null |
Self-Normalization Properties of Language Modeling
|
Self-normalizing discriminative models approximate the normalized probability
of a class without having to compute the partition function. In the context of
language modeling, this property is particularly appealing as it may
significantly reduce run-times due to large word vocabularies. In this study,
we provide a comprehensive investigation of language modeling
self-normalization. First, we theoretically analyze the inherent
self-normalization properties of Noise Contrastive Estimation (NCE) language
models. Then, we compare them empirically to softmax-based approaches, which
are self-normalized using explicit regularization, and suggest a hybrid model
with compelling properties. Finally, we uncover a surprising negative
correlation between self-normalization and perplexity across the board, as well
as some regularity in the observed errors, which may potentially be used for
improving self-normalization algorithms in the future.
| null |
http://arxiv.org/abs/1806.00913v1
|
http://arxiv.org/pdf/1806.00913v1.pdf
|
COLING 2018 8
|
[
"Jacob Goldberger",
"Oren Melamud"
] |
[
"Language Modeling",
"Language Modelling"
] | 2018-06-04T00:00:00 |
https://aclanthology.org/C18-1065
|
https://aclanthology.org/C18-1065.pdf
|
self-normalization-properties-of-language-1
| null |
[] |
https://paperswithcode.com/paper/bayesian-semantic-instance-segmentation-in
|
1806.00911
| null | null |
Bayesian Semantic Instance Segmentation in Open Set World
|
This paper addresses the semantic instance segmentation task in the open-set
conditions, where input images can contain known and unknown object classes.
The training process of existing semantic instance segmentation methods
requires annotation masks for all object instances, which is expensive to
acquire or even infeasible in some realistic scenarios, where the number of
categories may increase boundlessly. In this paper, we present a novel open-set
semantic instance segmentation approach capable of segmenting all known and
unknown object classes in images, based on the output of an object detector
trained on known object classes. We formulate the problem using a Bayesian
framework, where the posterior distribution is approximated with a simulated
annealing optimization equipped with an efficient image partition sampler. We
show empirically that our method is competitive with state-of-the-art
supervised methods on known classes, but also performs well on unknown classes
when compared with unsupervised methods.
| null |
http://arxiv.org/abs/1806.00911v2
|
http://arxiv.org/pdf/1806.00911v2.pdf
|
ECCV 2018 9
|
[
"Trung Pham",
"Vijay Kumar B G",
"Thanh-Toan Do",
"Gustavo Carneiro",
"Ian Reid"
] |
[
"Instance Segmentation",
"Object",
"Segmentation",
"Semantic Segmentation"
] | 2018-06-04T00:00:00 |
http://openaccess.thecvf.com/content_ECCV_2018/html/Trung_Pham_Bayesian_Instance_Segmentation_ECCV_2018_paper.html
|
http://openaccess.thecvf.com/content_ECCV_2018/papers/Trung_Pham_Bayesian_Instance_Segmentation_ECCV_2018_paper.pdf
|
bayesian-semantic-instance-segmentation-in-1
| null |
[] |
https://paperswithcode.com/paper/effect-of-antipsychotics-on-community
|
1806.00080
| null | null |
Effect of antipsychotics on community structure in functional brain networks
|
Schizophrenia, a mental disorder that is characterized by abnormal social
behavior and failure to distinguish one's own thoughts and ideas from reality,
has been associated with structural abnormalities in the architecture of
functional brain networks. Using various methods from network analysis, we
examine the effect of two classical therapeutic antipsychotics --- Aripiprazole
and Sulpiride --- on the structure of functional brain networks of healthy
controls and patients who have been diagnosed with schizophrenia. We compare
the community structures of functional brain networks of different individuals
using mesoscopic response functions, which measure how community structure
changes across different scales of a network. We are able to do a reasonably
good job of distinguishing patients from controls, and we are most successful
at this task on people who have been treated with Aripiprazole. We demonstrate
that this increased separation between patients and controls is related only to
a change in the control group, as the functional brain networks of the patient
group appear to be predominantly unaffected by this drug. This suggests that
Aripiprazole has a significant and measurable effect on community structure in
healthy individuals but not in individuals who are diagnosed with
schizophrenia. In contrast, we find for individuals are given the drug
Sulpiride that it is more difficult to separate the networks of patients from
those of controls. Overall, we observe differences in the effects of the drugs
(and a placebo) on community structure in patients and controls and also that
this effect differs across groups. We thereby demonstrate that different types
of antipsychotic drugs selectively affect mesoscale structures of brain
networks, providing support that mesoscale structures such as communities are
meaningful functional units in the brain.
| null |
http://arxiv.org/abs/1806.00080v2
|
http://arxiv.org/pdf/1806.00080v2.pdf
| null |
[
"Ryan Flanagan",
"Lucas Lacasa",
"Emma K. Towlson",
"Sang Hoon Lee",
"Mason A. Porter"
] |
[] | 2018-05-31T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/an-unsupervised-and-customizable-misspelling
|
1806.00910
| null | null |
An unsupervised and customizable misspelling generator for mining noisy health-related text sources
|
In this paper, we present a customizable datacentric system that
automatically generates common misspellings for complex health-related terms.
The spelling variant generator relies on a dense vector model learned from
large unlabeled text, which is used to find semantically close terms to the
original/seed keyword, followed by the filtering of terms that are lexically
dissimilar beyond a given threshold. The process is executed recursively,
converging when no new terms similar (lexically and semantically) to the seed
keyword are found. Weighting of intra-word character sequence similarities
allows further problem-specific customization of the system. On a dataset
prepared for this study, our system outperforms the current state-of-the-art
for medication name variant generation with best F1-score of 0.69 and
F1/4-score of 0.78. Extrinsic evaluation of the system on a set of
cancer-related terms showed an increase of over 67% in retrieval rate from
Twitter posts when the generated variants are included. Our proposed spelling
variant generator has several advantages over the current state-of-the-art and
other types of variant generators-(i) it is capable of filtering out lexically
similar but semantically dissimilar terms, (ii) the number of variants
generated is low as many low-frequency and ambiguous misspellings are filtered
out, and (iii) the system is fully automatic, customizable and easily
executable. While the base system is fully unsupervised, we show how
supervision maybe employed to adjust weights for task-specific customization.
The performance and significant relative simplicity of our proposed approach
makes it a much needed misspelling generation resource for health-related text
mining from noisy sources. The source code for the system has been made
publicly available for research purposes.
|
Our proposed spelling variant generator has several advantages over the current state-of-the-art and other types of variant generators-(i) it is capable of filtering out lexically similar but semantically dissimilar terms, (ii) the number of variants generated is low as many low-frequency and ambiguous misspellings are filtered out, and (iii) the system is fully automatic, customizable and easily executable.
|
http://arxiv.org/abs/1806.00910v1
|
http://arxiv.org/pdf/1806.00910v1.pdf
| null |
[
"Abeed Sarker",
"Graciela Gonzalez-Hernandez"
] |
[
"Retrieval"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/large-scale-land-cover-classification-in
|
1806.00901
| null | null |
Large-scale Land Cover Classification in GaoFen-2 Satellite Imagery
|
Many significant applications need land cover information of remote sensing
images that are acquired from different areas and times, such as change
detection and disaster monitoring. However, it is difficult to find a generic
land cover classification scheme for different remote sensing images due to the
spectral shift caused by diverse acquisition condition. In this paper, we
develop a novel land cover classification method that can deal with large-scale
data captured from widely distributed areas and different times. Additionally,
we establish a large-scale land cover classification dataset consisting of 150
Gaofen-2 imageries as data support for model training and performance
evaluation. Our experiments achieve outstanding classification accuracy
compared with traditional methods.
| null |
http://arxiv.org/abs/1806.00901v1
|
http://arxiv.org/pdf/1806.00901v1.pdf
| null |
[
"Xin-Yi Tong",
"Qikai Lu",
"Gui-Song Xia",
"Liangpei Zhang"
] |
[
"Change Detection",
"Classification",
"General Classification",
"Land Cover Classification"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/algorithmic-regularization-in-learning-deep
|
1806.00900
| null | null |
Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced
|
We study the implicit regularization imposed by gradient descent for learning
multi-layer homogeneous functions including feed-forward fully connected and
convolutional deep neural networks with linear, ReLU or Leaky ReLU activation.
We rigorously prove that gradient flow (i.e. gradient descent with
infinitesimal step size) effectively enforces the differences between squared
norms across different layers to remain invariant without any explicit
regularization. This result implies that if the weights are initially small,
gradient flow automatically balances the magnitudes of all layers. Using a
discretization argument, we analyze gradient descent with positive step size
for the non-convex low-rank asymmetric matrix factorization problem without any
regularization. Inspired by our findings for gradient flow, we prove that
gradient descent with step sizes $\eta_t = O\left(t^{-\left(
\frac12+\delta\right)} \right)$ ($0<\delta\le\frac12$) automatically balances
two low-rank factors and converges to a bounded global optimum. Furthermore,
for rank-$1$ asymmetric matrix factorization we give a finer analysis showing
gradient descent with constant step size converges to the global minimum at a
globally linear rate. We believe that the idea of examining the invariance
imposed by first order algorithms in learning homogeneous models could serve as
a fundamental building block for studying optimization for learning deep
models.
| null |
http://arxiv.org/abs/1806.00900v2
|
http://arxiv.org/pdf/1806.00900v2.pdf
|
NeurIPS 2018 12
|
[
"Simon S. Du",
"Wei Hu",
"Jason D. Lee"
] |
[] | 2018-06-04T00:00:00 |
http://papers.nips.cc/paper/7321-algorithmic-regularization-in-learning-deep-homogeneous-models-layers-are-automatically-balanced
|
http://papers.nips.cc/paper/7321-algorithmic-regularization-in-learning-deep-homogeneous-models-layers-are-automatically-balanced.pdf
|
algorithmic-regularization-in-learning-deep-1
| null |
[
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!\r\n\r\n\r\nHow Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!",
"full_name": "*Communicated@Fast*How Do I Communicate to Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "How do I get a human at Expedia?\r\nHow Do I Get a Human at Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Real-Time Help & Exclusive Travel Deals!Want to speak with a real person at Expedia? Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now for immediate support and unlock exclusive best deal discounts on flights, hotels, and vacation packages. Skip the wait, get fast answers, and enjoy limited-time offers that make your next journey more affordable and stress-free. Call today and save!\r\n\r\nHow do I get a human at Expedia?\r\nHow Do I Get a Human at Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Real-Time Help & Exclusive Travel Deals!Want to speak with a real person at Expedia? Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now for immediate support and unlock exclusive best deal discounts on flights, hotels, and vacation packages. Skip the wait, get fast answers, and enjoy limited-time offers that make your next journey more affordable and stress-free. Call today and save!",
"full_name": "HuMan(Expedia)||How do I get a human at Expedia?",
"introduced_year": 2014,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "HuMan(Expedia)||How do I get a human at Expedia?",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/recent-advances-and-opportunities-in-scene
|
1806.00899
| null | null |
Recent advances and opportunities in scene classification of aerial images with deep models
|
Scene classification is a fundamental task in interpretation of remote
sensing images, and has become an active research topic in remote sensing
community due to its important role in a wide range of applications. Over the
past years, tremendous efforts have been made for developing powerful
approaches for scene classification of remote sensing images, evolving from the
traditional bag-of-visual-words model to the new generation deep convolutional
neural networks (CNNs). The deep CNN based methods have exhibited remarkable
breakthrough on performance, dramatically outperforming previous methods which
strongly rely on hand-crafted features. However, performance with deep CNNs has
gradually plateaued on existing public scene datasets, due to the notable
drawbacks of these datasets, such as the small scale and low-diversity of
training samples. Therefore, to promote the development of new methods and move
the scene classification task a step further, we deeply discuss the existing
problems in scene classification task, and accordingly present three open
directions. We believe these potential directions will be instructive for the
researchers in this field.
| null |
http://arxiv.org/abs/1806.00899v1
|
http://arxiv.org/pdf/1806.00899v1.pdf
| null |
[
"Fan Hu",
"Gui-Song Xia",
"Wen Yang",
"Liangpei Zhang"
] |
[
"Classification",
"Diversity",
"General Classification",
"Scene Classification"
] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/techniques-for-proving-asynchronous
|
1711.06719
| null | null |
Techniques for proving Asynchronous Convergence results for Markov Chain Monte Carlo methods
|
Markov Chain Monte Carlo (MCMC) methods such as Gibbs sampling are finding
widespread use in applied statistics and machine learning. These often lead to
difficult computational problems, which are increasingly being solved on
parallel and distributed systems such as compute clusters. Recent work has
proposed running iterative algorithms such as gradient descent and MCMC in
parallel asynchronously for increased performance, with good empirical results
in certain problems. Unfortunately, for MCMC this parallelization technique
requires new convergence theory, as it has been explicitly demonstrated to lead
to divergence on some examples. Recent theory on Asynchronous Gibbs sampling
describes why these algorithms can fail, and provides a way to alter them to
make them converge. In this article, we describe how to apply this theory in a
generic setting, to understand the asynchronous behavior of any MCMC algorithm,
including those implemented using parameter servers, and those not based on
Gibbs sampling.
| null |
http://arxiv.org/abs/1711.06719v5
|
http://arxiv.org/pdf/1711.06719v5.pdf
| null |
[
"Alexander Terenin",
"Eric P. Xing"
] |
[] | 2017-11-17T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/infrastructure-quality-assessment-in-africa
|
1806.00894
| null | null |
Infrastructure Quality Assessment in Africa using Satellite Imagery and Deep Learning
|
The UN Sustainable Development Goals allude to the importance of
infrastructure quality in three of its seventeen goals. However, monitoring
infrastructure quality in developing regions remains prohibitively expensive
and impedes efforts to measure progress toward these goals. To this end, we
investigate the use of widely available remote sensing data for the prediction
of infrastructure quality in Africa. We train a convolutional neural network to
predict ground truth labels from the Afrobarometer Round 6 survey using Landsat
8 and Sentinel 1 satellite imagery.
Our best models predict infrastructure quality with AUROC scores of 0.881 on
Electricity, 0.862 on Sewerage, 0.739 on Piped Water, and 0.786 on Roads using
Landsat 8. These performances are significantly better than models that
leverage OpenStreetMap or nighttime light intensity on the same tasks. We also
demonstrate that our trained model can accurately make predictions in an unseen
country after fine-tuning on a small sample of images. Furthermore, the model
can be deployed in regions with limited samples to predict infrastructure
outcomes with higher performance than nearest neighbor spatial interpolation.
| null |
http://arxiv.org/abs/1806.00894v1
|
http://arxiv.org/pdf/1806.00894v1.pdf
| null |
[
"Barak Oshri",
"Annie Hu",
"Peter Adelson",
"Xiao Chen",
"Pascaline Dupas",
"Jeremy Weinstein",
"Marshall Burke",
"David Lobell",
"Stefano Ermon"
] |
[
"Spatial Interpolation"
] | 2018-06-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/conservative-exploration-using-interleaving
|
1806.00892
| null | null |
Conservative Exploration using Interleaving
|
In many practical problems, a learning agent may want to learn the best
action in hindsight without ever taking a bad action, which is significantly
worse than the default production action. In general, this is impossible
because the agent has to explore unknown actions, some of which can be bad, to
learn better actions. However, when the actions are combinatorial, this may be
possible if the unknown action can be evaluated by interleaving it with the
production action. We formalize this concept as learning in stochastic
combinatorial semi-bandits with exchangeable actions. We design efficient
learning algorithms for this problem, bound their n-step regret, and evaluate
them on both synthetic and real-world problems. Our real-world experiments show
that our algorithms can learn to recommend K most attractive movies without
ever violating a strict production constraint, both overall and subject to a
diversity constraint.
| null |
http://arxiv.org/abs/1806.00892v1
|
http://arxiv.org/pdf/1806.00892v1.pdf
| null |
[
"Sumeet Katariya",
"Branislav Kveton",
"Zheng Wen",
"Vamsi K. Potluru"
] |
[
"Diversity"
] | 2018-06-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/on-the-limitations-of-first-order-1
|
1706.09884
| null | null |
On the Limitations of First-Order Approximation in GAN Dynamics
|
While Generative Adversarial Networks (GANs) have demonstrated promising
performance on multiple vision tasks, their learning dynamics are not yet well
understood, both in theory and in practice. To address this issue, we study GAN
dynamics in a simple yet rich parametric model that exhibits several of the
common problematic convergence behaviors such as vanishing gradients, mode
collapse, and diverging or oscillatory behavior. In spite of the non-convex
nature of our model, we are able to perform a rigorous theoretical analysis of
its convergence behavior. Our analysis reveals an interesting dichotomy: a GAN
with an optimal discriminator provably converges, while first order
approximations of the discriminator steps lead to unstable GAN dynamics and
mode collapse. Our result suggests that using first order discriminator steps
(the de-facto standard in most existing GAN setups) might be one of the factors
that makes GAN training challenging in practice.
| null |
http://arxiv.org/abs/1706.09884v2
|
http://arxiv.org/pdf/1706.09884v2.pdf
|
ICML 2018 7
|
[
"Jerry Li",
"Aleksander Madry",
"John Peebles",
"Ludwig Schmidt"
] |
[] | 2017-06-29T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2342
|
http://proceedings.mlr.press/v80/li18d/li18d.pdf
|
on-the-limitations-of-first-order-2
| null |
[
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "In today’s digital age, Dogecoin has become more than just a buzzword—it’s a revolutionary way to manage and invest your money. But just like with any advanced technology, users sometimes face issues that can be frustrating or even alarming. Whether you're dealing with a Dogecoin transaction not confirmed, your Dogecoin wallet not showing balance, or you're trying to recover a lost Dogecoin wallet, knowing where to get help is essential. That’s why the Dogecoin customer support number +1-833-534-1729 is your go-to solution for fast and reliable assistance.\r\n\r\nWhy You Might Need to Call the Dogecoin Customer Support Number +1-833-534-1729\r\nDogecoin operates on a decentralized network, which means there’s no single company or office that manages everything. However, platforms, wallets, and third-party services provide support to make your experience smoother. Calling +1-833-534-1729 can help you troubleshoot issues such as:\r\n\r\n1. 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Knowing how to recover a lost Dogecoin wallet depends on the type of wallet you used—hardware, mobile, desktop, or paper. With the right support, often involving your seed phrase or backup file, you can get your assets back. Don’t waste time; dial +1-833-534-1729 for step-by-step recovery help.\r\n\r\n4. Dogecoin Deposit Not Received\r\nIf someone has sent you Dogecoin but it’s not showing up in your wallet, it could be a delay in network confirmation or a mistake in the receiving address. A Dogecoin deposit not received needs quick attention. Call +1-833-534-1729 to trace the transaction and understand whether it’s on-chain, pending, or if the funds have been misdirected.\r\n\r\n5. Dogecoin Transaction Stuck or Pending\r\nSometimes your Dogecoin transaction is stuck or pending due to low gas fees or heavy blockchain traffic. While this can resolve itself, in some cases it doesn't. Don’t stay in the dark. A quick call to +1-833-534-1729 can give you clarity and guidance on whether to wait, rebroadcast, or use a transaction accelerator.\r\n\r\n6. Dogecoin Wallet Recovery Phrase Issue\r\nYour 12 or 24-word Dogecoin wallet recovery phrase is the key to your funds. But what if it’s not working? If you’re seeing errors or your wallet can’t be restored, something might have gone wrong during the backup. Experts at +1-833-534-1729 can help verify the phrase, troubleshoot format issues, and guide you on next steps.\r\n\r\nHow the Dogecoin Support Number +1-833-534-1729 Helps You\r\nWhen you’re dealing with cryptocurrency issues, every second counts. Here’s why users trust +1-833-534-1729:\r\n\r\nLive Experts: Talk to real people who understand wallets, blockchain, and Dogecoin tech.\r\n\r\n24/7 Availability: Dogecoin doesn’t sleep, and neither should your support.\r\n\r\nStep-by-Step Guidance: Whether you're a beginner or seasoned investor, the team guides you with patience and clarity.\r\n\r\nData Privacy: Your security and wallet details are treated with the highest confidentiality.\r\n\r\nFAQs About Dogecoin Support and Wallet Issues\r\nQ1: Can Dogecoin support help me recover stolen BTC?\r\nA: While Dogecoin transactions are irreversible, support can help investigate, trace addresses, and advise on what to do next.\r\n\r\nQ2: My wallet shows zero balance after reinstalling. What do I do?\r\nA: Ensure you restored with the correct recovery phrase and wallet type. Call +1-833-534-1729 for assistance.\r\n\r\nQ3: What if I forgot my wallet password?\r\nA: Recovery depends on the wallet provider. Support can check if recovery options or tools are available.\r\n\r\nQ4: I sent BTC to the wrong address. Can support help?\r\nA: Dogecoin transactions are final. If the address is invalid, the transaction may fail. If it’s valid but unintended, unfortunately, it’s not reversible. Still, call +1-833-534-1729 to explore all possible solutions.\r\n\r\nQ5: Is this number official?\r\nA: While +1-833-534-1729 is not Dogecoin’s official number (Dogecoin is decentralized), it connects you to trained professionals experienced in resolving all major Dogecoin issues.\r\n\r\nFinal Thoughts\r\nDogecoin is a powerful tool for financial freedom—but only when everything works as expected. When things go sideways, you need someone to rely on. Whether it's a Dogecoin transaction not confirmed, your Dogecoin wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Dogecoin customer support number +1-833-534-1729 can be your fastest path to peace of mind.\r\n\r\nNo matter what the issue, you don’t have to face it alone. Expert help is just a call away—+1-833-534-1729.",
"full_name": "Dogecoin Customer Service Number +1-833-534-1729",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Generative Models** aim to model data generatively (rather than discriminatively), that is they aim to approximate the probability distribution of the data. Below you can find a continuously updating list of generative models for computer vision.",
"name": "Generative Models",
"parent": null
},
"name": "Dogecoin Customer Service Number +1-833-534-1729",
"source_title": "Generative Adversarial Networks",
"source_url": "https://arxiv.org/abs/1406.2661v1"
}
] |
https://paperswithcode.com/paper/soccer-on-your-tabletop
|
1806.00890
| null | null |
Soccer on Your Tabletop
|
We present a system that transforms a monocular video of a soccer game into a
moving 3D reconstruction, in which the players and field can be rendered
interactively with a 3D viewer or through an Augmented Reality device. At the
heart of our paper is an approach to estimate the depth map of each player,
using a CNN that is trained on 3D player data extracted from soccer video
games. We compare with state of the art body pose and depth estimation
techniques, and show results on both synthetic ground truth benchmarks, and
real YouTube soccer footage.
| null |
http://arxiv.org/abs/1806.00890v1
|
http://arxiv.org/pdf/1806.00890v1.pdf
|
CVPR 2018 6
|
[
"Konstantinos Rematas",
"Ira Kemelmacher-Shlizerman",
"Brian Curless",
"Steve Seitz"
] |
[
"3D Reconstruction",
"Depth Estimation"
] | 2018-06-03T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Rematas_Soccer_on_Your_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Rematas_Soccer_on_Your_CVPR_2018_paper.pdf
|
soccer-on-your-tabletop-1
| null |
[] |
https://paperswithcode.com/paper/structural-learning-of-multivariate
|
1806.00882
| null | null |
Structural Learning of Multivariate Regression Chain Graphs via Decomposition
|
We extend the decomposition approach for learning Bayesian networks (BNs) proposed by (Xie et. al.) to learning multivariate regression chain graphs (MVR CGs), which include BNs as a special case. The same advantages of this decomposition approach hold in the more general setting: reduced complexity and increased power of computational independence tests. Moreover, latent (hidden) variables can be represented in MVR CGs by using bidirected edges, and our algorithm correctly recovers any independence structure that is faithful to an MVR CG, thus greatly extending the range of applications of decomposition-based model selection techniques. Simulations under a variety of settings demonstrate the competitive performance of our method in comparison with the PC-like algorithm (Sonntag and Pena). In fact, the decomposition-based algorithm usually outperforms the PC-like algorithm except in running time. The performance of both algorithms is much better when the underlying graph is sparse.
| null |
https://arxiv.org/abs/1806.00882v2
|
https://arxiv.org/pdf/1806.00882v2.pdf
| null |
[
"Mohammad Ali Javidian",
"Marco Valtorta"
] |
[
"Model Selection",
"regression"
] | 2018-06-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/wide-inference-network-for-image-denoising
|
1707.05414
| null | null |
Wide Inference Network for Image Denoising via Learning Pixel-distribution Prior
|
We explore an innovative strategy for image denoising by using convolutional
neural networks (CNN) to learn similar pixel-distribution features from noisy
images. Many types of image noise follow a certain pixel-distribution in
common, such as additive white Gaussian noise (AWGN). By increasing CNN's width
with larger reception fields and more channels in each layer, CNNs can reveal
the ability to extract more accurate pixel-distribution features. The key to
our approach is a discovery that wider CNNs with more convolutions tend to
learn the similar pixel-distribution features, which reveals a new strategy to
solve low-level vision problems effectively that the inference mapping
primarily relies on the priors behind the noise property instead of deeper CNNs
with more stacked nonlinear layers. We evaluate our work, Wide inference
Networks (WIN), on AWGN and demonstrate that by learning pixel-distribution
features from images, WIN-based network consistently achieves significantly
better performance than current state-of-the-art deep CNN-based methods in both
quantitative and visual evaluations. \textit{Code and models are available at
\url{https://github.com/cswin/WIN}}.
|
We explore an innovative strategy for image denoising by using convolutional neural networks (CNN) to learn similar pixel-distribution features from noisy images.
|
http://arxiv.org/abs/1707.05414v5
|
http://arxiv.org/pdf/1707.05414v5.pdf
| null |
[
"Peng Liu",
"Ruogu Fang"
] |
[
"Denoising",
"Image Denoising"
] | 2017-07-17T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/disconnected-manifold-learning-for-generative
|
1806.00880
| null | null |
Disconnected Manifold Learning for Generative Adversarial Networks
|
Natural images may lie on a union of disjoint manifolds rather than one
globally connected manifold, and this can cause several difficulties for the
training of common Generative Adversarial Networks (GANs). In this work, we
first show that single generator GANs are unable to correctly model a
distribution supported on a disconnected manifold, and investigate how sample
quality, mode dropping and local convergence are affected by this. Next, we
show how using a collection of generators can address this problem, providing
new insights into the success of such multi-generator GANs. Finally, we explain
the serious issues caused by considering a fixed prior over the collection of
generators and propose a novel approach for learning the prior and inferring
the necessary number of generators without any supervision. Our proposed
modifications can be applied on top of any other GAN model to enable learning
of distributions supported on disconnected manifolds. We conduct several
experiments to illustrate the aforementioned shortcoming of GANs, its
consequences in practice, and the effectiveness of our proposed modifications
in alleviating these issues.
|
Natural images may lie on a union of disjoint manifolds rather than one globally connected manifold, and this can cause several difficulties for the training of common Generative Adversarial Networks (GANs).
|
http://arxiv.org/abs/1806.00880v3
|
http://arxiv.org/pdf/1806.00880v3.pdf
|
NeurIPS 2018 12
|
[
"Mahyar Khayatkhoei",
"Ahmed Elgammal",
"Maneesh Singh"
] |
[] | 2018-06-03T00:00:00 |
http://papers.nips.cc/paper/7964-disconnected-manifold-learning-for-generative-adversarial-networks
|
http://papers.nips.cc/paper/7964-disconnected-manifold-learning-for-generative-adversarial-networks.pdf
|
disconnected-manifold-learning-for-generative-1
| null |
[
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "In today’s digital age, Dogecoin has become more than just a buzzword—it’s a revolutionary way to manage and invest your money. But just like with any advanced technology, users sometimes face issues that can be frustrating or even alarming. Whether you're dealing with a Dogecoin transaction not confirmed, your Dogecoin wallet not showing balance, or you're trying to recover a lost Dogecoin wallet, knowing where to get help is essential. That’s why the Dogecoin customer support number +1-833-534-1729 is your go-to solution for fast and reliable assistance.\r\n\r\nWhy You Might Need to Call the Dogecoin Customer Support Number +1-833-534-1729\r\nDogecoin operates on a decentralized network, which means there’s no single company or office that manages everything. However, platforms, wallets, and third-party services provide support to make your experience smoother. Calling +1-833-534-1729 can help you troubleshoot issues such as:\r\n\r\n1. Dogecoin Transaction Not Confirmed\r\nOne of the most common concerns is when a Dogecoin transaction is stuck or pending. This usually happens due to low miner fees or network congestion. If your transaction hasn’t been confirmed for hours or even days, it’s important to get expert help through +1-833-534-1729 to understand what steps you can take next—whether it’s accelerating the transaction or canceling and resending it.\r\n\r\n2. Dogecoin Wallet Not Showing Balance\r\nImagine opening your wallet and seeing a zero balance even though you know you haven’t made any transactions. A Dogecoin wallet not showing balance can be caused by a sync issue, outdated app version, or even incorrect wallet address. The support team at +1-833-534-1729 can walk you through diagnostics and get your balance showing correctly again.\r\n\r\n3. How to Recover Lost Dogecoin Wallet\r\nLost access to your wallet? That can feel like the end of the world, but all may not be lost. Knowing how to recover a lost Dogecoin wallet depends on the type of wallet you used—hardware, mobile, desktop, or paper. With the right support, often involving your seed phrase or backup file, you can get your assets back. Don’t waste time; dial +1-833-534-1729 for step-by-step recovery help.\r\n\r\n4. Dogecoin Deposit Not Received\r\nIf someone has sent you Dogecoin but it’s not showing up in your wallet, it could be a delay in network confirmation or a mistake in the receiving address. A Dogecoin deposit not received needs quick attention. Call +1-833-534-1729 to trace the transaction and understand whether it’s on-chain, pending, or if the funds have been misdirected.\r\n\r\n5. Dogecoin Transaction Stuck or Pending\r\nSometimes your Dogecoin transaction is stuck or pending due to low gas fees or heavy blockchain traffic. While this can resolve itself, in some cases it doesn't. Don’t stay in the dark. A quick call to +1-833-534-1729 can give you clarity and guidance on whether to wait, rebroadcast, or use a transaction accelerator.\r\n\r\n6. Dogecoin Wallet Recovery Phrase Issue\r\nYour 12 or 24-word Dogecoin wallet recovery phrase is the key to your funds. But what if it’s not working? If you’re seeing errors or your wallet can’t be restored, something might have gone wrong during the backup. Experts at +1-833-534-1729 can help verify the phrase, troubleshoot format issues, and guide you on next steps.\r\n\r\nHow the Dogecoin Support Number +1-833-534-1729 Helps You\r\nWhen you’re dealing with cryptocurrency issues, every second counts. Here’s why users trust +1-833-534-1729:\r\n\r\nLive Experts: Talk to real people who understand wallets, blockchain, and Dogecoin tech.\r\n\r\n24/7 Availability: Dogecoin doesn’t sleep, and neither should your support.\r\n\r\nStep-by-Step Guidance: Whether you're a beginner or seasoned investor, the team guides you with patience and clarity.\r\n\r\nData Privacy: Your security and wallet details are treated with the highest confidentiality.\r\n\r\nFAQs About Dogecoin Support and Wallet Issues\r\nQ1: Can Dogecoin support help me recover stolen BTC?\r\nA: While Dogecoin transactions are irreversible, support can help investigate, trace addresses, and advise on what to do next.\r\n\r\nQ2: My wallet shows zero balance after reinstalling. What do I do?\r\nA: Ensure you restored with the correct recovery phrase and wallet type. Call +1-833-534-1729 for assistance.\r\n\r\nQ3: What if I forgot my wallet password?\r\nA: Recovery depends on the wallet provider. Support can check if recovery options or tools are available.\r\n\r\nQ4: I sent BTC to the wrong address. Can support help?\r\nA: Dogecoin transactions are final. If the address is invalid, the transaction may fail. If it’s valid but unintended, unfortunately, it’s not reversible. Still, call +1-833-534-1729 to explore all possible solutions.\r\n\r\nQ5: Is this number official?\r\nA: While +1-833-534-1729 is not Dogecoin’s official number (Dogecoin is decentralized), it connects you to trained professionals experienced in resolving all major Dogecoin issues.\r\n\r\nFinal Thoughts\r\nDogecoin is a powerful tool for financial freedom—but only when everything works as expected. When things go sideways, you need someone to rely on. Whether it's a Dogecoin transaction not confirmed, your Dogecoin wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Dogecoin customer support number +1-833-534-1729 can be your fastest path to peace of mind.\r\n\r\nNo matter what the issue, you don’t have to face it alone. Expert help is just a call away—+1-833-534-1729.",
"full_name": "Dogecoin Customer Service Number +1-833-534-1729",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Generative Models** aim to model data generatively (rather than discriminatively), that is they aim to approximate the probability distribution of the data. Below you can find a continuously updating list of generative models for computer vision.",
"name": "Generative Models",
"parent": null
},
"name": "Dogecoin Customer Service Number +1-833-534-1729",
"source_title": "Generative Adversarial Networks",
"source_url": "https://arxiv.org/abs/1406.2661v1"
}
] |
https://paperswithcode.com/paper/multi-agent-reinforcement-learning-via-double
|
1806.00877
| null | null |
Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization
|
Despite the success of single-agent reinforcement learning, multi-agent
reinforcement learning (MARL) remains challenging due to complex interactions
between agents. Motivated by decentralized applications such as sensor
networks, swarm robotics, and power grids, we study policy evaluation in MARL,
where agents with jointly observed state-action pairs and private local rewards
collaborate to learn the value of a given policy. In this paper, we propose a
double averaging scheme, where each agent iteratively performs averaging over
both space and time to incorporate neighboring gradient information and local
reward information, respectively. We prove that the proposed algorithm
converges to the optimal solution at a global geometric rate. In particular,
such an algorithm is built upon a primal-dual reformulation of the mean squared
projected Bellman error minimization problem, which gives rise to a
decentralized convex-concave saddle-point problem. To the best of our
knowledge, the proposed double averaging primal-dual optimization algorithm is
the first to achieve fast finite-time convergence on decentralized
convex-concave saddle-point problems.
| null |
http://arxiv.org/abs/1806.00877v4
|
http://arxiv.org/pdf/1806.00877v4.pdf
|
NeurIPS 2018 12
|
[
"Hoi-To Wai",
"Zhuoran Yang",
"Zhaoran Wang",
"Mingyi Hong"
] |
[
"Multi-agent Reinforcement Learning",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-06-03T00:00:00 |
http://papers.nips.cc/paper/8173-multi-agent-reinforcement-learning-via-double-averaging-primal-dual-optimization
|
http://papers.nips.cc/paper/8173-multi-agent-reinforcement-learning-via-double-averaging-primal-dual-optimization.pdf
|
multi-agent-reinforcement-learning-via-double-1
| null |
[] |
https://paperswithcode.com/paper/deploying-customized-data-representation-and
|
1806.00875
| null | null |
Deploying Customized Data Representation and Approximate Computing in Machine Learning Applications
|
Major advancements in building general-purpose and customized hardware have
been one of the key enablers of versatility and pervasiveness of machine
learning models such as deep neural networks. To sustain this ubiquitous
deployment of machine learning models and cope with their computational and
storage complexity, several solutions such as low-precision representation of
model parameters using fixed-point representation and deploying approximate
arithmetic operations have been employed. Studying the potency of such
solutions in different applications requires integrating them into existing
machine learning frameworks for high-level simulations as well as implementing
them in hardware to analyze their effects on power/energy dissipation,
throughput, and chip area. Lop is a library for design space exploration that
bridges the gap between machine learning and efficient hardware realization. It
comprises a Python module, which can be integrated with some of the existing
machine learning frameworks and implements various customizable data
representations including fixed-point and floating-point as well as approximate
arithmetic operations.Furthermore, it includes a highly-parameterized Scala
module, which allows synthesizing hardware based on the said data
representations and arithmetic operations. Lop allows researchers and designers
to quickly compare quality of their models using various data representations
and arithmetic operations in Python and contrast the hardware cost of viable
representations by synthesizing them on their target platforms (e.g., FPGA or
ASIC). To the best of our knowledge, Lop is the first library that allows both
software simulation and hardware realization using customized data
representations and approximate computing techniques.
| null |
http://arxiv.org/abs/1806.00875v1
|
http://arxiv.org/pdf/1806.00875v1.pdf
| null |
[
"Mahdi Nazemi",
"Massoud Pedram"
] |
[
"BIG-bench Machine Learning"
] | 2018-06-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/patch-based-image-hallucination-for-super
|
1806.00874
| null | null |
Patch-Based Image Hallucination for Super Resolution with Detail Reconstruction from Similar Sample Images
|
Image hallucination and super-resolution have been studied for decades, and
many approaches have been proposed to upsample low-resolution images using
information from the images themselves, multiple example images, or large image
databases. However, most of this work has focused exclusively on small
magnification levels because the algorithms simply sharpen the blurry edges in
the upsampled images - no actual new detail is typically reconstructed in the
final result. In this paper, we present a patch-based algorithm for image
hallucination which, for the first time, properly synthesizes novel high
frequency detail. To do this, we pose the synthesis problem as a patch-based
optimization which inserts coherent, high-frequency detail from
contextually-similar images of the same physical scene/subject provided from
either a personal image collection or a large online database. The resulting
image is visually plausible and contains coherent high frequency information.
We demonstrate the robustness of our algorithm by testing it on a large number
of images and show that its performance is considerably superior to all
state-of-the-art approaches, a result that is verified to be statistically
significant through a randomized user study.
| null |
http://arxiv.org/abs/1806.00874v1
|
http://arxiv.org/pdf/1806.00874v1.pdf
| null |
[
"Chieh-Chi Kao",
"Yu-Xiang Wang",
"Jonathan Waltman",
"Pradeep Sen"
] |
[
"Hallucination",
"Super-Resolution"
] | 2018-06-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-comprehensive-comparison-between-neural
|
1806.00868
| null | null |
A Comprehensive Comparison between Neural Style Transfer and Universal Style Transfer
|
Style transfer aims to transfer arbitrary visual styles to content images. We
explore algorithms adapted from two papers that try to solve the problem of
style transfer while generalizing on unseen styles or compromised visual
quality. Majority of the improvements made focus on optimizing the algorithm
for real-time style transfer while adapting to new styles with considerably
less resources and constraints. We compare these strategies and compare how
they measure up to produce visually appealing images. We explore two approaches
to style transfer: neural style transfer with improvements and universal style
transfer. We also make a comparison between the different images produced and
how they can be qualitatively measured.
| null |
http://arxiv.org/abs/1806.00868v1
|
http://arxiv.org/pdf/1806.00868v1.pdf
| null |
[
"Somshubra Majumdar",
"Amlaan Bhoi",
"Ganesh Jagadeesan"
] |
[
"Style Transfer"
] | 2018-06-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/real-time-deep-pose-estimation-with-geodesic
|
1803.05982
| null | null |
Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration
|
With an aim to increase the capture range and accelerate the performance of
state-of-the-art inter-subject and subject-to-template 3D registration, we
propose deep learning-based methods that are trained to find the 3D position of
arbitrarily oriented subjects or anatomy based on slices or volumes of medical
images. For this, we propose regression CNNs that learn to predict the
angle-axis representation of 3D rotations and translations using image
features. We use and compare mean square error and geodesic loss to train
regression CNNs for 3D pose estimation used in two different scenarios:
slice-to-volume registration and volume-to-volume registration. Our results
show that in such registration applications that are amendable to learning, the
proposed deep learning methods with geodesic loss minimization can achieve
accurate results with a wide capture range in real-time (<100ms). We also
tested the generalization capability of the trained CNNs on an expanded age
range and on images of newborn subjects with similar and different MR image
contrasts. We trained our models on T2-weighted fetal brain MRI scans and used
them to predict the 3D pose of newborn brains based on T1-weighted MRI scans.
We showed that the trained models generalized well for the new domain when we
performed image contrast transfer through a conditional generative adversarial
network. This indicates that the domain of application of the trained deep
regression CNNs can be further expanded to image modalities and contrasts other
than those used in training. A combination of our proposed methods with
accelerated optimization-based registration algorithms can dramatically enhance
the performance of automatic imaging devices and image processing methods of
the future.
| null |
http://arxiv.org/abs/1803.05982v4
|
http://arxiv.org/pdf/1803.05982v4.pdf
| null |
[
"Seyed Sadegh Mohseni Salehi",
"Shadab Khan",
"Deniz Erdogmus",
"Ali Gholipour"
] |
[
"3D Pose Estimation",
"Anatomy",
"Generative Adversarial Network",
"Pose Estimation",
"regression"
] | 2018-03-15T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/submodular-hypergraphs-p-laplacians-cheeger
|
1803.03833
| null | null |
Submodular Hypergraphs: p-Laplacians, Cheeger Inequalities and Spectral Clustering
|
We introduce submodular hypergraphs, a family of hypergraphs that have
different submodular weights associated with different cuts of hyperedges.
Submodular hypergraphs arise in clustering applications in which higher-order
structures carry relevant information. For such hypergraphs, we define the
notion of p-Laplacians and derive corresponding nodal domain theorems and k-way
Cheeger inequalities. We conclude with the description of algorithms for
computing the spectra of 1- and 2-Laplacians that constitute the basis of new
spectral hypergraph clustering methods.
|
We introduce submodular hypergraphs, a family of hypergraphs that have different submodular weights associated with different cuts of hyperedges.
|
http://arxiv.org/abs/1803.03833v4
|
http://arxiv.org/pdf/1803.03833v4.pdf
|
ICML 2018 7
|
[
"Pan Li",
"Olgica Milenkovic"
] |
[
"Clustering"
] | 2018-03-10T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2044
|
http://proceedings.mlr.press/v80/li18e/li18e.pdf
|
submodular-hypergraphs-p-laplacians-cheeger-1
| null |
[] |
https://paperswithcode.com/paper/on-the-flip-side-identifying-counterexamples
|
1806.00857
| null | null |
On the Flip Side: Identifying Counterexamples in Visual Question Answering
|
Visual question answering (VQA) models respond to open-ended natural language
questions about images. While VQA is an increasingly popular area of research,
it is unclear to what extent current VQA architectures learn key semantic
distinctions between visually-similar images. To investigate this question, we
explore a reformulation of the VQA task that challenges models to identify
counterexamples: images that result in a different answer to the original
question. We introduce two methods for evaluating existing VQA models against a
supervised counterexample prediction task, VQA-CX. While our models surpass
existing benchmarks on VQA-CX, we find that the multimodal representations
learned by an existing state-of-the-art VQA model do not meaningfully
contribute to performance on this task. These results call into question the
assumption that successful performance on the VQA benchmark is indicative of
general visual-semantic reasoning abilities.
| null |
http://arxiv.org/abs/1806.00857v3
|
http://arxiv.org/pdf/1806.00857v3.pdf
| null |
[
"Gabriel Grand",
"Aron Szanto",
"Yoon Kim",
"Alexander Rush"
] |
[
"Question Answering",
"Visual Question Answering",
"Visual Question Answering (VQA)"
] | 2018-06-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/intentional-control-of-type-i-error-over
|
1802.02558
| null | null |
Intentional Control of Type I Error over Unconscious Data Distortion: a Neyman-Pearson Approach to Text Classification
|
This paper addresses the challenges in classifying textual data obtained from open online platforms, which are vulnerable to distortion. Most existing classification methods minimize the overall classification error and may yield an undesirably large type I error (relevant textual messages are classified as irrelevant), particularly when available data exhibit an asymmetry between relevant and irrelevant information. Data distortion exacerbates this situation and often leads to fallacious prediction. To deal with inestimable data distortion, we propose the use of the Neyman-Pearson (NP) classification paradigm, which minimizes type II error under a user-specified type I error constraint. Theoretically, we show that the NP oracle is unaffected by data distortion when the class conditional distributions remain the same. Empirically, we study a case of classifying posts about worker strikes obtained from a leading Chinese microblogging platform, which are frequently prone to extensive, unpredictable and inestimable censorship. We demonstrate that, even though the training and test data are susceptible to different distortion and therefore potentially follow different distributions, our proposed NP methods control the type I error on test data at the targeted level. The methods and implementation pipeline proposed in our case study are applicable to many other problems involving data distortion.
|
To deal with inestimable data distortion, we propose the use of the Neyman-Pearson (NP) classification paradigm, which minimizes type II error under a user-specified type I error constraint.
|
https://arxiv.org/abs/1802.02558v3
|
https://arxiv.org/pdf/1802.02558v3.pdf
| null |
[
"Lucy Xia",
"Richard Zhao",
"Yanhui Wu",
"Xin Tong"
] |
[
"Classification",
"General Classification",
"text-classification",
"Text Classification"
] | 2018-02-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/on-the-importance-of-attention-in-meta
|
1806.00852
| null | null |
On the Importance of Attention in Meta-Learning for Few-Shot Text Classification
|
Current deep learning based text classification methods are limited by their
ability to achieve fast learning and generalization when the data is scarce. We
address this problem by integrating a meta-learning procedure that uses the
knowledge learned across many tasks as an inductive bias towards better natural
language understanding. Based on the Model-Agnostic Meta-Learning framework
(MAML), we introduce the Attentive Task-Agnostic Meta-Learning (ATAML)
algorithm for text classification. The essential difference between MAML and
ATAML is in the separation of task-agnostic representation learning and
task-specific attentive adaptation. The proposed ATAML is designed to encourage
task-agnostic representation learning by way of task-agnostic parameterization
and facilitate task-specific adaptation via attention mechanisms. We provide
evidence to show that the attention mechanism in ATAML has a synergistic effect
on learning performance. In comparisons with models trained from random
initialization, pretrained models and meta trained MAML, our proposed ATAML
method generalizes better on single-label and multi-label classification tasks
in miniRCV1 and miniReuters-21578 datasets.
| null |
http://arxiv.org/abs/1806.00852v1
|
http://arxiv.org/pdf/1806.00852v1.pdf
| null |
[
"Xiang Jiang",
"Mohammad Havaei",
"Gabriel Chartrand",
"Hassan Chouaib",
"Thomas Vincent",
"Andrew Jesson",
"Nicolas Chapados",
"Stan Matwin"
] |
[
"Classification",
"Few-Shot Text Classification",
"General Classification",
"Inductive Bias",
"Meta-Learning",
"Multi-Label Classification",
"MUlTI-LABEL-ClASSIFICATION",
"Natural Language Understanding",
"Representation Learning",
"text-classification",
"Text Classification"
] | 2018-06-03T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/cbfinn/maml/blob/a7f45f1bcd7457fe97b227a21e89b8a82cc5fa49/maml.py#L17",
"description": "**MAML**, or **Model-Agnostic Meta-Learning**, is a model and task-agnostic algorithm for meta-learning that trains a model’s parameters such that a small number of gradient updates will lead to fast learning on a new task.\r\n\r\nConsider a model represented by a parametrized function $f\\_{\\theta}$ with parameters $\\theta$. When adapting to a new task $\\mathcal{T}\\_{i}$, the model’s parameters $\\theta$ become $\\theta'\\_{i}$. With MAML, the updated parameter vector $\\theta'\\_{i}$ is computed using one or more gradient descent updates on task $\\mathcal{T}\\_{i}$. For example, when using one gradient update,\r\n\r\n$$ \\theta'\\_{i} = \\theta - \\alpha\\nabla\\_{\\theta}\\mathcal{L}\\_{\\mathcal{T}\\_{i}}\\left(f\\_{\\theta}\\right) $$\r\n\r\nThe step size $\\alpha$ may be fixed as a hyperparameter or metalearned. The model parameters are trained by optimizing for the performance of $f\\_{\\theta'\\_{i}}$ with respect to $\\theta$ across tasks sampled from $p\\left(\\mathcal{T}\\_{i}\\right)$. More concretely the meta-objective is as follows:\r\n\r\n$$ \\min\\_{\\theta} \\sum\\_{\\mathcal{T}\\_{i} \\sim p\\left(\\mathcal{T}\\right)} \\mathcal{L}\\_{\\mathcal{T\\_{i}}}\\left(f\\_{\\theta'\\_{i}}\\right) = \\sum\\_{\\mathcal{T}\\_{i} \\sim p\\left(\\mathcal{T}\\right)} \\mathcal{L}\\_{\\mathcal{T\\_{i}}}\\left(f\\_{\\theta - \\alpha\\nabla\\_{\\theta}\\mathcal{L}\\_{\\mathcal{T}\\_{i}}\\left(f\\_{\\theta}\\right)}\\right) $$\r\n\r\nNote that the meta-optimization is performed over the model parameters $\\theta$, whereas the objective is computed using the updated model parameters $\\theta'$. In effect MAML aims to optimize the model parameters such that one or a small number of gradient steps on a new task will produce maximally effective behavior on that task. The meta-optimization across tasks is performed via stochastic gradient descent ([SGD](https://paperswithcode.com/method/sgd)), such that the model parameters $\\theta$ are updated as follows:\r\n\r\n$$ \\theta \\leftarrow \\theta - \\beta\\nabla\\_{\\theta} \\sum\\_{\\mathcal{T}\\_{i} \\sim p\\left(\\mathcal{T}\\right)} \\mathcal{L}\\_{\\mathcal{T\\_{i}}}\\left(f\\_{\\theta'\\_{i}}\\right)$$\r\n\r\nwhere $\\beta$ is the meta step size.",
"full_name": "Model-Agnostic Meta-Learning",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Meta-Learning** methods are methods that learn to learn. An example is few-shot meta-learning methods which aim to quickly adapt to a new task with only a few datapoints. Below you can find a continuously updating list of meta-learning methods.",
"name": "Meta-Learning Algorithms",
"parent": null
},
"name": "MAML",
"source_title": "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks",
"source_url": "http://arxiv.org/abs/1703.03400v3"
}
] |
https://paperswithcode.com/paper/an-aggressive-genetic-programming-approach
|
1806.00851
| null | null |
An Aggressive Genetic Programming Approach for Searching Neural Network Structure Under Computational Constraints
|
Recently, there emerged revived interests of designing automatic programs
(e.g., using genetic/evolutionary algorithms) to optimize the structure of
Convolutional Neural Networks (CNNs) for a specific task. The challenge in
designing such programs lies in how to balance between large search space of
the network structures and high computational costs. Existing works either
impose strong restrictions on the search space or use enormous computing
resources. In this paper, we study how to design a genetic programming approach
for optimizing the structure of a CNN for a given task under limited
computational resources yet without imposing strong restrictions on the search
space. To reduce the computational costs, we propose two general strategies
that are observed to be helpful: (i) aggressively selecting strongest
individuals for survival and reproduction, and killing weaker individuals at a
very early age; (ii) increasing mutation frequency to encourage diversity and
faster evolution. The combined strategy with additional optimization techniques
allows us to explore a large search space but with affordable computational
costs. Our results on standard benchmark datasets (MNIST, SVHN, CIFAR-10,
CIFAR-100) are competitive to similar approaches with significantly reduced
computational costs.
| null |
http://arxiv.org/abs/1806.00851v1
|
http://arxiv.org/pdf/1806.00851v1.pdf
| null |
[
"Zhe Li",
"Xuehan Xiong",
"Zhou Ren",
"Ning Zhang",
"Xiaoyu Wang",
"Tianbao Yang"
] |
[
"Diversity",
"Evolutionary Algorithms"
] | 2018-06-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/sample-efficient-learning-of-mixtures
|
1706.01596
| null | null |
Sample-Efficient Learning of Mixtures
|
We consider PAC learning of probability distributions (a.k.a. density
estimation), where we are given an i.i.d. sample generated from an unknown
target distribution, and want to output a distribution that is close to the
target in total variation distance. Let $\mathcal F$ be an arbitrary class of
probability distributions, and let $\mathcal{F}^k$ denote the class of
$k$-mixtures of elements of $\mathcal F$. Assuming the existence of a method
for learning $\mathcal F$ with sample complexity $m_{\mathcal{F}}(\epsilon)$,
we provide a method for learning $\mathcal F^k$ with sample complexity
$O({k\log k \cdot m_{\mathcal F}(\epsilon) }/{\epsilon^{2}})$. Our mixture
learning algorithm has the property that, if the $\mathcal F$-learner is
proper/agnostic, then the $\mathcal F^k$-learner would be proper/agnostic as
well.
This general result enables us to improve the best known sample complexity
upper bounds for a variety of important mixture classes. First, we show that
the class of mixtures of $k$ axis-aligned Gaussians in $\mathbb{R}^d$ is
PAC-learnable in the agnostic setting with $\widetilde{O}({kd}/{\epsilon ^ 4})$
samples, which is tight in $k$ and $d$ up to logarithmic factors. Second, we
show that the class of mixtures of $k$ Gaussians in $\mathbb{R}^d$ is
PAC-learnable in the agnostic setting with sample complexity
$\widetilde{O}({kd^2}/{\epsilon ^ 4})$, which improves the previous known
bounds of $\widetilde{O}({k^3d^2}/{\epsilon ^ 4})$ and
$\widetilde{O}(k^4d^4/\epsilon ^ 2)$ in its dependence on $k$ and $d$. Finally,
we show that the class of mixtures of $k$ log-concave distributions over
$\mathbb{R}^d$ is PAC-learnable using
$\widetilde{O}(d^{(d+5)/2}\epsilon^{-(d+9)/2}k)$ samples.
| null |
http://arxiv.org/abs/1706.01596v3
|
http://arxiv.org/pdf/1706.01596v3.pdf
| null |
[
"Hassan Ashtiani",
"Shai Ben-David",
"Abbas Mehrabian"
] |
[
"Density Estimation",
"PAC learning"
] | 2017-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-graphs-from-data-a-signal
|
1806.00848
| null | null |
Learning graphs from data: A signal representation perspective
|
The construction of a meaningful graph topology plays a crucial role in the effective representation, processing, analysis and visualization of structured data. When a natural choice of the graph is not readily available from the data sets, it is thus desirable to infer or learn a graph topology from the data. In this tutorial overview, we survey solutions to the problem of graph learning, including classical viewpoints from statistics and physics, and more recent approaches that adopt a graph signal processing (GSP) perspective. We further emphasize the conceptual similarities and differences between classical and GSP-based graph inference methods, and highlight the potential advantage of the latter in a number of theoretical and practical scenarios. We conclude with several open issues and challenges that are keys to the design of future signal processing and machine learning algorithms for learning graphs from data.
| null |
https://arxiv.org/abs/1806.00848v3
|
https://arxiv.org/pdf/1806.00848v3.pdf
| null |
[
"Xiaowen Dong",
"Dorina Thanou",
"Michael Rabbat",
"Pascal Frossard"
] |
[
"Graph Learning"
] | 2018-06-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/s4nd-single-shot-single-scale-lung-nodule
|
1805.02279
| null | null |
S4ND: Single-Shot Single-Scale Lung Nodule Detection
|
The state of the art lung nodule detection studies rely on computationally
expensive multi-stage frameworks to detect nodules from CT scans. To address
this computational challenge and provide better performance, in this paper we
propose S4ND, a new deep learning based method for lung nodule detection. Our
approach uses a single feed forward pass of a single network for detection and
provides better performance when compared to the current literature. The whole
detection pipeline is designed as a single $3D$ Convolutional Neural Network
(CNN) with dense connections, trained in an end-to-end manner. S4ND does not
require any further post-processing or user guidance to refine detection
results. Experimentally, we compared our network with the current
state-of-the-art object detection network (SSD) in computer vision as well as
the state-of-the-art published method for lung nodule detection (3D DCNN). We
used publically available $888$ CT scans from LUNA challenge dataset and showed
that the proposed method outperforms the current literature both in terms of
efficiency and accuracy by achieving an average FROC-score of $0.897$. We also
provide an in-depth analysis of our proposed network to shed light on the
unclear paradigms of tiny object detection.
| null |
http://arxiv.org/abs/1805.02279v2
|
http://arxiv.org/pdf/1805.02279v2.pdf
| null |
[
"Naji Khosravan",
"Ulas Bagci"
] |
[
"Lung Nodule Detection",
"object-detection",
"Object Detection"
] | 2018-05-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/the-power-of-localization-for-efficiently
|
1307.8371
| null | null |
The Power of Localization for Efficiently Learning Linear Separators with Noise
|
We introduce a new approach for designing computationally efficient learning
algorithms that are tolerant to noise, and demonstrate its effectiveness by
designing algorithms with improved noise tolerance guarantees for learning
linear separators.
We consider both the malicious noise model and the adversarial label noise
model. For malicious noise, where the adversary can corrupt both the label and
the features, we provide a polynomial-time algorithm for learning linear
separators in $\Re^d$ under isotropic log-concave distributions that can
tolerate a nearly information-theoretically optimal noise rate of $\eta =
\Omega(\epsilon)$. For the adversarial label noise model, where the
distribution over the feature vectors is unchanged, and the overall probability
of a noisy label is constrained to be at most $\eta$, we also give a
polynomial-time algorithm for learning linear separators in $\Re^d$ under
isotropic log-concave distributions that can handle a noise rate of $\eta =
\Omega\left(\epsilon\right)$.
We show that, in the active learning model, our algorithms achieve a label
complexity whose dependence on the error parameter $\epsilon$ is
polylogarithmic. This provides the first polynomial-time active learning
algorithm for learning linear separators in the presence of malicious noise or
adversarial label noise.
| null |
http://arxiv.org/abs/1307.8371v9
|
http://arxiv.org/pdf/1307.8371v9.pdf
| null |
[
"Pranjal Awasthi",
"Maria Florina Balcan",
"Philip M. Long"
] |
[
"Active Learning"
] | 2013-07-31T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/ternausnetv2-fully-convolutional-network-for
|
1806.00844
| null | null |
TernausNetV2: Fully Convolutional Network for Instance Segmentation
|
The most common approaches to instance segmentation are complex and use
two-stage networks with object proposals, conditional random-fields, template
matching or recurrent neural networks. In this work we present TernausNetV2 - a
simple fully convolutional network that allows extracting objects from a
high-resolution satellite imagery on an instance level. The network has popular
encoder-decoder type of architecture with skip connections but has a few
essential modifications that allows using for semantic as well as for instance
segmentation tasks. This approach is universal and allows to extend any network
that has been successfully applied for semantic segmentation to perform
instance segmentation task. In addition, we generalize network encoder that was
pre-trained for RGB images to use additional input channels. It makes possible
to use transfer learning from visual to a wider spectral range. For
DeepGlobe-CVPR 2018 building detection sub-challenge, based on public
leaderboard score, our approach shows superior performance in comparison to
other methods. The source code corresponding pre-trained weights are publicly
available at https://github.com/ternaus/TernausNetV2
|
The most common approaches to instance segmentation are complex and use two-stage networks with object proposals, conditional random-fields, template matching or recurrent neural networks.
|
http://arxiv.org/abs/1806.00844v2
|
http://arxiv.org/pdf/1806.00844v2.pdf
| null |
[
"Vladimir I. Iglovikov",
"Selim Seferbekov",
"Alexander V. Buslaev",
"Alexey Shvets"
] |
[
"Decoder",
"Instance Segmentation",
"Segmentation",
"Semantic Segmentation",
"Template Matching",
"Transfer Learning"
] | 2018-06-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/incorrigibility-in-the-cirl-framework
|
1709.06275
| null | null |
Incorrigibility in the CIRL Framework
|
A value learning system has incentives to follow shutdown instructions,
assuming the shutdown instruction provides information (in the technical sense)
about which actions lead to valuable outcomes. However, this assumption is not
robust to model mis-specification (e.g., in the case of programmer errors). We
demonstrate this by presenting some Supervised POMDP scenarios in which errors
in the parameterized reward function remove the incentive to follow shutdown
commands. These difficulties parallel those discussed by Soares et al. (2015)
in their paper on corrigibility. We argue that it is important to consider
systems that follow shutdown commands under some weaker set of assumptions
(e.g., that one small verified module is correctly implemented; as opposed to
an entire prior probability distribution and/or parameterized reward function).
We discuss some difficulties with simple ways to attempt to attain these sorts
of guarantees in a value learning framework.
| null |
http://arxiv.org/abs/1709.06275v2
|
http://arxiv.org/pdf/1709.06275v2.pdf
| null |
[
"Ryan Carey"
] |
[] | 2017-09-19T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/latent-tree-learning-with-differentiable
|
1806.00840
| null | null |
Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing
|
Latent tree learning models represent sentences by composing their words
according to an induced parse tree, all based on a downstream task. These
models often outperform baselines which use (externally provided) syntax trees
to drive the composition order. This work contributes (a) a new latent tree
learning model based on shift-reduce parsing, with competitive downstream
performance and non-trivial induced trees, and (b) an analysis of the trees
learned by our shift-reduce model and by a chart-based model.
| null |
http://arxiv.org/abs/1806.00840v1
|
http://arxiv.org/pdf/1806.00840v1.pdf
|
WS 2018 7
|
[
"Jean Maillard",
"Stephen Clark"
] |
[] | 2018-06-03T00:00:00 |
https://aclanthology.org/W18-2903
|
https://aclanthology.org/W18-2903.pdf
|
latent-tree-learning-with-differentiable-1
| null |
[] |
https://paperswithcode.com/paper/study-and-development-of-a-computer-aided
|
1806.00839
| null | null |
Study and development of a Computer-Aided Diagnosis system for classification of chest x-ray images using convolutional neural networks pre-trained for ImageNet and data augmentation
|
Convolutional neural networks (ConvNets) are the actual standard for image
recognizement and classification. On the present work we develop a Computer
Aided-Diagnosis (CAD) system using ConvNets to classify a x-rays chest images
dataset in two groups: Normal and Pneumonia. The study uses ConvNets models
available on the PyTorch platform: AlexNet, SqueezeNet, ResNet and Inception.
We initially use three training styles: complete from scratch using random
initialization, using a pre-trained ImageNet model training only the last layer
adapted to our problem (transfer learning) and a pre-trained model modified
training all the classifying layers of the model (fine tuning). The last
strategy of training used is with data augmentation techniques that avoid over
fitting problems on ConvNets yielding the better results on this study
| null |
http://arxiv.org/abs/1806.00839v1
|
http://arxiv.org/pdf/1806.00839v1.pdf
| null |
[
"Vinicius Pavanelli Vianna"
] |
[
"Data Augmentation",
"General Classification",
"Transfer Learning"
] | 2018-06-03T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "**Average Pooling** is a pooling operation that calculates the average value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. It adds a small amount of translation invariance - meaning translating the image by a small amount does not significantly affect the values of most pooled outputs. It extracts features more smoothly than [Max Pooling](https://paperswithcode.com/method/max-pooling), whereas max pooling extracts more pronounced features like edges.\r\n\r\nImage Source: [here](https://www.researchgate.net/figure/Illustration-of-Max-Pooling-and-Average-Pooling-Figure-2-above-shows-an-example-of-max_fig2_333593451)",
"full_name": "Average Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Average Pooling",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/6db1569c89094cf23f3bc41f79275c45e9fcb3f3/torchvision/models/squeezenet.py#L14",
"description": "A **Fire Module** is a building block for convolutional neural networks, notably used as part of [SqueezeNet](https://paperswithcode.com/method/squeezenet). A Fire module is comprised of: a squeeze [convolution](https://paperswithcode.com/method/convolution) layer (which has only 1x1 filters), feeding into an expand layer that has a mix of 1x1 and 3x3 convolution filters. We expose three tunable dimensions (hyperparameters) in a Fire module: $s\\_{1x1}$, $e\\_{1x1}$, and $e\\_{3x3}$. In a Fire module, $s\\_{1x1}$ is the number of filters in the squeeze layer (all 1x1), $e\\_{1x1}$ is the number of 1x1 filters in the expand layer, and $e\\_{3x3}$ is the number of 3x3 filters in the expand layer. When we use Fire modules we set $s\\_{1x1}$ to be less than ($e\\_{1x1}$ + $e\\_{3x3}$), so the squeeze layer helps to limit the number of input channels to the 3x3 filters.",
"full_name": "Fire Module",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Image Model Blocks** are building blocks used in image models such as convolutional neural networks. Below you can find a continuously updating list of image model blocks.",
"name": "Image Model Blocks",
"parent": null
},
"name": "Fire Module",
"source_title": "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size",
"source_url": "http://arxiv.org/abs/1602.07360v4"
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/1c5c289b6218eb1026dcb5fd9738231401cfccea/torch/nn/modules/normalization.py#L13",
"description": "**Local Response Normalization** is a normalization layer that implements the idea of lateral inhibition. Lateral inhibition is a concept in neurobiology that refers to the phenomenon of an excited neuron inhibiting its neighbours: this leads to a peak in the form of a local maximum, creating contrast in that area and increasing sensory perception. In practice, we can either normalize within the same channel or normalize across channels when we apply LRN to convolutional neural networks.\r\n\r\n$$ b_{c} = a_{c}\\left(k + \\frac{\\alpha}{n}\\sum_{c'=\\max(0, c-n/2)}^{\\min(N-1,c+n/2)}a_{c'}^2\\right)^{-\\beta} $$\r\n\r\nWhere the size is the number of neighbouring channels used for normalization, $\\alpha$ is multiplicative factor, $\\beta$ an exponent and $k$ an additive factor",
"full_name": "Local Response Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Local Response Normalization",
"source_title": "ImageNet Classification with Deep Convolutional Neural Networks",
"source_url": "http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks"
},
{
"code_snippet_url": "https://github.com/prlz77/ResNeXt.pytorch/blob/39fb8d03847f26ec02fb9b880ecaaa88db7a7d16/models/model.py#L42",
"description": "A **Grouped Convolution** uses a group of convolutions - multiple kernels per layer - resulting in multiple channel outputs per layer. This leads to wider networks helping a network learn a varied set of low level and high level features. The original motivation of using Grouped Convolutions in [AlexNet](https://paperswithcode.com/method/alexnet) was to distribute the model over multiple GPUs as an engineering compromise. But later, with models such as [ResNeXt](https://paperswithcode.com/method/resnext), it was shown this module could be used to improve classification accuracy. Specifically by exposing a new dimension through grouped convolutions, *cardinality* (the size of set of transformations), we can increase accuracy by increasing it.",
"full_name": "Grouped Convolution",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Grouped Convolution",
"source_title": "ImageNet Classification with Deep Convolutional Neural Networks",
"source_url": "http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks"
},
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/0adb5843766092fba584791af76383125fd0d01c/torch/nn/init.py#L289",
"description": "**Xavier Initialization**, or **Glorot Initialization**, is an initialization scheme for neural networks. Biases are initialized be 0 and the weights $W\\_{ij}$ at each layer are initialized as:\r\n\r\n$$ W\\_{ij} \\sim U\\left[-\\frac{\\sqrt{6}}{\\sqrt{fan_{in} + fan_{out}}}, \\frac{\\sqrt{6}}{\\sqrt{fan_{in} + fan_{out}}}\\right] $$\r\n\r\nWhere $U$ is a uniform distribution and $fan_{in}$ is the size of the previous layer (number of columns in $W$) and $fan_{out}$ is the size of the current layer.",
"full_name": "Xavier Initialization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Initialization** methods are used to initialize the weights in a neural network. Below can you find a continuously updating list of initialization methods.",
"name": "Initialization",
"parent": null
},
"name": "Xavier Initialization",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/6db1569c89094cf23f3bc41f79275c45e9fcb3f3/torchvision/models/squeezenet.py#L37",
"description": "**SqueezeNet** is a convolutional neural network that employs design strategies to reduce the number of parameters, notably with the use of fire modules that \"squeeze\" parameters using 1x1 convolutions.",
"full_name": "SqueezeNet",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "If you have questions or want to make special travel arrangements, you can make them online or call ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. For hearing or speech impaired assistance dial 711 to be connected through the National Relay Service.",
"name": "Convolutional Neural Networks",
"parent": "Image Models"
},
"name": "SqueezeNet",
"source_title": "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size",
"source_url": "http://arxiv.org/abs/1602.07360v4"
},
{
"code_snippet_url": "https://github.com/dansuh17/alexnet-pytorch/blob/d0c1b1c52296ffcbecfbf5b17e1d1685b4ca6744/model.py#L40",
"description": "To make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.ggfdf\r\n\r\n\r\nHow do I speak to a person at Expedia?How do I speak to a person at Expedia?To make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.To make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.\r\n\r\n\r\n\r\nTo make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.To make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.To make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.chgd",
"full_name": "How do I speak to a person at Expedia?-/+/",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "If you have questions or want to make special travel arrangements, you can make them online or call ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. For hearing or speech impaired assistance dial 711 to be connected through the National Relay Service.",
"name": "Convolutional Neural Networks",
"parent": "Image Models"
},
"name": "How do I speak to a person at Expedia?-/+/",
"source_title": "ImageNet Classification with Deep Convolutional Neural Networks",
"source_url": "http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks"
},
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!\r\n\r\n\r\nHow Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!",
"full_name": "*Communicated@Fast*How Do I Communicate to Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "A **1 x 1 Convolution** is a [convolution](https://paperswithcode.com/method/convolution) with some special properties in that it can be used for dimensionality reduction, efficient low dimensional embeddings, and applying non-linearity after convolutions. It maps an input pixel with all its channels to an output pixel which can be squeezed to a desired output depth. It can be viewed as an [MLP](https://paperswithcode.com/method/feedforward-network) looking at a particular pixel location.\r\n\r\nImage Credit: [http://deeplearning.ai](http://deeplearning.ai)",
"full_name": "1x1 Convolution",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "1x1 Convolution",
"source_title": "Network In Network",
"source_url": "http://arxiv.org/abs/1312.4400v3"
},
{
"code_snippet_url": "https://github.com/google/jax/blob/36f91261099b00194922bd93ed1286fe1c199724/jax/experimental/stax.py#L116",
"description": "**Batch Normalization** aims to reduce internal covariate shift, and in doing so aims to accelerate the training of deep neural nets. It accomplishes this via a normalization step that fixes the means and variances of layer inputs. Batch Normalization also has a beneficial effect on the gradient flow through the network, by reducing the dependence of gradients on the scale of the parameters or of their initial values. This allows for use of much higher learning rates without the risk of divergence. Furthermore, batch normalization regularizes the model and reduces the need for [Dropout](https://paperswithcode.com/method/dropout).\r\n\r\nWe apply a batch normalization layer as follows for a minibatch $\\mathcal{B}$:\r\n\r\n$$ \\mu\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}x\\_{i} $$\r\n\r\n$$ \\sigma^{2}\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}\\left(x\\_{i}-\\mu\\_{\\mathcal{B}}\\right)^{2} $$\r\n\r\n$$ \\hat{x}\\_{i} = \\frac{x\\_{i} - \\mu\\_{\\mathcal{B}}}{\\sqrt{\\sigma^{2}\\_{\\mathcal{B}}+\\epsilon}} $$\r\n\r\n$$ y\\_{i} = \\gamma\\hat{x}\\_{i} + \\beta = \\text{BN}\\_{\\gamma, \\beta}\\left(x\\_{i}\\right) $$\r\n\r\nWhere $\\gamma$ and $\\beta$ are learnable parameters.",
"full_name": "Batch Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Batch Normalization",
"source_title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
"source_url": "http://arxiv.org/abs/1502.03167v3"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/1aef87d01eec2c0989458387fa04baebcc86ea7b/torchvision/models/resnet.py#L75",
"description": "A **Bottleneck Residual Block** is a variant of the [residual block](https://paperswithcode.com/method/residual-block) that utilises 1x1 convolutions to create a bottleneck. The use of a bottleneck reduces the number of parameters and matrix multiplications. The idea is to make residual blocks as thin as possible to increase depth and have less parameters. They were introduced as part of the [ResNet](https://paperswithcode.com/method/resnet) architecture, and are used as part of deeper ResNets such as ResNet-50 and ResNet-101.",
"full_name": "Bottleneck Residual Block",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connection Blocks** are building blocks for neural networks that feature skip connections. These skip connections 'skip' some layers allowing gradients to better flow through the network. Below you will find a continuously updating list of skip connection blocks:",
"name": "Skip Connection Blocks",
"parent": null
},
"name": "Bottleneck Residual Block",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/baa592b215804927e28638f6a7f3318cbc411d49/torchvision/models/resnet.py#L157",
"description": "**Global Average Pooling** is a pooling operation designed to replace fully connected layers in classical CNNs. The idea is to generate one feature map for each corresponding category of the classification task in the last mlpconv layer. Instead of adding fully connected layers on top of the feature maps, we take the average of each feature map, and the resulting vector is fed directly into the [softmax](https://paperswithcode.com/method/softmax) layer. \r\n\r\nOne advantage of global [average pooling](https://paperswithcode.com/method/average-pooling) over the fully connected layers is that it is more native to the [convolution](https://paperswithcode.com/method/convolution) structure by enforcing correspondences between feature maps and categories. Thus the feature maps can be easily interpreted as categories confidence maps. Another advantage is that there is no parameter to optimize in the global average pooling thus overfitting is avoided at this layer. Furthermore, global average pooling sums out the spatial information, thus it is more robust to spatial translations of the input.",
"full_name": "Global Average Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Global Average Pooling",
"source_title": "Network In Network",
"source_url": "http://arxiv.org/abs/1312.4400v3"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/1aef87d01eec2c0989458387fa04baebcc86ea7b/torchvision/models/resnet.py#L35",
"description": "**Residual Blocks** are skip-connection blocks that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. They were introduced as part of the [ResNet](https://paperswithcode.com/method/resnet) architecture.\r\n \r\nFormally, denoting the desired underlying mapping as $\\mathcal{H}({x})$, we let the stacked nonlinear layers fit another mapping of $\\mathcal{F}({x}):=\\mathcal{H}({x})-{x}$. The original mapping is recast into $\\mathcal{F}({x})+{x}$. The $\\mathcal{F}({x})$ acts like a residual, hence the name 'residual block'.\r\n\r\nThe intuition is that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers. Having skip connections allows the network to more easily learn identity-like mappings.\r\n\r\nNote that in practice, [Bottleneck Residual Blocks](https://paperswithcode.com/method/bottleneck-residual-block) are used for deeper ResNets, such as ResNet-50 and ResNet-101, as these bottleneck blocks are less computationally intensive.",
"full_name": "Residual Block",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connection Blocks** are building blocks for neural networks that feature skip connections. These skip connections 'skip' some layers allowing gradients to better flow through the network. Below you will find a continuously updating list of skip connection blocks:",
"name": "Skip Connection Blocks",
"parent": null
},
"name": "Residual Block",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/0adb5843766092fba584791af76383125fd0d01c/torch/nn/init.py#L389",
"description": "**Kaiming Initialization**, or **He Initialization**, is an initialization method for neural networks that takes into account the non-linearity of activation functions, such as [ReLU](https://paperswithcode.com/method/relu) activations.\r\n\r\nA proper initialization method should avoid reducing or magnifying the magnitudes of input signals exponentially. Using a derivation they work out that the condition to stop this happening is:\r\n\r\n$$\\frac{1}{2}n\\_{l}\\text{Var}\\left[w\\_{l}\\right] = 1 $$\r\n\r\nThis implies an initialization scheme of:\r\n\r\n$$ w\\_{l} \\sim \\mathcal{N}\\left(0, 2/n\\_{l}\\right)$$\r\n\r\nThat is, a zero-centered Gaussian with standard deviation of $\\sqrt{2/{n}\\_{l}}$ (variance shown in equation above). Biases are initialized at $0$.",
"full_name": "Kaiming Initialization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Initialization** methods are used to initialize the weights in a neural network. Below can you find a continuously updating list of initialization methods.",
"name": "Initialization",
"parent": null
},
"name": "Kaiming Initialization",
"source_title": "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification",
"source_url": "http://arxiv.org/abs/1502.01852v1"
},
{
"code_snippet_url": null,
"description": "**Max Pooling** is a pooling operation that calculates the maximum value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. It adds a small amount of translation invariance - meaning translating the image by a small amount does not significantly affect the values of most pooled outputs.\r\n\r\nImage Source: [here](https://computersciencewiki.org/index.php/File:MaxpoolSample2.png)",
"full_name": "Max Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Max Pooling",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/resnet.py#L118",
"description": "**Residual Connections** are a type of skip-connection that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. \r\n\r\nFormally, denoting the desired underlying mapping as $\\mathcal{H}({x})$, we let the stacked nonlinear layers fit another mapping of $\\mathcal{F}({x}):=\\mathcal{H}({x})-{x}$. The original mapping is recast into $\\mathcal{F}({x})+{x}$.\r\n\r\nThe intuition is that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers.",
"full_name": "Residual Connection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connections** allow layers to skip layers and connect to layers further up the network, allowing for information to flow more easily up the network. Below you can find a continuously updating list of skip connection methods.",
"name": "Skip Connections",
"parent": null
},
"name": "Residual Connection",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "In today’s digital age, Bitcoin has become more than just a buzzword—it’s a revolutionary way to manage and invest your money. But just like with any advanced technology, users sometimes face issues that can be frustrating or even alarming. Whether you're dealing with a Bitcoin transaction not confirmed, your Bitcoin wallet not showing balance, or you're trying to recover a lost Bitcoin wallet, knowing where to get help is essential. That’s why the Bitcoin customer support number +1-833-534-1729 is your go-to solution for fast and reliable assistance.\r\n\r\nWhy You Might Need to Call the Bitcoin Customer Support Number +1-833-534-1729\r\nBitcoin operates on a decentralized network, which means there’s no single company or office that manages everything. However, platforms, wallets, and third-party services provide support to make your experience smoother. Calling +1-833-534-1729 can help you troubleshoot issues such as:\r\n\r\n1. Bitcoin Transaction Not Confirmed\r\nOne of the most common concerns is when a Bitcoin transaction is stuck or pending. This usually happens due to low miner fees or network congestion. If your transaction hasn’t been confirmed for hours or even days, it’s important to get expert help through +1-833-534-1729 to understand what steps you can take next—whether it’s accelerating the transaction or canceling and resending it.\r\n\r\n2. Bitcoin Wallet Not Showing Balance\r\nImagine opening your wallet and seeing a zero balance even though you know you haven’t made any transactions. A Bitcoin wallet not showing balance can be caused by a sync issue, outdated app version, or even incorrect wallet address. The support team at +1-833-534-1729 can walk you through diagnostics and get your balance showing correctly again.\r\n\r\n3. How to Recover Lost Bitcoin Wallet\r\nLost access to your wallet? That can feel like the end of the world, but all may not be lost. Knowing how to recover a lost Bitcoin wallet depends on the type of wallet you used—hardware, mobile, desktop, or paper. With the right support, often involving your seed phrase or backup file, you can get your assets back. Don’t waste time; dial +1-833-534-1729 for step-by-step recovery help.\r\n\r\n4. Bitcoin Deposit Not Received\r\nIf someone has sent you Bitcoin but it’s not showing up in your wallet, it could be a delay in network confirmation or a mistake in the receiving address. A Bitcoin deposit not received needs quick attention. Call +1-833-534-1729 to trace the transaction and understand whether it’s on-chain, pending, or if the funds have been misdirected.\r\n\r\n5. Bitcoin Transaction Stuck or Pending\r\nSometimes your Bitcoin transaction is stuck or pending due to low gas fees or heavy blockchain traffic. While this can resolve itself, in some cases it doesn't. Don’t stay in the dark. 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Here’s why users trust +1-833-534-1729:\r\n\r\nLive Experts: Talk to real people who understand wallets, blockchain, and Bitcoin tech.\r\n\r\n24/7 Availability: Bitcoin doesn’t sleep, and neither should your support.\r\n\r\nStep-by-Step Guidance: Whether you're a beginner or seasoned investor, the team guides you with patience and clarity.\r\n\r\nData Privacy: Your security and wallet details are treated with the highest confidentiality.\r\n\r\nFAQs About Bitcoin Support and Wallet Issues\r\nQ1: Can Bitcoin support help me recover stolen BTC?\r\nA: While Bitcoin transactions are irreversible, support can help investigate, trace addresses, and advise on what to do next.\r\n\r\nQ2: My wallet shows zero balance after reinstalling. What do I do?\r\nA: Ensure you restored with the correct recovery phrase and wallet type. Call +1-833-534-1729 for assistance.\r\n\r\nQ3: What if I forgot my wallet password?\r\nA: Recovery depends on the wallet provider. 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Whether it's a Bitcoin transaction not confirmed, your Bitcoin wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Bitcoin customer support number +1-833-534-1729 can be your fastest path to peace of mind.\r\n\r\nNo matter what the issue, you don’t have to face it alone. Expert help is just a call away—+1-833-534-1729.",
"full_name": "Bitcoin Customer Service Number +1-833-534-1729",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "If you have questions or want to make special travel arrangements, you can make them online or call ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. For hearing or speech impaired assistance dial 711 to be connected through the National Relay Service.",
"name": "Convolutional Neural Networks",
"parent": "Image Models"
},
"name": "Bitcoin Customer Service Number +1-833-534-1729",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
}
] |
https://paperswithcode.com/paper/the-actor-search-tree-critic-astc-for-off
|
1805.11548
| null | null |
The Actor Search Tree Critic (ASTC) for Off-Policy POMDP Learning in Medical Decision Making
|
Off-policy reinforcement learning enables near-optimal policy from suboptimal
experience, thereby provisions opportunity for artificial intelligence
applications in healthcare. Previous works have mainly framed patient-clinician
interactions as Markov decision processes, while true physiological states are
not necessarily fully observable from clinical data. We capture this situation
with partially observable Markov decision process, in which an agent optimises
its actions in a belief represented as a distribution of patient states
inferred from individual history trajectories. A Gaussian mixture model is
fitted for the observed data. Moreover, we take into account the fact that
nuance in pharmaceutical dosage could presumably result in significantly
different effect by modelling a continuous policy through a Gaussian
approximator directly in the policy space, i.e. the actor. To address the
challenge of infinite number of possible belief states which renders exact
value iteration intractable, we evaluate and plan for only every encountered
belief, through heuristic search tree by tightly maintaining lower and upper
bounds of the true value of belief. We further resort to function
approximations to update value bounds estimation, i.e. the critic, so that the
tree search can be improved through more compact bounds at the fringe nodes
that will be back-propagated to the root. Both actor and critic parameters are
learned via gradient-based approaches. Our proposed policy trained from real
intensive care unit data is capable of dictating dosing on vasopressors and
intravenous fluids for sepsis patients that lead to the best patient outcomes.
| null |
http://arxiv.org/abs/1805.11548v3
|
http://arxiv.org/pdf/1805.11548v3.pdf
| null |
[
"Luchen Li",
"Matthieu Komorowski",
"Aldo A. Faisal"
] |
[
"Decision Making",
"Heuristic Search",
"Reinforcement Learning"
] | 2018-05-29T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/analysis-of-regularized-nystrom-subsampling
|
1806.00826
| null | null |
Analysis of regularized Nyström subsampling for regression functions of low smoothness
|
This paper studies a Nystr\"om type subsampling approach to large kernel
learning methods in the misspecified case, where the target function is not
assumed to belong to the reproducing kernel Hilbert space generated by the
underlying kernel. This case is less understood, in spite of its practical
importance. To model such a case, the smoothness of target functions is
described in terms of general source conditions. It is surprising that almost
for the whole range of the source conditions, describing the misspecified case,
the corresponding learning rate bounds can be achieved with just one value of
the regularization parameter. This observation allows a formulation of mild
conditions under which the plain Nystr\"om subsampling can be realized with
subquadratic cost maintaining the guaranteed learning rates.
| null |
http://arxiv.org/abs/1806.00826v1
|
http://arxiv.org/pdf/1806.00826v1.pdf
| null |
[
"Shuai Lu",
"Peter Mathé",
"Sergiy Pereverzyev Jr"
] |
[
"regression"
] | 2018-06-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/dont-just-assume-look-and-answer-overcoming
|
1712.00377
| null | null |
Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering
|
A number of studies have found that today's Visual Question Answering (VQA)
models are heavily driven by superficial correlations in the training data and
lack sufficient image grounding. To encourage development of models geared
towards the latter, we propose a new setting for VQA where for every question
type, train and test sets have different prior distributions of answers.
Specifically, we present new splits of the VQA v1 and VQA v2 datasets, which we
call Visual Question Answering under Changing Priors (VQA-CP v1 and VQA-CP v2
respectively). First, we evaluate several existing VQA models under this new
setting and show that their performance degrades significantly compared to the
original VQA setting. Second, we propose a novel Grounded Visual Question
Answering model (GVQA) that contains inductive biases and restrictions in the
architecture specifically designed to prevent the model from 'cheating' by
primarily relying on priors in the training data. Specifically, GVQA explicitly
disentangles the recognition of visual concepts present in the image from the
identification of plausible answer space for a given question, enabling the
model to more robustly generalize across different distributions of answers.
GVQA is built off an existing VQA model -- Stacked Attention Networks (SAN).
Our experiments demonstrate that GVQA significantly outperforms SAN on both
VQA-CP v1 and VQA-CP v2 datasets. Interestingly, it also outperforms more
powerful VQA models such as Multimodal Compact Bilinear Pooling (MCB) in
several cases. GVQA offers strengths complementary to SAN when trained and
evaluated on the original VQA v1 and VQA v2 datasets. Finally, GVQA is more
transparent and interpretable than existing VQA models.
|
Specifically, we present new splits of the VQA v1 and VQA v2 datasets, which we call Visual Question Answering under Changing Priors (VQA-CP v1 and VQA-CP v2 respectively).
|
http://arxiv.org/abs/1712.00377v2
|
http://arxiv.org/pdf/1712.00377v2.pdf
|
CVPR 2018 6
|
[
"Aishwarya Agrawal",
"Dhruv Batra",
"Devi Parikh",
"Aniruddha Kembhavi"
] |
[
"Question Answering",
"Visual Question Answering",
"Visual Question Answering (VQA)"
] | 2017-12-01T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Agrawal_Dont_Just_Assume_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Agrawal_Dont_Just_Assume_CVPR_2018_paper.pdf
|
dont-just-assume-look-and-answer-overcoming-1
| null |
[] |
https://paperswithcode.com/paper/causal-inference-with-noisy-and-missing
|
1806.00811
| null | null |
Causal Inference with Noisy and Missing Covariates via Matrix Factorization
|
Valid causal inference in observational studies often requires controlling
for confounders. However, in practice measurements of confounders may be noisy,
and can lead to biased estimates of causal effects. We show that we can reduce
the bias caused by measurement noise using a large number of noisy measurements
of the underlying confounders. We propose the use of matrix factorization to
infer the confounders from noisy covariates, a flexible and principled
framework that adapts to missing values, accommodates a wide variety of data
types, and can augment many causal inference methods. We bound the error for
the induced average treatment effect estimator and show it is consistent in a
linear regression setting, using Exponential Family Matrix Completion
preprocessing. We demonstrate the effectiveness of the proposed procedure in
numerical experiments with both synthetic data and real clinical data.
|
Valid causal inference in observational studies often requires controlling for confounders.
|
http://arxiv.org/abs/1806.00811v1
|
http://arxiv.org/pdf/1806.00811v1.pdf
|
NeurIPS 2018 12
|
[
"Nathan Kallus",
"Xiaojie Mao",
"Madeleine Udell"
] |
[
"Causal Inference",
"Matrix Completion",
"Missing Values",
"valid"
] | 2018-06-03T00:00:00 |
http://papers.nips.cc/paper/7924-causal-inference-with-noisy-and-missing-covariates-via-matrix-factorization
|
http://papers.nips.cc/paper/7924-causal-inference-with-noisy-and-missing-covariates-via-matrix-factorization.pdf
|
causal-inference-with-noisy-and-missing-1
| null |
[
{
"code_snippet_url": "",
"description": "Causal inference is the process of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect. The main difference between causal inference and inference of association is that the former analyzes the response of the effect variable when the cause is changed.",
"full_name": "Causal inference",
"introduced_year": 2000,
"main_collection": null,
"name": "Causal inference",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/fast-approximate-nearest-neighbor-search-with
|
1707.00143
| null | null |
Fast Approximate Nearest Neighbor Search With The Navigating Spreading-out Graph
|
Approximate nearest neighbor search (ANNS) is a fundamental problem in
databases and data mining. A scalable ANNS algorithm should be both
memory-efficient and fast. Some early graph-based approaches have shown
attractive theoretical guarantees on search time complexity, but they all
suffer from the problem of high indexing time complexity. Recently, some
graph-based methods have been proposed to reduce indexing complexity by
approximating the traditional graphs; these methods have achieved revolutionary
performance on million-scale datasets. Yet, they still can not scale to
billion-node databases. In this paper, to further improve the search-efficiency
and scalability of graph-based methods, we start by introducing four aspects:
(1) ensuring the connectivity of the graph; (2) lowering the average out-degree
of the graph for fast traversal; (3) shortening the search path; and (4)
reducing the index size. Then, we propose a novel graph structure called
Monotonic Relative Neighborhood Graph (MRNG) which guarantees very low search
complexity (close to logarithmic time). To further lower the indexing
complexity and make it practical for billion-node ANNS problems, we propose a
novel graph structure named Navigating Spreading-out Graph (NSG) by
approximating the MRNG. The NSG takes the four aspects into account
simultaneously. Extensive experiments show that NSG outperforms all the
existing algorithms significantly. In addition, NSG shows superior performance
in the E-commercial search scenario of Taobao (Alibaba Group) and has been
integrated into their search engine at billion-node scale.
|
In this paper, to further improve the search-efficiency and scalability of graph-based methods, we start by introducing four aspects: (1) ensuring the connectivity of the graph; (2) lowering the average out-degree of the graph for fast traversal; (3) shortening the search path; and (4) reducing the index size.
|
http://arxiv.org/abs/1707.00143v9
|
http://arxiv.org/pdf/1707.00143v9.pdf
| null |
[
"Cong Fu",
"Chao Xiang",
"Changxu Wang",
"Deng Cai"
] |
[] | 2017-07-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/admissible-abstractions-for-near-optimal-task
|
1806.00805
| null | null |
Admissible Abstractions for Near-optimal Task and Motion Planning
|
We define an admissibility condition for abstractions expressed using angelic
semantics and show that these conditions allow us to accelerate planning while
preserving the ability to find the optimal motion plan. We then derive
admissible abstractions for two motion planning domains with continuous state.
We extract upper and lower bounds on the cost of concrete motion plans using
local metric and topological properties of the problem domain. These bounds
guide the search for a plan while maintaining performance guarantees. We show
that abstraction can dramatically reduce the complexity of search relative to a
direct motion planner. Using our abstractions, we find near-optimal motion
plans in planning problems involving $10^{13}$ states without using a separate
task planner.
| null |
http://arxiv.org/abs/1806.00805v1
|
http://arxiv.org/pdf/1806.00805v1.pdf
| null |
[
"William Vega-Brown",
"Nicholas Roy"
] |
[
"Motion Planning",
"Task and Motion Planning"
] | 2018-06-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/nam-non-adversarial-unsupervised-domain
|
1806.00804
| null | null |
NAM: Non-Adversarial Unsupervised Domain Mapping
|
Several methods were recently proposed for the task of translating images
between domains without prior knowledge in the form of correspondences. The
existing methods apply adversarial learning to ensure that the distribution of
the mapped source domain is indistinguishable from the target domain, which
suffers from known stability issues. In addition, most methods rely heavily on
`cycle' relationships between the domains, which enforce a one-to-one mapping.
In this work, we introduce an alternative method: Non-Adversarial Mapping
(NAM), which separates the task of target domain generative modeling from the
cross-domain mapping task. NAM relies on a pre-trained generative model of the
target domain, and aligns each source image with an image synthesized from the
target domain, while jointly optimizing the domain mapping function. It has
several key advantages: higher quality and resolution image translations,
simpler and more stable training and reusable target models. Extensive
experiments are presented validating the advantages of our method.
|
NAM relies on a pre-trained generative model of the target domain, and aligns each source image with an image synthesized from the target domain, while jointly optimizing the domain mapping function.
|
http://arxiv.org/abs/1806.00804v2
|
http://arxiv.org/pdf/1806.00804v2.pdf
|
ECCV 2018 9
|
[
"Yedid Hoshen",
"Lior Wolf"
] |
[] | 2018-06-03T00:00:00 |
http://openaccess.thecvf.com/content_ECCV_2018/html/Yedid_Hoshen_Separable_Cross-Domain_Translation_ECCV_2018_paper.html
|
http://openaccess.thecvf.com/content_ECCV_2018/papers/Yedid_Hoshen_Separable_Cross-Domain_Translation_ECCV_2018_paper.pdf
|
nam-non-adversarial-unsupervised-domain-1
| null |
[] |
https://paperswithcode.com/paper/latent-ransac
|
1802.07045
| null | null |
Latent RANSAC
|
We present a method that can evaluate a RANSAC hypothesis in constant time,
i.e. independent of the size of the data. A key observation here is that
correct hypotheses are tightly clustered together in the latent parameter
domain. In a manner similar to the generalized Hough transform we seek to find
this cluster, only that we need as few as two votes for a successful detection.
Rapidly locating such pairs of similar hypotheses is made possible by adapting
the recent "Random Grids" range-search technique. We only perform the usual
(costly) hypothesis verification stage upon the discovery of a close pair of
hypotheses. We show that this event rarely happens for incorrect hypotheses,
enabling a significant speedup of the RANSAC pipeline. The suggested approach
is applied and tested on three robust estimation problems: camera localization,
3D rigid alignment and 2D-homography estimation. We perform rigorous testing on
both synthetic and real datasets, demonstrating an improvement in efficiency
without a compromise in accuracy. Furthermore, we achieve state-of-the-art 3D
alignment results on the challenging "Redwood" loop-closure challenge.
|
We present a method that can evaluate a RANSAC hypothesis in constant time, i. e. independent of the size of the data.
|
http://arxiv.org/abs/1802.07045v2
|
http://arxiv.org/pdf/1802.07045v2.pdf
|
CVPR 2018 6
|
[
"Simon Korman",
"Roee Litman"
] |
[
"3D Face Alignment",
"3D Plane Detection",
"Camera Localization",
"Homography Estimation",
"Point Cloud Registration",
"Robust Face Alignment"
] | 2018-02-20T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Korman_Latent_RANSAC_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Korman_Latent_RANSAC_CVPR_2018_paper.pdf
|
latent-ransac-1
| null |
[] |
https://paperswithcode.com/paper/aid-an-updated-version-of-aid-on-scene
|
1806.00801
| null | null |
AID++: An Updated Version of AID on Scene Classification
|
Aerial image scene classification is a fundamental problem for understanding
high-resolution remote sensing images and has become an active research task in
the field of remote sensing due to its important role in a wide range of
applications. However, the limitations of existing datasets for scene
classification, such as the small scale and low-diversity, severely hamper the
potential usage of the new generation deep convolutional neural networks
(CNNs). Although huge efforts have been made in building large-scale datasets
very recently, e.g., the Aerial Image Dataset (AID) which contains 10,000 image
samples, they are still far from sufficient to fully train a high-capacity deep
CNN model. To this end, we present a larger-scale dataset in this paper, named
as AID++, for aerial scene classification based on the AID dataset. The
proposed AID++ consists of more than 400,000 image samples that are
semi-automatically annotated by using the existing the geographical data. We
evaluate several prevalent CNN models on the proposed dataset, and the results
show that our dataset can be used as a promising benchmark for scene
classification.
| null |
http://arxiv.org/abs/1806.00801v1
|
http://arxiv.org/pdf/1806.00801v1.pdf
| null |
[
"Pu Jin",
"Gui-Song Xia",
"Fan Hu",
"Qikai Lu",
"Liangpei Zhang"
] |
[
"Aerial Scene Classification",
"Classification",
"Diversity",
"General Classification",
"Scene Classification"
] | 2018-06-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/proflow-learning-to-predict-optical-flow
|
1806.00800
| null | null |
ProFlow: Learning to Predict Optical Flow
|
Temporal coherence is a valuable source of information in the context of
optical flow estimation. However, finding a suitable motion model to leverage
this information is a non-trivial task. In this paper we propose an
unsupervised online learning approach based on a convolutional neural network
(CNN) that estimates such a motion model individually for each frame. By
relating forward and backward motion these learned models not only allow to
infer valuable motion information based on the backward flow, they also help to
improve the performance at occlusions, where a reliable prediction is
particularly useful. Moreover, our learned models are spatially variant and
hence allow to estimate non-rigid motion per construction. This, in turns,
allows to overcome the major limitation of recent rigidity-based approaches
that seek to improve the estimation by incorporating additional stereo/SfM
constraints. Experiments demonstrate the usefulness of our new approach. They
not only show a consistent improvement of up to 27% for all major benchmarks
(KITTI 2012, KITTI 2015, MPI Sintel) compared to a baseline without prediction,
they also show top results for the MPI Sintel benchmark -- the one of the three
benchmarks that contains the largest amount of non-rigid motion.
| null |
http://arxiv.org/abs/1806.00800v1
|
http://arxiv.org/pdf/1806.00800v1.pdf
| null |
[
"Daniel Maurer",
"Andrés Bruhn"
] |
[
"Optical Flow Estimation"
] | 2018-06-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/handwriting-trajectory-recovery-using-end-to
|
1801.07211
| null | null |
Handwriting Trajectory Recovery using End-to-End Deep Encoder-Decoder Network
|
In this paper, we introduce a novel technique to recover the pen trajectory
of offline characters which is a crucial step for handwritten character
recognition. Generally, online acquisition approach has more advantage than its
offline counterpart as the online technique keeps track of the pen movement.
Hence, pen tip trajectory retrieval from offline text can bridge the gap
between online and offline methods. Our proposed framework employs sequence to
sequence model which consists of an encoder-decoder LSTM module. Our encoder
module consists of Convolutional LSTM network, which takes an offline character
image as the input and encodes the feature sequence to a hidden representation.
The output of the encoder is fed to a decoder LSTM and we get the successive
coordinate points from every time step of the decoder LSTM. Although the
sequence to sequence model is a popular paradigm in various computer vision and
language translation tasks, the main contribution of our work lies in designing
an end-to-end network for a decade old popular problem in Document Image
Analysis community. Tamil, Telugu and Devanagari characters of LIPI Toolkit
dataset are used for our experiments. Our proposed method has achieved superior
performance compared to the other conventional approaches.
| null |
http://arxiv.org/abs/1801.07211v4
|
http://arxiv.org/pdf/1801.07211v4.pdf
| null |
[
"Ayan Kumar Bhunia",
"Abir Bhowmick",
"Ankan Kumar Bhunia",
"Aishik Konwer",
"Prithaj Banerjee",
"Partha Pratim Roy",
"Umapada Pal"
] |
[
"Decoder",
"Retrieval",
"Trajectory Recovery"
] | 2018-01-22T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L277",
"description": "**Sigmoid Activations** are a type of activation function for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{1}{\\left(1+\\exp\\left(-x\\right)\\right)}$$\r\n\r\nSome drawbacks of this activation that have been noted in the literature are: sharp damp gradients during backpropagation from deeper hidden layers to inputs, gradient saturation, and slow convergence.",
"full_name": "Sigmoid Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Sigmoid Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L329",
"description": "**Tanh Activation** is an activation function used for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$\r\n\r\nHistorically, the tanh function became preferred over the [sigmoid function](https://paperswithcode.com/method/sigmoid-activation) as it gave better performance for multi-layer neural networks. But it did not solve the vanishing gradient problem that sigmoids suffered, which was tackled more effectively with the introduction of [ReLU](https://paperswithcode.com/method/relu) activations.\r\n\r\nImage Source: [Junxi Feng](https://www.researchgate.net/profile/Junxi_Feng)",
"full_name": "Tanh Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Tanh Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "An **LSTM** is a type of [recurrent neural network](https://paperswithcode.com/methods/category/recurrent-neural-networks) that addresses the vanishing gradient problem in vanilla RNNs through additional cells, input and output gates. Intuitively, vanishing gradients are solved through additional *additive* components, and forget gate activations, that allow the gradients to flow through the network without vanishing as quickly.\r\n\r\n(Image Source [here](https://medium.com/datadriveninvestor/how-do-lstm-networks-solve-the-problem-of-vanishing-gradients-a6784971a577))\r\n\r\n(Introduced by Hochreiter and Schmidhuber)",
"full_name": "Long Short-Term Memory",
"introduced_year": 1997,
"main_collection": {
"area": "Sequential",
"description": "",
"name": "Recurrent Neural Networks",
"parent": null
},
"name": "LSTM",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/echo-state-networks-are-universal
|
1806.00797
| null | null |
Echo state networks are universal
|
This paper shows that echo state networks are universal uniform approximants
in the context of discrete-time fading memory filters with uniformly bounded
inputs defined on negative infinite times. This result guarantees that any
fading memory input/output system in discrete time can be realized as a simple
finite-dimensional neural network-type state-space model with a static linear
readout map. This approximation is valid for infinite time intervals. The proof
of this statement is based on fundamental results, also presented in this work,
about the topological nature of the fading memory property and about reservoir
computing systems generated by continuous reservoir maps.
| null |
http://arxiv.org/abs/1806.00797v2
|
http://arxiv.org/pdf/1806.00797v2.pdf
| null |
[
"Lyudmila Grigoryeva",
"Juan-Pablo Ortega"
] |
[
"valid"
] | 2018-06-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/transfer-topic-labeling-with-domain-specific
|
1806.00793
| null | null |
Transfer Topic Labeling with Domain-Specific Knowledge Base: An Analysis of UK House of Commons Speeches 1935-2014
|
Topic models are widely used in natural language processing, allowing
researchers to estimate the underlying themes in a collection of documents.
Most topic models use unsupervised methods and hence require the additional
step of attaching meaningful labels to estimated topics. This process of manual
labeling is not scalable and suffers from human bias. We present a
semi-automatic transfer topic labeling method that seeks to remedy these
problems. Domain-specific codebooks form the knowledge-base for automated topic
labeling. We demonstrate our approach with a dynamic topic model analysis of
the complete corpus of UK House of Commons speeches 1935-2014, using the coding
instructions of the Comparative Agendas Project to label topics. We show that
our method works well for a majority of the topics we estimate; but we also
find that institution-specific topics, in particular on subnational governance,
require manual input. We validate our results using human expert coding.
| null |
http://arxiv.org/abs/1806.00793v2
|
http://arxiv.org/pdf/1806.00793v2.pdf
| null |
[
"Alexander Herzog",
"Peter John",
"Slava Jankin Mikhaylov"
] |
[
"Topic Models"
] | 2018-06-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-comparison-of-audio-signal-preprocessing
|
1709.01922
| null | null |
A Comparison of Audio Signal Preprocessing Methods for Deep Neural Networks on Music Tagging
|
In this paper, we empirically investigate the effect of audio preprocessing on music tagging with deep neural networks. We perform comprehensive experiments involving audio preprocessing using different time-frequency representations, logarithmic magnitude compression, frequency weighting, and scaling. We show that many commonly used input preprocessing techniques are redundant except magnitude compression.
|
In this paper, we empirically investigate the effect of audio preprocessing on music tagging with deep neural networks.
|
https://arxiv.org/abs/1709.01922v3
|
https://arxiv.org/pdf/1709.01922v3.pdf
| null |
[
"Keunwoo Choi",
"György Fazekas",
"Kyunghyun Cho",
"Mark Sandler"
] |
[
"Music Tagging"
] | 2017-09-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/building-advanced-dialogue-managers-for-goal
|
1806.00780
| null | null |
Building Advanced Dialogue Managers for Goal-Oriented Dialogue Systems
|
Goal-Oriented (GO) Dialogue Systems, colloquially known as goal oriented
chatbots, help users achieve a predefined goal (e.g. book a movie ticket)
within a closed domain. A first step is to understand the user's goal by using
natural language understanding techniques. Once the goal is known, the bot must
manage a dialogue to achieve that goal, which is conducted with respect to a
learnt policy. The success of the dialogue system depends on the quality of the
policy, which is in turn reliant on the availability of high-quality training
data for the policy learning method, for instance Deep Reinforcement Learning.
Due to the domain specificity, the amount of available data is typically too
low to allow the training of good dialogue policies. In this master thesis we
introduce a transfer learning method to mitigate the effects of the low
in-domain data availability. Our transfer learning based approach improves the
bot's success rate by $20\%$ in relative terms for distant domains and we more
than double it for close domains, compared to the model without transfer
learning. Moreover, the transfer learning chatbots learn the policy up to 5 to
10 times faster. Finally, as the transfer learning approach is complementary to
additional processing such as warm-starting, we show that their joint
application gives the best outcomes.
| null |
http://arxiv.org/abs/1806.00780v1
|
http://arxiv.org/pdf/1806.00780v1.pdf
| null |
[
"Vladimir Ilievski"
] |
[
"Deep Reinforcement Learning",
"Goal-Oriented Dialogue Systems",
"Natural Language Understanding",
"Reinforcement Learning",
"Specificity",
"Transfer Learning"
] | 2018-06-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/multi-cast-attention-networks-for-retrieval
|
1806.00778
| null | null |
Multi-Cast Attention Networks for Retrieval-based Question Answering and Response Prediction
|
Attention is typically used to select informative sub-phrases that are used
for prediction. This paper investigates the novel use of attention as a form of
feature augmentation, i.e, casted attention. We propose Multi-Cast Attention
Networks (MCAN), a new attention mechanism and general model architecture for a
potpourri of ranking tasks in the conversational modeling and question
answering domains. Our approach performs a series of soft attention operations,
each time casting a scalar feature upon the inner word embeddings. The key idea
is to provide a real-valued hint (feature) to a subsequent encoder layer and is
targeted at improving the representation learning process. There are several
advantages to this design, e.g., it allows an arbitrary number of attention
mechanisms to be casted, allowing for multiple attention types (e.g.,
co-attention, intra-attention) and attention variants (e.g., alignment-pooling,
max-pooling, mean-pooling) to be executed simultaneously. This not only
eliminates the costly need to tune the nature of the co-attention layer, but
also provides greater extents of explainability to practitioners. Via extensive
experiments on four well-known benchmark datasets, we show that MCAN achieves
state-of-the-art performance. On the Ubuntu Dialogue Corpus, MCAN outperforms
existing state-of-the-art models by $9\%$. MCAN also achieves the best
performing score to date on the well-studied TrecQA dataset.
| null |
http://arxiv.org/abs/1806.00778v1
|
http://arxiv.org/pdf/1806.00778v1.pdf
| null |
[
"Yi Tay",
"Luu Anh Tuan",
"Siu Cheung Hui"
] |
[
"Question Answering",
"Representation Learning",
"Retrieval",
"Word Embeddings"
] | 2018-06-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/exploration-in-structured-reinforcement
|
1806.00775
| null | null |
Exploration in Structured Reinforcement Learning
|
We address reinforcement learning problems with finite state and action
spaces where the underlying MDP has some known structure that could be
potentially exploited to minimize the exploration rates of suboptimal (state,
action) pairs. For any arbitrary structure, we derive problem-specific regret
lower bounds satisfied by any learning algorithm. These lower bounds are made
explicit for unstructured MDPs and for those whose transition probabilities and
average reward functions are Lipschitz continuous w.r.t. the state and action.
For Lipschitz MDPs, the bounds are shown not to scale with the sizes $S$ and
$A$ of the state and action spaces, i.e., they are smaller than $c\log T$ where
$T$ is the time horizon and the constant $c$ only depends on the Lipschitz
structure, the span of the bias function, and the minimal action sub-optimality
gap. This contrasts with unstructured MDPs where the regret lower bound
typically scales as $SA\log T$. We devise DEL (Directed Exploration Learning),
an algorithm that matches our regret lower bounds. We further simplify the
algorithm for Lipschitz MDPs, and show that the simplified version is still
able to efficiently exploit the structure.
| null |
http://arxiv.org/abs/1806.00775v2
|
http://arxiv.org/pdf/1806.00775v2.pdf
|
NeurIPS 2018 12
|
[
"Jungseul Ok",
"Alexandre Proutiere",
"Damianos Tranos"
] |
[
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-06-03T00:00:00 |
http://papers.nips.cc/paper/8103-exploration-in-structured-reinforcement-learning
|
http://papers.nips.cc/paper/8103-exploration-in-structured-reinforcement-learning.pdf
|
exploration-in-structured-reinforcement-1
| null |
[] |
https://paperswithcode.com/paper/dual-primal-graph-convolutional-networks
|
1806.00770
| null | null |
Dual-Primal Graph Convolutional Networks
|
In recent years, there has been a surge of interest in developing deep
learning methods for non-Euclidean structured data such as graphs. In this
paper, we propose Dual-Primal Graph CNN, a graph convolutional architecture
that alternates convolution-like operations on the graph and its dual. Our
approach allows to learn both vertex- and edge features and generalizes the
previous graph attention (GAT) model. We provide extensive experimental
validation showing state-of-the-art results on a variety of tasks tested on
established graph benchmarks, including CORA and Citeseer citation networks as
well as MovieLens, Flixter, Douban and Yahoo Music graph-guided recommender
systems.
| null |
http://arxiv.org/abs/1806.00770v1
|
http://arxiv.org/pdf/1806.00770v1.pdf
| null |
[
"Federico Monti",
"Oleksandr Shchur",
"Aleksandar Bojchevski",
"Or Litany",
"Stephan Günnemann",
"Michael M. Bronstein"
] |
[
"Graph Attention",
"Recommendation Systems"
] | 2018-06-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/detecting-adversarial-image-examples-in-deep
|
1705.08378
| null | null |
Detecting Adversarial Image Examples in Deep Networks with Adaptive Noise Reduction
|
Recently, many studies have demonstrated deep neural network (DNN)
classifiers can be fooled by the adversarial example, which is crafted via
introducing some perturbations into an original sample. Accordingly, some
powerful defense techniques were proposed. However, existing defense techniques
often require modifying the target model or depend on the prior knowledge of
attacks. In this paper, we propose a straightforward method for detecting
adversarial image examples, which can be directly deployed into unmodified
off-the-shelf DNN models. We consider the perturbation to images as a kind of
noise and introduce two classic image processing techniques, scalar
quantization and smoothing spatial filter, to reduce its effect. The image
entropy is employed as a metric to implement an adaptive noise reduction for
different kinds of images. Consequently, the adversarial example can be
effectively detected by comparing the classification results of a given sample
and its denoised version, without referring to any prior knowledge of attacks.
More than 20,000 adversarial examples against some state-of-the-art DNN models
are used to evaluate the proposed method, which are crafted with different
attack techniques. The experiments show that our detection method can achieve a
high overall F1 score of 96.39% and certainly raises the bar for defense-aware
attacks.
|
Consequently, the adversarial example can be effectively detected by comparing the classification results of a given sample and its denoised version, without referring to any prior knowledge of attacks.
|
http://arxiv.org/abs/1705.08378v5
|
http://arxiv.org/pdf/1705.08378v5.pdf
| null |
[
"Bin Liang",
"Hongcheng Li",
"Miaoqiang Su",
"Xirong Li",
"Wenchang Shi",
"Xiao-Feng Wang"
] |
[
"Quantization"
] | 2017-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/what-we-really-want-to-find-by-sentiment
|
1704.03407
| null | null |
What we really want to find by Sentiment Analysis: The Relationship between Computational Models and Psychological State
|
As the first step to model emotional state of a person, we build sentiment
analysis models with existing deep neural network algorithms and compare the
models with psychological measurements to enlighten the relationship. In the
experiments, we first examined psychological state of 64 participants and asked
them to summarize the story of a book, Chronicle of a Death Foretold (Marquez,
1981). Secondly, we trained models using crawled 365,802 movie review data;
then we evaluated participants' summaries using the pretrained model as a
concept of transfer learning. With the background that emotion affects on
memories, we investigated the relationship between the evaluation score of the
summaries from computational models and the examined psychological
measurements. The result shows that although CNN performed the best among other
deep neural network algorithms (LSTM, GRU), its results are not related to the
psychological state. Rather, GRU shows more explainable results depending on
the psychological state. The contribution of this paper can be summarized as
follows: (1) we enlighten the relationship between computational models and
psychological measurements. (2) we suggest this framework as objective methods
to evaluate the emotion; the real sentiment analysis of a person.
| null |
http://arxiv.org/abs/1704.03407v2
|
http://arxiv.org/pdf/1704.03407v2.pdf
| null |
[
"Hwiyeol Jo",
"Soo-Min Kim",
"Jeong Ryu"
] |
[
"Sentiment Analysis",
"Transfer Learning"
] | 2017-04-11T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "A **Gated Recurrent Unit**, or **GRU**, is a type of recurrent neural network. It is similar to an [LSTM](https://paperswithcode.com/method/lstm), but only has two gates - a reset gate and an update gate - and notably lacks an output gate. Fewer parameters means GRUs are generally easier/faster to train than their LSTM counterparts.\r\n\r\nImage Source: [here](https://www.google.com/url?sa=i&url=https%3A%2F%2Fcommons.wikimedia.org%2Fwiki%2FFile%3AGated_Recurrent_Unit%2C_type_1.svg&psig=AOvVaw3EmNX8QXC5hvyxeenmJIUn&ust=1590332062671000&source=images&cd=vfe&ved=0CA0QjhxqFwoTCMiev9-eyukCFQAAAAAdAAAAABAR)",
"full_name": "Gated Recurrent Unit",
"introduced_year": 2000,
"main_collection": {
"area": "Sequential",
"description": "",
"name": "Recurrent Neural Networks",
"parent": null
},
"name": "GRU",
"source_title": "Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation",
"source_url": "http://arxiv.org/abs/1406.1078v3"
}
] |
https://paperswithcode.com/paper/psychological-state-in-text-a-limitation-of
|
1806.00754
| null | null |
Psychological State in Text: A Limitation of Sentiment Analysis
|
Starting with the idea that sentiment analysis models should be able to
predict not only positive or negative but also other psychological states of a
person, we implement a sentiment analysis model to investigate the relationship
between the model and emotional state. We first examine psychological
measurements of 64 participants and ask them to write a book report about a
story. After that, we train our sentiment analysis model using crawled movie
review data. We finally evaluate participants' writings, using the pretrained
model as a concept of transfer learning. The result shows that sentiment
analysis model performs good at predicting a score, but the score does not have
any correlation with human's self-checked sentiment.
| null |
http://arxiv.org/abs/1806.00754v1
|
http://arxiv.org/pdf/1806.00754v1.pdf
| null |
[
"Hwiyeol Jo",
"Jeong Ryu"
] |
[
"Sentiment Analysis",
"Transfer Learning"
] | 2018-06-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/multiple-models-for-recommending-temporal
|
1803.07890
| null | null |
Multiple Models for Recommending Temporal Aspects of Entities
|
Entity aspect recommendation is an emerging task in semantic search that helps users discover serendipitous and prominent information with respect to an entity, of which salience (e.g., popularity) is the most important factor in previous work. However, entity aspects are temporally dynamic and often driven by events happening over time. For such cases, aspect suggestion based solely on salience features can give unsatisfactory results, for two reasons. First, salience is often accumulated over a long time period and does not account for recency. Second, many aspects related to an event entity are strongly time-dependent. In this paper, we study the task of temporal aspect recommendation for a given entity, which aims at recommending the most relevant aspects and takes into account time in order to improve search experience. We propose a novel event-centric ensemble ranking method that learns from multiple time and type-dependent models and dynamically trades off salience and recency characteristics. Through extensive experiments on real-world query logs, we demonstrate that our method is robust and achieves better effectiveness than competitive baselines.
| null |
https://arxiv.org/abs/1803.07890v4
|
https://arxiv.org/pdf/1803.07890v4.pdf
| null |
[
"Tu Nguyen",
"Nattiya Kanhabua",
"Wolfgang Nejdl"
] |
[] | 2018-03-21T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/extrofitting-enriching-word-representation
|
1804.07946
| null | null |
Extrofitting: Enriching Word Representation and its Vector Space with Semantic Lexicons
|
We propose post-processing method for enriching not only word representation
but also its vector space using semantic lexicons, which we call extrofitting.
The method consists of 3 steps as follows: (i) Expanding 1 or more dimension(s)
on all the word vectors, filling with their representative value. (ii)
Transferring semantic knowledge by averaging each representative values of
synonyms and filling them in the expanded dimension(s). These two steps make
representations of the synonyms close together. (iii) Projecting the vector
space using Linear Discriminant Analysis, which eliminates the expanded
dimension(s) with semantic knowledge. When experimenting with GloVe, we find
that our method outperforms Faruqui's retrofitting on some of word similarity
task. We also report further analysis on our method in respect to word vector
dimensions, vocabulary size as well as other well-known pretrained word vectors
(e.g., Word2Vec, Fasttext).
|
The method consists of 3 steps as follows: (i) Expanding 1 or more dimension(s) on all the word vectors, filling with their representative value.
|
http://arxiv.org/abs/1804.07946v2
|
http://arxiv.org/pdf/1804.07946v2.pdf
|
WS 2018 7
|
[
"Hwiyeol Jo",
"Stanley Jungkyu Choi"
] |
[
"Word Similarity"
] | 2018-04-21T00:00:00 |
https://aclanthology.org/W18-3003
|
https://aclanthology.org/W18-3003.pdf
|
extrofitting-enriching-word-representation-1
| null |
[
{
"code_snippet_url": "",
"description": "**GloVe Embeddings** are a type of word embedding that encode the co-occurrence probability ratio between two words as vector differences. GloVe uses a weighted least squares objective $J$ that minimizes the difference between the dot product of the vectors of two words and the logarithm of their number of co-occurrences:\r\n\r\n$$ J=\\sum\\_{i, j=1}^{V}f\\left(𝑋\\_{i j}\\right)(w^{T}\\_{i}\\tilde{w}_{j} + b\\_{i} + \\tilde{b}\\_{j} - \\log{𝑋}\\_{ij})^{2} $$\r\n\r\nwhere $w\\_{i}$ and $b\\_{i}$ are the word vector and bias respectively of word $i$, $\\tilde{w}_{j}$ and $b\\_{j}$ are the context word vector and bias respectively of word $j$, $X\\_{ij}$ is the number of times word $i$ occurs in the context of word $j$, and $f$ is a weighting function that assigns lower weights to rare and frequent co-occurrences.",
"full_name": "GloVe Embeddings",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Word Embeddings",
"parent": null
},
"name": "GloVe",
"source_title": "GloVe: Global Vectors for Word Representation",
"source_url": "https://aclanthology.org/D14-1162"
}
] |
https://paperswithcode.com/paper/ti-cnn-convolutional-neural-networks-for-fake
|
1806.00749
| null | null |
TI-CNN: Convolutional Neural Networks for Fake News Detection
|
With the development of social networks, fake news for various commercial and political purposes has been appearing in large numbers and gotten widespread in the online world. With deceptive words, people can get infected by the fake news very easily and will share them without any fact-checking. For instance, during the 2016 US president election, various kinds of fake news about the candidates widely spread through both official news media and the online social networks. These fake news is usually released to either smear the opponents or support the candidate on their side. The erroneous information in the fake news is usually written to motivate the voters' irrational emotion and enthusiasm. Such kinds of fake news sometimes can bring about devastating effects, and an important goal in improving the credibility of online social networks is to identify the fake news timely. In this paper, we propose to study the fake news detection problem. Automatic fake news identification is extremely hard, since pure model based fact-checking for news is still an open problem, and few existing models can be applied to solve the problem. With a thorough investigation of a fake news data, lots of useful explicit features are identified from both the text words and images used in the fake news. Besides the explicit features, there also exist some hidden patterns in the words and images used in fake news, which can be captured with a set of latent features extracted via the multiple convolutional layers in our model. A model named as TI-CNN (Text and Image information based Convolutinal Neural Network) is proposed in this paper. By projecting the explicit and latent features into a unified feature space, TI-CNN is trained with both the text and image information simultaneously. Extensive experiments carried on the real-world fake news datasets have demonstrate the effectiveness of TI-CNN.
|
By projecting the explicit and latent features into a unified feature space, TI-CNN is trained with both the text and image information simultaneously.
|
https://arxiv.org/abs/1806.00749v3
|
https://arxiv.org/pdf/1806.00749v3.pdf
| null |
[
"Yang Yang",
"Lei Zheng",
"Jiawei Zhang",
"Qingcai Cui",
"Zhoujun Li",
"Philip S. Yu"
] |
[
"Fact Checking",
"Fake News Detection"
] | 2018-06-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/eye-in-the-sky-real-time-drone-surveillance
|
1806.00746
| null | null |
Eye in the Sky: Real-time Drone Surveillance System (DSS) for Violent Individuals Identification using ScatterNet Hybrid Deep Learning Network
|
Drone systems have been deployed by various law enforcement agencies to
monitor hostiles, spy on foreign drug cartels, conduct border control
operations, etc. This paper introduces a real-time drone surveillance system to
identify violent individuals in public areas. The system first uses the Feature
Pyramid Network to detect humans from aerial images. The image region with the
human is used by the proposed ScatterNet Hybrid Deep Learning (SHDL) network
for human pose estimation. The orientations between the limbs of the estimated
pose are next used to identify the violent individuals. The proposed deep
network can learn meaningful representations quickly using ScatterNet and
structural priors with relatively fewer labeled examples. The system detects
the violent individuals in real-time by processing the drone images in the
cloud. This research also introduces the aerial violent individual dataset used
for training the deep network which hopefully may encourage researchers
interested in using deep learning for aerial surveillance. The pose estimation
and violent individuals identification performance is compared with the
state-of-the-art techniques.
| null |
http://arxiv.org/abs/1806.00746v1
|
http://arxiv.org/pdf/1806.00746v1.pdf
| null |
[
"Amarjot Singh",
"Devendra Patil",
"SN Omkar"
] |
[
"Pose Estimation"
] | 2018-06-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/clustering-driven-deep-embedding-with
|
1803.08457
| null | null |
Clustering-driven Deep Embedding with Pairwise Constraints
|
Recently, there has been increasing interest to leverage the competence of
neural networks to analyze data. In particular, new clustering methods that
employ deep embeddings have been presented. In this paper, we depart from
centroid-based models and suggest a new framework, called Clustering-driven
deep embedding with PAirwise Constraints (CPAC), for non-parametric clustering
using a neural network. We present a clustering-driven embedding based on a
Siamese network that encourages pairs of data points to output similar
representations in the latent space. Our pair-based model allows augmenting the
information with labeled pairs to constitute a semi-supervised framework. Our
approach is based on analyzing the losses associated with each pair to refine
the set of constraints. We show that clustering performance increases when
using this scheme, even with a limited amount of user queries. We demonstrate
how our architecture is adapted for various types of data and present the first
deep framework to cluster 3D shapes.
|
In this paper, we depart from centroid-based models and suggest a new framework, called Clustering-driven deep embedding with PAirwise Constraints (CPAC), for non-parametric clustering using a neural network.
|
http://arxiv.org/abs/1803.08457v5
|
http://arxiv.org/pdf/1803.08457v5.pdf
| null |
[
"Sharon Fogel",
"Hadar Averbuch-Elor",
"Jacov Goldberger",
"Daniel Cohen-Or"
] |
[
"Clustering"
] | 2018-03-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/faster-deep-q-learning-using-neural-episodic
|
1801.01968
| null | null |
Faster Deep Q-learning using Neural Episodic Control
|
The research on deep reinforcement learning which estimates Q-value by deep
learning has been attracted the interest of researchers recently. In deep
reinforcement learning, it is important to efficiently learn the experiences
that an agent has collected by exploring environment. We propose NEC2DQN that
improves learning speed of a poor sample efficiency algorithm such as DQN by
using good one such as NEC at the beginning of learning. We show it is able to
learn faster than Double DQN or N-step DQN in the experiments of Pong.
| null |
http://arxiv.org/abs/1801.01968v4
|
http://arxiv.org/pdf/1801.01968v4.pdf
| null |
[
"Daichi Nishio",
"Satoshi Yamane"
] |
[
"Deep Reinforcement Learning",
"Q-Learning",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-01-06T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/lorenzopapa5/SPEED",
"description": "The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key components in autonomous and robotic systems. \r\nApproaches based on the state of the art vision transformer architectures are extremely deep and complex not suitable for real-time inference operations on edge and autonomous systems equipped with low resources (i.e. robot indoor navigation and surveillance). This paper presents SPEED, a Separable Pyramidal pooling EncodEr-Decoder architecture designed to achieve real-time frequency performances on multiple hardware platforms. The proposed model is a fast-throughput deep architecture for MDE able to obtain depth estimations with high accuracy from low resolution images using minimum hardware resources (i.e. edge devices). Our encoder-decoder model exploits two depthwise separable pyramidal pooling layers, which allow to increase the inference frequency while reducing the overall computational complexity. The proposed method performs better than other fast-throughput architectures in terms of both accuracy and frame rates, achieving real-time performances over cloud CPU, TPU and the NVIDIA Jetson TX1 on two indoor benchmarks: the NYU Depth v2 and the DIML Kinect v2 datasets.",
"full_name": "SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings",
"introduced_year": 2000,
"main_collection": null,
"name": "SPEED",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Experience Replay** is a replay memory technique used in reinforcement learning where we store the agent’s experiences at each time-step, $e\\_{t} = \\left(s\\_{t}, a\\_{t}, r\\_{t}, s\\_{t+1}\\right)$ in a data-set $D = e\\_{1}, \\cdots, e\\_{N}$ , pooled over many episodes into a replay memory. We then usually sample the memory randomly for a minibatch of experience, and use this to learn off-policy, as with Deep Q-Networks. This tackles the problem of autocorrelation leading to unstable training, by making the problem more like a supervised learning problem.\r\n\r\nImage Credit: [Hands-On Reinforcement Learning with Python, Sudharsan Ravichandiran](https://subscription.packtpub.com/book/big_data_and_business_intelligence/9781788836524)",
"full_name": "Experience Replay",
"introduced_year": 1993,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Replay Memory",
"parent": null
},
"name": "Experience Replay",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "**Double Q-learning** is an off-policy reinforcement learning algorithm that utilises double estimation to counteract overestimation problems with traditional Q-learning. \r\n\r\nThe max operator in standard [Q-learning](https://paperswithcode.com/method/q-learning) and [DQN](https://paperswithcode.com/method/dqn) uses the same values both to select and to evaluate an action. This makes it more likely to select overestimated values, resulting in overoptimistic value estimates. To prevent this, we can decouple the selection from the evaluation, which is the idea behind Double Q-learning:\r\n\r\n$$ Y^{Q}\\_{t} = R\\_{t+1} + \\gamma{Q}\\left(S\\_{t+1}, \\arg\\max\\_{a}Q\\left(S\\_{t+1}, a; \\mathbb{\\theta}\\_{t}\\right);\\mathbb{\\theta}\\_{t}\\right) $$\r\n\r\nThe Double Q-learning error can then be written as:\r\n\r\n$$ Y^{DoubleQ}\\_{t} = R\\_{t+1} + \\gamma{Q}\\left(S\\_{t+1}, \\arg\\max\\_{a}Q\\left(S\\_{t+1}, a; \\mathbb{\\theta}\\_{t}\\right);\\mathbb{\\theta}^{'}\\_{t}\\right) $$\r\n\r\nHere the selection of the action in the $\\arg\\max$ is still due to the online weights $\\theta\\_{t}$. But we use a second set of weights $\\mathbb{\\theta}^{'}\\_{t}$ to fairly evaluate the value of this policy.\r\n\r\nSource: [Deep Reinforcement Learning with Double Q-learning](https://paperswithcode.com/paper/deep-reinforcement-learning-with-double-q)",
"full_name": "Double Q-learning",
"introduced_year": 2000,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Off-Policy TD Control",
"parent": null
},
"name": "Double Q-learning",
"source_title": "Double Q-learning",
"source_url": "http://papers.nips.cc/paper/3964-double-q-learning"
},
{
"code_snippet_url": null,
"description": "**Q-Learning** is an off-policy temporal difference control algorithm:\r\n\r\n$$Q\\left(S\\_{t}, A\\_{t}\\right) \\leftarrow Q\\left(S\\_{t}, A\\_{t}\\right) + \\alpha\\left[R_{t+1} + \\gamma\\max\\_{a}Q\\left(S\\_{t+1}, a\\right) - Q\\left(S\\_{t}, A\\_{t}\\right)\\right] $$\r\n\r\nThe learned action-value function $Q$ directly approximates $q\\_{*}$, the optimal action-value function, independent of the policy being followed.\r\n\r\nSource: Sutton and Barto, Reinforcement Learning, 2nd Edition",
"full_name": "Q-Learning",
"introduced_year": 1984,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Off-Policy TD Control",
"parent": null
},
"name": "Q-Learning",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "A **Double Deep Q-Network**, or **Double DQN** utilises [Double Q-learning](https://paperswithcode.com/method/double-q-learning) to reduce overestimation by decomposing the max operation in the target into action selection and action evaluation. We evaluate the greedy policy according to the online network, but we use the target network to estimate its value. The update is the same as for [DQN](https://paperswithcode.com/method/dqn), but replacing the target $Y^{DQN}\\_{t}$ with:\r\n\r\n$$ Y^{DoubleDQN}\\_{t} = R\\_{t+1}+\\gamma{Q}\\left(S\\_{t+1}, \\arg\\max\\_{a}Q\\left(S\\_{t+1}, a; \\theta\\_{t}\\right);\\theta\\_{t}^{-}\\right) $$\r\n\r\nCompared to the original formulation of Double [Q-Learning](https://paperswithcode.com/method/q-learning), in Double DQN the weights of the second network $\\theta^{'}\\_{t}$ are replaced with the weights of the target network $\\theta\\_{t}^{-}$ for the evaluation of the current greedy policy.",
"full_name": "Double DQN",
"introduced_year": 2000,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Q-Learning Networks",
"parent": "Off-Policy TD Control"
},
"name": "Double DQN",
"source_title": "Deep Reinforcement Learning with Double Q-learning",
"source_url": "http://arxiv.org/abs/1509.06461v3"
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "A **DQN**, or Deep Q-Network, approximates a state-value function in a [Q-Learning](https://paperswithcode.com/method/q-learning) framework with a neural network. In the Atari Games case, they take in several frames of the game as an input and output state values for each action as an output. \r\n\r\nIt is usually used in conjunction with [Experience Replay](https://paperswithcode.com/method/experience-replay), for storing the episode steps in memory for off-policy learning, where samples are drawn from the replay memory at random. Additionally, the Q-Network is usually optimized towards a frozen target network that is periodically updated with the latest weights every $k$ steps (where $k$ is a hyperparameter). The latter makes training more stable by preventing short-term oscillations from a moving target. The former tackles autocorrelation that would occur from on-line learning, and having a replay memory makes the problem more like a supervised learning problem.\r\n\r\nImage Source: [here](https://www.researchgate.net/publication/319643003_Autonomous_Quadrotor_Landing_using_Deep_Reinforcement_Learning)",
"full_name": "Deep Q-Network",
"introduced_year": 2000,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Q-Learning Networks",
"parent": "Off-Policy TD Control"
},
"name": "DQN",
"source_title": "Playing Atari with Deep Reinforcement Learning",
"source_url": "http://arxiv.org/abs/1312.5602v1"
}
] |
https://paperswithcode.com/paper/contextualize-show-and-tell-a-neural-visual
|
1806.00738
| null | null |
Contextualize, Show and Tell: A Neural Visual Storyteller
|
We present a neural model for generating short stories from image sequences,
which extends the image description model by Vinyals et al. (Vinyals et al.,
2015). This extension relies on an encoder LSTM to compute a context vector of
each story from the image sequence. This context vector is used as the first
state of multiple independent decoder LSTMs, each of which generates the
portion of the story corresponding to each image in the sequence by taking the
image embedding as the first input. Our model showed competitive results with
the METEOR metric and human ratings in the internal track of the Visual
Storytelling Challenge 2018.
|
We present a neural model for generating short stories from image sequences, which extends the image description model by Vinyals et al. (Vinyals et al., 2015).
|
http://arxiv.org/abs/1806.00738v1
|
http://arxiv.org/pdf/1806.00738v1.pdf
| null |
[
"Diana Gonzalez-Rico",
"Gibran Fuentes-Pineda"
] |
[
"Decoder",
"Image Description",
"Visual Storytelling"
] | 2018-06-03T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L277",
"description": "**Sigmoid Activations** are a type of activation function for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{1}{\\left(1+\\exp\\left(-x\\right)\\right)}$$\r\n\r\nSome drawbacks of this activation that have been noted in the literature are: sharp damp gradients during backpropagation from deeper hidden layers to inputs, gradient saturation, and slow convergence.",
"full_name": "Sigmoid Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Sigmoid Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L329",
"description": "**Tanh Activation** is an activation function used for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$\r\n\r\nHistorically, the tanh function became preferred over the [sigmoid function](https://paperswithcode.com/method/sigmoid-activation) as it gave better performance for multi-layer neural networks. But it did not solve the vanishing gradient problem that sigmoids suffered, which was tackled more effectively with the introduction of [ReLU](https://paperswithcode.com/method/relu) activations.\r\n\r\nImage Source: [Junxi Feng](https://www.researchgate.net/profile/Junxi_Feng)",
"full_name": "Tanh Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Tanh Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "An **LSTM** is a type of [recurrent neural network](https://paperswithcode.com/methods/category/recurrent-neural-networks) that addresses the vanishing gradient problem in vanilla RNNs through additional cells, input and output gates. Intuitively, vanishing gradients are solved through additional *additive* components, and forget gate activations, that allow the gradients to flow through the network without vanishing as quickly.\r\n\r\n(Image Source [here](https://medium.com/datadriveninvestor/how-do-lstm-networks-solve-the-problem-of-vanishing-gradients-a6784971a577))\r\n\r\n(Introduced by Hochreiter and Schmidhuber)",
"full_name": "Long Short-Term Memory",
"introduced_year": 1997,
"main_collection": {
"area": "Sequential",
"description": "",
"name": "Recurrent Neural Networks",
"parent": null
},
"name": "LSTM",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/content-based-video-relevance-prediction
|
1806.00737
| null | null |
Content-based Video Relevance Prediction Challenge: Data, Protocol, and Baseline
|
Video relevance prediction is one of the most important tasks for online
streaming service. Given the relevance of videos and viewer feedbacks, the
system can provide personalized recommendations, which will help the user
discover more content of interest. In most online service, the computation of
video relevance table is based on users' implicit feedback, e.g. watch and
search history. However, this kind of method performs poorly for "cold-start"
problems - when a new video is added to the library, the recommendation system
needs to bootstrap the video relevance score with very little user behavior
known. One promising approach to solve it is analyzing video content itself,
i.e. predicting video relevance by video frame, audio, subtitle and metadata.
In this paper, we describe a challenge on Content-based Video Relevance
Prediction (CBVRP) that is hosted by Hulu in the ACM Multimedia Conference
2018. In this challenge, Hulu drives the study on an open problem of exploiting
content characteristics directly from original video for video relevance
prediction. We provide massive video assets and ground truth relevance derived
from our really system, to build up a common platform for algorithm development
and performance evaluation.
|
Video relevance prediction is one of the most important tasks for online streaming service.
|
http://arxiv.org/abs/1806.00737v1
|
http://arxiv.org/pdf/1806.00737v1.pdf
| null |
[
"Mengyi Liu",
"Xiaohui Xie",
"Hanning Zhou"
] |
[
"Prediction"
] | 2018-06-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/minnorm-training-an-algorithm-for-training
|
1806.00730
| null | null |
Minnorm training: an algorithm for training over-parameterized deep neural networks
|
In this work, we propose a new training method for finding minimum weight
norm solutions in over-parameterized neural networks (NNs). This method seeks
to improve training speed and generalization performance by framing NN training
as a constrained optimization problem wherein the sum of the norm of the
weights in each layer of the network is minimized, under the constraint of
exactly fitting training data. It draws inspiration from support vector
machines (SVMs), which are able to generalize well, despite often having an
infinite number of free parameters in their primal form, and from recent
theoretical generalization bounds on NNs which suggest that lower norm
solutions generalize better. To solve this constrained optimization problem,
our method employs Lagrange multipliers that act as integrators of error over
training and identify `support vector'-like examples. The method can be
implemented as a wrapper around gradient based methods and uses standard
back-propagation of gradients from the NN for both regression and
classification versions of the algorithm. We provide theoretical justifications
for the effectiveness of this algorithm in comparison to early stopping and
$L_2$-regularization using simple, analytically tractable settings. In
particular, we show faster convergence to the max-margin hyperplane in a
shallow network (compared to vanilla gradient descent); faster convergence to
the minimum-norm solution in a linear chain (compared to $L_2$-regularization);
and initialization-independent generalization performance in a deep linear
network. Finally, using the MNIST dataset, we demonstrate that this algorithm
can boost test accuracy and identify difficult examples in real-world datasets.
| null |
http://arxiv.org/abs/1806.00730v2
|
http://arxiv.org/pdf/1806.00730v2.pdf
| null |
[
"Yamini Bansal",
"Madhu Advani",
"David D. Cox",
"Andrew M. Saxe"
] |
[
"Generalization Bounds"
] | 2018-06-03T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/lorenzopapa5/SPEED",
"description": "The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key components in autonomous and robotic systems. \r\nApproaches based on the state of the art vision transformer architectures are extremely deep and complex not suitable for real-time inference operations on edge and autonomous systems equipped with low resources (i.e. robot indoor navigation and surveillance). This paper presents SPEED, a Separable Pyramidal pooling EncodEr-Decoder architecture designed to achieve real-time frequency performances on multiple hardware platforms. The proposed model is a fast-throughput deep architecture for MDE able to obtain depth estimations with high accuracy from low resolution images using minimum hardware resources (i.e. edge devices). Our encoder-decoder model exploits two depthwise separable pyramidal pooling layers, which allow to increase the inference frequency while reducing the overall computational complexity. The proposed method performs better than other fast-throughput architectures in terms of both accuracy and frame rates, achieving real-time performances over cloud CPU, TPU and the NVIDIA Jetson TX1 on two indoor benchmarks: the NYU Depth v2 and the DIML Kinect v2 datasets.",
"full_name": "SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings",
"introduced_year": 2000,
"main_collection": null,
"name": "SPEED",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "**Early Stopping** is a regularization technique for deep neural networks that stops training when parameter updates no longer begin to yield improves on a validation set. In essence, we store and update the current best parameters during training, and when parameter updates no longer yield an improvement (after a set number of iterations) we stop training and use the last best parameters. It works as a regularizer by restricting the optimization procedure to a smaller volume of parameter space.\r\n\r\nImage Source: [Ramazan Gençay](https://www.researchgate.net/figure/Early-stopping-based-on-cross-validation_fig1_3302948)",
"full_name": "Early Stopping",
"introduced_year": 1995,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Early Stopping",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/data-freedata-sparse-softmax-parameter
|
1806.00728
| null | null |
Data-Free/Data-Sparse Softmax Parameter Estimation with Structured Class Geometries
|
This note considers softmax parameter estimation when little/no labeled
training data is available, but a priori information about the relative
geometry of class label log-odds boundaries is available. It is shown that
`data-free' softmax model synthesis corresponds to solving a linear system of
parameter equations, wherein desired dominant class log-odds boundaries are
encoded via convex polytopes that decompose the input feature space. When
solvable, the linear equations yield closed-form softmax parameter solution
families using class boundary polytope specifications only. This allows softmax
parameter learning to be implemented without expensive brute force data
sampling and numerical optimization. The linear equations can also be adapted
to constrained maximum likelihood estimation in data-sparse settings. Since
solutions may also fail to exist for the linear parameter equations derived
from certain polytope specifications, it is thus also shown that there exist
probabilistic classification problems over m convexly separable classes for
which the log-odds boundaries cannot be learned using an m-class softmax model.
| null |
http://arxiv.org/abs/1806.00728v2
|
http://arxiv.org/pdf/1806.00728v2.pdf
| null |
[
"Nisar Ahmed"
] |
[
"parameter estimation"
] | 2018-06-03T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/closed-loop-bayesian-semantic-data-fusion-for
|
1806.00727
| null | null |
Closed-loop Bayesian Semantic Data Fusion for Collaborative Human-Autonomy Target Search
|
In search applications, autonomous unmanned vehicles must be able to
efficiently reacquire and localize mobile targets that can remain out of view
for long periods of time in large spaces. As such, all available information
sources must be actively leveraged -- including imprecise but readily available
semantic observations provided by humans. To achieve this, this work develops
and validates a novel collaborative human-machine sensing solution for dynamic
target search. Our approach uses continuous partially observable Markov
decision process (CPOMDP) planning to generate vehicle trajectories that
optimally exploit imperfect detection data from onboard sensors, as well as
semantic natural language observations that can be specifically requested from
human sensors. The key innovation is a scalable hierarchical Gaussian mixture
model formulation for efficiently solving CPOMDPs with semantic observations in
continuous dynamic state spaces. The approach is demonstrated and validated
with a real human-robot team engaged in dynamic indoor target search and
capture scenarios on a custom testbed.
| null |
http://arxiv.org/abs/1806.00727v1
|
http://arxiv.org/pdf/1806.00727v1.pdf
| null |
[
"Luke Burks",
"Ian Loefgren",
"Luke Barbier",
"Jeremy Muesing",
"Jamison McGinley",
"Sousheel Vunnam",
"Nisar Ahmed"
] |
[] | 2018-06-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/dense-information-flow-for-neural-machine
|
1806.00722
| null | null |
Dense Information Flow for Neural Machine Translation
|
Recently, neural machine translation has achieved remarkable progress by
introducing well-designed deep neural networks into its encoder-decoder
framework. From the optimization perspective, residual connections are adopted
to improve learning performance for both encoder and decoder in most of these
deep architectures, and advanced attention connections are applied as well.
Inspired by the success of the DenseNet model in computer vision problems, in
this paper, we propose a densely connected NMT architecture (DenseNMT) that is
able to train more efficiently for NMT. The proposed DenseNMT not only allows
dense connection in creating new features for both encoder and decoder, but
also uses the dense attention structure to improve attention quality. Our
experiments on multiple datasets show that DenseNMT structure is more
competitive and efficient.
|
Recently, neural machine translation has achieved remarkable progress by introducing well-designed deep neural networks into its encoder-decoder framework.
|
http://arxiv.org/abs/1806.00722v2
|
http://arxiv.org/pdf/1806.00722v2.pdf
|
NAACL 2018 6
|
[
"Yanyao Shen",
"Xu Tan",
"Di He",
"Tao Qin",
"Tie-Yan Liu"
] |
[
"Decoder",
"Machine Translation",
"NMT",
"Translation"
] | 2018-06-03T00:00:00 |
https://aclanthology.org/N18-1117
|
https://aclanthology.org/N18-1117.pdf
|
dense-information-flow-for-neural-machine-1
| null |
[
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!\r\n\r\n\r\nHow Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!",
"full_name": "*Communicated@Fast*How Do I Communicate to Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/google/jax/blob/36f91261099b00194922bd93ed1286fe1c199724/jax/experimental/stax.py#L116",
"description": "**Batch Normalization** aims to reduce internal covariate shift, and in doing so aims to accelerate the training of deep neural nets. It accomplishes this via a normalization step that fixes the means and variances of layer inputs. Batch Normalization also has a beneficial effect on the gradient flow through the network, by reducing the dependence of gradients on the scale of the parameters or of their initial values. This allows for use of much higher learning rates without the risk of divergence. Furthermore, batch normalization regularizes the model and reduces the need for [Dropout](https://paperswithcode.com/method/dropout).\r\n\r\nWe apply a batch normalization layer as follows for a minibatch $\\mathcal{B}$:\r\n\r\n$$ \\mu\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}x\\_{i} $$\r\n\r\n$$ \\sigma^{2}\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}\\left(x\\_{i}-\\mu\\_{\\mathcal{B}}\\right)^{2} $$\r\n\r\n$$ \\hat{x}\\_{i} = \\frac{x\\_{i} - \\mu\\_{\\mathcal{B}}}{\\sqrt{\\sigma^{2}\\_{\\mathcal{B}}+\\epsilon}} $$\r\n\r\n$$ y\\_{i} = \\gamma\\hat{x}\\_{i} + \\beta = \\text{BN}\\_{\\gamma, \\beta}\\left(x\\_{i}\\right) $$\r\n\r\nWhere $\\gamma$ and $\\beta$ are learnable parameters.",
"full_name": "Batch Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Batch Normalization",
"source_title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
"source_url": "http://arxiv.org/abs/1502.03167v3"
},
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "**Average Pooling** is a pooling operation that calculates the average value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. It adds a small amount of translation invariance - meaning translating the image by a small amount does not significantly affect the values of most pooled outputs. It extracts features more smoothly than [Max Pooling](https://paperswithcode.com/method/max-pooling), whereas max pooling extracts more pronounced features like edges.\r\n\r\nImage Source: [here](https://www.researchgate.net/figure/Illustration-of-Max-Pooling-and-Average-Pooling-Figure-2-above-shows-an-example-of-max_fig2_333593451)",
"full_name": "Average Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Average Pooling",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/densenet.py#L113",
"description": "A **Concatenated Skip Connection** is a type of skip connection that seeks to reuse features by concatenating them to new layers, allowing more information to be retained from previous layers of the network. This contrasts with say, residual connections, where element-wise summation is used instead to incorporate information from previous layers. This type of skip connection is prominently used in DenseNets (and also Inception networks), which the Figure to the right illustrates.",
"full_name": "Concatenated Skip Connection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connections** allow layers to skip layers and connect to layers further up the network, allowing for information to flow more easily up the network. Below you can find a continuously updating list of skip connection methods.",
"name": "Skip Connections",
"parent": null
},
"name": "Concatenated Skip Connection",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/baa592b215804927e28638f6a7f3318cbc411d49/torchvision/models/resnet.py#L157",
"description": "**Global Average Pooling** is a pooling operation designed to replace fully connected layers in classical CNNs. The idea is to generate one feature map for each corresponding category of the classification task in the last mlpconv layer. Instead of adding fully connected layers on top of the feature maps, we take the average of each feature map, and the resulting vector is fed directly into the [softmax](https://paperswithcode.com/method/softmax) layer. \r\n\r\nOne advantage of global [average pooling](https://paperswithcode.com/method/average-pooling) over the fully connected layers is that it is more native to the [convolution](https://paperswithcode.com/method/convolution) structure by enforcing correspondences between feature maps and categories. Thus the feature maps can be easily interpreted as categories confidence maps. Another advantage is that there is no parameter to optimize in the global average pooling thus overfitting is avoided at this layer. Furthermore, global average pooling sums out the spatial information, thus it is more robust to spatial translations of the input.",
"full_name": "Global Average Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Global Average Pooling",
"source_title": "Network In Network",
"source_url": "http://arxiv.org/abs/1312.4400v3"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/1aef87d01eec2c0989458387fa04baebcc86ea7b/torchvision/models/densenet.py#L93",
"description": "A **Dense Block** is a module used in convolutional neural networks that connects *all layers* (with matching feature-map sizes) directly with each other. It was originally proposed as part of the [DenseNet](https://paperswithcode.com/method/densenet) architecture. To preserve the feed-forward nature, each layer obtains additional inputs from all preceding layers and passes on its own feature-maps to all subsequent layers. In contrast to [ResNets](https://paperswithcode.com/method/resnet), we never combine features through summation before they are passed into a layer; instead, we combine features by concatenating them. Hence, the $\\ell^{th}$ layer has $\\ell$ inputs, consisting of the feature-maps of all preceding convolutional blocks. Its own feature-maps are passed on to all $L-\\ell$ subsequent layers. This introduces $\\frac{L(L+1)}{2}$ connections in an $L$-layer network, instead of just $L$, as in traditional architectures: \"dense connectivity\".",
"full_name": "Dense Block",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Image Model Blocks** are building blocks used in image models such as convolutional neural networks. Below you can find a continuously updating list of image model blocks.",
"name": "Image Model Blocks",
"parent": null
},
"name": "Dense Block",
"source_title": "Densely Connected Convolutional Networks",
"source_url": "http://arxiv.org/abs/1608.06993v5"
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/0adb5843766092fba584791af76383125fd0d01c/torch/nn/init.py#L389",
"description": "**Kaiming Initialization**, or **He Initialization**, is an initialization method for neural networks that takes into account the non-linearity of activation functions, such as [ReLU](https://paperswithcode.com/method/relu) activations.\r\n\r\nA proper initialization method should avoid reducing or magnifying the magnitudes of input signals exponentially. Using a derivation they work out that the condition to stop this happening is:\r\n\r\n$$\\frac{1}{2}n\\_{l}\\text{Var}\\left[w\\_{l}\\right] = 1 $$\r\n\r\nThis implies an initialization scheme of:\r\n\r\n$$ w\\_{l} \\sim \\mathcal{N}\\left(0, 2/n\\_{l}\\right)$$\r\n\r\nThat is, a zero-centered Gaussian with standard deviation of $\\sqrt{2/{n}\\_{l}}$ (variance shown in equation above). Biases are initialized at $0$.",
"full_name": "Kaiming Initialization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Initialization** methods are used to initialize the weights in a neural network. Below can you find a continuously updating list of initialization methods.",
"name": "Initialization",
"parent": null
},
"name": "Kaiming Initialization",
"source_title": "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification",
"source_url": "http://arxiv.org/abs/1502.01852v1"
},
{
"code_snippet_url": "",
"description": "A **1 x 1 Convolution** is a [convolution](https://paperswithcode.com/method/convolution) with some special properties in that it can be used for dimensionality reduction, efficient low dimensional embeddings, and applying non-linearity after convolutions. It maps an input pixel with all its channels to an output pixel which can be squeezed to a desired output depth. It can be viewed as an [MLP](https://paperswithcode.com/method/feedforward-network) looking at a particular pixel location.\r\n\r\nImage Credit: [http://deeplearning.ai](http://deeplearning.ai)",
"full_name": "1x1 Convolution",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "1x1 Convolution",
"source_title": "Network In Network",
"source_url": "http://arxiv.org/abs/1312.4400v3"
},
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Max Pooling** is a pooling operation that calculates the maximum value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. It adds a small amount of translation invariance - meaning translating the image by a small amount does not significantly affect the values of most pooled outputs.\r\n\r\nImage Source: [here](https://computersciencewiki.org/index.php/File:MaxpoolSample2.png)",
"full_name": "Max Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Max Pooling",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "In today’s digital age, XRP has become more than just a buzzword—it’s a revolutionary way to manage and invest your money. But just like with any advanced technology, users sometimes face issues that can be frustrating or even alarming. Whether you're dealing with a XRP transaction not confirmed, your XRP wallet not showing balance, or you're trying to recover a lost XRP wallet, knowing where to get help is essential. That’s why the XRP customer support number +1-833-534-1729 is your go-to solution for fast and reliable assistance.\r\n\r\nWhy You Might Need to Call the XRP Customer Support Number +1-833-534-1729\r\nXRP operates on a decentralized network, which means there’s no single company or office that manages everything. However, platforms, wallets, and third-party services provide support to make your experience smoother. Calling +1-833-534-1729 can help you troubleshoot issues such as:\r\n\r\n1. 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Here’s why users trust +1-833-534-1729:\r\n\r\nLive Experts: Talk to real people who understand wallets, blockchain, and XRP tech.\r\n\r\n24/7 Availability: XRP doesn’t sleep, and neither should your support.\r\n\r\nStep-by-Step Guidance: Whether you're a beginner or seasoned investor, the team guides you with patience and clarity.\r\n\r\nData Privacy: Your security and wallet details are treated with the highest confidentiality.\r\n\r\nFAQs About XRP Support and Wallet Issues\r\nQ1: Can XRP support help me recover stolen BTC?\r\nA: While XRP transactions are irreversible, support can help investigate, trace addresses, and advise on what to do next.\r\n\r\nQ2: My wallet shows zero balance after reinstalling. What do I do?\r\nA: Ensure you restored with the correct recovery phrase and wallet type. Call +1-833-534-1729 for assistance.\r\n\r\nQ3: What if I forgot my wallet password?\r\nA: Recovery depends on the wallet provider. 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Whether it's a XRP transaction not confirmed, your XRP wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the XRP customer support number +1-833-534-1729 can be your fastest path to peace of mind.\r\n\r\nNo matter what the issue, you don’t have to face it alone. Expert help is just a call away—+1-833-534-1729.",
"full_name": "XRP Customer Service Number +1-833-534-1729",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "If you have questions or want to make special travel arrangements, you can make them online or call ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. For hearing or speech impaired assistance dial 711 to be connected through the National Relay Service.",
"name": "Convolutional Neural Networks",
"parent": "Image Models"
},
"name": "XRP Customer Service Number +1-833-534-1729",
"source_title": "Densely Connected Convolutional Networks",
"source_url": "http://arxiv.org/abs/1608.06993v5"
}
] |
https://paperswithcode.com/paper/seeking-open-ended-evolution-in-swarm
|
1804.03304
| null | null |
Seeking Open-Ended Evolution in Swarm Chemistry II: Analyzing Long-Term Dynamics via Automated Object Harvesting
|
We studied the long-term dynamics of evolutionary Swarm Chemistry by
extending the simulation length ten-fold compared to earlier work and by
developing and using a new automated object harvesting method. Both macroscopic
dynamics and microscopic object features were characterized and tracked using
several measures. Results showed that the evolutionary dynamics tended to
settle down into a stable state after the initial transient period, and that
the extent of environmental perturbations also affected the evolutionary trends
substantially. In the meantime, the automated harvesting method successfully
produced a huge collection of spontaneously evolved objects, revealing the
system's autonomous creativity at an unprecedented scale.
| null |
http://arxiv.org/abs/1804.03304v2
|
http://arxiv.org/pdf/1804.03304v2.pdf
| null |
[
"Hiroki Sayama"
] |
[
"Object"
] | 2018-04-10T00:00:00 | null | null | null | null |
[] |
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