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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
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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. 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/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. 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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. 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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 Bitcoin Support Number +1-833-534-1729 Helps You\r\nWhen you’re dealing with cryptocurrency issues, every second counts. 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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. 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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
[]