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https://paperswithcode.com/paper/gain-missing-data-imputation-using-generative
|
1806.02920
| null | null |
GAIN: Missing Data Imputation using Generative Adversarial Nets
|
We propose a novel method for imputing missing data by adapting the
well-known Generative Adversarial Nets (GAN) framework. Accordingly, we call
our method Generative Adversarial Imputation Nets (GAIN). The generator (G)
observes some components of a real data vector, imputes the missing components
conditioned on what is actually observed, and outputs a completed vector. The
discriminator (D) then takes a completed vector and attempts to determine which
components were actually observed and which were imputed. To ensure that D
forces G to learn the desired distribution, we provide D with some additional
information in the form of a hint vector. The hint reveals to D partial
information about the missingness of the original sample, which is used by D to
focus its attention on the imputation quality of particular components. This
hint ensures that G does in fact learn to generate according to the true data
distribution. We tested our method on various datasets and found that GAIN
significantly outperforms state-of-the-art imputation methods.
|
Accordingly, we call our method Generative Adversarial Imputation Nets (GAIN).
|
http://arxiv.org/abs/1806.02920v1
|
http://arxiv.org/pdf/1806.02920v1.pdf
|
ICML 2018 7
|
[
"Jinsung Yoon",
"James Jordon",
"Mihaela van der Schaar"
] |
[
"Imputation",
"Multivariate Time Series Imputation"
] | 2018-06-07T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2025
|
http://proceedings.mlr.press/v80/yoon18a/yoon18a.pdf
|
gain-missing-data-imputation-using-generative-1
| null |
[] |
https://paperswithcode.com/paper/non-local-recurrent-network-for-image
|
1806.02919
| null | null |
Non-Local Recurrent Network for Image Restoration
|
Many classic methods have shown non-local self-similarity in natural images
to be an effective prior for image restoration. However, it remains unclear and
challenging to make use of this intrinsic property via deep networks. In this
paper, we propose a non-local recurrent network (NLRN) as the first attempt to
incorporate non-local operations into a recurrent neural network (RNN) for
image restoration. The main contributions of this work are: (1) Unlike existing
methods that measure self-similarity in an isolated manner, the proposed
non-local module can be flexibly integrated into existing deep networks for
end-to-end training to capture deep feature correlation between each location
and its neighborhood. (2) We fully employ the RNN structure for its parameter
efficiency and allow deep feature correlation to be propagated along adjacent
recurrent states. This new design boosts robustness against inaccurate
correlation estimation due to severely degraded images. (3) We show that it is
essential to maintain a confined neighborhood for computing deep feature
correlation given degraded images. This is in contrast to existing practice
that deploys the whole image. Extensive experiments on both image denoising and
super-resolution tasks are conducted. Thanks to the recurrent non-local
operations and correlation propagation, the proposed NLRN achieves superior
results to state-of-the-art methods with much fewer parameters.
|
The main contributions of this work are: (1) Unlike existing methods that measure self-similarity in an isolated manner, the proposed non-local module can be flexibly integrated into existing deep networks for end-to-end training to capture deep feature correlation between each location and its neighborhood.
|
http://arxiv.org/abs/1806.02919v2
|
http://arxiv.org/pdf/1806.02919v2.pdf
|
NeurIPS 2018 12
|
[
"Ding Liu",
"Bihan Wen",
"Yuchen Fan",
"Chen Change Loy",
"Thomas S. Huang"
] |
[
"Denoising",
"Feature Correlation",
"Image Denoising",
"Image Restoration",
"Image Super-Resolution",
"Super-Resolution"
] | 2018-06-07T00:00:00 |
http://papers.nips.cc/paper/7439-non-local-recurrent-network-for-image-restoration
|
http://papers.nips.cc/paper/7439-non-local-recurrent-network-for-image-restoration.pdf
|
non-local-recurrent-network-for-image-1
| null |
[] |
https://paperswithcode.com/paper/color-sails-discrete-continuous-palettes-for
|
1806.02918
| null | null |
Color Sails: Discrete-Continuous Palettes for Deep Color Exploration
|
We present color sails, a discrete-continuous color gamut representation that
extends the color gradient analogy to three dimensions and allows interactive
control of the color blending behavior. Our representation models a wide
variety of color distributions in a compact manner, and lends itself to
applications such as color exploration for graphic design, illustration and
similar fields. We propose a Neural Network that can fit a color sail to any
image. Then, the user can adjust color sail parameters to change the base
colors, their blending behavior and the number of colors, exploring a wide
range of options for the original design. In addition, we propose a Deep
Learning model that learns to automatically segment an image into
color-compatible alpha masks, each equipped with its own color sail. This
allows targeted color exploration by either editing their corresponding color
sails or using standard software packages. Our model is trained on a custom
diverse dataset of art and design. We provide both quantitative evaluations,
and a user study, demonstrating the effectiveness of color sail interaction.
Interactive demos are available at www.colorsails.com.
| null |
http://arxiv.org/abs/1806.02918v1
|
http://arxiv.org/pdf/1806.02918v1.pdf
| null |
[
"Maria Shugrina",
"Amlan Kar",
"Karan Singh",
"Sanja Fidler"
] |
[] | 2018-06-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/stein-variational-message-passing-for
|
1711.07168
| null | null |
Stein Variational Message Passing for Continuous Graphical Models
|
We propose a novel distributed inference algorithm for continuous graphical
models, by extending Stein variational gradient descent (SVGD) to leverage the
Markov dependency structure of the distribution of interest. Our approach
combines SVGD with a set of structured local kernel functions defined on the
Markov blanket of each node, which alleviates the curse of high dimensionality
and simultaneously yields a distributed algorithm for decentralized inference
tasks. We justify our method with theoretical analysis and show that the use of
local kernels can be viewed as a new type of localized approximation that
matches the target distribution on the conditional distributions of each node
over its Markov blanket. Our empirical results show that our method outperforms
a variety of baselines including standard MCMC and particle message passing
methods.
| null |
http://arxiv.org/abs/1711.07168v3
|
http://arxiv.org/pdf/1711.07168v3.pdf
|
ICML 2018 7
|
[
"Dilin Wang",
"Zhe Zeng",
"Qiang Liu"
] |
[] | 2017-11-20T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2214
|
http://proceedings.mlr.press/v80/wang18l/wang18l.pdf
|
stein-variational-message-passing-for-1
| null |
[] |
https://paperswithcode.com/paper/adversarial-label-learning
|
1805.08877
| null | null |
Adversarial Label Learning
|
We consider the task of training classifiers without labels. We propose a
weakly supervised method---adversarial label learning---that trains classifiers
to perform well against an adversary that chooses labels for training data. The
weak supervision constrains what labels the adversary can choose. The method
therefore minimizes an upper bound of the classifier's error rate using
projected primal-dual subgradient descent. Minimizing this bound protects
against bias and dependencies in the weak supervision. Experiments on three
real datasets show that our method can train without labels and outperforms
other approaches for weakly supervised learning.
| null |
http://arxiv.org/abs/1805.08877v3
|
http://arxiv.org/pdf/1805.08877v3.pdf
| null |
[
"Chidubem Arachie",
"Bert Huang"
] |
[
"Weakly-supervised Learning"
] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/efficient-discovery-of-heterogeneous
|
1803.09159
| null | null |
Efficient Discovery of Heterogeneous Quantile Treatment Effects in Randomized Experiments via Anomalous Pattern Detection
|
In the recent literature on estimating heterogeneous treatment effects, each proposed method makes its own set of restrictive assumptions about the intervention's effects and which subpopulations to explicitly estimate. Moreover, the majority of the literature provides no mechanism to identify which subpopulations are the most affected--beyond manual inspection--and provides little guarantee on the correctness of the identified subpopulations. Therefore, we propose Treatment Effect Subset Scan (TESS), a new method for discovering which subpopulation in a randomized experiment is most significantly affected by a treatment. We frame this challenge as a pattern detection problem where we efficiently maximize a nonparametric scan statistic (a measure of the conditional quantile treatment effect) over subpopulations. Furthermore, we identify the subpopulation which experiences the largest distributional change as a result of the intervention, while making minimal assumptions about the intervention's effects or the underlying data generating process. In addition to the algorithm, we demonstrate that under the sharp null hypothesis of no treatment effect, the asymptotic Type I and II error can be controlled, and provide sufficient conditions for detection consistency--i.e., exact identification of the affected subpopulation. Finally, we validate the efficacy of the method by discovering heterogeneous treatment effects in simulations and in real-world data from a well-known program evaluation study.
| null |
https://arxiv.org/abs/1803.09159v3
|
https://arxiv.org/pdf/1803.09159v3.pdf
| null |
[
"Edward McFowland III",
"Sriram Somanchi",
"Daniel B. Neill"
] |
[] | 2018-03-24T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/towards-fast-computation-of-certified
|
1804.09699
| null | null |
Towards Fast Computation of Certified Robustness for ReLU Networks
|
Verifying the robustness property of a general Rectified Linear Unit (ReLU)
network is an NP-complete problem [Katz, Barrett, Dill, Julian and Kochenderfer
CAV17]. Although finding the exact minimum adversarial distortion is hard,
giving a certified lower bound of the minimum distortion is possible. Current
available methods of computing such a bound are either time-consuming or
delivering low quality bounds that are too loose to be useful. In this paper,
we exploit the special structure of ReLU networks and provide two
computationally efficient algorithms Fast-Lin and Fast-Lip that are able to
certify non-trivial lower bounds of minimum distortions, by bounding the ReLU
units with appropriate linear functions Fast-Lin, or by bounding the local
Lipschitz constant Fast-Lip. Experiments show that (1) our proposed methods
deliver bounds close to (the gap is 2-3X) exact minimum distortion found by
Reluplex in small MNIST networks while our algorithms are more than 10,000
times faster; (2) our methods deliver similar quality of bounds (the gap is
within 35% and usually around 10%; sometimes our bounds are even better) for
larger networks compared to the methods based on solving linear programming
problems but our algorithms are 33-14,000 times faster; (3) our method is
capable of solving large MNIST and CIFAR networks up to 7 layers with more than
10,000 neurons within tens of seconds on a single CPU core.
In addition, we show that, in fact, there is no polynomial time algorithm
that can approximately find the minimum $\ell_1$ adversarial distortion of a
ReLU network with a $0.99\ln n$ approximation ratio unless
$\mathsf{NP}$=$\mathsf{P}$, where $n$ is the number of neurons in the network.
|
Verifying the robustness property of a general Rectified Linear Unit (ReLU) network is an NP-complete problem [Katz, Barrett, Dill, Julian and Kochenderfer CAV17].
|
http://arxiv.org/abs/1804.09699v4
|
http://arxiv.org/pdf/1804.09699v4.pdf
|
ICML 2018 7
|
[
"Tsui-Wei Weng",
"huan zhang",
"Hongge Chen",
"Zhao Song",
"Cho-Jui Hsieh",
"Duane Boning",
"Inderjit S. Dhillon",
"Luca Daniel"
] |
[
"CPU"
] | 2018-04-25T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2052
|
http://proceedings.mlr.press/v80/weng18a/weng18a.pdf
|
towards-fast-computation-of-certified-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
}
] |
https://paperswithcode.com/paper/fast-decoding-in-sequence-models-using
|
1803.03382
| null | null |
Fast Decoding in Sequence Models using Discrete Latent Variables
|
Autoregressive sequence models based on deep neural networks, such as RNNs,
Wavenet and the Transformer attain state-of-the-art results on many tasks.
However, they are difficult to parallelize and are thus slow at processing long
sequences. RNNs lack parallelism both during training and decoding, while
architectures like WaveNet and Transformer are much more parallelizable during
training, yet still operate sequentially during decoding.
Inspired by [arxiv:1711.00937], we present a method to extend sequence models
using discrete latent variables that makes decoding much more parallelizable.
We first auto-encode the target sequence into a shorter sequence of discrete
latent variables, which at inference time is generated autoregressively, and
finally decode the output sequence from this shorter latent sequence in
parallel. To this end, we introduce a novel method for constructing a sequence
of discrete latent variables and compare it with previously introduced methods.
Finally, we evaluate our model end-to-end on the task of neural machine
translation, where it is an order of magnitude faster at decoding than
comparable autoregressive models. While lower in BLEU than purely
autoregressive models, our model achieves higher scores than previously
proposed non-autoregressive translation models.
| null |
http://arxiv.org/abs/1803.03382v6
|
http://arxiv.org/pdf/1803.03382v6.pdf
|
ICML 2018 7
|
[
"Łukasz Kaiser",
"Aurko Roy",
"Ashish Vaswani",
"Niki Parmar",
"Samy Bengio",
"Jakob Uszkoreit",
"Noam Shazeer"
] |
[
"Machine Translation",
"Translation"
] | 2018-03-09T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2438
|
http://proceedings.mlr.press/v80/kaiser18a/kaiser18a.pdf
|
fast-decoding-in-sequence-models-using-1
| 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": null,
"description": "**Mixture of Logistic Distributions (MoL)** is a type of output function, and an alternative to a [softmax](https://paperswithcode.com/method/softmax) layer. Discretized logistic mixture likelihood is used in [PixelCNN](https://paperswithcode.com/method/pixelcnn)++ and [WaveNet](https://paperswithcode.com/method/wavenet) to predict discrete values.\r\n\r\nImage Credit: [Hao Gao](https://medium.com/@smallfishbigsea/an-explanation-of-discretized-logistic-mixture-likelihood-bdfe531751f0)",
"full_name": "Mixture of Logistic Distributions",
"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": "Mixture of Logistic Distributions",
"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": 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": null,
"description": "A **Dilated Causal Convolution** is a [causal convolution](https://paperswithcode.com/method/causal-convolution) where the filter is applied over an area larger than its length by skipping input values with a certain step. A dilated causal [convolution](https://paperswithcode.com/method/convolution) effectively allows the network to have very large receptive fields with just a few layers.",
"full_name": "Dilated Causal Convolution",
"introduced_year": 2000,
"main_collection": {
"area": "Sequential",
"description": "",
"name": "Temporal Convolutions",
"parent": null
},
"name": "Dilated Causal Convolution",
"source_title": "WaveNet: A Generative Model for Raw Audio",
"source_url": "http://arxiv.org/abs/1609.03499v2"
},
{
"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": null,
"description": "**WaveNet** is an audio generative model based on the [PixelCNN](https://paperswithcode.com/method/pixelcnn) architecture. In order to deal with long-range temporal dependencies needed for raw audio generation, architectures are developed based on dilated causal convolutions, which exhibit very large receptive fields.\r\n\r\nThe joint probability of a waveform $\\vec{x} = \\{ x_1, \\dots, x_T \\}$ is factorised as a product of conditional probabilities as follows:\r\n\r\n$$p\\left(\\vec{x}\\right) = \\prod_{t=1}^{T} p\\left(x_t \\mid x_1, \\dots ,x_{t-1}\\right)$$\r\n\r\nEach audio sample $x_t$ is therefore conditioned on the samples at all previous timesteps.",
"full_name": "WaveNet",
"introduced_year": 2000,
"main_collection": {
"area": "Audio",
"description": "",
"name": "Generative Audio Models",
"parent": null
},
"name": "WaveNet",
"source_title": "WaveNet: A Generative Model for Raw Audio",
"source_url": "http://arxiv.org/abs/1609.03499v2"
},
{
"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/from-nodes-to-networks-evolving-recurrent
|
1803.04439
| null |
S1lVniC5Y7
|
From Nodes to Networks: Evolving Recurrent Neural Networks
|
Gated recurrent networks such as those composed of Long Short-Term Memory
(LSTM) nodes have recently been used to improve state of the art in many
sequential processing tasks such as speech recognition and machine translation.
However, the basic structure of the LSTM node is essentially the same as when
it was first conceived 25 years ago. Recently, evolutionary and reinforcement
learning mechanisms have been employed to create new variations of this
structure. This paper proposes a new method, evolution of a tree-based encoding
of the gated memory nodes, and shows that it makes it possible to explore new
variations more effectively than other methods. The method discovers nodes with
multiple recurrent paths and multiple memory cells, which lead to significant
improvement in the standard language modeling benchmark task. The paper also
shows how the search process can be speeded up by training an LSTM network to
estimate performance of candidate structures, and by encouraging exploration of
novel solutions. Thus, evolutionary design of complex neural network structures
promises to improve performance of deep learning architectures beyond human
ability to do so.
| null |
http://arxiv.org/abs/1803.04439v2
|
http://arxiv.org/pdf/1803.04439v2.pdf
| null |
[
"Aditya Rawal",
"Risto Miikkulainen"
] |
[
"Language Modeling",
"Language Modelling",
"Machine Translation",
"Reinforcement Learning",
"speech-recognition",
"Speech Recognition",
"Translation"
] | 2018-03-12T00:00:00 |
https://openreview.net/forum?id=S1lVniC5Y7
|
https://openreview.net/pdf?id=S1lVniC5Y7
| 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/capsgan-using-dynamic-routing-for-generative
|
1806.03968
| null | null |
CapsGAN: Using Dynamic Routing for Generative Adversarial Networks
|
In this paper, we propose a novel technique for generating images in the 3D
domain from images with high degree of geometrical transformations. By
coalescing two popular concurrent methods that have seen rapid ascension to the
machine learning zeitgeist in recent years: GANs (Goodfellow et. al.) and
Capsule networks (Sabour, Hinton et. al.) - we present: \textbf{CapsGAN}. We
show that CapsGAN performs better than or equal to traditional CNN based GANs
in generating images with high geometric transformations using rotated MNIST.
In the process, we also show the efficacy of using capsules architecture in the
GANs domain. Furthermore, we tackle the Gordian Knot in training GANs - the
performance control and training stability by experimenting with using
Wasserstein distance (gradient clipping, penalty) and Spectral Normalization.
The experimental findings of this paper should propel the application of
capsules and GANs in the still exciting and nascent domain of 3D image
generation, and plausibly video (frame) generation.
|
We show that CapsGAN performs better than or equal to traditional CNN based GANs in generating images with high geometric transformations using rotated MNIST.
|
http://arxiv.org/abs/1806.03968v1
|
http://arxiv.org/pdf/1806.03968v1.pdf
| null |
[
"Raeid Saqur",
"Sal Vivona"
] |
[
"Image Generation",
"Rotated MNIST"
] | 2018-06-07T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/christiancosgrove/pytorch-spectral-normalization-gan/blob/12dcf945a6359301d63d1e0da3708cd0f0590b19/spectral_normalization.py#L14",
"description": "**Spectral Normalization** is a normalization technique used for generative adversarial networks, used to stabilize training of the discriminator. Spectral normalization has the convenient property that the Lipschitz constant is the only hyper-parameter to be tuned.\r\n\r\nIt controls the Lipschitz constant of the discriminator $f$ by constraining the spectral norm of each layer $g : \\textbf{h}\\_{in} \\rightarrow \\textbf{h}_{out}$. The Lipschitz norm $\\Vert{g}\\Vert\\_{\\text{Lip}}$ is equal to $\\sup\\_{\\textbf{h}}\\sigma\\left(\\nabla{g}\\left(\\textbf{h}\\right)\\right)$, where $\\sigma\\left(a\\right)$ is the spectral norm of the matrix $A$ ($L\\_{2}$ matrix norm of $A$):\r\n\r\n$$ \\sigma\\left(a\\right) = \\max\\_{\\textbf{h}:\\textbf{h}\\neq{0}}\\frac{\\Vert{A\\textbf{h}}\\Vert\\_{2}}{\\Vert\\textbf{h}\\Vert\\_{2}} = \\max\\_{\\Vert\\textbf{h}\\Vert\\_{2}\\leq{1}}{\\Vert{A\\textbf{h}}\\Vert\\_{2}} $$\r\n\r\nwhich is equivalent to the largest singular value of $A$. Therefore for a [linear layer](https://paperswithcode.com/method/linear-layer) $g\\left(\\textbf{h}\\right) = W\\textbf{h}$ the norm is given by $\\Vert{g}\\Vert\\_{\\text{Lip}} = \\sup\\_{\\textbf{h}}\\sigma\\left(\\nabla{g}\\left(\\textbf{h}\\right)\\right) = \\sup\\_{\\textbf{h}}\\sigma\\left(W\\right) = \\sigma\\left(W\\right) $. Spectral normalization normalizes the spectral norm of the weight matrix $W$ so it satisfies the Lipschitz constraint $\\sigma\\left(W\\right) = 1$:\r\n\r\n$$ \\bar{W}\\_{\\text{SN}}\\left(W\\right) = W / \\sigma\\left(W\\right) $$",
"full_name": "Spectral 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": "Spectral Normalization",
"source_title": "Spectral Normalization for Generative Adversarial Networks",
"source_url": "http://arxiv.org/abs/1802.05957v1"
}
] |
https://paperswithcode.com/paper/accelerating-natural-gradient-with-higher
|
1803.01273
| null | null |
Accelerating Natural Gradient with Higher-Order Invariance
|
An appealing property of the natural gradient is that it is invariant to
arbitrary differentiable reparameterizations of the model. However, this
invariance property requires infinitesimal steps and is lost in practical
implementations with small but finite step sizes. In this paper, we study
invariance properties from a combined perspective of Riemannian geometry and
numerical differential equation solving. We define the order of invariance of a
numerical method to be its convergence order to an invariant solution. We
propose to use higher-order integrators and geodesic corrections to obtain more
invariant optimization trajectories. We prove the numerical convergence
properties of geodesic corrected updates and show that they can be as
computationally efficient as plain natural gradient. Experimentally, we
demonstrate that invariance leads to faster optimization and our techniques
improve on traditional natural gradient in deep neural network training and
natural policy gradient for reinforcement learning.
|
An appealing property of the natural gradient is that it is invariant to arbitrary differentiable reparameterizations of the model.
|
http://arxiv.org/abs/1803.01273v2
|
http://arxiv.org/pdf/1803.01273v2.pdf
|
ICML 2018 7
|
[
"Yang Song",
"Jiaming Song",
"Stefano Ermon"
] |
[
"Reinforcement Learning"
] | 2018-03-04T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2386
|
http://proceedings.mlr.press/v80/song18a/song18a.pdf
|
accelerating-natural-gradient-with-higher-1
| null |
[] |
https://paperswithcode.com/paper/is-preprocessing-of-text-really-worth-your
|
1806.02908
| null | null |
Is preprocessing of text really worth your time for online comment classification?
|
A large proportion of online comments present on public domains are
constructive, however a significant proportion are toxic in nature. The
comments contain lot of typos which increases the number of features manifold,
making the ML model difficult to train. Considering the fact that the data
scientists spend approximately 80% of their time in collecting, cleaning and
organizing their data [1], we explored how much effort should we invest in the
preprocessing (transformation) of raw comments before feeding it to the
state-of-the-art classification models. With the help of four models on Jigsaw
toxic comment classification data, we demonstrated that the training of model
without any transformation produce relatively decent model. Applying even basic
transformations, in some cases, lead to worse performance and should be applied
with caution.
|
A large proportion of online comments present on public domains are constructive, however a significant proportion are toxic in nature.
|
http://arxiv.org/abs/1806.02908v2
|
http://arxiv.org/pdf/1806.02908v2.pdf
| null |
[
"Fahim Mohammad"
] |
[
"General Classification",
"Toxic Comment Classification"
] | 2018-06-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/copy-move-forgery-using-hus-invariant-moments
|
1806.02907
| null | null |
Copy Move Forgery using Hus Invariant Moments and Log Polar Transformations
|
With the increase in interchange of data, there is a growing necessity of
security. Considering the volumes of digital data that is transmitted, they are
in need to be secure. Among the many forms of tampering possible, one
widespread technique is Copy Move Forgery CMF. This forgery occurs when parts
of the image are copied and duplicated elsewhere in the same image. There exist
a number of algorithms to detect such a forgery in which the primary step
involved is feature extraction. The feature extraction techniques employed must
have lesser time and space complexity involved for an efficient and faster
processing of media. Also, majority of the existing state of art techniques
often tend to falsely match similar genuine objects as copy move forged during
the detection process. To tackle these problems, the paper proposes a novel
algorithm that recognizes a unique approach of using Hus Invariant Moments and
Log polar Transformations to reduce feature vector dimension to one feature per
block simultaneously detecting CMF among genuine similar objects in an image.
The qualitative and quantitative results obtained demonstrate the effectiveness
of this algorithm.
| null |
http://arxiv.org/abs/1806.02907v1
|
http://arxiv.org/pdf/1806.02907v1.pdf
| null |
[
"Tejas K",
"Swathi C",
"Rajesh Kumar M"
] |
[] | 2018-06-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/adversarial-time-to-event-modeling
|
1804.03184
| null | null |
Adversarial Time-to-Event Modeling
|
Modern health data science applications leverage abundant molecular and
electronic health data, providing opportunities for machine learning to build
statistical models to support clinical practice. Time-to-event analysis, also
called survival analysis, stands as one of the most representative examples of
such statistical models. We present a deep-network-based approach that
leverages adversarial learning to address a key challenge in modern
time-to-event modeling: nonparametric estimation of event-time distributions.
We also introduce a principled cost function to exploit information from
censored events (events that occur subsequent to the observation window).
Unlike most time-to-event models, we focus on the estimation of time-to-event
distributions, rather than time ordering. We validate our model on both
benchmark and real datasets, demonstrating that the proposed formulation yields
significant performance gains relative to a parametric alternative, which we
also propose.
|
Modern health data science applications leverage abundant molecular and electronic health data, providing opportunities for machine learning to build statistical models to support clinical practice.
|
http://arxiv.org/abs/1804.03184v2
|
http://arxiv.org/pdf/1804.03184v2.pdf
|
ICML 2018 7
|
[
"Paidamoyo Chapfuwa",
"Chenyang Tao",
"Chunyuan Li",
"Courtney Page",
"Benjamin Goldstein",
"Lawrence Carin",
"Ricardo Henao"
] |
[
"Survival Analysis"
] | 2018-04-09T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2332
|
http://proceedings.mlr.press/v80/chapfuwa18a/chapfuwa18a.pdf
|
adversarial-time-to-event-modeling-1
| null |
[] |
https://paperswithcode.com/paper/convolutional-imputation-of-matrix-networks
|
1606.00925
| null | null |
Convolutional Imputation of Matrix Networks
|
A matrix network is a family of matrices, with relatedness modeled by a
weighted graph. We consider the task of completing a partially observed matrix
network. We assume a novel sampling scheme where a fraction of matrices might
be completely unobserved. How can we recover the entire matrix network from
incomplete observations? This mathematical problem arises in many applications
including medical imaging and social networks.
To recover the matrix network, we propose a structural assumption that the
matrices have a graph Fourier transform which is low-rank. We formulate a
convex optimization problem and prove an exact recovery guarantee for the
optimization problem. Furthermore, we numerically characterize the exact
recovery regime for varying rank and sampling rate and discover a new phase
transition phenomenon. Then we give an iterative imputation algorithm to
efficiently solve the optimization problem and complete large scale matrix
networks. We demonstrate the algorithm with a variety of applications such as
MRI and Facebook user network.
| null |
http://arxiv.org/abs/1606.00925v3
|
http://arxiv.org/pdf/1606.00925v3.pdf
|
ICML 2018 7
|
[
"Qingyun Sun",
"Mengyuan Yan David Donoho",
"Stephen Boyd"
] |
[
"Imputation"
] | 2016-06-02T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2135
|
http://proceedings.mlr.press/v80/sun18d/sun18d.pdf
|
convolutional-imputation-of-matrix-networks-1
| null |
[] |
https://paperswithcode.com/paper/modeling-4d-fmri-data-via-spatio-temporal
|
1805.12564
| null | null |
Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN)
|
Simultaneous modeling of the spatio-temporal variation patterns of brain
functional network from 4D fMRI data has been an important yet challenging
problem for the field of cognitive neuroscience and medical image analysis.
Inspired by the recent success in applying deep learning for functional brain
decoding and encoding, in this work we propose a spatio-temporal convolutional
neural network (ST-CNN)to jointly learn the spatial and temporal patterns of
targeted network from the training data and perform automatic, pin-pointing
functional network identification. The proposed ST-CNN is evaluated by the task
of identifying the Default Mode Network (DMN) from fMRI data. Results show that
while the framework is only trained on one fMRI dataset,it has the sufficient
generalizability to identify the DMN from different populations of data as well
as different cognitive tasks. Further investigation into the results show that
the superior performance of ST-CNN is driven by the jointly-learning scheme,
which capture the intrinsic relationship between the spatial and temporal
characteristic of DMN and ensures the accurate identification.
| null |
http://arxiv.org/abs/1805.12564v3
|
http://arxiv.org/pdf/1805.12564v3.pdf
| null |
[
"Yu Zhao",
"Xiang Li",
"Wei zhang",
"Shijie Zhao",
"Milad Makkie",
"Mo Zhang",
"Quanzheng Li",
"Tianming Liu"
] |
[
"Brain Decoding",
"Medical Image Analysis",
"Network Identification"
] | 2018-05-31T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/probabilistic-fasttext-for-multi-sense-word
|
1806.02901
| null | null |
Probabilistic FastText for Multi-Sense Word Embeddings
|
We introduce Probabilistic FastText, a new model for word embeddings that can
capture multiple word senses, sub-word structure, and uncertainty information.
In particular, we represent each word with a Gaussian mixture density, where
the mean of a mixture component is given by the sum of n-grams. This
representation allows the model to share statistical strength across sub-word
structures (e.g. Latin roots), producing accurate representations of rare,
misspelt, or even unseen words. Moreover, each component of the mixture can
capture a different word sense. Probabilistic FastText outperforms both
FastText, which has no probabilistic model, and dictionary-level probabilistic
embeddings, which do not incorporate subword structures, on several
word-similarity benchmarks, including English RareWord and foreign language
datasets. We also achieve state-of-art performance on benchmarks that measure
ability to discern different meanings. Thus, the proposed model is the first to
achieve multi-sense representations while having enriched semantics on rare
words.
|
We introduce Probabilistic FastText, a new model for word embeddings that can capture multiple word senses, sub-word structure, and uncertainty information.
|
http://arxiv.org/abs/1806.02901v1
|
http://arxiv.org/pdf/1806.02901v1.pdf
|
ACL 2018 7
|
[
"Ben Athiwaratkun",
"Andrew Gordon Wilson",
"Anima Anandkumar"
] |
[
"Word Embeddings",
"Word Similarity"
] | 2018-06-07T00:00:00 |
https://aclanthology.org/P18-1001
|
https://aclanthology.org/P18-1001.pdf
|
probabilistic-fasttext-for-multi-sense-word-1
| null |
[
{
"code_snippet_url": null,
"description": "**fastText** embeddings exploit subword information to construct word embeddings. Representations are learnt of character $n$-grams, and words represented as the sum of the $n$-gram vectors. This extends the word2vec type models with subword information. This helps the embeddings understand suffixes and prefixes. Once a word is represented using character $n$-grams, a skipgram model is trained to learn the embeddings.",
"full_name": "fastText",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Word Embeddings",
"parent": null
},
"name": "fastText",
"source_title": "Enriching Word Vectors with Subword Information",
"source_url": "http://arxiv.org/abs/1607.04606v2"
}
] |
https://paperswithcode.com/paper/training-faster-by-separating-modes-of
|
1806.02892
| null | null |
Training Faster by Separating Modes of Variation in Batch-normalized Models
|
Batch Normalization (BN) is essential to effectively train state-of-the-art
deep Convolutional Neural Networks (CNN). It normalizes inputs to the layers
during training using the statistics of each mini-batch. In this work, we study
BN from the viewpoint of Fisher kernels. We show that assuming samples within a
mini-batch are from the same probability density function, then BN is identical
to the Fisher vector of a Gaussian distribution. That means BN can be explained
in terms of kernels that naturally emerge from the probability density function
of the underlying data distribution. However, given the rectifying
non-linearities employed in CNN architectures, distribution of inputs to the
layers show heavy tail and asymmetric characteristics. Therefore, we propose
approximating underlying data distribution not with one, but a mixture of
Gaussian densities. Deriving Fisher vector for a Gaussian Mixture Model (GMM),
reveals that BN can be improved by independently normalizing with respect to
the statistics of disentangled sub-populations. We refer to our proposed soft
piecewise version of BN as Mixture Normalization (MN). Through extensive set of
experiments on CIFAR-10 and CIFAR-100, we show that MN not only effectively
accelerates training image classification and Generative Adversarial networks,
but also reaches higher quality models.
|
We show that assuming samples within a mini-batch are from the same probability density function, then BN is identical to the Fisher vector of a Gaussian distribution.
|
http://arxiv.org/abs/1806.02892v2
|
http://arxiv.org/pdf/1806.02892v2.pdf
| null |
[
"Mahdi M. Kalayeh",
"Mubarak Shah"
] |
[
"image-classification",
"Image Classification"
] | 2018-06-07T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Mixture Normalization** is normalization technique that relies on an approximation of the probability density function of the internal representations. Any continuous distribution can be approximated with arbitrary precision using a Gaussian Mixture Model (GMM). Hence, instead of computing one set of statistical measures from the entire population (of instances in the mini-batch) as [Batch Normalization](https://paperswithcode.com/method/batch-normalization) does, Mixture Normalization works on sub-populations which can be identified by disentangling modes of the distribution, estimated via GMM. \r\n\r\nWhile BN can only scale and/or shift the whole underlying probability density function, mixture normalization operates like a soft piecewise normalizing transform, capable of completely re-structuring the data distribution by independently scaling and/or shifting individual modes of distribution.",
"full_name": "Mixture 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": "Mixture Normalization",
"source_title": "Training Faster by Separating Modes of Variation in Batch-normalized Models",
"source_url": "http://arxiv.org/abs/1806.02892v2"
}
] |
https://paperswithcode.com/paper/revisiting-the-importance-of-individual-units
|
1806.02891
| null | null |
Revisiting the Importance of Individual Units in CNNs via Ablation
|
We revisit the importance of the individual units in Convolutional Neural
Networks (CNNs) for visual recognition. By conducting unit ablation experiments
on CNNs trained on large scale image datasets, we demonstrate that, though
ablating any individual unit does not hurt overall classification accuracy, it
does lead to significant damage on the accuracy of specific classes. This
result shows that an individual unit is specialized to encode information
relevant to a subset of classes. We compute the correlation between the
accuracy drop under unit ablation and various attributes of an individual unit
such as class selectivity and weight L1 norm. We confirm that unit attributes
such as class selectivity are a poor predictor for impact on overall accuracy
as found previously in recent work \cite{morcos2018importance}. However, our
results show that class selectivity along with other attributes are good
predictors of the importance of one unit to individual classes. We evaluate the
impact of random rotation, batch normalization, and dropout to the importance
of units to specific classes. Our results show that units with high selectivity
play an important role in network classification power at the individual class
level. Understanding and interpreting the behavior of these units is necessary
and meaningful.
| null |
http://arxiv.org/abs/1806.02891v1
|
http://arxiv.org/pdf/1806.02891v1.pdf
| null |
[
"Bolei Zhou",
"Yiyou Sun",
"David Bau",
"Antonio Torralba"
] |
[
"General Classification"
] | 2018-06-07T00:00:00 | null | null | null | 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"
}
] |
https://paperswithcode.com/paper/inertial-odometry-on-handheld-smartphones
|
1703.00154
| null | null |
Inertial Odometry on Handheld Smartphones
|
Building a complete inertial navigation system using the limited quality data
provided by current smartphones has been regarded challenging, if not
impossible. This paper shows that by careful crafting and accounting for the
weak information in the sensor samples, smartphones are capable of pure
inertial navigation. We present a probabilistic approach for orientation and
use-case free inertial odometry, which is based on double-integrating rotated
accelerations. The strength of the model is in learning additive and
multiplicative IMU biases online. We are able to track the phone position,
velocity, and pose in real-time and in a computationally lightweight fashion by
solving the inference with an extended Kalman filter. The information fusion is
completed with zero-velocity updates (if the phone remains stationary),
altitude correction from barometric pressure readings (if available), and
pseudo-updates constraining the momentary speed. We demonstrate our approach
using an iPad and iPhone in several indoor dead-reckoning applications and in a
measurement tool setup.
|
Building a complete inertial navigation system using the limited quality data provided by current smartphones has been regarded challenging, if not impossible.
|
http://arxiv.org/abs/1703.00154v2
|
http://arxiv.org/pdf/1703.00154v2.pdf
| null |
[
"Arno Solin",
"Santiago Cortes",
"Esa Rahtu",
"Juho Kannala"
] |
[] | 2017-03-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/real-time-coherent-diffraction-inversion
|
1806.03992
| null | null |
Real-time coherent diffraction inversion using deep generative networks
|
Phase retrieval, or the process of recovering phase information in reciprocal
space to reconstruct images from measured intensity alone, is the underlying
basis to a variety of imaging applications including coherent diffraction
imaging (CDI). Typical phase retrieval algorithms are iterative in nature, and
hence, are time-consuming and computationally expensive, precluding real-time
imaging. Furthermore, iterative phase retrieval algorithms struggle to converge
to the correct solution especially in the presence of strong phase structures.
In this work, we demonstrate the training and testing of CDI NN, a pair of deep
deconvolutional networks trained to predict structure and phase in real space
of a 2D object from its corresponding far-field diffraction intensities alone.
Once trained, CDI NN can invert a diffraction pattern to an image within a few
milliseconds of compute time on a standard desktop machine, opening the door to
real-time imaging.
| null |
http://arxiv.org/abs/1806.03992v1
|
http://arxiv.org/pdf/1806.03992v1.pdf
| null |
[
"Mathew J. Cherukara",
"Youssef S. G. Nashed",
"Ross J. Harder"
] |
[
"Retrieval"
] | 2018-06-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/generating-artificial-data-for-private-deep
|
1803.03148
| null | null |
Generating Artificial Data for Private Deep Learning
|
In this paper, we propose generating artificial data that retain statistical
properties of real data as the means of providing privacy with respect to the
original dataset. We use generative adversarial network to draw
privacy-preserving artificial data samples and derive an empirical method to
assess the risk of information disclosure in a differential-privacy-like way.
Our experiments show that we are able to generate artificial data of high
quality and successfully train and validate machine learning models on this
data while limiting potential privacy loss.
| null |
http://arxiv.org/abs/1803.03148v3
|
http://arxiv.org/pdf/1803.03148v3.pdf
| null |
[
"Aleksei Triastcyn",
"Boi Faltings"
] |
[
"BIG-bench Machine Learning",
"Deep Learning",
"Generative Adversarial Network",
"Privacy Preserving"
] | 2018-03-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/constant-time-predictive-distributions-for
|
1803.06058
| null | null |
Constant-Time Predictive Distributions for Gaussian Processes
|
One of the most compelling features of Gaussian process (GP) regression is
its ability to provide well-calibrated posterior distributions. Recent advances
in inducing point methods have sped up GP marginal likelihood and posterior
mean computations, leaving posterior covariance estimation and sampling as the
remaining computational bottlenecks. In this paper we address these
shortcomings by using the Lanczos algorithm to rapidly approximate the
predictive covariance matrix. Our approach, which we refer to as LOVE (LanczOs
Variance Estimates), substantially improves time and space complexity. In our
experiments, LOVE computes covariances up to 2,000 times faster and draws
samples 18,000 times faster than existing methods, all without sacrificing
accuracy.
|
One of the most compelling features of Gaussian process (GP) regression is its ability to provide well-calibrated posterior distributions.
|
http://arxiv.org/abs/1803.06058v4
|
http://arxiv.org/pdf/1803.06058v4.pdf
|
ICML 2018 7
|
[
"Geoff Pleiss",
"Jacob R. Gardner",
"Kilian Q. Weinberger",
"Andrew Gordon Wilson"
] |
[
"Gaussian Processes",
"regression"
] | 2018-03-16T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2067
|
http://proceedings.mlr.press/v80/pleiss18a/pleiss18a.pdf
|
constant-time-predictive-distributions-for-1
| null |
[
{
"code_snippet_url": null,
"description": "**Gaussian Processes** are non-parametric models for approximating functions. They rely upon a measure of similarity between points (the kernel function) to predict the value for an unseen point from training data. The models are fully probabilistic so uncertainty bounds are baked in with the model.\r\n\r\nImage Source: Gaussian Processes for Machine Learning, C. E. Rasmussen & C. K. I. Williams",
"full_name": "Gaussian Process",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Non-Parametric Classification** methods perform classification where we use non-parametric methods to approximate the functional form of the relationship. Below you can find a continuously updating list of non-parametric classification methods.",
"name": "Non-Parametric Classification",
"parent": null
},
"name": "Gaussian Process",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/correspondence-of-deep-neural-networks-and
|
1806.02888
| null | null |
Correspondence of Deep Neural Networks and the Brain for Visual Textures
|
Deep convolutional neural networks (CNNs) trained on objects and scenes have
shown intriguing ability to predict some response properties of visual cortical
neurons. However, the factors and computations that give rise to such ability,
and the role of intermediate processing stages in explaining changes that
develop across areas of the cortical hierarchy, are poorly understood. We
focused on the sensitivity to textures as a paradigmatic example, since recent
neurophysiology experiments provide rich data pointing to texture sensitivity
in secondary but not primary visual cortex. We developed a quantitative
approach for selecting a subset of the neural unit population from the CNN that
best describes the brain neural recordings. We found that the first two layers
of the CNN showed qualitative and quantitative correspondence to the cortical
data across a number of metrics. This compatibility was reduced for the
architecture alone rather than the learned weights, for some other related
hierarchical models, and only mildly in the absence of a nonlinear computation
akin to local divisive normalization. Our results show that the CNN class of
model is effective for capturing changes that develop across early areas of
cortex, and has the potential to facilitate understanding of the computations
that give rise to hierarchical processing in the brain.
|
Deep convolutional neural networks (CNNs) trained on objects and scenes have shown intriguing ability to predict some response properties of visual cortical neurons.
|
http://arxiv.org/abs/1806.02888v1
|
http://arxiv.org/pdf/1806.02888v1.pdf
| null |
[
"Md Nasir Uddin Laskar",
"Luis G. Sanchez Giraldo",
"Odelia Schwartz"
] |
[
"Sensitivity"
] | 2018-06-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/residual-unfairness-in-fair-machine-learning
|
1806.02887
| null | null |
Residual Unfairness in Fair Machine Learning from Prejudiced Data
|
Recent work in fairness in machine learning has proposed adjusting for
fairness by equalizing accuracy metrics across groups and has also studied how
datasets affected by historical prejudices may lead to unfair decision
policies. We connect these lines of work and study the residual unfairness that
arises when a fairness-adjusted predictor is not actually fair on the target
population due to systematic censoring of training data by existing biased
policies. This scenario is particularly common in the same applications where
fairness is a concern. We characterize theoretically the impact of such
censoring on standard fairness metrics for binary classifiers and provide
criteria for when residual unfairness may or may not appear. We prove that,
under certain conditions, fairness-adjusted classifiers will in fact induce
residual unfairness that perpetuates the same injustices, against the same
groups, that biased the data to begin with, thus showing that even
state-of-the-art fair machine learning can have a "bias in, bias out" property.
When certain benchmark data is available, we show how sample reweighting can
estimate and adjust fairness metrics while accounting for censoring. We use
this to study the case of Stop, Question, and Frisk (SQF) and demonstrate that
attempting to adjust for fairness perpetuates the same injustices that the
policy is infamous for.
| null |
http://arxiv.org/abs/1806.02887v1
|
http://arxiv.org/pdf/1806.02887v1.pdf
|
ICML 2018 7
|
[
"Nathan Kallus",
"Angela Zhou"
] |
[
"BIG-bench Machine Learning",
"Fairness"
] | 2018-06-07T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2314
|
http://proceedings.mlr.press/v80/kallus18a/kallus18a.pdf
|
residual-unfairness-in-fair-machine-learning-1
| null |
[] |
https://paperswithcode.com/paper/hilbert-space-methods-for-reduced-rank
|
1401.5508
| null | null |
Hilbert Space Methods for Reduced-Rank Gaussian Process Regression
|
This paper proposes a novel scheme for reduced-rank Gaussian process regression. The method is based on an approximate series expansion of the covariance function in terms of an eigenfunction expansion of the Laplace operator in a compact subset of $\mathbb{R}^d$. On this approximate eigenbasis the eigenvalues of the covariance function can be expressed as simple functions of the spectral density of the Gaussian process, which allows the GP inference to be solved under a computational cost scaling as $\mathcal{O}(nm^2)$ (initial) and $\mathcal{O}(m^3)$ (hyperparameter learning) with $m$ basis functions and $n$ data points. Furthermore, the basis functions are independent of the parameters of the covariance function, which allows for very fast hyperparameter learning. The approach also allows for rigorous error analysis with Hilbert space theory, and we show that the approximation becomes exact when the size of the compact subset and the number of eigenfunctions go to infinity. We also show that the convergence rate of the truncation error is independent of the input dimensionality provided that the differentiability order of the covariance function is increases appropriately, and for the squared exponential covariance function it is always bounded by ${\sim}1/m$ regardless of the input dimensionality. The expansion generalizes to Hilbert spaces with an inner product which is defined as an integral over a specified input density. The method is compared to previously proposed methods theoretically and through empirical tests with simulated and real data.
|
On this approximate eigenbasis the eigenvalues of the covariance function can be expressed as simple functions of the spectral density of the Gaussian process, which allows the GP inference to be solved under a computational cost scaling as $\mathcal{O}(nm^2)$ (initial) and $\mathcal{O}(m^3)$ (hyperparameter learning) with $m$ basis functions and $n$ data points.
|
https://arxiv.org/abs/1401.5508v3
|
https://arxiv.org/pdf/1401.5508v3.pdf
| null |
[
"Arno Solin",
"Simo Särkkä"
] |
[
"regression"
] | 2014-01-21T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Gaussian Processes** are non-parametric models for approximating functions. They rely upon a measure of similarity between points (the kernel function) to predict the value for an unseen point from training data. The models are fully probabilistic so uncertainty bounds are baked in with the model.\r\n\r\nImage Source: Gaussian Processes for Machine Learning, C. E. Rasmussen & C. K. I. Williams",
"full_name": "Gaussian Process",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Non-Parametric Classification** methods perform classification where we use non-parametric methods to approximate the functional form of the relationship. Below you can find a continuously updating list of non-parametric classification methods.",
"name": "Non-Parametric Classification",
"parent": null
},
"name": "Gaussian Process",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/mine-mutual-information-neural-estimation
|
1801.04062
| null | null |
MINE: Mutual Information Neural Estimation
|
We argue that the estimation of mutual information between high dimensional continuous random variables can be achieved by gradient descent over neural networks. We present a Mutual Information Neural Estimator (MINE) that is linearly scalable in dimensionality as well as in sample size, trainable through back-prop, and strongly consistent. We present a handful of applications on which MINE can be used to minimize or maximize mutual information. We apply MINE to improve adversarially trained generative models. We also use MINE to implement Information Bottleneck, applying it to supervised classification; our results demonstrate substantial improvement in flexibility and performance in these settings.
|
We argue that the estimation of mutual information between high dimensional continuous random variables can be achieved by gradient descent over neural networks.
|
https://arxiv.org/abs/1801.04062v5
|
https://arxiv.org/pdf/1801.04062v5.pdf
| null |
[
"Mohamed Ishmael Belghazi",
"Aristide Baratin",
"Sai Rajeswar",
"Sherjil Ozair",
"Yoshua Bengio",
"Aaron Courville",
"R. Devon Hjelm"
] |
[
"General Classification"
] | 2018-01-12T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/pros-and-cons-of-gan-evaluation-measures
|
1802.03446
| null | null |
Pros and Cons of GAN Evaluation Measures
|
Generative models, in particular generative adversarial networks (GANs), have
received significant attention recently. A number of GAN variants have been
proposed and have been utilized in many applications. Despite large strides in
terms of theoretical progress, evaluating and comparing GANs remains a daunting
task. While several measures have been introduced, as of yet, there is no
consensus as to which measure best captures strengths and limitations of models
and should be used for fair model comparison. As in other areas of computer
vision and machine learning, it is critical to settle on one or few good
measures to steer the progress in this field. In this paper, I review and
critically discuss more than 24 quantitative and 5 qualitative measures for
evaluating generative models with a particular emphasis on GAN-derived models.
I also provide a set of 7 desiderata followed by an evaluation of whether a
given measure or a family of measures is compatible with them.
|
Generative models, in particular generative adversarial networks (GANs), have received significant attention recently.
|
http://arxiv.org/abs/1802.03446v5
|
http://arxiv.org/pdf/1802.03446v5.pdf
| null |
[
"Ali Borji"
] |
[] | 2018-02-09T00:00:00 | null | null | null | 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/learning-tasks-for-multitask-learning
|
1806.02878
| null | null |
Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU
|
Machine learning approaches have been effective in predicting adverse
outcomes in different clinical settings. These models are often developed and
evaluated on datasets with heterogeneous patient populations. However, good
predictive performance on the aggregate population does not imply good
performance for specific groups.
In this work, we present a two-step framework to 1) learn relevant patient
subgroups, and 2) predict an outcome for separate patient populations in a
multi-task framework, where each population is a separate task. We demonstrate
how to discover relevant groups in an unsupervised way with a
sequence-to-sequence autoencoder. We show that using these groups in a
multi-task framework leads to better predictive performance of in-hospital
mortality both across groups and overall. We also highlight the need for more
granular evaluation of performance when dealing with heterogeneous populations.
|
In this work, we present a two-step framework to 1) learn relevant patient subgroups, and 2) predict an outcome for separate patient populations in a multi-task framework, where each population is a separate task.
|
http://arxiv.org/abs/1806.02878v1
|
http://arxiv.org/pdf/1806.02878v1.pdf
| null |
[
"Harini Suresh",
"Jen J. Gong",
"John Guttag"
] |
[] | 2018-06-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/an-exploration-of-unreliable-news
|
1806.02875
| null | null |
An Exploration of Unreliable News Classification in Brazil and The U.S
|
The propagation of unreliable information is on the rise in many places
around the world. This expansion is facilitated by the rapid spread of
information and anonymity granted by the Internet. The spread of unreliable
information is a wellstudied issue and it is associated with negative social
impacts. In a previous work, we have identified significant differences in the
structure of news articles from reliable and unreliable sources in the US
media. Our goal in this work was to explore such differences in the Brazilian
media. We found significant features in two data sets: one with Brazilian news
in Portuguese and another one with US news in English. Our results show that
features related to the writing style were prominent in both data sets and,
despite the language difference, some features have a universal behavior, being
significant to both US and Brazilian news articles. Finally, we combined both
data sets and used the universal features to build a machine learning
classifier to predict the source type of a news article as reliable or
unreliable.
| null |
http://arxiv.org/abs/1806.02875v1
|
http://arxiv.org/pdf/1806.02875v1.pdf
| null |
[
"Mauricio Gruppi",
"Benjamin D. Horne",
"Sibel Adali"
] |
[
"Articles",
"General Classification",
"News Classification"
] | 2018-06-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/on-the-topic-of-jets-disentangling-quarks-and
|
1802.00008
| null | null |
On the Topic of Jets: Disentangling Quarks and Gluons at Colliders
|
We introduce jet topics: a framework to identify underlying classes of jets
from collider data. Because of a close mathematical relationship between
distributions of observables in jets and emergent themes in sets of documents,
we can apply recent techniques in "topic modeling" to extract jet topics from
data with minimal or no input from simulation or theory. As a proof of concept
with parton shower samples, we apply jet topics to determine separate quark and
gluon jet distributions for constituent multiplicity. We also determine
separate quark and gluon rapidity spectra from a mixed Z-plus-jet sample. While
jet topics are defined directly from hadron-level multi-differential cross
sections, one can also predict jet topics from first-principles theoretical
calculations, with potential implications for how to define quark and gluon
jets beyond leading-logarithmic accuracy. These investigations suggest that jet
topics will be useful for extracting underlying jet distributions and fractions
in a wide range of contexts at the Large Hadron Collider.
| null |
http://arxiv.org/abs/1802.00008v2
|
http://arxiv.org/pdf/1802.00008v2.pdf
| null |
[
"Eric M. Metodiev",
"Jesse Thaler"
] |
[] | 2018-01-31T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/direct-optimization-through-arg-max-for
|
1806.02867
| null |
S1ey2sRcYQ
|
Direct Optimization through $\arg \max$ for Discrete Variational Auto-Encoder
|
Reparameterization of variational auto-encoders with continuous random variables is an effective method for reducing the variance of their gradient estimates. In the discrete case, one can perform reparametrization using the Gumbel-Max trick, but the resulting objective relies on an $\arg \max$ operation and is non-differentiable. In contrast to previous works which resort to softmax-based relaxations, we propose to optimize it directly by applying the direct loss minimization approach. Our proposal extends naturally to structured discrete latent variable models when evaluating the $\arg \max$ operation is tractable. We demonstrate empirically the effectiveness of the direct loss minimization technique in variational autoencoders with both unstructured and structured discrete latent variables.
|
We demonstrate empirically the effectiveness of the direct loss minimization technique in variational autoencoders with both unstructured and structured discrete latent variables.
|
https://arxiv.org/abs/1806.02867v5
|
https://arxiv.org/pdf/1806.02867v5.pdf
|
ICLR 2019 5
|
[
"Guy Lorberbom",
"Andreea Gane",
"Tommi Jaakkola",
"Tamir Hazan"
] |
[] | 2018-06-07T00:00:00 |
https://openreview.net/forum?id=S1ey2sRcYQ
|
https://openreview.net/pdf?id=S1ey2sRcYQ
|
direct-optimization-through-arg-max-for-1
| null |
[] |
https://paperswithcode.com/paper/kernel-machines-with-missing-responses
|
1806.02865
| null | null |
Kernel Machines With Missing Responses
|
Missing responses is a missing data format in which outcomes are not always
observed. In this work we develop kernel machines that can handle missing
responses. First, we propose a kernel machine family that uses mainly the
complete cases. For the quadratic loss, we then propose a family of
doubly-robust kernel machines. The proposed kernel-machine estimators can be
applied to both regression and classification problems. We prove oracle
inequalities for the finite-sample differences between the kernel machine risk
and Bayes risk. We use these oracle inequalities to prove consistency and to
calculate convergence rates. We demonstrate the performance of the two proposed
kernel machine families using both a simulation study and a real-world data
analysis.
| null |
http://arxiv.org/abs/1806.02865v1
|
http://arxiv.org/pdf/1806.02865v1.pdf
| null |
[
"Tiantian Liu",
"Yair Goldberg"
] |
[
"General Classification",
"regression"
] | 2018-06-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/semi-supervised-and-transfer-learning
|
1806.02863
| null | null |
Semi-supervised and Transfer learning approaches for low resource sentiment classification
|
Sentiment classification involves quantifying the affective reaction of a
human to a document, media item or an event. Although researchers have
investigated several methods to reliably infer sentiment from lexical, speech
and body language cues, training a model with a small set of labeled datasets
is still a challenge. For instance, in expanding sentiment analysis to new
languages and cultures, it may not always be possible to obtain comprehensive
labeled datasets. In this paper, we investigate the application of
semi-supervised and transfer learning methods to improve performances on low
resource sentiment classification tasks. We experiment with extracting dense
feature representations, pre-training and manifold regularization in enhancing
the performance of sentiment classification systems. Our goal is a coherent
implementation of these methods and we evaluate the gains achieved by these
methods in matched setting involving training and testing on a single corpus
setting as well as two cross corpora settings. In both the cases, our
experiments demonstrate that the proposed methods can significantly enhance the
model performance against a purely supervised approach, particularly in cases
involving a handful of training data.
| null |
http://arxiv.org/abs/1806.02863v1
|
http://arxiv.org/pdf/1806.02863v1.pdf
| null |
[
"Rahul Gupta",
"Saurabh Sahu",
"Carol Espy-Wilson",
"Shrikanth Narayanan"
] |
[
"Classification",
"General Classification",
"Sentiment Analysis",
"Sentiment Classification",
"Transfer Learning"
] | 2018-06-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-large-scale-multi-institutional-evaluation
|
1803.03729
| null | null |
A Large-Scale Multi-Institutional Evaluation of Advanced Discrimination Algorithms for Buried Threat Detection in Ground Penetrating Radar
|
In this paper we consider the development of algorithms for the automatic
detection of buried threats using ground penetrating radar (GPR) measurements.
GPR is one of the most studied and successful modalities for automatic buried
threat detection (BTD), and a large variety of BTD algorithms have been
proposed for it. Despite this, large-scale comparisons of GPR-based BTD
algorithms are rare in the literature. In this work we report the results of a
multi-institutional effort to develop advanced buried threat detection
algorithms for a real-world GPR BTD system. The effort involved five
institutions with substantial experience with the development of GPR-based BTD
algorithms. In this paper we report the technical details of the advanced
algorithms submitted by each institution, representing their latest technical
advances, and many state-of-the-art GPR-based BTD algorithms. We also report
the results of evaluating the algorithms from each institution on the large
experimental dataset used for development. The experimental dataset comprised
120,000 m^2 of GPR data using surface area, from 13 different lanes across two
US test sites. The data was collected using a vehicle-mounted GPR system, the
variants of which have supplied data for numerous publications. Using these
results, we identify the most successful and common processing strategies among
the submitted algorithms, and make recommendations for GPR-based BTD algorithm
design.
| null |
http://arxiv.org/abs/1803.03729v2
|
http://arxiv.org/pdf/1803.03729v2.pdf
| null |
[
"Jordan M. Malof",
"Daniel Reichman",
"Andrew Karem",
"Hichem Frigui",
"Dominic K. C. Ho",
"Joseph N. Wilson",
"Wen-Hsiung Lee",
"William Cummings",
"Leslie M. Collins"
] |
[
"GPR"
] | 2018-03-10T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-credible-models
|
1711.03190
| null | null |
Learning Credible Models
|
In many settings, it is important that a model be capable of providing
reasons for its predictions (i.e., the model must be interpretable). However,
the model's reasoning may not conform with well-established knowledge. In such
cases, while interpretable, the model lacks \textit{credibility}. In this work,
we formally define credibility in the linear setting and focus on techniques
for learning models that are both accurate and credible. In particular, we
propose a regularization penalty, expert yielded estimates (EYE), that
incorporates expert knowledge about well-known relationships among covariates
and the outcome of interest. We give both theoretical and empirical results
comparing our proposed method to several other regularization techniques.
Across a range of settings, experiments on both synthetic and real data show
that models learned using the EYE penalty are significantly more credible than
those learned using other penalties. Applied to a large-scale patient risk
stratification task, our proposed technique results in a model whose top
features overlap significantly with known clinical risk factors, while still
achieving good predictive performance.
|
In this work, we formally define credibility in the linear setting and focus on techniques for learning models that are both accurate and credible.
|
http://arxiv.org/abs/1711.03190v3
|
http://arxiv.org/pdf/1711.03190v3.pdf
| null |
[
"Jiaxuan Wang",
"Jeeheh Oh",
"Haozhu Wang",
"Jenna Wiens"
] |
[] | 2017-11-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/scalable-natural-gradient-langevin-dynamics
|
1806.02855
| null | null |
Scalable Natural Gradient Langevin Dynamics in Practice
|
Stochastic Gradient Langevin Dynamics (SGLD) is a sampling scheme for
Bayesian modeling adapted to large datasets and models. SGLD relies on the
injection of Gaussian Noise at each step of a Stochastic Gradient Descent (SGD)
update. In this scheme, every component in the noise vector is independent and
has the same scale, whereas the parameters we seek to estimate exhibit strong
variations in scale and significant correlation structures, leading to poor
convergence and mixing times. We compare different preconditioning approaches
to the normalization of the noise vector and benchmark these approaches on the
following criteria: 1) mixing times of the multivariate parameter vector, 2)
regularizing effect on small dataset where it is easy to overfit, 3) covariate
shift detection and 4) resistance to adversarial examples.
| null |
http://arxiv.org/abs/1806.02855v1
|
http://arxiv.org/pdf/1806.02855v1.pdf
| null |
[
"Henri Palacci",
"Henry Hess"
] |
[] | 2018-06-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/addressing-function-approximation-error-in
|
1802.09477
| null | null |
Addressing Function Approximation Error in Actor-Critic Methods
|
In value-based reinforcement learning methods such as deep Q-learning,
function approximation errors are known to lead to overestimated value
estimates and suboptimal policies. We show that this problem persists in an
actor-critic setting and propose novel mechanisms to minimize its effects on
both the actor and the critic. Our algorithm builds on Double Q-learning, by
taking the minimum value between a pair of critics to limit overestimation. We
draw the connection between target networks and overestimation bias, and
suggest delaying policy updates to reduce per-update error and further improve
performance. We evaluate our method on the suite of OpenAI gym tasks,
outperforming the state of the art in every environment tested.
|
In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated value estimates and suboptimal policies.
|
http://arxiv.org/abs/1802.09477v3
|
http://arxiv.org/pdf/1802.09477v3.pdf
|
ICML 2018 7
|
[
"Scott Fujimoto",
"Herke van Hoof",
"David Meger"
] |
[
"Continuous Control",
"OpenAI Gym",
"Q-Learning",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-02-26T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2227
|
http://proceedings.mlr.press/v80/fujimoto18a/fujimoto18a.pdf
|
addressing-function-approximation-error-in-1
| 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": "**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": 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": "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": "https://github.com/sfujim/TD3/blob/ade6260da88864d1ab0ed592588e090d3d97d679/main.py#L86",
"description": "**Target Policy Smoothing** is a regularization strategy for the value function in reinforcement learning. Deterministic policies can overfit to narrow peaks in the value estimate, making them highly susceptible to functional approximation error, increasing the variance of the target. To reduce this variance, target policy smoothing adds a small amount of random noise to the target policy and averages over mini-batches - approximating a [SARSA](https://paperswithcode.com/method/sarsa)-like expectation/integral.\r\n\r\nThe modified target update is:\r\n\r\n$$ y = r + \\gamma{Q}\\_{\\theta'}\\left(s', \\pi\\_{\\theta'}\\left(s'\\right) + \\epsilon \\right) $$\r\n\r\n$$ \\epsilon \\sim \\text{clip}\\left(\\mathcal{N}\\left(0, \\sigma\\right), -c, c \\right) $$\r\n\r\nwhere the added noise is clipped to keep the target close to the original action. The outcome is an algorithm reminiscent of [Expected SARSA](https://paperswithcode.com/method/expected-sarsa), where the value estimate is instead learned off-policy and the noise added to the target policy is chosen independently of the exploration policy. The value estimate learned is with respect to a noisy policy defined by the parameter $\\sigma$.",
"full_name": "Target Policy Smoothing",
"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": "Target Policy Smoothing",
"source_title": "Addressing Function Approximation Error in Actor-Critic Methods",
"source_url": "http://arxiv.org/abs/1802.09477v3"
},
{
"code_snippet_url": null,
"description": "**Clipped Double Q-learning** is a variant on [Double Q-learning](https://paperswithcode.com/method/double-q-learning) that upper-bounds the less biased Q estimate $Q\\_{\\theta\\_{2}}$ by the biased estimate $Q\\_{\\theta\\_{1}}$. This is equivalent to taking the minimum of the two estimates, resulting in the following target update:\r\n\r\n$$ y\\_{1} = r + \\gamma\\min\\_{i=1,2}Q\\_{\\theta'\\_{i}}\\left(s', \\pi\\_{\\phi\\_{1}}\\left(s'\\right)\\right) $$\r\n\r\nThe motivation for this extension is that vanilla double [Q-learning](https://paperswithcode.com/method/q-learning) is sometimes ineffective if the target and current networks are too similar, e.g. with a slow-changing policy in an actor-critic framework.",
"full_name": "Clipped Double Q-learning",
"introduced_year": 2000,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Off-Policy TD Control",
"parent": null
},
"name": "Clipped Double Q-learning",
"source_title": "Addressing Function Approximation Error in Actor-Critic Methods",
"source_url": "http://arxiv.org/abs/1802.09477v3"
},
{
"code_snippet_url": null,
"description": "**TD3** builds on the [DDPG](https://paperswithcode.com/method/ddpg) algorithm for reinforcement learning, with a couple of modifications aimed at tackling overestimation bias with the value function. In particular, it utilises [clipped double Q-learning](https://paperswithcode.com/method/clipped-double-q-learning), delayed update of target and policy networks, and [target policy smoothing](https://paperswithcode.com/method/target-policy-smoothing) (which is similar to a [SARSA](https://paperswithcode.com/method/sarsa) based update; a safer update, as they provide higher value to actions resistant to perturbations).",
"full_name": "Twin Delayed Deep Deterministic",
"introduced_year": 2000,
"main_collection": {
"area": "Reinforcement Learning",
"description": "**Policy Gradient Methods** try to optimize the policy function directly in reinforcement learning. This contrasts with, for example, Q-Learning, where the policy manifests itself as maximizing a value function. Below you can find a continuously updating catalog of policy gradient methods.",
"name": "Policy Gradient Methods",
"parent": null
},
"name": "TD3",
"source_title": "Addressing Function Approximation Error in Actor-Critic Methods",
"source_url": "http://arxiv.org/abs/1802.09477v3"
}
] |
https://paperswithcode.com/paper/model-based-active-learning-to-detect
|
1806.02850
| null | null |
Model-based active learning to detect isometric deformable objects in the wild with deep architectures
|
In the recent past, algorithms based on Convolutional Neural Networks (CNNs)
have achieved significant milestones in object recognition. With large examples
of each object class, standard datasets train well for inter-class variability.
However, gathering sufficient data to train for a particular instance of an
object within a class is impractical. Furthermore, quantitatively assessing the
imaging conditions for each image in a given dataset is not feasible. By
generating sufficient images with known imaging conditions, we study to what
extent CNNs can cope with hard imaging conditions for instance-level
recognition in an active learning regime.
Leveraging powerful rendering techniques to achieve instance-level detection,
we present results of training three state-of-the-art object detection
algorithms namely, Fast R-CNN, Faster R-CNN and YOLO9000, for hard imaging
conditions imposed into the scene by rendering. Our extensive experiments
produce a mean Average Precision score of 0.92 on synthetic images and 0.83 on
real images using the best performing Faster R-CNN. We show for the first time
how well detection algorithms based on deep architectures fare for each hard
imaging condition studied.
| null |
http://arxiv.org/abs/1806.02850v1
|
http://arxiv.org/pdf/1806.02850v1.pdf
| null |
[
"Shrinivasan Sankar",
"Adrien Bartoli"
] |
[
"Active Learning",
"Object",
"object-detection",
"Object Detection",
"Object Recognition"
] | 2018-06-07T00:00:00 | null | null | null | 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": "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": "**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": "",
"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/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/longcw/yolo2-pytorch/blob/17056ca69f097a07884135d9031c53d4ef217a6a/darknet.py#L140",
"description": "**Darknet-19** is a convolutional neural network that is used as the backbone of [YOLOv2](https://paperswithcode.com/method/yolov2). Similar to the [VGG](https://paperswithcode.com/method/vgg) models it mostly uses $3 \\times 3$ filters and doubles the number of channels after every pooling step. Following the work on Network in Network (NIN) it uses [global average pooling](https://paperswithcode.com/method/global-average-pooling) to make predictions as well as $1 \\times 1$ filters to compress the feature representation between $3 \\times 3$ convolutions. [Batch Normalization](https://paperswithcode.com/method/batch-normalization) is used to stabilize training, speed up convergence, and regularize the model batch.",
"full_name": "Darknet-19",
"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": "Darknet-19",
"source_title": "YOLO9000: Better, Faster, Stronger",
"source_url": "http://arxiv.org/abs/1612.08242v1"
},
{
"code_snippet_url": "https://github.com/pjreddie/darknet",
"description": "**YOLOv2**, or [**YOLO9000**](https://www.youtube.com/watch?v=QsDDXSmGJZA), is a single-stage real-time object detection model. It improves upon [YOLOv1](https://paperswithcode.com/method/yolov1) in several ways, including the use of [Darknet-19](https://paperswithcode.com/method/darknet-19) as a backbone, [batch normalization](https://paperswithcode.com/method/batch-normalization), use of a high-resolution classifier, and the use of anchor boxes to predict bounding boxes, and more.",
"full_name": "YOLOv2",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Object Detection Models** are architectures used to perform the task of object detection. Below you can find a continuously updating list of object detection models.",
"name": "Object Detection Models",
"parent": null
},
"name": "YOLOv2",
"source_title": "YOLO9000: Better, Faster, Stronger",
"source_url": "http://arxiv.org/abs/1612.08242v1"
},
{
"code_snippet_url": "",
"description": "**Fast R-CNN** is an object detection model that improves in its predecessor [R-CNN](https://paperswithcode.com/method/r-cnn) in a number of ways. Instead of extracting CNN features independently for each region of interest, Fast R-CNN aggregates them into a single forward pass over the image; i.e. regions of interest from the same image share computation and memory in the forward and backward passes.",
"full_name": "Fast R-CNN",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Object Detection Models** are architectures used to perform the task of object detection. Below you can find a continuously updating list of object detection models.",
"name": "Object Detection Models",
"parent": null
},
"name": "Fast R-CNN",
"source_title": "Fast R-CNN",
"source_url": "http://arxiv.org/abs/1504.08083v2"
},
{
"code_snippet_url": null,
"description": "A **Region Proposal Network**, or **RPN**, is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals. RPN and algorithms like [Fast R-CNN](https://paperswithcode.com/method/fast-r-cnn) can be merged into a single network by sharing their convolutional features - using the recently popular terminology of neural networks with attention mechanisms, the RPN component tells the unified network where to look.\r\n\r\nRPNs are designed to efficiently predict region proposals with a wide range of scales and aspect ratios. RPNs use anchor boxes that serve as references at multiple scales and aspect ratios. The scheme can be thought of as a pyramid of regression references, which avoids enumerating images or filters of multiple scales or aspect ratios.",
"full_name": "Region Proposal Network",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Region Proposal",
"parent": null
},
"name": "RPN",
"source_title": "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks",
"source_url": "http://arxiv.org/abs/1506.01497v3"
},
{
"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": "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": "https://github.com/pytorch/vision/blob/5e9ebe8dadc0ea2841a46cfcd82a93b4ce0d4519/torchvision/ops/roi_pool.py#L10",
"description": "**Region of Interest Pooling**, or **RoIPool**, is an operation for extracting a small feature map (e.g., $7×7$) from each RoI in detection and segmentation based tasks. Features are extracted from each candidate box, and thereafter in models like [Fast R-CNN](https://paperswithcode.com/method/fast-r-cnn), are then classified and bounding box regression performed.\r\n\r\nThe actual scaling to, e.g., $7×7$, occurs by dividing the region proposal into equally sized sections, finding the largest value in each section, and then copying these max values to the output buffer. In essence, **RoIPool** is [max pooling](https://paperswithcode.com/method/max-pooling) on a discrete grid based on a box.\r\n\r\nImage Source: [Joyce Xu](https://towardsdatascience.com/deep-learning-for-object-detection-a-comprehensive-review-73930816d8d9)",
"full_name": "RoIPool",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**RoI Feature Extractors** are used to extract regions of interest features for tasks such as object detection. Below you can find a continuously updating list of RoI Feature Extractors.",
"name": "RoI Feature Extractors",
"parent": null
},
"name": "RoIPool",
"source_title": "Rich feature hierarchies for accurate object detection and semantic segmentation",
"source_url": "http://arxiv.org/abs/1311.2524v5"
},
{
"code_snippet_url": "https://github.com/chenyuntc/simple-faster-rcnn-pytorch/blob/367db367834efd8a2bc58ee0023b2b628a0e474d/model/faster_rcnn.py#L22",
"description": "**Faster R-CNN** is an object detection model that improves on [Fast R-CNN](https://paperswithcode.com/method/fast-r-cnn) by utilising a region proposal network ([RPN](https://paperswithcode.com/method/rpn)) with the CNN model. The RPN shares full-image convolutional features with the detection network, enabling nearly cost-free region proposals. It is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by [Fast R-CNN](https://paperswithcode.com/method/fast-r-cnn) for detection. RPN and Fast [R-CNN](https://paperswithcode.com/method/r-cnn) are merged into a single network by sharing their convolutional features: the RPN component tells the unified network where to look.\r\n\r\nAs a whole, Faster R-CNN consists of two modules. The first module is a deep fully convolutional network that proposes regions, and the second module is the Fast R-CNN detector that uses the proposed regions.",
"full_name": "Faster R-CNN",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Object Detection Models** are architectures used to perform the task of object detection. Below you can find a continuously updating list of object detection models.",
"name": "Object Detection Models",
"parent": null
},
"name": "Faster R-CNN",
"source_title": "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks",
"source_url": "http://arxiv.org/abs/1506.01497v3"
}
] |
https://paperswithcode.com/paper/black-box-adversarial-attacks-with-limited
|
1804.08598
| null | null |
Black-box Adversarial Attacks with Limited Queries and Information
|
Current neural network-based classifiers are susceptible to adversarial
examples even in the black-box setting, where the attacker only has query
access to the model. In practice, the threat model for real-world systems is
often more restrictive than the typical black-box model where the adversary can
observe the full output of the network on arbitrarily many chosen inputs. We
define three realistic threat models that more accurately characterize many
real-world classifiers: the query-limited setting, the partial-information
setting, and the label-only setting. We develop new attacks that fool
classifiers under these more restrictive threat models, where previous methods
would be impractical or ineffective. We demonstrate that our methods are
effective against an ImageNet classifier under our proposed threat models. We
also demonstrate a targeted black-box attack against a commercial classifier,
overcoming the challenges of limited query access, partial information, and
other practical issues to break the Google Cloud Vision API.
|
Current neural network-based classifiers are susceptible to adversarial examples even in the black-box setting, where the attacker only has query access to the model.
|
http://arxiv.org/abs/1804.08598v3
|
http://arxiv.org/pdf/1804.08598v3.pdf
|
ICML 2018 7
|
[
"Andrew Ilyas",
"Logan Engstrom",
"Anish Athalye",
"Jessy Lin"
] |
[] | 2018-04-23T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2426
|
http://proceedings.mlr.press/v80/ilyas18a/ilyas18a.pdf
|
black-box-adversarial-attacks-with-limited-1
| null |
[] |
https://paperswithcode.com/paper/a-simple-method-for-commonsense-reasoning
|
1806.02847
| null | null |
A Simple Method for Commonsense Reasoning
|
Commonsense reasoning is a long-standing challenge for deep learning. For example, it is difficult to use neural networks to tackle the Winograd Schema dataset (Levesque et al., 2011). In this paper, we present a simple method for commonsense reasoning with neural networks, using unsupervised learning. Key to our method is the use of language models, trained on a massive amount of unlabled data, to score multiple choice questions posed by commonsense reasoning tests. On both Pronoun Disambiguation and Winograd Schema challenges, our models outperform previous state-of-the-art methods by a large margin, without using expensive annotated knowledge bases or hand-engineered features. We train an array of large RNN language models that operate at word or character level on LM-1-Billion, CommonCrawl, SQuAD, Gutenberg Books, and a customized corpus for this task and show that diversity of training data plays an important role in test performance. Further analysis also shows that our system successfully discovers important features of the context that decide the correct answer, indicating a good grasp of commonsense knowledge.
|
Commonsense reasoning is a long-standing challenge for deep learning.
|
https://arxiv.org/abs/1806.02847v2
|
https://arxiv.org/pdf/1806.02847v2.pdf
| null |
[
"Trieu H. Trinh",
"Quoc V. Le"
] |
[
"Common Sense Reasoning",
"Coreference Resolution",
"Diversity",
"Multiple-choice",
"Natural Language Understanding"
] | 2018-06-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/fast-neural-machine-translation
|
1805.09863
| null | null |
Fast Neural Machine Translation Implementation
|
This paper describes the submissions to the efficiency track for GPUs at the
Workshop for Neural Machine Translation and Generation by members of the
University of Edinburgh, Adam Mickiewicz University, Tilde and University of
Alicante. We focus on efficient implementation of the recurrent deep-learning
model as implemented in Amun, the fast inference engine for neural machine
translation. We improve the performance with an efficient mini-batching
algorithm, and by fusing the softmax operation with the k-best extraction
algorithm. Submissions using Amun were first, second and third fastest in the
GPU efficiency track.
| null |
http://arxiv.org/abs/1805.09863v3
|
http://arxiv.org/pdf/1805.09863v3.pdf
|
WS 2018 7
|
[
"Hieu Hoang",
"Tomasz Dwojak",
"Rihards Krislauks",
"Daniel Torregrosa",
"Kenneth Heafield"
] |
[
"GPU",
"Machine Translation",
"Translation"
] | 2018-05-24T00:00:00 |
https://aclanthology.org/W18-2714
|
https://aclanthology.org/W18-2714.pdf
|
fast-neural-machine-translation-1
| 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
}
] |
https://paperswithcode.com/paper/probabilistic-model-agnostic-meta-learning
|
1806.02817
| null | null |
Probabilistic Model-Agnostic Meta-Learning
|
Meta-learning for few-shot learning entails acquiring a prior over previous tasks and experiences, such that new tasks be learned from small amounts of data. However, a critical challenge in few-shot learning is task ambiguity: even when a powerful prior can be meta-learned from a large number of prior tasks, a small dataset for a new task can simply be too ambiguous to acquire a single model (e.g., a classifier) for that task that is accurate. In this paper, we propose a probabilistic meta-learning algorithm that can sample models for a new task from a model distribution. Our approach extends model-agnostic meta-learning, which adapts to new tasks via gradient descent, to incorporate a parameter distribution that is trained via a variational lower bound. At meta-test time, our algorithm adapts via a simple procedure that injects noise into gradient descent, and at meta-training time, the model is trained such that this stochastic adaptation procedure produces samples from the approximate model posterior. Our experimental results show that our method can sample plausible classifiers and regressors in ambiguous few-shot learning problems. We also show how reasoning about ambiguity can also be used for downstream active learning problems.
|
However, a critical challenge in few-shot learning is task ambiguity: even when a powerful prior can be meta-learned from a large number of prior tasks, a small dataset for a new task can simply be too ambiguous to acquire a single model (e. g., a classifier) for that task that is accurate.
|
https://arxiv.org/abs/1806.02817v2
|
https://arxiv.org/pdf/1806.02817v2.pdf
|
NeurIPS 2018 12
|
[
"Chelsea Finn",
"Kelvin Xu",
"Sergey Levine"
] |
[
"Active Learning",
"Few-Shot Image Classification",
"Few-Shot Learning",
"Meta-Learning",
"model"
] | 2018-06-07T00:00:00 |
http://papers.nips.cc/paper/8161-probabilistic-model-agnostic-meta-learning
|
http://papers.nips.cc/paper/8161-probabilistic-model-agnostic-meta-learning.pdf
|
probabilistic-model-agnostic-meta-learning-1
| null |
[] |
https://paperswithcode.com/paper/data-summarization-at-scale-a-two-stage
|
1806.02815
| null | null |
Data Summarization at Scale: A Two-Stage Submodular Approach
|
The sheer scale of modern datasets has resulted in a dire need for
summarization techniques that identify representative elements in a dataset.
Fortunately, the vast majority of data summarization tasks satisfy an intuitive
diminishing returns condition known as submodularity, which allows us to find
nearly-optimal solutions in linear time. We focus on a two-stage submodular
framework where the goal is to use some given training functions to reduce the
ground set so that optimizing new functions (drawn from the same distribution)
over the reduced set provides almost as much value as optimizing them over the
entire ground set. In this paper, we develop the first streaming and
distributed solutions to this problem. In addition to providing strong
theoretical guarantees, we demonstrate both the utility and efficiency of our
algorithms on real-world tasks including image summarization and ride-share
optimization.
| null |
http://arxiv.org/abs/1806.02815v1
|
http://arxiv.org/pdf/1806.02815v1.pdf
|
ICML 2018 7
|
[
"Marko Mitrovic",
"Ehsan Kazemi",
"Morteza Zadimoghaddam",
"Amin Karbasi"
] |
[
"Data Summarization",
"Vocal Bursts Valence Prediction"
] | 2018-06-07T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2144
|
http://proceedings.mlr.press/v80/mitrovic18a/mitrovic18a.pdf
|
data-summarization-at-scale-a-two-stage-1
| null |
[] |
https://paperswithcode.com/paper/embedding-transfer-for-low-resource-medical
|
1806.02814
| null | null |
Embedding Transfer for Low-Resource Medical Named Entity Recognition: A Case Study on Patient Mobility
|
Functioning is gaining recognition as an important indicator of global
health, but remains under-studied in medical natural language processing
research. We present the first analysis of automatically extracting
descriptions of patient mobility, using a recently-developed dataset of free
text electronic health records. We frame the task as a named entity recognition
(NER) problem, and investigate the applicability of NER techniques to mobility
extraction. As text corpora focused on patient functioning are scarce, we
explore domain adaptation of word embeddings for use in a recurrent neural
network NER system. We find that embeddings trained on a small in-domain corpus
perform nearly as well as those learned from large out-of-domain corpora, and
that domain adaptation techniques yield additional improvements in both
precision and recall. Our analysis identifies several significant challenges in
extracting descriptions of patient mobility, including the length and
complexity of annotated entities and high linguistic variability in mobility
descriptions.
|
Functioning is gaining recognition as an important indicator of global health, but remains under-studied in medical natural language processing research.
|
http://arxiv.org/abs/1806.02814v1
|
http://arxiv.org/pdf/1806.02814v1.pdf
|
WS 2018 7
|
[
"Denis Newman-Griffis",
"Ayah Zirikly"
] |
[
"Domain Adaptation",
"Medical Named Entity Recognition",
"named-entity-recognition",
"Named Entity Recognition",
"Named Entity Recognition (NER)",
"NER",
"Word Embeddings"
] | 2018-06-07T00:00:00 |
https://aclanthology.org/W18-2301
|
https://aclanthology.org/W18-2301.pdf
|
embedding-transfer-for-low-resource-medical-1
| null |
[] |
https://paperswithcode.com/paper/self-consistent-trajectory-autoencoder
|
1806.02813
| null | null |
Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings
|
In this work, we take a representation learning perspective on hierarchical
reinforcement learning, where the problem of learning lower layers in a
hierarchy is transformed into the problem of learning trajectory-level
generative models. We show that we can learn continuous latent representations
of trajectories, which are effective in solving temporally extended and
multi-stage problems. Our proposed model, SeCTAR, draws inspiration from
variational autoencoders, and learns latent representations of trajectories. A
key component of this method is to learn both a latent-conditioned policy and a
latent-conditioned model which are consistent with each other. Given the same
latent, the policy generates a trajectory which should match the trajectory
predicted by the model. This model provides a built-in prediction mechanism, by
predicting the outcome of closed loop policy behavior. We propose a novel
algorithm for performing hierarchical RL with this model, combining model-based
planning in the learned latent space with an unsupervised exploration
objective. We show that our model is effective at reasoning over long horizons
with sparse rewards for several simulated tasks, outperforming standard
reinforcement learning methods and prior methods for hierarchical reasoning,
model-based planning, and exploration.
| null |
http://arxiv.org/abs/1806.02813v1
|
http://arxiv.org/pdf/1806.02813v1.pdf
|
ICML 2018 7
|
[
"John D. Co-Reyes",
"Yuxuan Liu",
"Abhishek Gupta",
"Benjamin Eysenbach",
"Pieter Abbeel",
"Sergey Levine"
] |
[
"Hierarchical Reinforcement Learning",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)",
"Representation Learning"
] | 2018-06-07T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2337
|
http://proceedings.mlr.press/v80/co-reyes18a/co-reyes18a.pdf
|
self-consistent-trajectory-autoencoder-1
| null |
[] |
https://paperswithcode.com/paper/towards-riemannian-accelerated-gradient
|
1806.02812
| null | null |
Towards Riemannian Accelerated Gradient Methods
|
We propose a Riemannian version of Nesterov's Accelerated Gradient algorithm
(RAGD), and show that for geodesically smooth and strongly convex problems,
within a neighborhood of the minimizer whose radius depends on the condition
number as well as the sectional curvature of the manifold, RAGD converges to
the minimizer with acceleration. Unlike the algorithm in (Liu et al., 2017)
that requires the exact solution to a nonlinear equation which in turn may be
intractable, our algorithm is constructive and computationally tractable. Our
proof exploits a new estimate sequence and a novel bound on the nonlinear
metric distortion, both ideas may be of independent interest.
| null |
http://arxiv.org/abs/1806.02812v1
|
http://arxiv.org/pdf/1806.02812v1.pdf
| null |
[
"Hongyi Zhang",
"Suvrit Sra"
] |
[] | 2018-06-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/concentration-of-tempered-posteriors-and-of
|
1706.09293
| null | null |
Concentration of tempered posteriors and of their variational approximations
|
While Bayesian methods are extremely popular in statistics and machine
learning, their application to massive datasets is often challenging, when
possible at all. Indeed, the classical MCMC algorithms are prohibitively slow
when both the model dimension and the sample size are large. Variational
Bayesian methods aim at approximating the posterior by a distribution in a
tractable family. Thus, MCMC are replaced by an optimization algorithm which is
orders of magnitude faster. VB methods have been applied in such
computationally demanding applications as including collaborative filtering,
image and video processing, NLP and text processing... However, despite very
nice results in practice, the theoretical properties of these approximations
are usually not known. In this paper, we propose a general approach to prove
the concentration of variational approximations of fractional posteriors. We
apply our theory to two examples: matrix completion, and Gaussian VB.
| null |
http://arxiv.org/abs/1706.09293v3
|
http://arxiv.org/pdf/1706.09293v3.pdf
| null |
[
"Pierre Alquier",
"James Ridgway"
] |
[
"Collaborative Filtering",
"Matrix Completion"
] | 2017-06-28T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/efficient-contextual-bandits-in-non
|
1708.01799
| null | null |
Efficient Contextual Bandits in Non-stationary Worlds
|
Most contextual bandit algorithms minimize regret against the best fixed
policy, a questionable benchmark for non-stationary environments that are
ubiquitous in applications. In this work, we develop several efficient
contextual bandit algorithms for non-stationary environments by equipping
existing methods for i.i.d. problems with sophisticated statistical tests so as
to dynamically adapt to a change in distribution.
We analyze various standard notions of regret suited to non-stationary
environments for these algorithms, including interval regret, switching regret,
and dynamic regret. When competing with the best policy at each time, one of
our algorithms achieves regret $\mathcal{O}(\sqrt{ST})$ if there are $T$ rounds
with $S$ stationary periods, or more generally
$\mathcal{O}(\Delta^{1/3}T^{2/3})$ where $\Delta$ is some non-stationarity
measure. These results almost match the optimal guarantees achieved by an
inefficient baseline that is a variant of the classic Exp4 algorithm. The
dynamic regret result is also the first one for efficient and fully adversarial
contextual bandit.
Furthermore, while the results above require tuning a parameter based on the
unknown quantity $S$ or $\Delta$, we also develop a parameter free algorithm
achieving regret $\min\{S^{1/4}T^{3/4}, \Delta^{1/5}T^{4/5}\}$. This improves
and generalizes the best existing result $\Delta^{0.18}T^{0.82}$ by Karnin and
Anava (2016) which only holds for the two-armed bandit problem.
| null |
http://arxiv.org/abs/1708.01799v4
|
http://arxiv.org/pdf/1708.01799v4.pdf
| null |
[
"Haipeng Luo",
"Chen-Yu Wei",
"Alekh Agarwal",
"John Langford"
] |
[
"Multi-Armed Bandits"
] | 2017-08-05T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/unbiased-estimation-of-the-value-of-an
|
1806.02794
| null | null |
Unbiased Estimation of the Value of an Optimized Policy
|
Randomized trials, also known as A/B tests, are used to select between two
policies: a control and a treatment. Given a corresponding set of features, we
can ideally learn an optimized policy P that maps the A/B test data features to
action space and optimizes reward. However, although A/B testing provides an
unbiased estimator for the value of deploying B (i.e., switching from policy A
to B), direct application of those samples to learn the the optimized policy P
generally does not provide an unbiased estimator of the value of P as the
samples were observed when constructing P. In situations where the cost and
risks associated of deploying a policy are high, such an unbiased estimator is
highly desirable.
We present a procedure for learning optimized policies and getting unbiased
estimates for the value of deploying them. We wrap any policy learning
procedure with a bagging process and obtain out-of-bag policy inclusion
decisions for each sample. We then prove that inverse-propensity-weighting
effect estimator is unbiased when applied to the optimized subset. Likewise, we
apply the same idea to obtain out-of-bag unbiased per-sample value estimate of
the measurement that is independent of the randomized treatment, and use these
estimates to build an unbiased doubly-robust effect estimator. Lastly, we
empirically shown that even when the average treatment effect is negative we
can find a positive optimized policy.
| null |
http://arxiv.org/abs/1806.02794v1
|
http://arxiv.org/pdf/1806.02794v1.pdf
| null |
[
"Elon Portugaly",
"Joseph J. Pfeiffer III"
] |
[] | 2018-06-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/mild-net-minimal-information-loss-dilated
|
1806.01963
| null | null |
MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images
|
The analysis of glandular morphology within colon histopathology images is an
important step in determining the grade of colon cancer. Despite the importance
of this task, manual segmentation is laborious, time-consuming and can suffer
from subjectivity among pathologists. The rise of computational pathology has
led to the development of automated methods for gland segmentation that aim to
overcome the challenges of manual segmentation. However, this task is
non-trivial due to the large variability in glandular appearance and the
difficulty in differentiating between certain glandular and non-glandular
histological structures. Furthermore, a measure of uncertainty is essential for
diagnostic decision making. To address these challenges, we propose a fully
convolutional neural network that counters the loss of information caused by
max-pooling by re-introducing the original image at multiple points within the
network. We also use atrous spatial pyramid pooling with varying dilation rates
for preserving the resolution and multi-level aggregation. To incorporate
uncertainty, we introduce random transformations during test time for an
enhanced segmentation result that simultaneously generates an uncertainty map,
highlighting areas of ambiguity. We show that this map can be used to define a
metric for disregarding predictions with high uncertainty. The proposed network
achieves state-of-the-art performance on the GlaS challenge dataset and on a
second independent colorectal adenocarcinoma dataset. In addition, we perform
gland instance segmentation on whole-slide images from two further datasets to
highlight the generalisability of our method. As an extension, we introduce
MILD-Net+ for simultaneous gland and lumen segmentation, to increase the
diagnostic power of the network.
| null |
http://arxiv.org/abs/1806.01963v4
|
http://arxiv.org/pdf/1806.01963v4.pdf
| null |
[
"Simon Graham",
"Hao Chen",
"Jevgenij Gamper",
"Qi Dou",
"Pheng-Ann Heng",
"David Snead",
"Yee Wah Tsang",
"Nasir Rajpoot"
] |
[
"Colorectal Gland Segmentation:",
"Decision Making",
"Diagnostic",
"Instance Segmentation",
"Segmentation",
"Semantic Segmentation",
"whole slide images"
] | 2018-06-05T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/yueruchen/sppnet-pytorch/blob/270529337baa5211538bf553bda222b9140838b3/spp_layer.py#L2",
"description": "** Spatial Pyramid Pooling (SPP)** is a pooling layer that removes the fixed-size constraint of the network, i.e. a CNN does not require a fixed-size input image. Specifically, we add an SPP layer on top of the last convolutional layer. The SPP layer pools the features and generates fixed-length outputs, which are then fed into the fully-connected layers (or other classifiers). In other words, we perform some information aggregation at a deeper stage of the network hierarchy (between convolutional layers and fully-connected layers) to avoid the need for cropping or warping at the beginning.",
"full_name": "Spatial Pyramid 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": "Spatial Pyramid Pooling",
"source_title": "Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition",
"source_url": "http://arxiv.org/abs/1406.4729v4"
}
] |
https://paperswithcode.com/paper/domain-adversarial-training-for-accented
|
1806.02786
| null | null |
Domain Adversarial Training for Accented Speech Recognition
|
In this paper, we propose a domain adversarial training (DAT) algorithm to
alleviate the accented speech recognition problem. In order to reduce the
mismatch between labeled source domain data ("standard" accent) and unlabeled
target domain data (with heavy accents), we augment the learning objective for
a Kaldi TDNN network with a domain adversarial training (DAT) objective to
encourage the model to learn accent-invariant features. In experiments with
three Mandarin accents, we show that DAT yields up to 7.45% relative character
error rate reduction when we do not have transcriptions of the accented speech,
compared with the baseline trained on standard accent data only. We also find a
benefit from DAT when used in combination with training from automatic
transcriptions on the accented data. Furthermore, we find that DAT is superior
to multi-task learning for accented speech recognition.
| null |
http://arxiv.org/abs/1806.02786v1
|
http://arxiv.org/pdf/1806.02786v1.pdf
| null |
[
"Sining Sun",
"Ching-Feng Yeh",
"Mei-Yuh Hwang",
"Mari Ostendorf",
"Lei Xie"
] |
[
"Accented Speech Recognition",
"Multi-Task Learning",
"speech-recognition",
"Speech Recognition"
] | 2018-06-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/zeroth-order-stochastic-variance-reduction
|
1805.10367
| null | null |
Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization
|
As application demands for zeroth-order (gradient-free) optimization
accelerate, the need for variance reduced and faster converging approaches is
also intensifying. This paper addresses these challenges by presenting: a) a
comprehensive theoretical analysis of variance reduced zeroth-order (ZO)
optimization, b) a novel variance reduced ZO algorithm, called ZO-SVRG, and c)
an experimental evaluation of our approach in the context of two compelling
applications, black-box chemical material classification and generation of
adversarial examples from black-box deep neural network models. Our theoretical
analysis uncovers an essential difficulty in the analysis of ZO-SVRG: the
unbiased assumption on gradient estimates no longer holds. We prove that
compared to its first-order counterpart, ZO-SVRG with a two-point random
gradient estimator could suffer an additional error of order $O(1/b)$, where
$b$ is the mini-batch size. To mitigate this error, we propose two accelerated
versions of ZO-SVRG utilizing variance reduced gradient estimators, which
achieve the best rate known for ZO stochastic optimization (in terms of
iterations). Our extensive experimental results show that our approaches
outperform other state-of-the-art ZO algorithms, and strike a balance between
the convergence rate and the function query complexity.
|
As application demands for zeroth-order (gradient-free) optimization accelerate, the need for variance reduced and faster converging approaches is also intensifying.
|
http://arxiv.org/abs/1805.10367v2
|
http://arxiv.org/pdf/1805.10367v2.pdf
|
NeurIPS 2018 12
|
[
"Sijia Liu",
"Bhavya Kailkhura",
"Pin-Yu Chen",
"Pai-Shun Ting",
"Shiyu Chang",
"Lisa Amini"
] |
[
"Material Classification",
"Stochastic Optimization"
] | 2018-05-25T00:00:00 |
http://papers.nips.cc/paper/7630-zeroth-order-stochastic-variance-reduction-for-nonconvex-optimization
|
http://papers.nips.cc/paper/7630-zeroth-order-stochastic-variance-reduction-for-nonconvex-optimization.pdf
|
zeroth-order-stochastic-variance-reduction-1
| null |
[] |
https://paperswithcode.com/paper/more-adaptive-algorithms-for-adversarial
|
1801.03265
| null | null |
More Adaptive Algorithms for Adversarial Bandits
|
We develop a novel and generic algorithm for the adversarial multi-armed
bandit problem (or more generally the combinatorial semi-bandit problem). When
instantiated differently, our algorithm achieves various new data-dependent
regret bounds improving previous work. Examples include: 1) a regret bound
depending on the variance of only the best arm; 2) a regret bound depending on
the first-order path-length of only the best arm; 3) a regret bound depending
on the sum of first-order path-lengths of all arms as well as an important
negative term, which together lead to faster convergence rates for some normal
form games with partial feedback; 4) a regret bound that simultaneously implies
small regret when the best arm has small loss and logarithmic regret when there
exists an arm whose expected loss is always smaller than those of others by a
fixed gap (e.g. the classic i.i.d. setting). In some cases, such as the last
two results, our algorithm is completely parameter-free.
The main idea of our algorithm is to apply the optimism and adaptivity
techniques to the well-known Online Mirror Descent framework with a special
log-barrier regularizer. The challenges are to come up with appropriate
optimistic predictions and correction terms in this framework. Some of our
results also crucially rely on using a sophisticated increasing learning rate
schedule.
| null |
http://arxiv.org/abs/1801.03265v3
|
http://arxiv.org/pdf/1801.03265v3.pdf
| null |
[
"Chen-Yu Wei",
"Haipeng Luo"
] |
[] | 2018-01-10T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/stein-variational-gradient-descent-without
|
1806.02775
| null | null |
Stein Variational Gradient Descent Without Gradient
|
Stein variational gradient decent (SVGD) has been shown to be a powerful
approximate inference algorithm for complex distributions. However, the
standard SVGD requires calculating the gradient of the target density and
cannot be applied when the gradient is unavailable. In this work, we develop a
gradient-free variant of SVGD (GF-SVGD), which replaces the true gradient with
a surrogate gradient, and corrects the induced bias by re-weighting the
gradients in a proper form. We show that our GF-SVGD can be viewed as the
standard SVGD with a special choice of kernel, and hence directly inherits the
theoretical properties of SVGD. We shed insights on the empirical choice of the
surrogate gradient and propose an annealed GF-SVGD that leverages the idea of
simulated annealing to improve the performance on high dimensional complex
distributions. Empirical studies show that our method consistently outperforms
a number of recent advanced gradient-free MCMC methods.
| null |
http://arxiv.org/abs/1806.02775v1
|
http://arxiv.org/pdf/1806.02775v1.pdf
|
ICML 2018 7
|
[
"Jun Han",
"Qiang Liu"
] |
[] | 2018-06-07T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2105
|
http://proceedings.mlr.press/v80/han18b/han18b.pdf
|
stein-variational-gradient-descent-without-1
| null |
[] |
https://paperswithcode.com/paper/obfuscated-gradients-give-a-false-sense-of
|
1802.00420
| null | null |
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
|
We identify obfuscated gradients, a kind of gradient masking, as a phenomenon
that leads to a false sense of security in defenses against adversarial
examples. While defenses that cause obfuscated gradients appear to defeat
iterative optimization-based attacks, we find defenses relying on this effect
can be circumvented. We describe characteristic behaviors of defenses
exhibiting the effect, and for each of the three types of obfuscated gradients
we discover, we develop attack techniques to overcome it. In a case study,
examining non-certified white-box-secure defenses at ICLR 2018, we find
obfuscated gradients are a common occurrence, with 7 of 9 defenses relying on
obfuscated gradients. Our new attacks successfully circumvent 6 completely, and
1 partially, in the original threat model each paper considers.
|
We identify obfuscated gradients, a kind of gradient masking, as a phenomenon that leads to a false sense of security in defenses against adversarial examples.
|
http://arxiv.org/abs/1802.00420v4
|
http://arxiv.org/pdf/1802.00420v4.pdf
|
ICML 2018 7
|
[
"Anish Athalye",
"Nicholas Carlini",
"David Wagner"
] |
[
"Adversarial Attack",
"Adversarial Defense"
] | 2018-02-01T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2217
|
http://proceedings.mlr.press/v80/athalye18a/athalye18a.pdf
|
obfuscated-gradients-give-a-false-sense-of-1
| null |
[] |
https://paperswithcode.com/paper/synthesizing-robust-adversarial-examples
|
1707.07397
| null |
BJDH5M-AW
|
Synthesizing Robust Adversarial Examples
|
Standard methods for generating adversarial examples for neural networks do
not consistently fool neural network classifiers in the physical world due to a
combination of viewpoint shifts, camera noise, and other natural
transformations, limiting their relevance to real-world systems. We demonstrate
the existence of robust 3D adversarial objects, and we present the first
algorithm for synthesizing examples that are adversarial over a chosen
distribution of transformations. We synthesize two-dimensional adversarial
images that are robust to noise, distortion, and affine transformation. We
apply our algorithm to complex three-dimensional objects, using 3D-printing to
manufacture the first physical adversarial objects. Our results demonstrate the
existence of 3D adversarial objects in the physical world.
|
We demonstrate the existence of robust 3D adversarial objects, and we present the first algorithm for synthesizing examples that are adversarial over a chosen distribution of transformations.
|
http://arxiv.org/abs/1707.07397v3
|
http://arxiv.org/pdf/1707.07397v3.pdf
| null |
[
"Anish Athalye",
"Logan Engstrom",
"Andrew Ilyas",
"Kevin Kwok"
] |
[] | 2017-07-24T00:00:00 |
https://openreview.net/forum?id=BJDH5M-AW
|
https://openreview.net/pdf?id=BJDH5M-AW
|
synthesizing-robust-adversarial-examples-1
| null |
[] |
https://paperswithcode.com/paper/deepeye-a-compact-and-accurate-video
|
1805.07935
| null | null |
DEEPEYE: A Compact and Accurate Video Comprehension at Terminal Devices Compressed with Quantization and Tensorization
|
As it requires a huge number of parameters when exposed to high dimensional
inputs in video detection and classification, there is a grand challenge to
develop a compact yet accurate video comprehension at terminal devices. Current
works focus on optimizations of video detection and classification in a
separated fashion. In this paper, we introduce a video comprehension (object
detection and action recognition) system for terminal devices, namely DEEPEYE.
Based on You Only Look Once (YOLO), we have developed an 8-bit quantization
method when training YOLO; and also developed a tensorized-compression method
of Recurrent Neural Network (RNN) composed of features extracted from YOLO. The
developed quantization and tensorization can significantly compress the
original network model yet with maintained accuracy. Using the challenging
video datasets: MOMENTS and UCF11 as benchmarks, the results show that the
proposed DEEPEYE achieves 3.994x model compression rate with only 0.47% mAP
decreased; and 15,047x parameter reduction and 2.87x speed-up with 16.58%
accuracy improvement.
| null |
http://arxiv.org/abs/1805.07935v2
|
http://arxiv.org/pdf/1805.07935v2.pdf
| null |
[
"Yuan Cheng",
"Guangya Li",
"Hai-Bao Chen",
"Sheldon X. -D. Tan",
"Hao Yu"
] |
[
"Action Recognition",
"General Classification",
"Model Compression",
"object-detection",
"Object Detection",
"Quantization",
"Temporal Action Localization"
] | 2018-05-21T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/evaluating-surgical-skills-from-kinematic
|
1806.02750
| null | null |
Evaluating surgical skills from kinematic data using convolutional neural networks
|
The need for automatic surgical skills assessment is increasing, especially
because manual feedback from senior surgeons observing junior surgeons is prone
to subjectivity and time consuming. Thus, automating surgical skills evaluation
is a very important step towards improving surgical practice. In this paper, we
designed a Convolutional Neural Network (CNN) to evaluate surgeon skills by
extracting patterns in the surgeon motions performed in robotic surgery. The
proposed method is validated on the JIGSAWS dataset and achieved very
competitive results with 100% accuracy on the suturing and needle passing
tasks. While we leveraged from the CNNs efficiency, we also managed to mitigate
its black-box effect using class activation map. This feature allows our method
to automatically highlight which parts of the surgical task influenced the
skill prediction and can be used to explain the classification and to provide
personalized feedback to the trainee.
|
The need for automatic surgical skills assessment is increasing, especially because manual feedback from senior surgeons observing junior surgeons is prone to subjectivity and time consuming.
|
http://arxiv.org/abs/1806.02750v1
|
http://arxiv.org/pdf/1806.02750v1.pdf
| null |
[
"Hassan Ismail Fawaz",
"Germain Forestier",
"Jonathan Weber",
"Lhassane Idoumghar",
"Pierre-Alain Muller"
] |
[
"General Classification",
"Skills Assessment",
"Skills Evaluation",
"Surgical Skills Evaluation"
] | 2018-06-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/discovering-space-grounding-spatial-topology
|
1806.02739
| null | null |
Discovering space - Grounding spatial topology and metric regularity in a naive agent's sensorimotor experience
|
In line with the sensorimotor contingency theory, we investigate the problem
of the perception of space from a fundamental sensorimotor perspective. Despite
its pervasive nature in our perception of the world, the origin of the concept
of space remains largely mysterious. For example in the context of artificial
perception, this issue is usually circumvented by having engineers pre-define
the spatial structure of the problem the agent has to face. We here show that
the structure of space can be autonomously discovered by a naive agent in the
form of sensorimotor regularities, that correspond to so called compensable
sensory experiences: these are experiences that can be generated either by the
agent or its environment. By detecting such compensable experiences the agent
can infer the topological and metric structure of the external space in which
its body is moving. We propose a theoretical description of the nature of these
regularities and illustrate the approach on a simulated robotic arm equipped
with an eye-like sensor, and which interacts with an object. Finally we show
how these regularities can be used to build an internal representation of the
sensor's external spatial configuration.
| null |
http://arxiv.org/abs/1806.02739v2
|
http://arxiv.org/pdf/1806.02739v2.pdf
| null |
[
"Alban Laflaquière",
"J. Kevin O'Regan",
"Bruno Gas",
"Alexander Terekhov"
] |
[] | 2018-06-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/joint-mean-and-covariance-estimation-with
|
1611.04208
| null | null |
Joint mean and covariance estimation with unreplicated matrix-variate data
|
It has been proposed that complex populations, such as those that arise in
genomics studies, may exhibit dependencies among observations as well as among
variables. This gives rise to the challenging problem of analyzing unreplicated
high-dimensional data with unknown mean and dependence structures.
Matrix-variate approaches that impose various forms of (inverse) covariance
sparsity allow flexible dependence structures to be estimated, but cannot
directly be applied when the mean and covariance matrices are estimated
jointly. We present a practical method utilizing generalized least squares and
penalized (inverse) covariance estimation to address this challenge. We
establish consistency and obtain rates of convergence for estimating the mean
parameters and covariance matrices. The advantages of our approaches are: (i)
dependence graphs and covariance structures can be estimated in the presence of
unknown mean structure, (ii) the mean structure becomes more efficiently
estimated when accounting for the dependence structure among observations; and
(iii) inferences about the mean parameters become correctly calibrated. We use
simulation studies and analysis of genomic data from a twin study of ulcerative
colitis to illustrate the statistical convergence and the performance of our
methods in practical settings. Several lines of evidence show that the test
statistics for differential gene expression produced by our methods are
correctly calibrated and improve power over conventional methods.
| null |
http://arxiv.org/abs/1611.04208v4
|
http://arxiv.org/pdf/1611.04208v4.pdf
| null |
[
"Michael Hornstein",
"Roger Fan",
"Kerby Shedden",
"Shuheng Zhou"
] |
[] | 2016-11-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/where-are-we-now-a-large-benchmark-study-of
|
1804.09331
| null | null |
Where are we now? A large benchmark study of recent symbolic regression methods
|
In this paper we provide a broad benchmarking of recent genetic programming
approaches to symbolic regression in the context of state of the art machine
learning approaches. We use a set of nearly 100 regression benchmark problems
culled from open source repositories across the web. We conduct a rigorous
benchmarking of four recent symbolic regression approaches as well as nine
machine learning approaches from scikit-learn. The results suggest that
symbolic regression performs strongly compared to state-of-the-art gradient
boosting algorithms, although in terms of running times is among the slowest of
the available methodologies. We discuss the results in detail and point to
future research directions that may allow symbolic regression to gain wider
adoption in the machine learning community.
|
In this paper we provide a broad benchmarking of recent genetic programming approaches to symbolic regression in the context of state of the art machine learning approaches.
|
http://arxiv.org/abs/1804.09331v2
|
http://arxiv.org/pdf/1804.09331v2.pdf
| null |
[
"Patryk Orzechowski",
"William La Cava",
"Jason H. Moore"
] |
[
"Benchmarking",
"BIG-bench Machine Learning",
"regression",
"Symbolic Regression"
] | 2018-04-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/online-adaptive-machine-learning-based
|
1706.01833
| null | null |
Online Adaptive Machine Learning Based Algorithm for Implied Volatility Surface Modeling
|
In this work, we design a machine learning based method, online adaptive
primal support vector regression (SVR), to model the implied volatility surface
(IVS). The algorithm proposed is the first derivation and implementation of an
online primal kernel SVR. It features enhancements that allow efficient online
adaptive learning by embedding the idea of local fitness and budget maintenance
to dynamically update support vectors upon pattern drifts. For algorithm
acceleration, we implement its most computationally intensive parts in a Field
Programmable Gate Arrays hardware, where a 132x speedup over CPU is achieved
during online prediction. Using intraday tick data from the E-mini S&P 500
options market, we show that the Gaussian kernel outperforms the linear kernel
in regulating the size of support vectors, and that our empirical IVS algorithm
beats two competing online methods with regards to model complexity and
regression errors (the mean absolute percentage error of our algorithm is up to
13%). Best results are obtained at the center of the IVS grid due to its larger
number of adjacent support vectors than the edges of the grid. Sensitivity
analysis is also presented to demonstrate how hyper parameters affect the error
rates and model complexity.
| null |
http://arxiv.org/abs/1706.01833v2
|
http://arxiv.org/pdf/1706.01833v2.pdf
| null |
[
"Yaxiong Zeng",
"Diego Klabjan"
] |
[
"BIG-bench Machine Learning",
"CPU",
"regression"
] | 2017-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/can-machines-design-an-artificial-general
|
1806.02091
| null | null |
Can Machines Design? An Artificial General Intelligence Approach
|
Can machines design? Can they come up with creative solutions to problems and
build tools and artifacts across a wide range of domains? Recent advances in
the field of computational creativity and formal Artificial General
Intelligence (AGI) provide frameworks for machines with the general ability to
design. In this paper we propose to integrate a formal computational creativity
framework into the G\"odel machine framework. We call the resulting framework
design G\"odel machine. Such a machine could solve a variety of design problems
by generating novel concepts. In addition, it could change the way these
concepts are generated by modifying itself. The design G\"odel machine is able
to improve its initial design program, once it has proven that a modification
would increase its return on the utility function. Finally, we sketch out a
specific version of the design G\"odel machine which specifically addresses the
design of complex software and hardware systems. Future work aims at the
development of a more formal version of the design G\"odel machine and a proof
of concept implementation.
| null |
http://arxiv.org/abs/1806.02091v4
|
http://arxiv.org/pdf/1806.02091v4.pdf
| null |
[
"Andreas Makoto Hein",
"Hélène Condat"
] |
[
"Novel Concepts"
] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/assessing-the-impact-of-machine-intelligence
|
1806.03192
| null | null |
Assessing the impact of machine intelligence on human behaviour: an interdisciplinary endeavour
|
This document contains the outcome of the first Human behaviour and machine
intelligence (HUMAINT) workshop that took place 5-6 March 2018 in Barcelona,
Spain. The workshop was organized in the context of a new research programme at
the Centre for Advanced Studies, Joint Research Centre of the European
Commission, which focuses on studying the potential impact of artificial
intelligence on human behaviour. The workshop gathered an interdisciplinary
group of experts to establish the state of the art research in the field and a
list of future research challenges to be addressed on the topic of human and
machine intelligence, algorithm's potential impact on human cognitive
capabilities and decision making, and evaluation and regulation needs. The
document is made of short position statements and identification of challenges
provided by each expert, and incorporates the result of the discussions carried
out during the workshop. In the conclusion section, we provide a list of
emerging research topics and strategies to be addressed in the near future.
| null |
http://arxiv.org/abs/1806.03192v1
|
http://arxiv.org/pdf/1806.03192v1.pdf
| null |
[
"Emilia Gómez",
"Carlos Castillo",
"Vicky Charisi",
"Verónica Dahl",
"Gustavo Deco",
"Blagoj Delipetrev",
"Nicole Dewandre",
"Miguel Ángel González-Ballester",
"Fabien Gouyon",
"José Hernández-Orallo",
"Perfecto Herrera",
"Anders Jonsson",
"Ansgar Koene",
"Martha Larson",
"Ramón López de Mántaras",
"Bertin Martens",
"Marius Miron",
"Rubén Moreno-Bote",
"Nuria Oliver",
"Antonio Puertas Gallardo",
"Heike Schweitzer",
"Nuria Sebastian",
"Xavier Serra",
"Joan Serrà",
"Songül Tolan",
"Karina Vold"
] |
[
"Decision Making"
] | 2018-06-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/taylors-law-for-human-linguistic-sequences
|
1804.07893
| null | null |
Taylor's law for Human Linguistic Sequences
|
Taylor's law describes the fluctuation characteristics underlying a system in
which the variance of an event within a time span grows by a power law with
respect to the mean. Although Taylor's law has been applied in many natural and
social systems, its application for language has been scarce. This article
describes a new quantification of Taylor's law in natural language and reports
an analysis of over 1100 texts across 14 languages. The Taylor exponents of
written natural language texts were found to exhibit almost the same value. The
exponent was also compared for other language-related data, such as the
child-directed speech, music, and programming language code. The results show
how the Taylor exponent serves to quantify the fundamental structural
complexity underlying linguistic time series. The article also shows the
applicability of these findings in evaluating language models.
|
Taylor's law describes the fluctuation characteristics underlying a system in which the variance of an event within a time span grows by a power law with respect to the mean.
|
http://arxiv.org/abs/1804.07893v2
|
http://arxiv.org/pdf/1804.07893v2.pdf
|
ACL 2018 7
|
[
"Tatsuru Kobayashi",
"Kumiko Tanaka-Ishii"
] |
[
"Time Series",
"Time Series Analysis"
] | 2018-04-21T00:00:00 |
https://aclanthology.org/P18-1105
|
https://aclanthology.org/P18-1105.pdf
|
tayloras-law-for-human-linguistic-sequences
| null |
[] |
https://paperswithcode.com/paper/speaker-follower-models-for-vision-and
|
1806.02724
| null | null |
Speaker-Follower Models for Vision-and-Language Navigation
|
Navigation guided by natural language instructions presents a challenging
reasoning problem for instruction followers. Natural language instructions
typically identify only a few high-level decisions and landmarks rather than
complete low-level motor behaviors; much of the missing information must be
inferred based on perceptual context. In machine learning settings, this is
doubly challenging: it is difficult to collect enough annotated data to enable
learning of this reasoning process from scratch, and also difficult to
implement the reasoning process using generic sequence models. Here we describe
an approach to vision-and-language navigation that addresses both these issues
with an embedded speaker model. We use this speaker model to (1) synthesize new
instructions for data augmentation and to (2) implement pragmatic reasoning,
which evaluates how well candidate action sequences explain an instruction.
Both steps are supported by a panoramic action space that reflects the
granularity of human-generated instructions. Experiments show that all three
components of this approach---speaker-driven data augmentation, pragmatic
reasoning and panoramic action space---dramatically improve the performance of
a baseline instruction follower, more than doubling the success rate over the
best existing approach on a standard benchmark.
|
We use this speaker model to (1) synthesize new instructions for data augmentation and to (2) implement pragmatic reasoning, which evaluates how well candidate action sequences explain an instruction.
|
http://arxiv.org/abs/1806.02724v2
|
http://arxiv.org/pdf/1806.02724v2.pdf
|
NeurIPS 2018 12
|
[
"Daniel Fried",
"Ronghang Hu",
"Volkan Cirik",
"Anna Rohrbach",
"Jacob Andreas",
"Louis-Philippe Morency",
"Taylor Berg-Kirkpatrick",
"Kate Saenko",
"Dan Klein",
"Trevor Darrell"
] |
[
"Data Augmentation",
"Vision and Language Navigation"
] | 2018-06-07T00:00:00 |
http://papers.nips.cc/paper/7592-speaker-follower-models-for-vision-and-language-navigation
|
http://papers.nips.cc/paper/7592-speaker-follower-models-for-vision-and-language-navigation.pdf
|
speaker-follower-models-for-vision-and-1
| null |
[] |
https://paperswithcode.com/paper/a-representer-theorem-for-deep-kernel
|
1709.10441
| null | null |
A representer theorem for deep kernel learning
|
In this paper we provide a finite-sample and an infinite-sample representer
theorem for the concatenation of (linear combinations of) kernel functions of
reproducing kernel Hilbert spaces. These results serve as mathematical
foundation for the analysis of machine learning algorithms based on
compositions of functions. As a direct consequence in the finite-sample case,
the corresponding infinite-dimensional minimization problems can be recast into
(nonlinear) finite-dimensional minimization problems, which can be tackled with
nonlinear optimization algorithms. Moreover, we show how concatenated machine
learning problems can be reformulated as neural networks and how our
representer theorem applies to a broad class of state-of-the-art deep learning
methods.
| null |
http://arxiv.org/abs/1709.10441v3
|
http://arxiv.org/pdf/1709.10441v3.pdf
| null |
[
"Bastian Bohn",
"Michael Griebel",
"Christian Rieger"
] |
[
"BIG-bench Machine Learning"
] | 2017-09-29T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/condensenet-an-efficient-densenet-using
|
1711.09224
| null | null |
CondenseNet: An Efficient DenseNet using Learned Group Convolutions
|
Deep neural networks are increasingly used on mobile devices, where
computational resources are limited. In this paper we develop CondenseNet, a
novel network architecture with unprecedented efficiency. It combines dense
connectivity with a novel module called learned group convolution. The dense
connectivity facilitates feature re-use in the network, whereas learned group
convolutions remove connections between layers for which this feature re-use is
superfluous. At test time, our model can be implemented using standard group
convolutions, allowing for efficient computation in practice. Our experiments
show that CondenseNets are far more efficient than state-of-the-art compact
convolutional networks such as MobileNets and ShuffleNets.
|
It combines dense connectivity with a novel module called learned group convolution.
|
http://arxiv.org/abs/1711.09224v2
|
http://arxiv.org/pdf/1711.09224v2.pdf
|
CVPR 2018 6
|
[
"Gao Huang",
"Shichen Liu",
"Laurens van der Maaten",
"Kilian Q. Weinberger"
] |
[] | 2017-11-25T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Huang_CondenseNet_An_Efficient_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Huang_CondenseNet_An_Efficient_CVPR_2018_paper.pdf
|
condensenet-an-efficient-densenet-using-1
| null |
[] |
https://paperswithcode.com/paper/a-study-of-ev-bms-cyber-security-based-on
|
1806.02714
| null | null |
A Study of EV BMS Cyber Security Based on Neural Network SOC Prediction
|
Recent changes to greenhouse gas emission policies are catalyzing the
electric vehicle (EV) market making it readily accessible to consumers. While
there are challenges that arise with dense deployment of EVs, one of the major
future concerns is cyber security threat. In this paper, cyber security threats
in the form of tampering with EV battery's State of Charge (SOC) was explored.
A Back Propagation (BP) Neural Network (NN) was trained and tested based on
experimental data to estimate SOC of battery under normal operation and
cyber-attack scenarios. NeuralWare software was used to run scenarios.
Different statistic metrics of the predicted values were compared against the
actual values of the specific battery tested to measure the stability and
accuracy of the proposed BP network under different operating conditions. The
results showed that BP NN was able to capture and detect the false entries due
to a cyber-attack on its network.
| null |
http://arxiv.org/abs/1806.02714v1
|
http://arxiv.org/pdf/1806.02714v1.pdf
| null |
[
"Syed Rahman",
"Haneen Aburub",
"Yemeserach Mekonnen",
"Arif I. Sarwat"
] |
[] | 2018-06-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/sbnet-sparse-blocks-network-for-fast
|
1801.02108
| null | null |
SBNet: Sparse Blocks Network for Fast Inference
|
Conventional deep convolutional neural networks (CNNs) apply convolution
operators uniformly in space across all feature maps for hundreds of layers -
this incurs a high computational cost for real-time applications. For many
problems such as object detection and semantic segmentation, we are able to
obtain a low-cost computation mask, either from a priori problem knowledge, or
from a low-resolution segmentation network. We show that such computation masks
can be used to reduce computation in the high-resolution main network. Variants
of sparse activation CNNs have previously been explored on small-scale tasks
and showed no degradation in terms of object classification accuracy, but often
measured gains in terms of theoretical FLOPs without realizing a practical
speed-up when compared to highly optimized dense convolution implementations.
In this work, we leverage the sparsity structure of computation masks and
propose a novel tiling-based sparse convolution algorithm. We verified the
effectiveness of our sparse CNN on LiDAR-based 3D object detection, and we
report significant wall-clock speed-ups compared to dense convolution without
noticeable loss of accuracy.
|
Conventional deep convolutional neural networks (CNNs) apply convolution operators uniformly in space across all feature maps for hundreds of layers - this incurs a high computational cost for real-time applications.
|
http://arxiv.org/abs/1801.02108v2
|
http://arxiv.org/pdf/1801.02108v2.pdf
|
CVPR 2018 6
|
[
"Mengye Ren",
"Andrei Pokrovsky",
"Bin Yang",
"Raquel Urtasun"
] |
[
"3D Object Detection",
"Object",
"object-detection",
"Object Detection",
"Semantic Segmentation"
] | 2018-01-07T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Ren_SBNet_Sparse_Blocks_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Ren_SBNet_Sparse_Blocks_CVPR_2018_paper.pdf
|
sbnet-sparse-blocks-network-for-fast-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
}
] |
https://paperswithcode.com/paper/randomized-optimal-transport-on-a-graph
|
1806.03232
| null | null |
Randomized Optimal Transport on a Graph: framework and new distance measures
|
The recently developed bag-of-paths (BoP) framework consists in setting a
Gibbs-Boltzmann distribution on all feasible paths of a graph. This probability
distribution favors short paths over long ones, with a free parameter (the
temperature $T$) controlling the entropic level of the distribution. This
formalism enables the computation of new distances or dissimilarities,
interpolating between the shortest-path and the resistance distance, which have
been shown to perform well in clustering and classification tasks. In this
work, the bag-of-paths formalism is extended by adding two independent equality
constraints fixing starting and ending nodes distributions of paths (margins).
When the temperature is low, this formalism is shown to be equivalent to a
relaxation of the optimal transport problem on a network where paths carry a
flow between two discrete distributions on nodes. The randomization is achieved
by considering free energy minimization instead of traditional cost
minimization. Algorithms computing the optimal free energy solution are
developed for two types of paths: hitting (or absorbing) paths and non-hitting,
regular, paths, and require the inversion of an $n \times n$ matrix with $n$
being the number of nodes. Interestingly, for regular paths on an undirected
graph, the resulting optimal policy interpolates between the deterministic
optimal transport policy ($T \rightarrow 0^{+}$) and the solution to the
corresponding electrical circuit ($T \rightarrow \infty$). Two distance
measures between nodes and a dissimilarity between groups of nodes, both
integrating weights on nodes, are derived from this framework.
| null |
http://arxiv.org/abs/1806.03232v2
|
http://arxiv.org/pdf/1806.03232v2.pdf
| null |
[
"Guillaume Guex",
"Ilkka Kivimäki",
"Marco Saerens"
] |
[
"Clustering"
] | 2018-06-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/multi-scale-dense-networks-for-resource
|
1703.09844
| null |
Hk2aImxAb
|
Multi-Scale Dense Networks for Resource Efficient Image Classification
|
In this paper we investigate image classification with computational resource
limits at test time. Two such settings are: 1. anytime classification, where
the network's prediction for a test example is progressively updated,
facilitating the output of a prediction at any time; and 2. budgeted batch
classification, where a fixed amount of computation is available to classify a
set of examples that can be spent unevenly across "easier" and "harder" inputs.
In contrast to most prior work, such as the popular Viola and Jones algorithm,
our approach is based on convolutional neural networks. We train multiple
classifiers with varying resource demands, which we adaptively apply during
test time. To maximally re-use computation between the classifiers, we
incorporate them as early-exits into a single deep convolutional neural network
and inter-connect them with dense connectivity. To facilitate high quality
classification early on, we use a two-dimensional multi-scale network
architecture that maintains coarse and fine level features all-throughout the
network. Experiments on three image-classification tasks demonstrate that our
framework substantially improves the existing state-of-the-art in both
settings.
|
In this paper we investigate image classification with computational resource limits at test time.
|
http://arxiv.org/abs/1703.09844v5
|
http://arxiv.org/pdf/1703.09844v5.pdf
|
ICLR 2018 1
|
[
"Gao Huang",
"Danlu Chen",
"Tianhong Li",
"Felix Wu",
"Laurens van der Maaten",
"Kilian Q. Weinberger"
] |
[
"General Classification",
"Handwritten Mathmatical Expression Recognition",
"image-classification",
"Image Classification"
] | 2017-03-29T00:00:00 |
https://openreview.net/forum?id=Hk2aImxAb
|
https://openreview.net/pdf?id=Hk2aImxAb
|
multi-scale-dense-networks-for-resource-1
| null |
[] |
https://paperswithcode.com/paper/multiobjective-test-problems-with-degenerate
|
1806.02706
| null | null |
Multiobjective Test Problems with Degenerate Pareto Fronts
|
In multiobjective optimisation, a set of scalable test problems with a variety of features allow researchers to investigate and evaluate the abilities of different optimisation algorithms, and thus can help them to design and develop more effective and efficient approaches. Existing test problem suites mainly focus on situations where all the objectives are fully conflicting with each other. In such cases, an m-objective optimisation problem has an (m-1)-dimensional Pareto front in the objective space. However, in some optimisation problems, there may be unexpected characteristics among objectives, e.g., redundancy. The redundancy of some objectives can lead to the multiobjective problem having a degenerate Pareto front, i.e., the dimension of the Pareto front of the $m$-objective problem be less than (m-1). In this paper, we systematically study degenerate multiobjective problems. We abstract three general characteristics of degenerate problems, which are not formulated and systematically investigated in the literature. Based on these characteristics, we present a set of test problems to support the investigation of multiobjective optimisation algorithms under situations with redundant objectives. To the best of our knowledge, this work is the first one that explicitly formulates these three characteristics of degenerate problems, thus allowing the resulting test problems to be featured by their generality, in contrast to existing test problems designed for specific purposes (e.g., visualisation).
| null |
https://arxiv.org/abs/1806.02706v2
|
https://arxiv.org/pdf/1806.02706v2.pdf
| null |
[
"Liangli Zhen",
"Miqing Li",
"Ran Cheng",
"Dezhong Peng",
"Xin Yao"
] |
[
"Multiobjective Optimization"
] | 2018-06-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/efficient-semantic-image-segmentation-with
|
1806.02705
| null | null |
Efficient semantic image segmentation with superpixel pooling
|
In this work, we evaluate the use of superpixel pooling layers in deep
network architectures for semantic segmentation. Superpixel pooling is a
flexible and efficient replacement for other pooling strategies that
incorporates spatial prior information. We propose a simple and efficient
GPU-implementation of the layer and explore several designs for the integration
of the layer into existing network architectures. We provide experimental
results on the IBSR and Cityscapes dataset, demonstrating that superpixel
pooling can be leveraged to consistently increase network accuracy with minimal
computational overhead. Source code is available at
https://github.com/bermanmaxim/superpixPool
|
In this work, we evaluate the use of superpixel pooling layers in deep network architectures for semantic segmentation.
|
http://arxiv.org/abs/1806.02705v1
|
http://arxiv.org/pdf/1806.02705v1.pdf
| null |
[
"Mathijs Schuurmans",
"Maxim Berman",
"Matthew B. Blaschko"
] |
[
"GPU",
"Image Segmentation",
"Semantic Segmentation"
] | 2018-06-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/first-order-generative-adversarial-networks
|
1802.04591
| null | null |
First Order Generative Adversarial Networks
|
GANs excel at learning high dimensional distributions, but they can update
generator parameters in directions that do not correspond to the steepest
descent direction of the objective. Prominent examples of problematic update
directions include those used in both Goodfellow's original GAN and the
WGAN-GP. To formally describe an optimal update direction, we introduce a
theoretical framework which allows the derivation of requirements on both the
divergence and corresponding method for determining an update direction, with
these requirements guaranteeing unbiased mini-batch updates in the direction of
steepest descent. We propose a novel divergence which approximates the
Wasserstein distance while regularizing the critic's first order information.
Together with an accompanying update direction, this divergence fulfills the
requirements for unbiased steepest descent updates. We verify our method, the
First Order GAN, with image generation on CelebA, LSUN and CIFAR-10 and set a
new state of the art on the One Billion Word language generation task. Code to
reproduce experiments is available.
|
To formally describe an optimal update direction, we introduce a theoretical framework which allows the derivation of requirements on both the divergence and corresponding method for determining an update direction, with these requirements guaranteeing unbiased mini-batch updates in the direction of steepest descent.
|
http://arxiv.org/abs/1802.04591v2
|
http://arxiv.org/pdf/1802.04591v2.pdf
|
ICML 2018 7
|
[
"Calvin Seward",
"Thomas Unterthiner",
"Urs Bergmann",
"Nikolay Jetchev",
"Sepp Hochreiter"
] |
[
"Image Generation",
"Text Generation"
] | 2018-02-13T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2073
|
http://proceedings.mlr.press/v80/seward18a/seward18a.pdf
|
first-order-generative-adversarial-networks-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/does-distributionally-robust-supervised
|
1611.02041
| null | null |
Does Distributionally Robust Supervised Learning Give Robust Classifiers?
|
Distributionally Robust Supervised Learning (DRSL) is necessary for building
reliable machine learning systems. When machine learning is deployed in the
real world, its performance can be significantly degraded because test data may
follow a different distribution from training data. DRSL with f-divergences
explicitly considers the worst-case distribution shift by minimizing the
adversarially reweighted training loss. In this paper, we analyze this DRSL,
focusing on the classification scenario. Since the DRSL is explicitly
formulated for a distribution shift scenario, we naturally expect it to give a
robust classifier that can aggressively handle shifted distributions. However,
surprisingly, we prove that the DRSL just ends up giving a classifier that
exactly fits the given training distribution, which is too pessimistic. This
pessimism comes from two sources: the particular losses used in classification
and the fact that the variety of distributions to which the DRSL tries to be
robust is too wide. Motivated by our analysis, we propose simple DRSL that
overcomes this pessimism and empirically demonstrate its effectiveness.
| null |
http://arxiv.org/abs/1611.02041v6
|
http://arxiv.org/pdf/1611.02041v6.pdf
|
ICML 2018 7
|
[
"Weihua Hu",
"Gang Niu",
"Issei Sato",
"Masashi Sugiyama"
] |
[
"BIG-bench Machine Learning",
"General Classification",
"Image Classification"
] | 2016-11-07T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2181
|
http://proceedings.mlr.press/v80/hu18a/hu18a.pdf
|
does-distributionally-robust-supervised-1
| null |
[] |
https://paperswithcode.com/paper/differential-diagnosis-for-pancreatic-cysts
|
1806.01023
| null | null |
Differential Diagnosis for Pancreatic Cysts in CT Scans Using Densely-Connected Convolutional Networks
|
The lethal nature of pancreatic ductal adenocarcinoma (PDAC) calls for early
differential diagnosis of pancreatic cysts, which are identified in up to 16%
of normal subjects, and some of which may develop into PDAC. Previous
computer-aided developments have achieved certain accuracy for classification
on segmented cystic lesions in CT. However, pancreatic cysts have a large
variation in size and shape, and the precise segmentation of them remains
rather challenging, which restricts the computer-aided interpretation of CT
images acquired for differential diagnosis. We propose a computer-aided
framework for early differential diagnosis of pancreatic cysts without
pre-segmenting the lesions using densely-connected convolutional networks
(Dense-Net). The Dense-Net learns high-level features from whole abnormal
pancreas and builds mappings between medical imaging appearance to different
pathological types of pancreatic cysts. To enhance the clinical applicability,
we integrate saliency maps in the framework to assist the physicians to
understand the decision of the deep learning method. The test on a cohort of
206 patients with 4 pathologically confirmed subtypes of pancreatic cysts has
achieved an overall accuracy of 72.8%, which is significantly higher than the
baseline accuracy of 48.1%, which strongly supports the clinical potential of
our developed method.
| null |
http://arxiv.org/abs/1806.01023v3
|
http://arxiv.org/pdf/1806.01023v3.pdf
| null |
[
"Hongwei Li",
"Kanru Lin",
"Maximilian Reichert",
"Lina Xu",
"Rickmer Braren",
"Deliang Fu",
"Roland Schmid",
"Ji Li",
"Bjoern Menze",
"Kuangyu Shi"
] |
[] | 2018-06-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/semi-blind-spatially-variant-deconvolution-in
|
1803.07452
| null | null |
Semi-Blind Spatially-Variant Deconvolution in Optical Microscopy with Local Point Spread Function Estimation By Use Of Convolutional Neural Networks
|
We present a semi-blind, spatially-variant deconvolution technique aimed at optical microscopy that combines a local estimation step of the point spread function (PSF) and deconvolution using a spatially variant, regularized Richardson-Lucy algorithm. To find the local PSF map in a computationally tractable way, we train a convolutional neural network to perform regression of an optical parametric model on synthetically blurred image patches. We deconvolved both synthetic and experimentally-acquired data, and achieved an improvement of image SNR of 1.00 dB on average, compared to other deconvolution algorithms.
|
We present a semi-blind, spatially-variant deconvolution technique aimed at optical microscopy that combines a local estimation step of the point spread function (PSF) and deconvolution using a spatially variant, regularized Richardson-Lucy algorithm.
|
https://arxiv.org/abs/1803.07452v4
|
https://arxiv.org/pdf/1803.07452v4.pdf
| null |
[
"Adrian Shajkofci",
"Michael Liebling"
] |
[
"regression"
] | 2018-03-20T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/semi-supervised-learning-via-compact-latent
|
1806.02679
| null | null |
Semi-Supervised Learning via Compact Latent Space Clustering
|
We present a novel cost function for semi-supervised learning of neural
networks that encourages compact clustering of the latent space to facilitate
separation. The key idea is to dynamically create a graph over embeddings of
labeled and unlabeled samples of a training batch to capture underlying
structure in feature space, and use label propagation to estimate its high and
low density regions. We then devise a cost function based on Markov chains on
the graph that regularizes the latent space to form a single compact cluster
per class, while avoiding to disturb existing clusters during optimization. We
evaluate our approach on three benchmarks and compare to state-of-the art with
promising results. Our approach combines the benefits of graph-based
regularization with efficient, inductive inference, does not require
modifications to a network architecture, and can thus be easily applied to
existing networks to enable an effective use of unlabeled data.
| null |
http://arxiv.org/abs/1806.02679v2
|
http://arxiv.org/pdf/1806.02679v2.pdf
|
ICML 2018 7
|
[
"Konstantinos Kamnitsas",
"Daniel C. Castro",
"Loic Le Folgoc",
"Ian Walker",
"Ryutaro Tanno",
"Daniel Rueckert",
"Ben Glocker",
"Antonio Criminisi",
"Aditya Nori"
] |
[
"Clustering"
] | 2018-06-07T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2417
|
http://proceedings.mlr.press/v80/kamnitsas18a/kamnitsas18a.pdf
|
semi-supervised-learning-via-compact-latent-1
| null |
[] |
https://paperswithcode.com/paper/attention-based-deep-multiple-instance
|
1802.04712
| null | null |
Attention-based Deep Multiple Instance Learning
|
Multiple instance learning (MIL) is a variation of supervised learning where
a single class label is assigned to a bag of instances. In this paper, we state
the MIL problem as learning the Bernoulli distribution of the bag label where
the bag label probability is fully parameterized by neural networks.
Furthermore, we propose a neural network-based permutation-invariant
aggregation operator that corresponds to the attention mechanism. Notably, an
application of the proposed attention-based operator provides insight into the
contribution of each instance to the bag label. We show empirically that our
approach achieves comparable performance to the best MIL methods on benchmark
MIL datasets and it outperforms other methods on a MNIST-based MIL dataset and
two real-life histopathology datasets without sacrificing interpretability.
|
Multiple instance learning (MIL) is a variation of supervised learning where a single class label is assigned to a bag of instances.
|
http://arxiv.org/abs/1802.04712v4
|
http://arxiv.org/pdf/1802.04712v4.pdf
|
ICML 2018 7
|
[
"Maximilian Ilse",
"Jakub M. Tomczak",
"Max Welling"
] |
[
"Aerial Scene Classification",
"Multiple Instance Learning"
] | 2018-02-13T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2029
|
http://proceedings.mlr.press/v80/ilse18a/ilse18a.pdf
|
attention-based-deep-multiple-instance-1
| null |
[] |
https://paperswithcode.com/paper/faceshop-deep-sketch-based-face-image-editing
|
1804.08972
| null | null |
FaceShop: Deep Sketch-based Face Image Editing
|
We present a novel system for sketch-based face image editing, enabling users
to edit images intuitively by sketching a few strokes on a region of interest.
Our interface features tools to express a desired image manipulation by
providing both geometry and color constraints as user-drawn strokes. As an
alternative to the direct user input, our proposed system naturally supports a
copy-paste mode, which allows users to edit a given image region by using parts
of another exemplar image without the need of hand-drawn sketching at all. The
proposed interface runs in real-time and facilitates an interactive and
iterative workflow to quickly express the intended edits. Our system is based
on a novel sketch domain and a convolutional neural network trained end-to-end
to automatically learn to render image regions corresponding to the input
strokes. To achieve high quality and semantically consistent results we train
our neural network on two simultaneous tasks, namely image completion and image
translation. To the best of our knowledge, we are the first to combine these
two tasks in a unified framework for interactive image editing. Our results
show that the proposed sketch domain, network architecture, and training
procedure generalize well to real user input and enable high quality synthesis
results without additional post-processing.
| null |
http://arxiv.org/abs/1804.08972v2
|
http://arxiv.org/pdf/1804.08972v2.pdf
| null |
[
"Tiziano Portenier",
"Qiyang Hu",
"Attila Szabó",
"Siavash Arjomand Bigdeli",
"Paolo Favaro",
"Matthias Zwicker"
] |
[
"Image Manipulation"
] | 2018-04-24T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/probabilistic-and-or-attribute-grouping-for
|
1806.02664
| null | null |
Probabilistic AND-OR Attribute Grouping for Zero-Shot Learning
|
In zero-shot learning (ZSL), a classifier is trained to recognize visual
classes without any image samples. Instead, it is given semantic information
about the class, like a textual description or a set of attributes. Learning
from attributes could benefit from explicitly modeling structure of the
attribute space. Unfortunately, learning of general structure from empirical
samples is hard with typical dataset sizes.
Here we describe LAGO, a probabilistic model designed to capture natural soft
and-or relations across groups of attributes. We show how this model can be
learned end-to-end with a deep attribute-detection model. The soft group
structure can be learned from data jointly as part of the model, and can also
readily incorporate prior knowledge about groups if available. The soft and-or
structure succeeds to capture meaningful and predictive structures, improving
the accuracy of zero-shot learning on two of three benchmarks.
Finally, LAGO reveals a unified formulation over two ZSL approaches: DAP
(Lampert et al., 2009) and ESZSL (Romera-Paredes & Torr, 2015). Interestingly,
taking only one singleton group for each attribute, introduces a new
soft-relaxation of DAP, that outperforms DAP by ~40.
|
The soft group structure can be learned from data jointly as part of the model, and can also readily incorporate prior knowledge about groups if available.
|
http://arxiv.org/abs/1806.02664v2
|
http://arxiv.org/pdf/1806.02664v2.pdf
| null |
[
"Yuval Atzmon",
"Gal Chechik"
] |
[
"Attribute",
"Zero-Shot Learning"
] | 2018-06-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/quantization-mimic-towards-very-tiny-cnn-for
|
1805.02152
| null | null |
Quantization Mimic: Towards Very Tiny CNN for Object Detection
|
In this paper, we propose a simple and general framework for training very
tiny CNNs for object detection. Due to limited representation ability, it is
challenging to train very tiny networks for complicated tasks like detection.
To the best of our knowledge, our method, called Quantization Mimic, is the
first one focusing on very tiny networks. We utilize two types of acceleration
methods: mimic and quantization. Mimic improves the performance of a student
network by transfering knowledge from a teacher network. Quantization converts
a full-precision network to a quantized one without large degradation of
performance. If the teacher network is quantized, the search scope of the
student network will be smaller. Using this feature of the quantization, we
propose Quantization Mimic. It first quantizes the large network, then mimic a
quantized small network. The quantization operation can help student network to
better match the feature maps from teacher network. To evaluate our approach,
we carry out experiments on various popular CNNs including VGG and Resnet, as
well as different detection frameworks including Faster R-CNN and R-FCN.
Experiments on Pascal VOC and WIDER FACE verify that our Quantization Mimic
algorithm can be applied on various settings and outperforms state-of-the-art
model acceleration methods given limited computing resouces.
| null |
http://arxiv.org/abs/1805.02152v3
|
http://arxiv.org/pdf/1805.02152v3.pdf
|
ECCV 2018 9
|
[
"Yi Wei",
"Xinyu Pan",
"Hongwei Qin",
"Wanli Ouyang",
"Junjie Yan"
] |
[
"Object",
"object-detection",
"Object Detection",
"Quantization"
] | 2018-05-06T00:00:00 |
http://openaccess.thecvf.com/content_ECCV_2018/html/Yi_Wei_Quantization_Mimic_Towards_ECCV_2018_paper.html
|
http://openaccess.thecvf.com/content_ECCV_2018/papers/Yi_Wei_Quantization_Mimic_Towards_ECCV_2018_paper.pdf
|
quantization-mimic-towards-very-tiny-cnn-for-1
| 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/pytorch/vision/blob/971c3e45b96bc5aa5868c45cd40e4f3c3d90d126/torchvision/ops/ps_roi_pool.py#L10",
"description": "**Position-Sensitive RoI Pooling layer** aggregates the outputs of the last convolutional layer and generates scores for each RoI. Unlike [RoI Pooling](https://paperswithcode.com/method/roi-pooling), PS RoI Pooling conducts selective pooling, and each of the $k$ × $k$ bin aggregates responses from only one score map out of the bank of $k$ × $k$ score maps. With end-to-end training, this RoI layer shepherds the last convolutional layer to learn specialized position-sensitive score maps.",
"full_name": "Position-Sensitive RoI Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**RoI Feature Extractors** are used to extract regions of interest features for tasks such as object detection. Below you can find a continuously updating list of RoI Feature Extractors.",
"name": "RoI Feature Extractors",
"parent": null
},
"name": "Position-Sensitive RoI Pooling",
"source_title": "R-FCN: Object Detection via Region-based Fully Convolutional Networks",
"source_url": "http://arxiv.org/abs/1605.06409v2"
},
{
"code_snippet_url": "https://github.com/facebookresearch/Detectron/blob/8170b25b425967f8f1c7d715bea3c5b8d9536cd8/detectron/modeling/rfcn_heads.py",
"description": "**Region-based Fully Convolutional Networks**, or **R-FCNs**, are a type of region-based object detector. In contrast to previous region-based object detectors such as Fast/[Faster R-CNN](https://paperswithcode.com/method/faster-r-cnn) that apply a costly per-region subnetwork hundreds of times, R-FCN is fully convolutional with almost all computation shared on the entire image.\r\n\r\nTo achieve this, R-FCN utilises position-sensitive score maps to address a dilemma between translation-invariance in image classification and translation-variance in object detection.",
"full_name": "Region-based Fully Convolutional Network",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Object Detection Models** are architectures used to perform the task of object detection. Below you can find a continuously updating list of object detection models.",
"name": "Object Detection Models",
"parent": null
},
"name": "R-FCN",
"source_title": "R-FCN: Object Detection via Region-based Fully Convolutional Networks",
"source_url": "http://arxiv.org/abs/1605.06409v2"
},
{
"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": "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": null,
"description": "A **Region Proposal Network**, or **RPN**, is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals. RPN and algorithms like [Fast R-CNN](https://paperswithcode.com/method/fast-r-cnn) can be merged into a single network by sharing their convolutional features - using the recently popular terminology of neural networks with attention mechanisms, the RPN component tells the unified network where to look.\r\n\r\nRPNs are designed to efficiently predict region proposals with a wide range of scales and aspect ratios. RPNs use anchor boxes that serve as references at multiple scales and aspect ratios. The scheme can be thought of as a pyramid of regression references, which avoids enumerating images or filters of multiple scales or aspect ratios.",
"full_name": "Region Proposal Network",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Region Proposal",
"parent": null
},
"name": "RPN",
"source_title": "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks",
"source_url": "http://arxiv.org/abs/1506.01497v3"
},
{
"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": "",
"description": "In today’s digital age, Ethereum 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 Ethereum transaction not confirmed, your Ethereum wallet not showing balance, or you're trying to recover a lost Ethereum wallet, knowing where to get help is essential. That’s why the Ethereum 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 Ethereum Customer Support Number +1-833-534-1729\r\nEthereum operates on a decentralized network, which means there’s no single company or office that manages everything. However, platforms, wallets, and third-party services provide support to make your experience smoother. Calling +1-833-534-1729 can help you troubleshoot issues such as:\r\n\r\n1. 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"full_name": "Ethereum 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": "Ethereum Customer Service Number +1-833-534-1729",
"source_title": "Very Deep Convolutional Networks for Large-Scale Image Recognition",
"source_url": "http://arxiv.org/abs/1409.1556v6"
},
{
"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": "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": "https://github.com/pytorch/vision/blob/5e9ebe8dadc0ea2841a46cfcd82a93b4ce0d4519/torchvision/ops/roi_pool.py#L10",
"description": "**Region of Interest Pooling**, or **RoIPool**, is an operation for extracting a small feature map (e.g., $7×7$) from each RoI in detection and segmentation based tasks. Features are extracted from each candidate box, and thereafter in models like [Fast R-CNN](https://paperswithcode.com/method/fast-r-cnn), are then classified and bounding box regression performed.\r\n\r\nThe actual scaling to, e.g., $7×7$, occurs by dividing the region proposal into equally sized sections, finding the largest value in each section, and then copying these max values to the output buffer. In essence, **RoIPool** is [max pooling](https://paperswithcode.com/method/max-pooling) on a discrete grid based on a box.\r\n\r\nImage Source: [Joyce Xu](https://towardsdatascience.com/deep-learning-for-object-detection-a-comprehensive-review-73930816d8d9)",
"full_name": "RoIPool",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**RoI Feature Extractors** are used to extract regions of interest features for tasks such as object detection. Below you can find a continuously updating list of RoI Feature Extractors.",
"name": "RoI Feature Extractors",
"parent": null
},
"name": "RoIPool",
"source_title": "Rich feature hierarchies for accurate object detection and semantic segmentation",
"source_url": "http://arxiv.org/abs/1311.2524v5"
},
{
"code_snippet_url": "https://github.com/chenyuntc/simple-faster-rcnn-pytorch/blob/367db367834efd8a2bc58ee0023b2b628a0e474d/model/faster_rcnn.py#L22",
"description": "**Faster R-CNN** is an object detection model that improves on [Fast R-CNN](https://paperswithcode.com/method/fast-r-cnn) by utilising a region proposal network ([RPN](https://paperswithcode.com/method/rpn)) with the CNN model. The RPN shares full-image convolutional features with the detection network, enabling nearly cost-free region proposals. It is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by [Fast R-CNN](https://paperswithcode.com/method/fast-r-cnn) for detection. RPN and Fast [R-CNN](https://paperswithcode.com/method/r-cnn) are merged into a single network by sharing their convolutional features: the RPN component tells the unified network where to look.\r\n\r\nAs a whole, Faster R-CNN consists of two modules. The first module is a deep fully convolutional network that proposes regions, and the second module is the Fast R-CNN detector that uses the proposed regions.",
"full_name": "Faster R-CNN",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Object Detection Models** are architectures used to perform the task of object detection. Below you can find a continuously updating list of object detection models.",
"name": "Object Detection Models",
"parent": null
},
"name": "Faster R-CNN",
"source_title": "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks",
"source_url": "http://arxiv.org/abs/1506.01497v3"
}
] |
https://paperswithcode.com/paper/scalable-multi-class-bayesian-support-vector
|
1806.02659
| null | null |
Scalable Multi-Class Bayesian Support Vector Machines for Structured and Unstructured Data
|
We introduce a new Bayesian multi-class support vector machine by formulating
a pseudo-likelihood for a multi-class hinge loss in the form of a
location-scale mixture of Gaussians. We derive a variational-inference-based
training objective for gradient-based learning. Additionally, we employ an
inducing point approximation which scales inference to large data sets.
Furthermore, we develop hybrid Bayesian neural networks that combine standard
deep learning components with the proposed model to enable learning for
unstructured data. We provide empirical evidence that our model outperforms the
competitor methods with respect to both training time and accuracy in
classification experiments on 68 structured and two unstructured data sets.
Finally, we highlight the key capability of our model in yielding prediction
uncertainty for classification by demonstrating its effectiveness in the tasks
of large-scale active learning and detection of adversarial images.
| null |
http://arxiv.org/abs/1806.02659v1
|
http://arxiv.org/pdf/1806.02659v1.pdf
| null |
[
"Martin Wistuba",
"Ambrish Rawat"
] |
[
"Active Learning",
"General Classification",
"Variational Inference"
] | 2018-06-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/super-resolution-using-convolutional-neural
|
1806.02658
| null | null |
Super-Resolution using Convolutional Neural Networks without Any Checkerboard Artifacts
|
It is well-known that a number of excellent super-resolution (SR) methods
using convolutional neural networks (CNNs) generate checkerboard artifacts. A
condition to avoid the checkerboard artifacts is proposed in this paper. So
far, checkerboard artifacts have been mainly studied for linear multirate
systems, but the condition to avoid checkerboard artifacts can not be applied
to CNNs due to the non-linearity of CNNs. We extend the avoiding condition for
CNNs, and apply the proposed structure to some typical SR methods to confirm
the effectiveness of the new scheme. Experiment results demonstrate that the
proposed structure can perfectly avoid to generate checkerboard artifacts under
two loss conditions: mean square error and perceptual loss, while keeping
excellent properties that the SR methods have.
|
It is well-known that a number of excellent super-resolution (SR) methods using convolutional neural networks (CNNs) generate checkerboard artifacts.
|
http://arxiv.org/abs/1806.02658v1
|
http://arxiv.org/pdf/1806.02658v1.pdf
| null |
[
"Yusuke Sugawara",
"Sayaka Shiota",
"Hitoshi Kiya"
] |
[
"Super-Resolution"
] | 2018-06-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/improving-the-gaussian-mechanism-for
|
1805.06530
| null | null |
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising
|
The Gaussian mechanism is an essential building block used in multitude of
differentially private data analysis algorithms. In this paper we revisit the
Gaussian mechanism and show that the original analysis has several important
limitations. Our analysis reveals that the variance formula for the original
mechanism is far from tight in the high privacy regime ($\varepsilon \to 0$)
and it cannot be extended to the low privacy regime ($\varepsilon \to \infty$).
We address these limitations by developing an optimal Gaussian mechanism whose
variance is calibrated directly using the Gaussian cumulative density function
instead of a tail bound approximation. We also propose to equip the Gaussian
mechanism with a post-processing step based on adaptive estimation techniques
by leveraging that the distribution of the perturbation is known. Our
experiments show that analytical calibration removes at least a third of the
variance of the noise compared to the classical Gaussian mechanism, and that
denoising dramatically improves the accuracy of the Gaussian mechanism in the
high-dimensional regime.
|
The Gaussian mechanism is an essential building block used in multitude of differentially private data analysis algorithms.
|
http://arxiv.org/abs/1805.06530v2
|
http://arxiv.org/pdf/1805.06530v2.pdf
|
ICML 2018 7
|
[
"Borja Balle",
"Yu-Xiang Wang"
] |
[
"Denoising"
] | 2018-05-16T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2245
|
http://proceedings.mlr.press/v80/balle18a/balle18a.pdf
|
improving-the-gaussian-mechanism-for-1
| null |
[] |
https://paperswithcode.com/paper/augment-and-reduce-stochastic-inference-for-1
|
1802.04220
| null | null |
Augment and Reduce: Stochastic Inference for Large Categorical Distributions
|
Categorical distributions are ubiquitous in machine learning, e.g., in
classification, language models, and recommendation systems. However, when the
number of possible outcomes is very large, using categorical distributions
becomes computationally expensive, as the complexity scales linearly with the
number of outcomes. To address this problem, we propose augment and reduce
(A&R), a method to alleviate the computational complexity. A&R uses two ideas:
latent variable augmentation and stochastic variational inference. It maximizes
a lower bound on the marginal likelihood of the data. Unlike existing methods
which are specific to softmax, A&R is more general and is amenable to other
categorical models, such as multinomial probit. On several large-scale
classification problems, we show that A&R provides a tighter bound on the
marginal likelihood and has better predictive performance than existing
approaches.
|
It maximizes a lower bound on the marginal likelihood of the data.
|
http://arxiv.org/abs/1802.04220v3
|
http://arxiv.org/pdf/1802.04220v3.pdf
|
ICML 2018
|
[
"Francisco J. R. Ruiz",
"Michalis K. Titsias",
"Adji B. Dieng",
"David M. Blei"
] |
[
"General Classification",
"Recommendation Systems",
"Variational Inference"
] | 2018-02-12T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/re-evaluating-evaluation
|
1806.02643
| null | null |
Re-evaluating Evaluation
|
Progress in machine learning is measured by careful evaluation on problems of
outstanding common interest. However, the proliferation of benchmark suites and
environments, adversarial attacks, and other complications has diluted the
basic evaluation model by overwhelming researchers with choices. Deliberate or
accidental cherry picking is increasingly likely, and designing well-balanced
evaluation suites requires increasing effort. In this paper we take a step back
and propose Nash averaging. The approach builds on a detailed analysis of the
algebraic structure of evaluation in two basic scenarios: agent-vs-agent and
agent-vs-task. The key strength of Nash averaging is that it automatically
adapts to redundancies in evaluation data, so that results are not biased by
the incorporation of easy tasks or weak agents. Nash averaging thus encourages
maximally inclusive evaluation -- since there is no harm (computational cost
aside) from including all available tasks and agents.
|
Progress in machine learning is measured by careful evaluation on problems of outstanding common interest.
|
http://arxiv.org/abs/1806.02643v2
|
http://arxiv.org/pdf/1806.02643v2.pdf
|
NeurIPS 2018 12
|
[
"David Balduzzi",
"Karl Tuyls",
"Julien Perolat",
"Thore Graepel"
] |
[] | 2018-06-07T00:00:00 |
http://papers.nips.cc/paper/7588-re-evaluating-evaluation
|
http://papers.nips.cc/paper/7588-re-evaluating-evaluation.pdf
|
re-evaluating-evaluation-1
| null |
[] |
https://paperswithcode.com/paper/visual-place-recognition-with-probabilistic
|
1610.03548
| null | null |
Visual Place Recognition with Probabilistic Vertex Voting
|
We propose a novel scoring concept for visual place recognition based on
nearest neighbor descriptor voting and demonstrate how the algorithm naturally
emerges from the problem formulation. Based on the observation that the number
of votes for matching places can be evaluated using a binomial distribution
model, loop closures can be detected with high precision. By casting the
problem into a probabilistic framework, we not only remove the need for
commonly employed heuristic parameters but also provide a powerful score to
classify matching and non-matching places. We present methods for both a 2D-2D
pose-graph vertex matching and a 2D-3D landmark matching based on the above
scoring. The approach maintains accuracy while being efficient enough for
online application through the use of compact (low dimensional) descriptors and
fast nearest neighbor retrieval techniques. The proposed methods are evaluated
on several challenging datasets in varied environments, showing
state-of-the-art results with high precision and high recall.
| null |
http://arxiv.org/abs/1610.03548v2
|
http://arxiv.org/pdf/1610.03548v2.pdf
| null |
[
"Mathias Gehrig",
"Elena Stumm",
"Timo Hinzmann",
"Roland Siegwart"
] |
[
"Retrieval",
"Visual Place Recognition"
] | 2016-10-11T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/effects-of-word-embeddings-on-neural-network
|
1805.05237
| null | null |
Effects of Word Embeddings on Neural Network-based Pitch Accent Detection
|
Pitch accent detection often makes use of both acoustic and lexical features
based on the fact that pitch accents tend to correlate with certain words. In
this paper, we extend a pitch accent detector that involves a convolutional
neural network to include word embeddings, which are state-of-the-art vector
representations of words. We examine the effect these features have on
within-corpus and cross-corpus experiments on three English datasets. The
results show that while word embeddings can improve the performance in
corpus-dependent experiments, they also have the potential to make
generalization to unseen data more challenging.
| null |
http://arxiv.org/abs/1805.05237v2
|
http://arxiv.org/pdf/1805.05237v2.pdf
| null |
[
"Sabrina Stehwien",
"Ngoc Thang Vu",
"Antje Schweitzer"
] |
[
"Cross-corpus",
"Word Embeddings"
] | 2018-05-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/path-level-network-transformation-for
|
1806.02639
| null | null |
Path-Level Network Transformation for Efficient Architecture Search
|
We introduce a new function-preserving transformation for efficient neural
architecture search. This network transformation allows reusing previously
trained networks and existing successful architectures that improves sample
efficiency. We aim to address the limitation of current network transformation
operations that can only perform layer-level architecture modifications, such
as adding (pruning) filters or inserting (removing) a layer, which fails to
change the topology of connection paths. Our proposed path-level transformation
operations enable the meta-controller to modify the path topology of the given
network while keeping the merits of reusing weights, and thus allow efficiently
designing effective structures with complex path topologies like Inception
models. We further propose a bidirectional tree-structured reinforcement
learning meta-controller to explore a simple yet highly expressive
tree-structured architecture space that can be viewed as a generalization of
multi-branch architectures. We experimented on the image classification
datasets with limited computational resources (about 200 GPU-hours), where we
observed improved parameter efficiency and better test results (97.70% test
accuracy on CIFAR-10 with 14.3M parameters and 74.6% top-1 accuracy on ImageNet
in the mobile setting), demonstrating the effectiveness and transferability of
our designed architectures.
|
We introduce a new function-preserving transformation for efficient neural architecture search.
|
http://arxiv.org/abs/1806.02639v1
|
http://arxiv.org/pdf/1806.02639v1.pdf
|
ICML 2018 7
|
[
"Han Cai",
"Jiacheng Yang",
"Wei-Nan Zhang",
"Song Han",
"Yong Yu"
] |
[
"GPU",
"image-classification",
"Image Classification",
"Neural Architecture Search",
"Reinforcement Learning"
] | 2018-06-07T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2208
|
http://proceedings.mlr.press/v80/cai18a/cai18a.pdf
|
path-level-network-transformation-for-1
| null |
[] |
https://paperswithcode.com/paper/multi-objective-analysis-of-map-elites
|
1803.05174
| null | null |
Multi-objective Analysis of MAP-Elites Performance
|
In certain complex optimization tasks, it becomes necessary to use multiple
measures to characterize the performance of different algorithms. This paper
presents a method that combines ordinal effect sizes with Pareto dominance to
analyze such cases. Since the method is ordinal, it can also generalize across
different optimization tasks even when the performance measurements are
differently scaled. Through a case study, we show that this method can discover
and quantify relations that would be difficult to deduce using a conventional
measure-by-measure analysis. This case study applies the method to the
evolution of robot controller repertoires using the MAP-Elites algorithm. Here,
we analyze the search performance across a large set of parametrizations;
varying mutation size and operator type, as well as map resolution, across four
different robot morphologies. We show that the average magnitude of mutations
has a bigger effect on outcomes than their precise distributions.
| null |
http://arxiv.org/abs/1803.05174v2
|
http://arxiv.org/pdf/1803.05174v2.pdf
| null |
[
"Eivind Samuelsen",
"Kyrre Glette"
] |
[] | 2018-03-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/reducing-deep-network-complexity-with-fourier
|
1801.01451
| null | null |
Reducing Deep Network Complexity via Sparse Hierarchical Fourier Interaction Networks
|
This paper presents a Sparse Hierarchical Fourier Interaction Networks, an architectural building block that unifies three complementary principles of frequency domain modeling: A hierarchical patch wise Fourier transform that affords simultaneous access to local detail and global context; A learnable, differentiable top K masking mechanism which retains only the most informative spectral coefficients, thereby exploiting the natural compressibility of visual and linguistic signals.
| null |
https://arxiv.org/abs/1801.01451v3
|
https://arxiv.org/pdf/1801.01451v3.pdf
| null |
[
"Andrew Kiruluta",
"Samantha Williams"
] |
[
"General Classification"
] | 2017-12-15T00:00:00 | null | null | null | 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/layer-wise-learning-of-stochastic-neural
|
1712.01272
| null | null |
Layer-wise Learning of Stochastic Neural Networks with Information Bottleneck
|
Information Bottleneck (IB) is a generalization of rate-distortion theory that naturally incorporates compression and relevance trade-offs for learning. Though the original IB has been extensively studied, there has not been much understanding of multiple bottlenecks which better fit in the context of neural networks. In this work, we propose Information Multi-Bottlenecks (IMBs) as an extension of IB to multiple bottlenecks which has a direct application to training neural networks by considering layers as multiple bottlenecks and weights as parameterized encoders and decoders. We show that the multiple optimality of IMB is not simultaneously achievable for stochastic encoders. We thus propose a simple compromised scheme of IMB which in turn generalizes maximum likelihood estimate (MLE) principle in the context of stochastic neural networks. We demonstrate the effectiveness of IMB on classification tasks and adversarial robustness in MNIST and CIFAR10.
| null |
https://arxiv.org/abs/1712.01272v6
|
https://arxiv.org/pdf/1712.01272v6.pdf
| null |
[
"Thanh T. Nguyen",
"Jaesik Choi"
] |
[
"Adversarial Robustness"
] | 2017-12-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/recommendations-with-negative-feedback-via
|
1802.06501
| null | null |
Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning
|
Recommender systems play a crucial role in mitigating the problem of
information overload by suggesting users' personalized items or services. The
vast majority of traditional recommender systems consider the recommendation
procedure as a static process and make recommendations following a fixed
strategy. In this paper, we propose a novel recommender system with the
capability of continuously improving its strategies during the interactions
with users. We model the sequential interactions between users and a
recommender system as a Markov Decision Process (MDP) and leverage
Reinforcement Learning (RL) to automatically learn the optimal strategies via
recommending trial-and-error items and receiving reinforcements of these items
from users' feedback. Users' feedback can be positive and negative and both
types of feedback have great potentials to boost recommendations. However, the
number of negative feedback is much larger than that of positive one; thus
incorporating them simultaneously is challenging since positive feedback could
be buried by negative one. In this paper, we develop a novel approach to
incorporate them into the proposed deep recommender system (DEERS) framework.
The experimental results based on real-world e-commerce data demonstrate the
effectiveness of the proposed framework. Further experiments have been
conducted to understand the importance of both positive and negative feedback
in recommendations.
|
Users' feedback can be positive and negative and both types of feedback have great potentials to boost recommendations.
|
http://arxiv.org/abs/1802.06501v3
|
http://arxiv.org/pdf/1802.06501v3.pdf
| null |
[
"Xiangyu Zhao",
"Liang Zhang",
"Zhuoye Ding",
"Long Xia",
"Jiliang Tang",
"Dawei Yin"
] |
[
"Deep Reinforcement Learning",
"Recommendation Systems",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-02-19T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/challenges-in-monocular-visual-odometry
|
1705.04300
| null | null |
Challenges in Monocular Visual Odometry: Photometric Calibration, Motion Bias and Rolling Shutter Effect
|
Monocular visual odometry (VO) and simultaneous localization and mapping
(SLAM) have seen tremendous improvements in accuracy, robustness and
efficiency, and have gained increasing popularity over recent years.
Nevertheless, not so many discussions have been carried out to reveal the
influences of three very influential yet easily overlooked aspects: photometric
calibration, motion bias and rolling shutter effect. In this work, we evaluate
these three aspects quantitatively on the state of the art of direct,
feature-based and semi-direct methods, providing the community with useful
practical knowledge both for better applying existing methods and developing
new algorithms of VO and SLAM. Conclusions (some of which are
counter-intuitive) are drawn with both technical and empirical analyses to all
of our experiments. Possible improvements on existing methods are directed or
proposed, such as a sub-pixel accuracy refinement of ORB-SLAM which boosts its
performance.
| null |
http://arxiv.org/abs/1705.04300v4
|
http://arxiv.org/pdf/1705.04300v4.pdf
| null |
[
"Nan Yang",
"Rui Wang",
"Xiang Gao",
"Daniel Cremers"
] |
[
"Monocular Visual Odometry",
"Simultaneous Localization and Mapping",
"Visual Odometry"
] | 2017-05-11T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/upper-bounds-on-the-runtime-of-the-univariate
|
1704.00026
| null | null |
Upper Bounds on the Runtime of the Univariate Marginal Distribution Algorithm on OneMax
|
A runtime analysis of the Univariate Marginal Distribution Algorithm (UMDA)
is presented on the OneMax function for wide ranges of its parameters $\mu$ and
$\lambda$. If $\mu\ge c\log n$ for some constant $c>0$ and
$\lambda=(1+\Theta(1))\mu$, a general bound $O(\mu n)$ on the expected runtime
is obtained. This bound crucially assumes that all marginal probabilities of
the algorithm are confined to the interval $[1/n,1-1/n]$. If $\mu\ge c'
\sqrt{n}\log n$ for a constant $c'>0$ and $\lambda=(1+\Theta(1))\mu$, the
behavior of the algorithm changes and the bound on the expected runtime becomes
$O(\mu\sqrt{n})$, which typically even holds if the borders on the marginal
probabilities are omitted.
The results supplement the recently derived lower bound
$\Omega(\mu\sqrt{n}+n\log n)$ by Krejca and Witt (FOGA 2017) and turn out as
tight for the two very different values $\mu=c\log n$ and $\mu=c'\sqrt{n}\log
n$. They also improve the previously best known upper bound $O(n\log n\log\log
n)$ by Dang and Lehre (GECCO 2015).
| null |
http://arxiv.org/abs/1704.00026v4
|
http://arxiv.org/pdf/1704.00026v4.pdf
| null |
[
"Carsten Witt"
] |
[] | 2017-03-31T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/an-evaluation-of-trajectory-prediction
|
1805.07663
| null | null |
An Evaluation of Trajectory Prediction Approaches and Notes on the TrajNet Benchmark
|
In recent years, there is a shift from modeling the tracking problem based on
Bayesian formulation towards using deep neural networks. Towards this end, in
this paper the effectiveness of various deep neural networks for predicting
future pedestrian paths are evaluated. The analyzed deep networks solely rely,
like in the traditional approaches, on observed tracklets without human-human
interaction information. The evaluation is done on the publicly available
TrajNet benchmark dataset, which builds up a repository of considerable and
popular datasets for trajectory-based activity forecasting. We show that a
Recurrent-Encoder with a Dense layer stacked on top, referred to as
RED-predictor, is able to achieve sophisticated results compared to elaborated
models in such scenarios. Further, we investigate failure cases and give
explanations for observed phenomena and give some recommendations for
overcoming demonstrated shortcomings.
| null |
http://arxiv.org/abs/1805.07663v6
|
http://arxiv.org/pdf/1805.07663v6.pdf
| null |
[
"Stefan Becker",
"Ronny Hug",
"Wolfgang Hübner",
"Michael Arens"
] |
[
"Trajectory Prediction"
] | 2018-05-19T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/asynchronous-stochastic-quasi-newton-mcmc-for
|
1806.02617
| null | null |
Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization
|
Recent studies have illustrated that stochastic gradient Markov Chain Monte
Carlo techniques have a strong potential in non-convex optimization, where
local and global convergence guarantees can be shown under certain conditions.
By building up on this recent theory, in this study, we develop an
asynchronous-parallel stochastic L-BFGS algorithm for non-convex optimization.
The proposed algorithm is suitable for both distributed and shared-memory
settings. We provide formal theoretical analysis and show that the proposed
method achieves an ergodic convergence rate of ${\cal O}(1/\sqrt{N})$ ($N$
being the total number of iterations) and it can achieve a linear speedup under
certain conditions. We perform several experiments on both synthetic and real
datasets. The results support our theory and show that the proposed algorithm
provides a significant speedup over the recently proposed synchronous
distributed L-BFGS algorithm.
| null |
http://arxiv.org/abs/1806.02617v1
|
http://arxiv.org/pdf/1806.02617v1.pdf
|
ICML 2018
|
[
"Umut Şimşekli",
"Çağatay Yıldız",
"Thanh Huy Nguyen",
"Gaël Richard",
"A. Taylan Cemgil"
] |
[] | 2018-06-07T00:00:00 | null | null | null | null |
[] |
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