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https://paperswithcode.com/paper/age-and-gender-classification-from-ear-images
|
1806.05742
| null | null |
Age and Gender Classification From Ear Images
|
In this paper, we present a detailed analysis on extracting soft biometric
traits, age and gender, from ear images. Although there have been a few
previous work on gender classification using ear images, to the best of our
knowledge, this study is the first work on age classification from ear images.
In the study, we have utilized both geometric features and appearance-based
features for ear representation. The utilized geometric features are based on
eight anthropometric landmarks and consist of 14 distance measurements and two
area calculations. The appearance-based methods employ deep convolutional
neural networks for representation and classification. The well-known
convolutional neural network models, namely, AlexNet, VGG-16, GoogLeNet, and
SqueezeNet have been adopted for the study. They have been fine-tuned on a
large-scale ear dataset that has been built from the profile and
close-to-profile face images in the Multi-PIE face dataset. This way, we have
performed a domain adaptation. The updated models have been fine-tuned once
more time on the small-scale target ear dataset, which contains only around 270
ear images for training. According to the experimental results,
appearance-based methods have been found to be superior to the methods based on
geometric features. We have achieved 94\% accuracy for gender classification,
whereas 52\% accuracy has been obtained for age classification. These results
indicate that ear images provide useful cues for age and gender classification,
however, further work is required for age estimation.
| null |
http://arxiv.org/abs/1806.05742v1
|
http://arxiv.org/pdf/1806.05742v1.pdf
| null |
[
"Dogucan Yaman",
"Fevziye Irem Eyiokur",
"Nurdan Sezgin",
"Hazim Kemal Ekenel"
] |
[
"Age And Gender Classification",
"Age Classification",
"Age Estimation",
"Classification",
"Domain Adaptation",
"Gender Classification",
"General Classification"
] | 2018-06-14T00:00:00 | null | null | null | 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": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "**Average Pooling** is a pooling operation that calculates the average value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. It adds a small amount of translation invariance - meaning translating the image by a small amount does not significantly affect the values of most pooled outputs. It extracts features more smoothly than [Max Pooling](https://paperswithcode.com/method/max-pooling), whereas max pooling extracts more pronounced features like edges.\r\n\r\nImage Source: [here](https://www.researchgate.net/figure/Illustration-of-Max-Pooling-and-Average-Pooling-Figure-2-above-shows-an-example-of-max_fig2_333593451)",
"full_name": "Average Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Average Pooling",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/1c5c289b6218eb1026dcb5fd9738231401cfccea/torch/nn/modules/normalization.py#L13",
"description": "**Local Response Normalization** is a normalization layer that implements the idea of lateral inhibition. Lateral inhibition is a concept in neurobiology that refers to the phenomenon of an excited neuron inhibiting its neighbours: this leads to a peak in the form of a local maximum, creating contrast in that area and increasing sensory perception. In practice, we can either normalize within the same channel or normalize across channels when we apply LRN to convolutional neural networks.\r\n\r\n$$ b_{c} = a_{c}\\left(k + \\frac{\\alpha}{n}\\sum_{c'=\\max(0, c-n/2)}^{\\min(N-1,c+n/2)}a_{c'}^2\\right)^{-\\beta} $$\r\n\r\nWhere the size is the number of neighbouring channels used for normalization, $\\alpha$ is multiplicative factor, $\\beta$ an exponent and $k$ an additive factor",
"full_name": "Local Response Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Local Response Normalization",
"source_title": "ImageNet Classification with Deep Convolutional Neural Networks",
"source_url": "http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks"
},
{
"code_snippet_url": "",
"description": "**Auxiliary Classifiers** are type of architectural component that seek to improve the convergence of very deep networks. They are classifier heads we attach to layers before the end of the network. The motivation is to push useful gradients to the lower layers to make them immediately useful and improve the convergence during training by combatting the vanishing gradient problem. They are notably used in the Inception family of convolutional neural networks.",
"full_name": "Auxiliary Classifier",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "The following is a list of miscellaneous components used in neural networks.",
"name": "Miscellaneous Components",
"parent": null
},
"name": "Auxiliary Classifier",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/hskang9/Googlenet/blob/654d126b6a2cd3ac944cf5613419deb73da5311e/keras/googlenet.py#L39",
"description": "An **Inception Module** is an image model block that aims to approximate an optimal local sparse structure in a CNN. Put simply, it allows for us to use multiple types of filter size, instead of being restricted to a single filter size, in a single image block, which we then concatenate and pass onto the next layer.",
"full_name": "Inception Module",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Image Model Blocks** are building blocks used in image models such as convolutional neural networks. Below you can find a continuously updating list of image model blocks.",
"name": "Image Model Blocks",
"parent": null
},
"name": "Inception Module",
"source_title": "Going Deeper with Convolutions",
"source_url": "http://arxiv.org/abs/1409.4842v1"
},
{
"code_snippet_url": "https://github.com/prlz77/ResNeXt.pytorch/blob/39fb8d03847f26ec02fb9b880ecaaa88db7a7d16/models/model.py#L42",
"description": "A **Grouped Convolution** uses a group of convolutions - multiple kernels per layer - resulting in multiple channel outputs per layer. This leads to wider networks helping a network learn a varied set of low level and high level features. The original motivation of using Grouped Convolutions in [AlexNet](https://paperswithcode.com/method/alexnet) was to distribute the model over multiple GPUs as an engineering compromise. But later, with models such as [ResNeXt](https://paperswithcode.com/method/resnext), it was shown this module could be used to improve classification accuracy. Specifically by exposing a new dimension through grouped convolutions, *cardinality* (the size of set of transformations), we can increase accuracy by increasing it.",
"full_name": "Grouped Convolution",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Grouped Convolution",
"source_title": "ImageNet Classification with Deep Convolutional Neural Networks",
"source_url": "http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks"
},
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!\r\n\r\n\r\nHow Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!",
"full_name": "*Communicated@Fast*How Do I Communicate to Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Max Pooling** is a pooling operation that calculates the maximum value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. It adds a small amount of translation invariance - meaning translating the image by a small amount does not significantly affect the values of most pooled outputs.\r\n\r\nImage Source: [here](https://computersciencewiki.org/index.php/File:MaxpoolSample2.png)",
"full_name": "Max Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Max Pooling",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/dansuh17/alexnet-pytorch/blob/d0c1b1c52296ffcbecfbf5b17e1d1685b4ca6744/model.py#L40",
"description": "To make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.ggfdf\r\n\r\n\r\nHow do I speak to a person at Expedia?How do I speak to a person at Expedia?To make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.To make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.\r\n\r\n\r\n\r\nTo make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.To make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.To make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.chgd",
"full_name": "How do I speak to a person at Expedia?-/+/",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "If you have questions or want to make special travel arrangements, you can make them online or call ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. For hearing or speech impaired assistance dial 711 to be connected through the National Relay Service.",
"name": "Convolutional Neural Networks",
"parent": "Image Models"
},
"name": "How do I speak to a person at Expedia?-/+/",
"source_title": "ImageNet Classification with Deep Convolutional Neural Networks",
"source_url": "http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/6db1569c89094cf23f3bc41f79275c45e9fcb3f3/torchvision/models/googlenet.py#L62",
"description": "**GoogLeNet** is a type of convolutional neural network based on the [Inception](https://paperswithcode.com/method/inception-module) architecture. It utilises Inception modules, which allow the network to choose between multiple convolutional filter sizes in each block. An Inception network stacks these modules on top of each other, with occasional max-pooling layers with stride 2 to halve the resolution of the grid.",
"full_name": "GoogLeNet",
"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": "GoogLeNet",
"source_title": "Going Deeper with Convolutions",
"source_url": "http://arxiv.org/abs/1409.4842v1"
}
] |
https://paperswithcode.com/paper/psyphy-a-psychophysics-driven-evaluation
|
1611.06448
| null | null |
PsyPhy: A Psychophysics Driven Evaluation Framework for Visual Recognition
|
By providing substantial amounts of data and standardized evaluation
protocols, datasets in computer vision have helped fuel advances across all
areas of visual recognition. But even in light of breakthrough results on
recent benchmarks, it is still fair to ask if our recognition algorithms are
doing as well as we think they are. The vision sciences at large make use of a
very different evaluation regime known as Visual Psychophysics to study visual
perception. Psychophysics is the quantitative examination of the relationships
between controlled stimuli and the behavioral responses they elicit in
experimental test subjects. Instead of using summary statistics to gauge
performance, psychophysics directs us to construct item-response curves made up
of individual stimulus responses to find perceptual thresholds, thus allowing
one to identify the exact point at which a subject can no longer reliably
recognize the stimulus class. In this article, we introduce a comprehensive
evaluation framework for visual recognition models that is underpinned by this
methodology. Over millions of procedurally rendered 3D scenes and 2D images, we
compare the performance of well-known convolutional neural networks. Our
results bring into question recent claims of human-like performance, and
provide a path forward for correcting newly surfaced algorithmic deficiencies.
| null |
http://arxiv.org/abs/1611.06448v6
|
http://arxiv.org/pdf/1611.06448v6.pdf
| null |
[
"Brandon RichardWebster",
"Samuel E. Anthony",
"Walter J. Scheirer"
] |
[] | 2016-11-19T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/using-search-queries-to-understand-health
|
1806.05740
| null | null |
Using Search Queries to Understand Health Information Needs in Africa
|
The lack of comprehensive, high-quality health data in developing nations
creates a roadblock for combating the impacts of disease. One key challenge is
understanding the health information needs of people in these nations. Without
understanding people's everyday needs, concerns, and misconceptions, health
organizations and policymakers lack the ability to effectively target education
and programming efforts. In this paper, we propose a bottom-up approach that
uses search data from individuals to uncover and gain insight into health
information needs in Africa. We analyze Bing searches related to HIV/AIDS,
malaria, and tuberculosis from all 54 African nations. For each disease, we
automatically derive a set of common search themes or topics, revealing a
wide-spread interest in various types of information, including disease
symptoms, drugs, concerns about breastfeeding, as well as stigma, beliefs in
natural cures, and other topics that may be hard to uncover through traditional
surveys. We expose the different patterns that emerge in health information
needs by demographic groups (age and sex) and country. We also uncover
discrepancies in the quality of content returned by search engines to users by
topic. Combined, our results suggest that search data can help illuminate
health information needs in Africa and inform discussions on health policy and
targeted education efforts both on- and offline.
| null |
http://arxiv.org/abs/1806.05740v2
|
http://arxiv.org/pdf/1806.05740v2.pdf
| null |
[
"Rediet Abebe",
"Shawndra Hill",
"Jennifer Wortman Vaughan",
"Peter M. Small",
"H. Andrew Schwartz"
] |
[
"Misconceptions"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-sauer-shelah-perles-lemma-for-sumsets
|
1806.05737
| null | null |
A Sauer-Shelah-Perles Lemma for Sumsets
|
We show that any family of subsets $A\subseteq 2^{[n]}$ satisfies $\lvert
A\rvert \leq O\bigl(n^{\lceil{d}/{2}\rceil}\bigr)$, where $d$ is the VC
dimension of $\{S\triangle T \,\vert\, S,T\in A\}$, and $\triangle$ is the
symmetric difference operator. We also observe that replacing $\triangle$ by
either $\cup$ or $\cap$ fails to satisfy an analogous statement. Our proof is
based on the polynomial method; specifically, on an argument due to [Croot,
Lev, Pach '17].
| null |
http://arxiv.org/abs/1806.05737v2
|
http://arxiv.org/pdf/1806.05737v2.pdf
| null |
[
"Zeev Dvir",
"Shay Moran"
] |
[
"LEMMA"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/efficient-sampling-for-gaussian-linear
|
1806.05738
| null | null |
Efficient sampling for Gaussian linear regression with arbitrary priors
|
This paper develops a slice sampler for Bayesian linear regression models
with arbitrary priors. The new sampler has two advantages over current
approaches. One, it is faster than many custom implementations that rely on
auxiliary latent variables, if the number of regressors is large. Two, it can
be used with any prior with a density function that can be evaluated up to a
normalizing constant, making it ideal for investigating the properties of new
shrinkage priors without having to develop custom sampling algorithms. The new
sampler takes advantage of the special structure of the linear regression
likelihood, allowing it to produce better effective sample size per second than
common alternative approaches.
| null |
http://arxiv.org/abs/1806.05738v1
|
http://arxiv.org/pdf/1806.05738v1.pdf
| null |
[
"P. Richard Hahn",
"Jingyu He",
"Hedibert Lopes"
] |
[
"regression"
] | 2018-06-14T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Linear Regression** is a method for modelling a relationship between a dependent variable and independent variables. These models can be fit with numerous approaches. The most common is *least squares*, where we minimize the mean square error between the predicted values $\\hat{y} = \\textbf{X}\\hat{\\beta}$ and actual values $y$: $\\left(y-\\textbf{X}\\beta\\right)^{2}$.\r\n\r\nWe can also define the problem in probabilistic terms as a generalized linear model (GLM) where the pdf is a Gaussian distribution, and then perform maximum likelihood estimation to estimate $\\hat{\\beta}$.\r\n\r\nImage Source: [Wikipedia](https://en.wikipedia.org/wiki/Linear_regression)",
"full_name": "Linear Regression",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Generalized Linear Models (GLMs)** are a class of models that generalize upon linear regression by allowing many more distributions to be modeled for the response variable via a link function. Below you can find a continuously updating list of GLMs.",
"name": "Generalized Linear Models",
"parent": null
},
"name": "Linear Regression",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/learning-influence-receptivity-network
|
1806.05730
| null | null |
Learning Influence-Receptivity Network Structure with Guarantee
|
Traditional works on community detection from observations of information
cascade assume that a single adjacency matrix parametrizes all the observed
cascades. However, in reality the connection structure usually does not stay
the same across cascades. For example, different people have different topics
of interest, therefore the connection structure depends on the
information/topic content of the cascade. In this paper we consider the case
where we observe a sequence of noisy adjacency matrices triggered by
information/event with different topic distributions. We propose a novel latent
model using the intuition that a connection is more likely to exist between two
nodes if they are interested in similar topics, which are common with the
information/event. Specifically, we endow each node with two node-topic
vectors: an influence vector that measures how influential/authoritative they
are on each topic; and a receptivity vector that measures how
receptive/susceptible they are to each topic. We show how these two node-topic
structures can be estimated from observed adjacency matrices with theoretical
guarantee on estimation error, in cases where the topic distributions of the
information/event are known, as well as when they are unknown. Experiments on
synthetic and real data demonstrate the effectiveness of our model and superior
performance compared to state-of-the-art methods.
| null |
http://arxiv.org/abs/1806.05730v2
|
http://arxiv.org/pdf/1806.05730v2.pdf
| null |
[
"Ming Yu",
"Varun Gupta",
"Mladen Kolar"
] |
[
"Community Detection"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/action-learning-for-3d-point-cloud-based
|
1806.05724
| null | null |
Action Learning for 3D Point Cloud Based Organ Segmentation
|
We propose a novel point cloud based 3D organ segmentation pipeline utilizing
deep Q-learning. In order to preserve shape properties, the learning process is
guided using a statistical shape model. The trained agent directly predicts
piece-wise linear transformations for all vertices in each iteration. This
mapping between the ideal transformation for an object outline estimation is
learned based on image features. To this end, we introduce aperture features
that extract gray values by sampling the 3D volume within the cone centered
around the associated vertex and its normal vector. Our approach is also
capable of estimating a hierarchical pyramid of non rigid deformations for
multi-resolution meshes. In the application phase, we use a marginal approach
to gradually estimate affine as well as non-rigid transformations. We performed
extensive evaluations to highlight the robust performance of our approach on a
variety of challenge data as well as clinical data. Additionally, our method
has a run time ranging from 0.3 to 2.7 seconds to segment each organ. In
addition, we show that the proposed method can be applied to different organs,
X-ray based modalities, and scanning protocols without the need of transfer
learning. As we learn actions, even unseen reference meshes can be processed as
demonstrated in an example with the Visible Human. From this we conclude that
our method is robust, and we believe that our method can be successfully
applied to many more applications, in particular, in the interventional imaging
space.
| null |
http://arxiv.org/abs/1806.05724v1
|
http://arxiv.org/pdf/1806.05724v1.pdf
| null |
[
"Xia Zhong",
"Mario Amrehn",
"Nishant Ravikumar",
"Shuqing Chen",
"Norbert Strobel",
"Annette Birkhold",
"Markus Kowarschik",
"Rebecca Fahrig",
"Andreas Maier"
] |
[
"Organ Segmentation",
"Q-Learning",
"Transfer Learning"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/differentiable-submodular-maximization
|
1803.01785
| null | null |
Differentiable Submodular Maximization
|
We consider learning of submodular functions from data. These functions are
important in machine learning and have a wide range of applications, e.g. data
summarization, feature selection and active learning. Despite their
combinatorial nature, submodular functions can be maximized approximately with
strong theoretical guarantees in polynomial time. Typically, learning the
submodular function and optimization of that function are treated separately,
i.e. the function is first learned using a proxy objective and subsequently
maximized. In contrast, we show how to perform learning and optimization
jointly. By interpreting the output of greedy maximization algorithms as
distributions over sequences of items and smoothening these distributions, we
obtain a differentiable objective. In this way, we can differentiate through
the maximization algorithms and optimize the model to work well with the
optimization algorithm. We theoretically characterize the error made by our
approach, yielding insights into the tradeoff of smoothness and accuracy. We
demonstrate the effectiveness of our approach for jointly learning and
optimizing on synthetic maximum cut data, and on real world applications such
as product recommendation and image collection summarization.
| null |
http://arxiv.org/abs/1803.01785v2
|
http://arxiv.org/pdf/1803.01785v2.pdf
| null |
[
"Sebastian Tschiatschek",
"Aytunc Sahin",
"Andreas Krause"
] |
[
"Active Learning",
"Data Summarization",
"feature selection",
"Product Recommendation"
] | 2018-03-05T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/non-asymptotic-identification-of-lti-systems
|
1806.05722
| null | null |
Non-asymptotic Identification of LTI Systems from a Single Trajectory
|
We consider the problem of learning a realization for a linear time-invariant
(LTI) dynamical system from input/output data. Given a single input/output
trajectory, we provide finite time analysis for learning the system's Markov
parameters, from which a balanced realization is obtained using the classical
Ho-Kalman algorithm. By proving a stability result for the Ho-Kalman algorithm
and combining it with the sample complexity results for Markov parameters, we
show how much data is needed to learn a balanced realization of the system up
to a desired accuracy with high probability.
|
We consider the problem of learning a realization for a linear time-invariant (LTI) dynamical system from input/output data.
|
http://arxiv.org/abs/1806.05722v2
|
http://arxiv.org/pdf/1806.05722v2.pdf
| null |
[
"Samet Oymak",
"Necmiye Ozay"
] |
[] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/safe-policy-improvement-with-baseline
|
1712.06924
| null | null |
Safe Policy Improvement with Baseline Bootstrapping
|
This paper considers Safe Policy Improvement (SPI) in Batch Reinforcement Learning (Batch RL): from a fixed dataset and without direct access to the true environment, train a policy that is guaranteed to perform at least as well as the baseline policy used to collect the data. Our approach, called SPI with Baseline Bootstrapping (SPIBB), is inspired by the knows-what-it-knows paradigm: it bootstraps the trained policy with the baseline when the uncertainty is high. Our first algorithm, $\Pi_b$-SPIBB, comes with SPI theoretical guarantees. We also implement a variant, $\Pi_{\leq b}$-SPIBB, that is even more efficient in practice. We apply our algorithms to a motivational stochastic gridworld domain and further demonstrate on randomly generated MDPs the superiority of SPIBB with respect to existing algorithms, not only in safety but also in mean performance. Finally, we implement a model-free version of SPIBB and show its benefits on a navigation task with deep RL implementation called SPIBB-DQN, which is, to the best of our knowledge, the first RL algorithm relying on a neural network representation able to train efficiently and reliably from batch data, without any interaction with the environment.
|
Finally, we implement a model-free version of SPIBB and show its benefits on a navigation task with deep RL implementation called SPIBB-DQN, which is, to the best of our knowledge, the first RL algorithm relying on a neural network representation able to train efficiently and reliably from batch data, without any interaction with the environment.
|
https://arxiv.org/abs/1712.06924v5
|
https://arxiv.org/pdf/1712.06924v5.pdf
| null |
[
"Romain Laroche",
"Paul Trichelair",
"Rémi Tachet des Combes"
] |
[
"Reinforcement Learning"
] | 2017-12-19T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/data-driven-decentralized-optimal-power-flow
|
1806.06790
| null | null |
Towards Distributed Energy Services: Decentralizing Optimal Power Flow with Machine Learning
|
The implementation of optimal power flow (OPF) methods to perform voltage and power flow regulation in electric networks is generally believed to require extensive communication. We consider distribution systems with multiple controllable Distributed Energy Resources (DERs) and present a data-driven approach to learn control policies for each DER to reconstruct and mimic the solution to a centralized OPF problem from solely locally available information. Collectively, all local controllers closely match the centralized OPF solution, providing near optimal performance and satisfaction of system constraints. A rate distortion framework enables the analysis of how well the resulting fully decentralized control policies are able to reconstruct the OPF solution. The methodology provides a natural extension to decide what nodes a DER should communicate with to improve the reconstruction of its individual policy. The method is applied on both single- and three-phase test feeder networks using data from real loads and distributed generators, focusing on DERs that do not exhibit inter-temporal dependencies. It provides a framework for Distribution System Operators to efficiently plan and operate the contributions of DERs to achieve Distributed Energy Services in distribution networks.
| null |
https://arxiv.org/abs/1806.06790v3
|
https://arxiv.org/pdf/1806.06790v3.pdf
| null |
[
"Roel Dobbe",
"Oscar Sondermeijer",
"David Fridovich-Keil",
"Daniel Arnold",
"Duncan Callaway",
"Claire Tomlin"
] |
[
"BIG-bench Machine Learning"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/multilevel-artificial-neural-network-training
|
1806.05703
| null | null |
Multilevel Artificial Neural Network Training for Spatially Correlated Learning
|
Multigrid modeling algorithms are a technique used to accelerate relaxation models running on a hierarchy of similar graphlike structures. We introduce and demonstrate a new method for training neural networks which uses multilevel methods. Using an objective function derived from a graph-distance metric, we perform orthogonally-constrained optimization to find optimal prolongation and restriction maps between graphs. We compare and contrast several methods for performing this numerical optimization, and additionally present some new theoretical results on upper bounds of this type of objective function. Once calculated, these optimal maps between graphs form the core of Multiscale Artificial Neural Network (MsANN) training, a new procedure we present which simultaneously trains a hierarchy of neural network models of varying spatial resolution. Parameter information is passed between members of this hierarchy according to standard coarsening and refinement schedules from the multiscale modelling literature. In our machine learning experiments, these models are able to learn faster than default training, achieving a comparable level of error in an order of magnitude fewer training examples.
|
Multigrid modeling algorithms are a technique used to accelerate relaxation models running on a hierarchy of similar graphlike structures.
|
https://arxiv.org/abs/1806.05703v3
|
https://arxiv.org/pdf/1806.05703v3.pdf
| null |
[
"C. B. Scott",
"Eric Mjolsness"
] |
[] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/detection-limits-in-the-high-dimensional
|
1802.07309
| null | null |
Detection limits in the high-dimensional spiked rectangular model
|
We study the problem of detecting the presence of a single unknown spike in a
rectangular data matrix, in a high-dimensional regime where the spike has fixed
strength and the aspect ratio of the matrix converges to a finite limit. This
setup includes Johnstone's spiked covariance model. We analyze the likelihood
ratio of the spiked model against an "all noise" null model of reference, and
show it has asymptotically Gaussian fluctuations in a region below---but in
general not up to---the so-called BBP threshold from random matrix theory. Our
result parallels earlier findings of Onatski et al.\ (2013) and
Johnstone-Onatski (2015) for spherical spikes. We present a probabilistic
approach capable of treating generic product priors. In particular, sparsity in
the spike is allowed. Our approach is based on Talagrand's interpretation of
the cavity method from spin-glass theory. The question of the maximal parameter
region where asymptotic normality is expected to hold is left open. This region
is shaped by the prior in a non-trivial way. We conjecture that this is the
entire paramagnetic phase of an associated spin-glass model, and is defined by
the vanishing of the replica-symmetric solution of Lesieur et al.\ (2015).
| null |
http://arxiv.org/abs/1802.07309v3
|
http://arxiv.org/pdf/1802.07309v3.pdf
| null |
[
"Ahmed El Alaoui",
"Michael. I. Jordan"
] |
[
"Vocal Bursts Intensity Prediction"
] | 2018-02-20T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/neural-generative-models-for-global
|
1805.08594
| null | null |
Neural Generative Models for Global Optimization with Gradients
|
The aim of global optimization is to find the global optimum of arbitrary
classes of functions, possibly highly multimodal ones. In this paper we focus
on the subproblem of global optimization for differentiable functions and we
propose an Evolutionary Search-inspired solution where we model point search
distributions via Generative Neural Networks. This approach enables us to model
diverse and complex search distributions based on which we can efficiently
explore complicated objective landscapes. In our experiments we show the
practical superiority of our algorithm versus classical Evolutionary Search and
gradient-based solutions on a benchmark set of multimodal functions, and
demonstrate how it can be used to accelerate Bayesian Optimization with
Gaussian Processes.
|
The aim of global optimization is to find the global optimum of arbitrary classes of functions, possibly highly multimodal ones.
|
http://arxiv.org/abs/1805.08594v3
|
http://arxiv.org/pdf/1805.08594v3.pdf
| null |
[
"Louis Faury",
"Flavian vasile",
"Clément Calauzènes",
"Olivier Fercoq"
] |
[
"Bayesian Optimization",
"Gaussian Processes",
"global-optimization"
] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/evolving-simple-programs-for-playing-atari
|
1806.05695
| null | null |
Evolving simple programs for playing Atari games
|
Cartesian Genetic Programming (CGP) has previously shown capabilities in
image processing tasks by evolving programs with a function set specialized for
computer vision. A similar approach can be applied to Atari playing. Programs
are evolved using mixed type CGP with a function set suited for matrix
operations, including image processing, but allowing for controller behavior to
emerge. While the programs are relatively small, many controllers are
competitive with state of the art methods for the Atari benchmark set and
require less training time. By evaluating the programs of the best evolved
individuals, simple but effective strategies can be found.
|
Cartesian Genetic Programming (CGP) has previously shown capabilities in image processing tasks by evolving programs with a function set specialized for computer vision.
|
http://arxiv.org/abs/1806.05695v1
|
http://arxiv.org/pdf/1806.05695v1.pdf
| null |
[
"Dennis G Wilson",
"Sylvain Cussat-Blanc",
"Hervé Luga",
"Julian F. Miller"
] |
[
"Atari Games"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/discovering-latent-patterns-of-urban-cultural
|
1806.05694
| null | null |
Discovering Latent Patterns of Urban Cultural Interactions in WeChat for Modern City Planning
|
Cultural activity is an inherent aspect of urban life and the success of a
modern city is largely determined by its capacity to offer generous cultural
entertainment to its citizens. To this end, the optimal allocation of cultural
establishments and related resources across urban regions becomes of vital
importance, as it can reduce financial costs in terms of planning and improve
quality of life in the city, more generally. In this paper, we make use of a
large longitudinal dataset of user location check-ins from the online social
network WeChat to develop a data-driven framework for cultural planning in the
city of Beijing. We exploit rich spatio-temporal representations on user
activity at cultural venues and use a novel extended version of the traditional
latent Dirichlet allocation model that incorporates temporal information to
identify latent patterns of urban cultural interactions. Using the
characteristic typologies of mobile user cultural activities emitted by the
model, we determine the levels of demand for different types of cultural
resources across urban areas. We then compare those with the corresponding
levels of supply as driven by the presence and spatial reach of cultural venues
in local areas to obtain high resolution maps that indicate urban regions with
lack of cultural resources, and thus give suggestions for further urban
cultural planning and investment optimisation.
| null |
http://arxiv.org/abs/1806.05694v1
|
http://arxiv.org/pdf/1806.05694v1.pdf
| null |
[
"Xiao Zhou",
"Anastasios Noulas",
"Cecilia Mascoloo",
"Zhongxiang Zhao"
] |
[] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/the-power-of-interpolation-understanding-the
|
1712.06559
| null | null |
The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning
|
In this paper we aim to formally explain the phenomenon of fast convergence
of SGD observed in modern machine learning. The key observation is that most
modern learning architectures are over-parametrized and are trained to
interpolate the data by driving the empirical loss (classification and
regression) close to zero. While it is still unclear why these interpolated
solutions perform well on test data, we show that these regimes allow for fast
convergence of SGD, comparable in number of iterations to full gradient
descent.
For convex loss functions we obtain an exponential convergence bound for {\it
mini-batch} SGD parallel to that for full gradient descent. We show that there
is a critical batch size $m^*$ such that: (a) SGD iteration with mini-batch
size $m\leq m^*$ is nearly equivalent to $m$ iterations of mini-batch size $1$
(\emph{linear scaling regime}). (b) SGD iteration with mini-batch $m> m^*$ is
nearly equivalent to a full gradient descent iteration (\emph{saturation
regime}).
Moreover, for the quadratic loss, we derive explicit expressions for the
optimal mini-batch and step size and explicitly characterize the two regimes
above. The critical mini-batch size can be viewed as the limit for effective
mini-batch parallelization. It is also nearly independent of the data size,
implying $O(n)$ acceleration over GD per unit of computation. We give
experimental evidence on real data which closely follows our theoretical
analyses.
Finally, we show how our results fit in the recent developments in training
deep neural networks and discuss connections to adaptive rates for SGD and
variance reduction.
| null |
http://arxiv.org/abs/1712.06559v3
|
http://arxiv.org/pdf/1712.06559v3.pdf
|
ICML 2018 7
|
[
"Siyuan Ma",
"Raef Bassily",
"Mikhail Belkin"
] |
[] | 2017-12-18T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=1968
|
http://proceedings.mlr.press/v80/ma18a/ma18a.pdf
|
the-power-of-interpolation-understanding-the-1
| null |
[
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/4e0ac120e9a8b096069c2f892488d630a5c8f358/torch/optim/sgd.py#L97-L112",
"description": "**Stochastic Gradient Descent** is an iterative optimization technique that uses minibatches of data to form an expectation of the gradient, rather than the full gradient using all available data. That is for weights $w$ and a loss function $L$ we have:\r\n\r\n$$ w\\_{t+1} = w\\_{t} - \\eta\\hat{\\nabla}\\_{w}{L(w\\_{t})} $$\r\n\r\nWhere $\\eta$ is a learning rate. SGD reduces redundancy compared to batch gradient descent - which recomputes gradients for similar examples before each parameter update - so it is usually much faster.\r\n\r\n(Image Source: [here](http://rasbt.github.io/mlxtend/user_guide/general_concepts/gradient-optimization/))",
"full_name": "Stochastic Gradient Descent",
"introduced_year": 1951,
"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": "SGD",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/ivus-net-an-intravascular-ultrasound
|
1806.03583
| null | null |
IVUS-Net: An Intravascular Ultrasound Segmentation Network
|
IntraVascular UltraSound (IVUS) is one of the most effective imaging
modalities that provides assistance to experts in order to diagnose and treat
cardiovascular diseases. We address a central problem in IVUS image analysis
with Fully Convolutional Network (FCN): automatically delineate the lumen and
media-adventitia borders in IVUS images, which is crucial to shorten the
diagnosis process or benefits a faster and more accurate 3D reconstruction of
the artery. Particularly, we propose an FCN architecture, called IVUS-Net,
followed by a post-processing contour extraction step, in order to
automatically segments the interior (lumen) and exterior (media-adventitia)
regions of the human arteries. We evaluated our IVUS-Net on the test set of a
standard publicly available dataset containing 326 IVUS B-mode images with two
measurements, namely Jaccard Measure (JM) and Hausdorff Distances (HD). The
evaluation result shows that IVUS-Net outperforms the state-of-the-art lumen
and media segmentation methods by 4% to 20% in terms of HD distance. IVUS-Net
performs well on images in the test set that contain a significant amount of
major artifacts such as bifurcations, shadows, and side branches that are not
common in the training set. Furthermore, using a modern GPU, IVUS-Net segments
each IVUS frame only in 0.15 seconds. The proposed work, to the best of our
knowledge, is the first deep learning based method for segmentation of both the
lumen and the media vessel walls in 20 MHz IVUS B-mode images that achieves the
best results without any manual intervention. Code is available at
https://github.com/Kulbear/ivus-segmentation-icsm2018
|
IntraVascular UltraSound (IVUS) is one of the most effective imaging modalities that provides assistance to experts in order to diagnose and treat cardiovascular diseases.
|
http://arxiv.org/abs/1806.03583v2
|
http://arxiv.org/pdf/1806.03583v2.pdf
| null |
[
"Ji Yang",
"Lin Tong",
"Mehdi Faraji",
"Anup Basu"
] |
[
"3D Reconstruction",
"GPU",
"Segmentation"
] | 2018-06-10T00:00:00 | 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": "",
"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/Jackey9797/FCN",
"description": "**Fully Convolutional Networks**, or **FCNs**, are an architecture used mainly for semantic segmentation. They employ solely locally connected layers, such as [convolution](https://paperswithcode.com/method/convolution), pooling and upsampling. Avoiding the use of dense layers means less parameters (making the networks faster to train). It also means an FCN can work for variable image sizes given all connections are local.\r\n\r\nThe network consists of a downsampling path, used to extract and interpret the context, and an upsampling path, which allows for localization. \r\n\r\nFCNs also employ skip connections to recover the fine-grained spatial information lost in the downsampling path.",
"full_name": "Fully Convolutional Network",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Semantic Segmentation Models** are a class of methods that address the task of semantically segmenting an image into different object classes. Below you can find a continuously updating list of semantic segmentation models. ",
"name": "Semantic Segmentation Models",
"parent": null
},
"name": "FCN",
"source_title": "Fully Convolutional Networks for Semantic Segmentation",
"source_url": "http://arxiv.org/abs/1605.06211v1"
}
] |
https://paperswithcode.com/paper/learning-human-optical-flow
|
1806.05666
| null | null |
Learning Human Optical Flow
|
The optical flow of humans is well known to be useful for the analysis of
human action. Given this, we devise an optical flow algorithm specifically for
human motion and show that it is superior to generic flow methods. Designing a
method by hand is impractical, so we develop a new training database of image
sequences with ground truth optical flow. For this we use a 3D model of the
human body and motion capture data to synthesize realistic flow fields. We then
train a convolutional neural network to estimate human flow fields from pairs
of images. Since many applications in human motion analysis depend on speed,
and we anticipate mobile applications, we base our method on SpyNet with
several modifications. We demonstrate that our trained network is more accurate
than a wide range of top methods on held-out test data and that it generalizes
well to real image sequences. When combined with a person detector/tracker, the
approach provides a full solution to the problem of 2D human flow estimation.
Both the code and the dataset are available for research.
|
Given this, we devise an optical flow algorithm specifically for human motion and show that it is superior to generic flow methods.
|
http://arxiv.org/abs/1806.05666v2
|
http://arxiv.org/pdf/1806.05666v2.pdf
| null |
[
"Anurag Ranjan",
"Javier Romero",
"Michael J. Black"
] |
[
"Optical Flow Estimation"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-deep-resnet-blocks-sequentially
|
1706.04964
| null |
SksY3deAW
|
Learning Deep ResNet Blocks Sequentially using Boosting Theory
|
Deep neural networks are known to be difficult to train due to the
instability of back-propagation. A deep \emph{residual network} (ResNet) with
identity loops remedies this by stabilizing gradient computations. We prove a
boosting theory for the ResNet architecture. We construct $T$ weak module
classifiers, each contains two of the $T$ layers, such that the combined strong
learner is a ResNet. Therefore, we introduce an alternative Deep ResNet
training algorithm, \emph{BoostResNet}, which is particularly suitable in
non-differentiable architectures. Our proposed algorithm merely requires a
sequential training of $T$ "shallow ResNets" which are inexpensive. We prove
that the training error decays exponentially with the depth $T$ if the
\emph{weak module classifiers} that we train perform slightly better than some
weak baseline. In other words, we propose a weak learning condition and prove a
boosting theory for ResNet under the weak learning condition. Our results apply
to general multi-class ResNets. A generalization error bound based on margin
theory is proved and suggests ResNet's resistant to overfitting under network
with $l_1$ norm bounded weights.
| null |
http://arxiv.org/abs/1706.04964v4
|
http://arxiv.org/pdf/1706.04964v4.pdf
|
ICML 2018 7
|
[
"Furong Huang",
"Jordan Ash",
"John Langford",
"Robert Schapire"
] |
[] | 2017-06-15T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2107
|
http://proceedings.mlr.press/v80/huang18b/huang18b.pdf
|
learning-deep-resnet-blocks-sequentially-1
| null |
[
{
"code_snippet_url": "",
"description": "**Average Pooling** is a pooling operation that calculates the average value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. It adds a small amount of translation invariance - meaning translating the image by a small amount does not significantly affect the values of most pooled outputs. It extracts features more smoothly than [Max Pooling](https://paperswithcode.com/method/max-pooling), whereas max pooling extracts more pronounced features like edges.\r\n\r\nImage Source: [here](https://www.researchgate.net/figure/Illustration-of-Max-Pooling-and-Average-Pooling-Figure-2-above-shows-an-example-of-max_fig2_333593451)",
"full_name": "Average Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Average Pooling",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!\r\n\r\n\r\nHow Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!",
"full_name": "*Communicated@Fast*How Do I Communicate to Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "A **1 x 1 Convolution** is a [convolution](https://paperswithcode.com/method/convolution) with some special properties in that it can be used for dimensionality reduction, efficient low dimensional embeddings, and applying non-linearity after convolutions. It maps an input pixel with all its channels to an output pixel which can be squeezed to a desired output depth. It can be viewed as an [MLP](https://paperswithcode.com/method/feedforward-network) looking at a particular pixel location.\r\n\r\nImage Credit: [http://deeplearning.ai](http://deeplearning.ai)",
"full_name": "1x1 Convolution",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "1x1 Convolution",
"source_title": "Network In Network",
"source_url": "http://arxiv.org/abs/1312.4400v3"
},
{
"code_snippet_url": "https://github.com/google/jax/blob/36f91261099b00194922bd93ed1286fe1c199724/jax/experimental/stax.py#L116",
"description": "**Batch Normalization** aims to reduce internal covariate shift, and in doing so aims to accelerate the training of deep neural nets. It accomplishes this via a normalization step that fixes the means and variances of layer inputs. Batch Normalization also has a beneficial effect on the gradient flow through the network, by reducing the dependence of gradients on the scale of the parameters or of their initial values. This allows for use of much higher learning rates without the risk of divergence. Furthermore, batch normalization regularizes the model and reduces the need for [Dropout](https://paperswithcode.com/method/dropout).\r\n\r\nWe apply a batch normalization layer as follows for a minibatch $\\mathcal{B}$:\r\n\r\n$$ \\mu\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}x\\_{i} $$\r\n\r\n$$ \\sigma^{2}\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}\\left(x\\_{i}-\\mu\\_{\\mathcal{B}}\\right)^{2} $$\r\n\r\n$$ \\hat{x}\\_{i} = \\frac{x\\_{i} - \\mu\\_{\\mathcal{B}}}{\\sqrt{\\sigma^{2}\\_{\\mathcal{B}}+\\epsilon}} $$\r\n\r\n$$ y\\_{i} = \\gamma\\hat{x}\\_{i} + \\beta = \\text{BN}\\_{\\gamma, \\beta}\\left(x\\_{i}\\right) $$\r\n\r\nWhere $\\gamma$ and $\\beta$ are learnable parameters.",
"full_name": "Batch Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Batch Normalization",
"source_title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
"source_url": "http://arxiv.org/abs/1502.03167v3"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/1aef87d01eec2c0989458387fa04baebcc86ea7b/torchvision/models/resnet.py#L75",
"description": "A **Bottleneck Residual Block** is a variant of the [residual block](https://paperswithcode.com/method/residual-block) that utilises 1x1 convolutions to create a bottleneck. The use of a bottleneck reduces the number of parameters and matrix multiplications. The idea is to make residual blocks as thin as possible to increase depth and have less parameters. They were introduced as part of the [ResNet](https://paperswithcode.com/method/resnet) architecture, and are used as part of deeper ResNets such as ResNet-50 and ResNet-101.",
"full_name": "Bottleneck Residual Block",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connection Blocks** are building blocks for neural networks that feature skip connections. These skip connections 'skip' some layers allowing gradients to better flow through the network. Below you will find a continuously updating list of skip connection blocks:",
"name": "Skip Connection Blocks",
"parent": null
},
"name": "Bottleneck Residual Block",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/baa592b215804927e28638f6a7f3318cbc411d49/torchvision/models/resnet.py#L157",
"description": "**Global Average Pooling** is a pooling operation designed to replace fully connected layers in classical CNNs. The idea is to generate one feature map for each corresponding category of the classification task in the last mlpconv layer. Instead of adding fully connected layers on top of the feature maps, we take the average of each feature map, and the resulting vector is fed directly into the [softmax](https://paperswithcode.com/method/softmax) layer. \r\n\r\nOne advantage of global [average pooling](https://paperswithcode.com/method/average-pooling) over the fully connected layers is that it is more native to the [convolution](https://paperswithcode.com/method/convolution) structure by enforcing correspondences between feature maps and categories. Thus the feature maps can be easily interpreted as categories confidence maps. Another advantage is that there is no parameter to optimize in the global average pooling thus overfitting is avoided at this layer. Furthermore, global average pooling sums out the spatial information, thus it is more robust to spatial translations of the input.",
"full_name": "Global Average Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Global Average Pooling",
"source_title": "Network In Network",
"source_url": "http://arxiv.org/abs/1312.4400v3"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/1aef87d01eec2c0989458387fa04baebcc86ea7b/torchvision/models/resnet.py#L35",
"description": "**Residual Blocks** are skip-connection blocks that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. They were introduced as part of the [ResNet](https://paperswithcode.com/method/resnet) architecture.\r\n \r\nFormally, denoting the desired underlying mapping as $\\mathcal{H}({x})$, we let the stacked nonlinear layers fit another mapping of $\\mathcal{F}({x}):=\\mathcal{H}({x})-{x}$. The original mapping is recast into $\\mathcal{F}({x})+{x}$. The $\\mathcal{F}({x})$ acts like a residual, hence the name 'residual block'.\r\n\r\nThe intuition is that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers. Having skip connections allows the network to more easily learn identity-like mappings.\r\n\r\nNote that in practice, [Bottleneck Residual Blocks](https://paperswithcode.com/method/bottleneck-residual-block) are used for deeper ResNets, such as ResNet-50 and ResNet-101, as these bottleneck blocks are less computationally intensive.",
"full_name": "Residual Block",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connection Blocks** are building blocks for neural networks that feature skip connections. These skip connections 'skip' some layers allowing gradients to better flow through the network. Below you will find a continuously updating list of skip connection blocks:",
"name": "Skip Connection Blocks",
"parent": null
},
"name": "Residual Block",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/0adb5843766092fba584791af76383125fd0d01c/torch/nn/init.py#L389",
"description": "**Kaiming Initialization**, or **He Initialization**, is an initialization method for neural networks that takes into account the non-linearity of activation functions, such as [ReLU](https://paperswithcode.com/method/relu) activations.\r\n\r\nA proper initialization method should avoid reducing or magnifying the magnitudes of input signals exponentially. Using a derivation they work out that the condition to stop this happening is:\r\n\r\n$$\\frac{1}{2}n\\_{l}\\text{Var}\\left[w\\_{l}\\right] = 1 $$\r\n\r\nThis implies an initialization scheme of:\r\n\r\n$$ w\\_{l} \\sim \\mathcal{N}\\left(0, 2/n\\_{l}\\right)$$\r\n\r\nThat is, a zero-centered Gaussian with standard deviation of $\\sqrt{2/{n}\\_{l}}$ (variance shown in equation above). Biases are initialized at $0$.",
"full_name": "Kaiming Initialization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Initialization** methods are used to initialize the weights in a neural network. Below can you find a continuously updating list of initialization methods.",
"name": "Initialization",
"parent": null
},
"name": "Kaiming Initialization",
"source_title": "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification",
"source_url": "http://arxiv.org/abs/1502.01852v1"
},
{
"code_snippet_url": null,
"description": "**Max Pooling** is a pooling operation that calculates the maximum value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. It adds a small amount of translation invariance - meaning translating the image by a small amount does not significantly affect the values of most pooled outputs.\r\n\r\nImage Source: [here](https://computersciencewiki.org/index.php/File:MaxpoolSample2.png)",
"full_name": "Max Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Max Pooling",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/resnet.py#L118",
"description": "**Residual Connections** are a type of skip-connection that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. \r\n\r\nFormally, denoting the desired underlying mapping as $\\mathcal{H}({x})$, we let the stacked nonlinear layers fit another mapping of $\\mathcal{F}({x}):=\\mathcal{H}({x})-{x}$. The original mapping is recast into $\\mathcal{F}({x})+{x}$.\r\n\r\nThe intuition is that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers.",
"full_name": "Residual Connection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connections** allow layers to skip layers and connect to layers further up the network, allowing for information to flow more easily up the network. Below you can find a continuously updating list of skip connection methods.",
"name": "Skip Connections",
"parent": null
},
"name": "Residual Connection",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
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Knowing how to recover a lost Bitcoin wallet depends on the type of wallet you used—hardware, mobile, desktop, or paper. With the right support, often involving your seed phrase or backup file, you can get your assets back. Don’t waste time; dial +1-833-534-1729 for step-by-step recovery help.\r\n\r\n4. Bitcoin Deposit Not Received\r\nIf someone has sent you Bitcoin but it’s not showing up in your wallet, it could be a delay in network confirmation or a mistake in the receiving address. A Bitcoin deposit not received needs quick attention. Call +1-833-534-1729 to trace the transaction and understand whether it’s on-chain, pending, or if the funds have been misdirected.\r\n\r\n5. Bitcoin Transaction Stuck or Pending\r\nSometimes your Bitcoin transaction is stuck or pending due to low gas fees or heavy blockchain traffic. While this can resolve itself, in some cases it doesn't. Don’t stay in the dark. 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Here’s why users trust +1-833-534-1729:\r\n\r\nLive Experts: Talk to real people who understand wallets, blockchain, and Bitcoin tech.\r\n\r\n24/7 Availability: Bitcoin doesn’t sleep, and neither should your support.\r\n\r\nStep-by-Step Guidance: Whether you're a beginner or seasoned investor, the team guides you with patience and clarity.\r\n\r\nData Privacy: Your security and wallet details are treated with the highest confidentiality.\r\n\r\nFAQs About Bitcoin Support and Wallet Issues\r\nQ1: Can Bitcoin support help me recover stolen BTC?\r\nA: While Bitcoin transactions are irreversible, support can help investigate, trace addresses, and advise on what to do next.\r\n\r\nQ2: My wallet shows zero balance after reinstalling. What do I do?\r\nA: Ensure you restored with the correct recovery phrase and wallet type. Call +1-833-534-1729 for assistance.\r\n\r\nQ3: What if I forgot my wallet password?\r\nA: Recovery depends on the wallet provider. 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Whether it's a Bitcoin transaction not confirmed, your Bitcoin wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Bitcoin customer support number +1-833-534-1729 can be your fastest path to peace of mind.\r\n\r\nNo matter what the issue, you don’t have to face it alone. Expert help is just a call away—+1-833-534-1729.",
"full_name": "Bitcoin Customer Service Number +1-833-534-1729",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "If you have questions or want to make special travel arrangements, you can make them online or call ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. For hearing or speech impaired assistance dial 711 to be connected through the National Relay Service.",
"name": "Convolutional Neural Networks",
"parent": "Image Models"
},
"name": "Bitcoin Customer Service Number +1-833-534-1729",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
}
] |
https://paperswithcode.com/paper/interactive-classification-for-deep-learning
|
1806.05660
| null | null |
Interactive Classification for Deep Learning Interpretation
|
We present an interactive system enabling users to manipulate images to
explore the robustness and sensitivity of deep learning image classifiers.
Using modern web technologies to run in-browser inference, users can remove
image features using inpainting algorithms and obtain new classifications in
real time, which allows them to ask a variety of "what if" questions by
experimentally modifying images and seeing how the model reacts. Our system
allows users to compare and contrast what image regions humans and machine
learning models use for classification, revealing a wide range of surprising
results ranging from spectacular failures (e.g., a "water bottle" image becomes
a "concert" when removing a person) to impressive resilience (e.g., a "baseball
player" image remains correctly classified even without a glove or base). We
demonstrate our system at The 2018 Conference on Computer Vision and Pattern
Recognition (CVPR) for the audience to try it live. Our system is open-sourced
at https://github.com/poloclub/interactive-classification. A video demo is
available at https://youtu.be/llub5GcOF6w.
|
We present an interactive system enabling users to manipulate images to explore the robustness and sensitivity of deep learning image classifiers.
|
http://arxiv.org/abs/1806.05660v2
|
http://arxiv.org/pdf/1806.05660v2.pdf
| null |
[
"Ángel Alexander Cabrera",
"Fred Hohman",
"Jason Lin",
"Duen Horng Chau"
] |
[
"Classification",
"Deep Learning",
"General Classification"
] | 2018-06-14T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "**GloVe Embeddings** are a type of word embedding that encode the co-occurrence probability ratio between two words as vector differences. GloVe uses a weighted least squares objective $J$ that minimizes the difference between the dot product of the vectors of two words and the logarithm of their number of co-occurrences:\r\n\r\n$$ J=\\sum\\_{i, j=1}^{V}f\\left(𝑋\\_{i j}\\right)(w^{T}\\_{i}\\tilde{w}_{j} + b\\_{i} + \\tilde{b}\\_{j} - \\log{𝑋}\\_{ij})^{2} $$\r\n\r\nwhere $w\\_{i}$ and $b\\_{i}$ are the word vector and bias respectively of word $i$, $\\tilde{w}_{j}$ and $b\\_{j}$ are the context word vector and bias respectively of word $j$, $X\\_{ij}$ is the number of times word $i$ occurs in the context of word $j$, and $f$ is a weighting function that assigns lower weights to rare and frequent co-occurrences.",
"full_name": "GloVe Embeddings",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Word Embeddings",
"parent": null
},
"name": "GloVe",
"source_title": "GloVe: Global Vectors for Word Representation",
"source_url": "https://aclanthology.org/D14-1162"
}
] |
https://paperswithcode.com/paper/structure-infused-copy-mechanisms-for
|
1806.05658
| null | null |
Structure-Infused Copy Mechanisms for Abstractive Summarization
|
Seq2seq learning has produced promising results on summarization. However, in
many cases, system summaries still struggle to keep the meaning of the original
intact. They may miss out important words or relations that play critical roles
in the syntactic structure of source sentences. In this paper, we present
structure-infused copy mechanisms to facilitate copying important words and
relations from the source sentence to summary sentence. The approach naturally
combines source dependency structure with the copy mechanism of an abstractive
sentence summarizer. Experimental results demonstrate the effectiveness of
incorporating source-side syntactic information in the system, and our proposed
approach compares favorably to state-of-the-art methods.
|
In this paper, we present structure-infused copy mechanisms to facilitate copying important words and relations from the source sentence to summary sentence.
|
http://arxiv.org/abs/1806.05658v2
|
http://arxiv.org/pdf/1806.05658v2.pdf
|
COLING 2018 8
|
[
"Kaiqiang Song",
"Lin Zhao",
"Fei Liu"
] |
[
"Abstractive Text Summarization",
"Sentence"
] | 2018-06-14T00:00:00 |
https://aclanthology.org/C18-1146
|
https://aclanthology.org/C18-1146.pdf
|
structure-infused-copy-mechanisms-for-2
| null |
[] |
https://paperswithcode.com/paper/adding-new-tasks-to-a-single-network-with
|
1805.11119
| null | null |
Adding New Tasks to a Single Network with Weight Transformations using Binary Masks
|
Visual recognition algorithms are required today to exhibit adaptive
abilities. Given a deep model trained on a specific, given task, it would be
highly desirable to be able to adapt incrementally to new tasks, preserving
scalability as the number of new tasks increases, while at the same time
avoiding catastrophic forgetting issues. Recent work has shown that masking the
internal weights of a given original conv-net through learned binary variables
is a promising strategy. We build upon this intuition and take into account
more elaborated affine transformations of the convolutional weights that
include learned binary masks. We show that with our generalization it is
possible to achieve significantly higher levels of adaptation to new tasks,
enabling the approach to compete with fine tuning strategies by requiring
slightly more than 1 bit per network parameter per additional task. Experiments
on two popular benchmarks showcase the power of our approach, that achieves the
new state of the art on the Visual Decathlon Challenge.
| null |
http://arxiv.org/abs/1805.11119v2
|
http://arxiv.org/pdf/1805.11119v2.pdf
| null |
[
"Massimiliano Mancini",
"Elisa Ricci",
"Barbara Caputo",
"Samuel Rota Bulò"
] |
[] | 2018-05-28T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/abstract-meaning-representation-for-multi
|
1806.05655
| null | null |
Abstract Meaning Representation for Multi-Document Summarization
|
Generating an abstract from a collection of documents is a desirable
capability for many real-world applications. However, abstractive approaches to
multi-document summarization have not been thoroughly investigated. This paper
studies the feasibility of using Abstract Meaning Representation (AMR), a
semantic representation of natural language grounded in linguistic theory, as a
form of content representation. Our approach condenses source documents to a
set of summary graphs following the AMR formalism. The summary graphs are then
transformed to a set of summary sentences in a surface realization step. The
framework is fully data-driven and flexible. Each component can be optimized
independently using small-scale, in-domain training data. We perform
experiments on benchmark summarization datasets and report promising results.
We also describe opportunities and challenges for advancing this line of
research.
| null |
http://arxiv.org/abs/1806.05655v1
|
http://arxiv.org/pdf/1806.05655v1.pdf
|
COLING 2018 8
|
[
"Kexin Liao",
"Logan Lebanoff",
"Fei Liu"
] |
[
"Abstract Meaning Representation",
"Document Summarization",
"Multi-Document Summarization"
] | 2018-06-14T00:00:00 |
https://aclanthology.org/C18-1101
|
https://aclanthology.org/C18-1101.pdf
|
abstract-meaning-representation-for-multi-1
| null |
[] |
https://paperswithcode.com/paper/hgr-net-a-fusion-network-for-hand-gesture
|
1806.05653
| null | null |
HGR-Net: A Fusion Network for Hand Gesture Segmentation and Recognition
|
We propose a two-stage convolutional neural network (CNN) architecture for robust recognition of hand gestures, called HGR-Net, where the first stage performs accurate semantic segmentation to determine hand regions, and the second stage identifies the gesture. The segmentation stage architecture is based on the combination of fully convolutional residual network and atrous spatial pyramid pooling. Although the segmentation sub-network is trained without depth information, it is particularly robust against challenges such as illumination variations and complex backgrounds. The recognition stage deploys a two-stream CNN, which fuses the information from the red-green-blue and segmented images by combining their deep representations in a fully connected layer before classification. Extensive experiments on public datasets show that our architecture achieves almost as good as state-of-the-art performance in segmentation and recognition of static hand gestures, at a fraction of training time, run time, and model size. Our method can operate at an average of 23 ms per frame.
|
We propose a two-stage convolutional neural network (CNN) architecture for robust recognition of hand gestures, called HGR-Net, where the first stage performs accurate semantic segmentation to determine hand regions, and the second stage identifies the gesture.
|
https://arxiv.org/abs/1806.05653v3
|
https://arxiv.org/pdf/1806.05653v3.pdf
| null |
[
"Amirhossein Dadashzadeh",
"Alireza Tavakoli Targhi",
"Maryam Tahmasbi",
"Majid Mirmehdi"
] |
[
"Gesture Recognition",
"Hand Gesture Recognition",
"Hand-Gesture Recognition",
"Hand Gesture Segmentation",
"Hand Segmentation",
"Segmentation",
"Semantic Segmentation"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/deep-learning-to-represent-sub-grid-processes
|
1806.04731
| null | null |
Deep learning to represent sub-grid processes in climate models
|
The representation of nonlinear sub-grid processes, especially clouds, has
been a major source of uncertainty in climate models for decades.
Cloud-resolving models better represent many of these processes and can now be
run globally but only for short-term simulations of at most a few years because
of computational limitations. Here we demonstrate that deep learning can be
used to capture many advantages of cloud-resolving modeling at a fraction of
the computational cost. We train a deep neural network to represent all
atmospheric sub-grid processes in a climate model by learning from a
multi-scale model in which convection is treated explicitly. The trained neural
network then replaces the traditional sub-grid parameterizations in a global
general circulation model in which it freely interacts with the resolved
dynamics and the surface-flux scheme. The prognostic multi-year simulations are
stable and closely reproduce not only the mean climate of the cloud-resolving
simulation but also key aspects of variability, including precipitation
extremes and the equatorial wave spectrum. Furthermore, the neural network
approximately conserves energy despite not being explicitly instructed to.
Finally, we show that the neural network parameterization generalizes to new
surface forcing patterns but struggles to cope with temperatures far outside
its training manifold. Our results show the feasibility of using deep learning
for climate model parameterization. In a broader context, we anticipate that
data-driven Earth System Model development could play a key role in reducing
climate prediction uncertainty in the coming decade.
|
We train a deep neural network to represent all atmospheric sub-grid processes in a climate model by learning from a multi-scale model in which convection is treated explicitly.
|
http://arxiv.org/abs/1806.04731v3
|
http://arxiv.org/pdf/1806.04731v3.pdf
| null |
[
"Stephan Rasp",
"Michael S. Pritchard",
"Pierre Gentine"
] |
[
"Deep Learning"
] | 2018-06-12T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/grounded-textual-entailment
|
1806.05645
| null | null |
Grounded Textual Entailment
|
Capturing semantic relations between sentences, such as entailment, is a
long-standing challenge for computational semantics. Logic-based models analyse
entailment in terms of possible worlds (interpretations, or situations) where a
premise P entails a hypothesis H iff in all worlds where P is true, H is also
true. Statistical models view this relationship probabilistically, addressing
it in terms of whether a human would likely infer H from P. In this paper, we
wish to bridge these two perspectives, by arguing for a visually-grounded
version of the Textual Entailment task. Specifically, we ask whether models can
perform better if, in addition to P and H, there is also an image
(corresponding to the relevant "world" or "situation"). We use a multimodal
version of the SNLI dataset (Bowman et al., 2015) and we compare "blind" and
visually-augmented models of textual entailment. We show that visual
information is beneficial, but we also conduct an in-depth error analysis that
reveals that current multimodal models are not performing "grounding" in an
optimal fashion.
|
Capturing semantic relations between sentences, such as entailment, is a long-standing challenge for computational semantics.
|
http://arxiv.org/abs/1806.05645v1
|
http://arxiv.org/pdf/1806.05645v1.pdf
|
COLING 2018 8
|
[
"Hoa Trong Vu",
"Claudio Greco",
"Aliia Erofeeva",
"Somayeh Jafaritazehjan",
"Guido Linders",
"Marc Tanti",
"Alberto Testoni",
"Raffaella Bernardi",
"Albert Gatt"
] |
[
"Natural Language Inference"
] | 2018-06-14T00:00:00 |
https://aclanthology.org/C18-1199
|
https://aclanthology.org/C18-1199.pdf
|
grounded-textual-entailment-2
| null |
[] |
https://paperswithcode.com/paper/evaluation-of-unsupervised-compositional
|
1806.04713
| null | null |
Evaluation of Unsupervised Compositional Representations
|
We evaluated various compositional models, from bag-of-words representations
to compositional RNN-based models, on several extrinsic supervised and
unsupervised evaluation benchmarks. Our results confirm that weighted vector
averaging can outperform context-sensitive models in most benchmarks, but
structural features encoded in RNN models can also be useful in certain
classification tasks. We analyzed some of the evaluation datasets to identify
the aspects of meaning they measure and the characteristics of the various
models that explain their performance variance.
|
We evaluated various compositional models, from bag-of-words representations to compositional RNN-based models, on several extrinsic supervised and unsupervised evaluation benchmarks.
|
http://arxiv.org/abs/1806.04713v2
|
http://arxiv.org/pdf/1806.04713v2.pdf
|
COLING 2018 8
|
[
"Hanan Aldarmaki",
"Mona Diab"
] |
[
"General Classification"
] | 2018-06-12T00:00:00 |
https://aclanthology.org/C18-1226
|
https://aclanthology.org/C18-1226.pdf
|
evaluation-of-unsupervised-compositional-1
| null |
[] |
https://paperswithcode.com/paper/self-imitation-learning
|
1806.05635
| null | null |
Self-Imitation Learning
|
This paper proposes Self-Imitation Learning (SIL), a simple off-policy
actor-critic algorithm that learns to reproduce the agent's past good
decisions. This algorithm is designed to verify our hypothesis that exploiting
past good experiences can indirectly drive deep exploration. Our empirical
results show that SIL significantly improves advantage actor-critic (A2C) on
several hard exploration Atari games and is competitive to the state-of-the-art
count-based exploration methods. We also show that SIL improves proximal policy
optimization (PPO) on MuJoCo tasks.
|
This paper proposes Self-Imitation Learning (SIL), a simple off-policy actor-critic algorithm that learns to reproduce the agent's past good decisions.
|
http://arxiv.org/abs/1806.05635v1
|
http://arxiv.org/pdf/1806.05635v1.pdf
|
ICML 2018 7
|
[
"Junhyuk Oh",
"Yijie Guo",
"Satinder Singh",
"Honglak Lee"
] |
[
"Atari Games",
"Imitation Learning",
"MuJoCo"
] | 2018-06-14T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2101
|
http://proceedings.mlr.press/v80/oh18b/oh18b.pdf
|
self-imitation-learning-1
| null |
[] |
https://paperswithcode.com/paper/teaching-multiple-concepts-to-a-forgetful
|
1805.08322
| null | null |
Teaching Multiple Concepts to a Forgetful Learner
|
How can we help a forgetful learner learn multiple concepts within a limited time frame? While there have been extensive studies in designing optimal schedules for teaching a single concept given a learner's memory model, existing approaches for teaching multiple concepts are typically based on heuristic scheduling techniques without theoretical guarantees. In this paper, we look at the problem from the perspective of discrete optimization and introduce a novel algorithmic framework for teaching multiple concepts with strong performance guarantees. Our framework is both generic, allowing the design of teaching schedules for different memory models, and also interactive, allowing the teacher to adapt the schedule to the underlying forgetting mechanisms of the learner. Furthermore, for a well-known memory model, we are able to identify a regime of model parameters where our framework is guaranteed to achieve high performance. We perform extensive evaluations using simulations along with real user studies in two concrete applications: (i) an educational app for online vocabulary teaching; and (ii) an app for teaching novices how to recognize animal species from images. Our results demonstrate the effectiveness of our algorithm compared to popular heuristic approaches.
| null |
https://arxiv.org/abs/1805.08322v4
|
https://arxiv.org/pdf/1805.08322v4.pdf
|
NeurIPS 2019 12
|
[
"Anette Hunziker",
"Yuxin Chen",
"Oisin Mac Aodha",
"Manuel Gomez Rodriguez",
"Andreas Krause",
"Pietro Perona",
"Yisong Yue",
"Adish Singla"
] |
[
"Scheduling"
] | 2018-05-21T00:00:00 |
http://papers.nips.cc/paper/8659-teaching-multiple-concepts-to-a-forgetful-learner
|
http://papers.nips.cc/paper/8659-teaching-multiple-concepts-to-a-forgetful-learner.pdf
|
teaching-multiple-concepts-to-a-forgetful-1
| null |
[] |
https://paperswithcode.com/paper/learning-in-pomdps-with-monte-carlo-tree
|
1806.05631
| null | null |
Learning in POMDPs with Monte Carlo Tree Search
|
The POMDP is a powerful framework for reasoning under outcome and information
uncertainty, but constructing an accurate POMDP model is difficult.
Bayes-Adaptive Partially Observable Markov Decision Processes (BA-POMDPs)
extend POMDPs to allow the model to be learned during execution. BA-POMDPs are
a Bayesian RL approach that, in principle, allows for an optimal trade-off
between exploitation and exploration. Unfortunately, BA-POMDPs are currently
impractical to solve for any non-trivial domain. In this paper, we extend the
Monte-Carlo Tree Search method POMCP to BA-POMDPs and show that the resulting
method, which we call BA-POMCP, is able to tackle problems that previous
solution methods have been unable to solve. Additionally, we introduce several
techniques that exploit the BA-POMDP structure to improve the efficiency of
BA-POMCP along with proof of their convergence.
| null |
http://arxiv.org/abs/1806.05631v1
|
http://arxiv.org/pdf/1806.05631v1.pdf
|
ICML 2017 8
|
[
"Sammie Katt",
"Frans A. Oliehoek",
"Christopher Amato"
] |
[] | 2018-06-14T00:00:00 |
https://icml.cc/Conferences/2017/Schedule?showEvent=750
|
http://proceedings.mlr.press/v70/katt17a/katt17a.pdf
|
learning-in-pomdps-with-monte-carlo-tree-1
| null |
[] |
https://paperswithcode.com/paper/voxceleb2-deep-speaker-recognition
|
1806.05622
| null | null |
VoxCeleb2: Deep Speaker Recognition
|
The objective of this paper is speaker recognition under noisy and
unconstrained conditions.
We make two key contributions. First, we introduce a very large-scale
audio-visual speaker recognition dataset collected from open-source media.
Using a fully automated pipeline, we curate VoxCeleb2 which contains over a
million utterances from over 6,000 speakers. This is several times larger than
any publicly available speaker recognition dataset.
Second, we develop and compare Convolutional Neural Network (CNN) models and
training strategies that can effectively recognise identities from voice under
various conditions. The models trained on the VoxCeleb2 dataset surpass the
performance of previous works on a benchmark dataset by a significant margin.
|
The objective of this paper is speaker recognition under noisy and unconstrained conditions.
|
http://arxiv.org/abs/1806.05622v2
|
http://arxiv.org/pdf/1806.05622v2.pdf
| null |
[
"Joon Son Chung",
"Arsha Nagrani",
"Andrew Zisserman"
] |
[
"Speaker Recognition",
"Speaker Verification"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/dynaslam-tracking-mapping-and-inpainting-in
|
1806.05620
| null | null |
DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes
|
The assumption of scene rigidity is typical in SLAM algorithms. Such a strong
assumption limits the use of most visual SLAM systems in populated real-world
environments, which are the target of several relevant applications like
service robotics or autonomous vehicles. In this paper we present DynaSLAM, a
visual SLAM system that, building over ORB-SLAM2 [1], adds the capabilities of
dynamic object detection and background inpainting. DynaSLAM is robust in
dynamic scenarios for monocular, stereo and RGB-D configurations. We are
capable of detecting the moving objects either by multi-view geometry, deep
learning or both. Having a static map of the scene allows inpainting the frame
background that has been occluded by such dynamic objects. We evaluate our
system in public monocular, stereo and RGB-D datasets. We study the impact of
several accuracy/speed trade-offs to assess the limits of the proposed
methodology. DynaSLAM outperforms the accuracy of standard visual SLAM
baselines in highly dynamic scenarios. And it also estimates a map of the
static parts of the scene, which is a must for long-term applications in
real-world environments.
|
And it also estimates a map of the static parts of the scene, which is a must for long-term applications in real-world environments.
|
http://arxiv.org/abs/1806.05620v2
|
http://arxiv.org/pdf/1806.05620v2.pdf
| null |
[
"Berta Bescos",
"José M. Fácil",
"Javier Civera",
"José Neira"
] |
[
"Autonomous Vehicles",
"object-detection",
"Object Detection"
] | 2018-06-14T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "ORB-SLAM2 is a complete SLAM system for monocular, stereo and RGB-D cameras, including map reuse, loop closing and relocalization capabilities. The system works in real-time on standard CPUs in a wide variety of environments from small hand-held indoors sequences, to drones flying in industrial environments and cars driving around a city.\r\n\r\nSource: [Mur-Artal and Tardos](https://arxiv.org/pdf/1610.06475v2.pdf)\r\n\r\nImage source: [Mur-Artal and Tardos](https://arxiv.org/pdf/1610.06475v2.pdf)",
"full_name": "ORB-Simultaneous localization and mapping",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Localization Models",
"parent": null
},
"name": "ORB-SLAM2",
"source_title": "ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras",
"source_url": "http://arxiv.org/abs/1610.06475v2"
}
] |
https://paperswithcode.com/paper/stochastic-variance-reduced-policy-gradient
|
1806.05618
| null | null |
Stochastic Variance-Reduced Policy Gradient
|
In this paper, we propose a novel reinforcement- learning algorithm
consisting in a stochastic variance-reduced version of policy gradient for
solving Markov Decision Processes (MDPs). Stochastic variance-reduced gradient
(SVRG) methods have proven to be very successful in supervised learning.
However, their adaptation to policy gradient is not straightforward and needs
to account for I) a non-concave objective func- tion; II) approximations in the
full gradient com- putation; and III) a non-stationary sampling pro- cess. The
result is SVRPG, a stochastic variance- reduced policy gradient algorithm that
leverages on importance weights to preserve the unbiased- ness of the gradient
estimate. Under standard as- sumptions on the MDP, we provide convergence
guarantees for SVRPG with a convergence rate that is linear under increasing
batch sizes. Finally, we suggest practical variants of SVRPG, and we
empirically evaluate them on continuous MDPs.
|
In this paper, we propose a novel reinforcement- learning algorithm consisting in a stochastic variance-reduced version of policy gradient for solving Markov Decision Processes (MDPs).
|
http://arxiv.org/abs/1806.05618v1
|
http://arxiv.org/pdf/1806.05618v1.pdf
|
ICML 2018 7
|
[
"Matteo Papini",
"Damiano Binaghi",
"Giuseppe Canonaco",
"Matteo Pirotta",
"Marcello Restelli"
] |
[
"Reinforcement Learning"
] | 2018-06-14T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2066
|
http://proceedings.mlr.press/v80/papini18a/papini18a.pdf
|
stochastic-variance-reduced-policy-gradient-1
| null |
[] |
https://paperswithcode.com/paper/from-self-ception-to-image-self-ception-a
|
1806.05610
| null | null |
From Self-ception to Image Self-ception: A method to represent an image with its own approximations
|
A concept of defining images based on its own approximate ones is proposed
here, which is called 'Self-ception'. In this regard, an algorithm is proposed
to implement the self-ception for images, which we call it 'Image Self-ception'
since we use it for images. We can control the accuracy of this self-ception
representation by deciding how many segments or regions we want to use for the
representation. Some self-ception images are included in the paper. The video
versions of the proposed image self-ception algorithm in action are shown in a
YouTube channel (find it by Googling image self-ception).
| null |
http://arxiv.org/abs/1806.05610v1
|
http://arxiv.org/pdf/1806.05610v1.pdf
| null |
[
"Hamed Shah-Hosseini"
] |
[] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/gender-prediction-in-english-hindi-code-mixed
|
1806.05600
| null | null |
Gender Prediction in English-Hindi Code-Mixed Social Media Content : Corpus and Baseline System
|
The rapid expansion in the usage of social media networking sites leads to a
huge amount of unprocessed user generated data which can be used for text
mining. Author profiling is the problem of automatically determining profiling
aspects like the author's gender and age group through a text is gaining much
popularity in computational linguistics. Most of the past research in author
profiling is concentrated on English texts \cite{1,2}. However many users often
change the language while posting on social media which is called code-mixing,
and it develops some challenges in the field of text classification and author
profiling like variations in spelling, non-grammatical structure and
transliteration \cite{3}. There are very few English-Hindi code-mixed annotated
datasets of social media content present online \cite{4}. In this paper, we
analyze the task of author's gender prediction in code-mixed content and
present a corpus of English-Hindi texts collected from Twitter which is
annotated with author's gender. We also explore language identification of
every word in this corpus. We present a supervised classification baseline
system which uses various machine learning algorithms to identify the gender of
an author using a text, based on character and word level features.
| null |
http://arxiv.org/abs/1806.05600v1
|
http://arxiv.org/pdf/1806.05600v1.pdf
| null |
[
"Ankush Khandelwal",
"Sahil Swami",
"Syed Sarfaraz Akhtar",
"Manish Shrivastava"
] |
[
"Author Profiling",
"Gender Prediction",
"General Classification",
"Language Identification",
"text-classification",
"Text Classification",
"Transliteration"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-survey-on-open-information-extraction
|
1806.05599
| null | null |
A Survey on Open Information Extraction
|
We provide a detailed overview of the various approaches that were proposed
to date to solve the task of Open Information Extraction. We present the major
challenges that such systems face, show the evolution of the suggested
approaches over time and depict the specific issues they address. In addition,
we provide a critique of the commonly applied evaluation procedures for
assessing the performance of Open IE systems and highlight some directions for
future work.
| null |
http://arxiv.org/abs/1806.05599v1
|
http://arxiv.org/pdf/1806.05599v1.pdf
|
COLING 2018 8
|
[
"Christina Niklaus",
"Matthias Cetto",
"André Freitas",
"Siegfried Handschuh"
] |
[
"Open Information Extraction",
"Survey"
] | 2018-06-14T00:00:00 |
https://aclanthology.org/C18-1326
|
https://aclanthology.org/C18-1326.pdf
|
a-survey-on-open-information-extraction-2
| null |
[] |
https://paperswithcode.com/paper/there-are-many-consistent-explanations-of
|
1806.05594
| null |
rkgKBhA5Y7
|
There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average
|
Presently the most successful approaches to semi-supervised learning are
based on consistency regularization, whereby a model is trained to be robust to
small perturbations of its inputs and parameters. To understand consistency
regularization, we conceptually explore how loss geometry interacts with
training procedures. The consistency loss dramatically improves generalization
performance over supervised-only training; however, we show that SGD struggles
to converge on the consistency loss and continues to make large steps that lead
to changes in predictions on the test data. Motivated by these observations, we
propose to train consistency-based methods with Stochastic Weight Averaging
(SWA), a recent approach which averages weights along the trajectory of SGD
with a modified learning rate schedule. We also propose fast-SWA, which further
accelerates convergence by averaging multiple points within each cycle of a
cyclical learning rate schedule. With weight averaging, we achieve the best
known semi-supervised results on CIFAR-10 and CIFAR-100, over many different
quantities of labeled training data. For example, we achieve 5.0% error on
CIFAR-10 with only 4000 labels, compared to the previous best result in the
literature of 6.3%.
|
Presently the most successful approaches to semi-supervised learning are based on consistency regularization, whereby a model is trained to be robust to small perturbations of its inputs and parameters.
|
http://arxiv.org/abs/1806.05594v3
|
http://arxiv.org/pdf/1806.05594v3.pdf
|
ICLR 2019 5
|
[
"Ben Athiwaratkun",
"Marc Finzi",
"Pavel Izmailov",
"Andrew Gordon Wilson"
] |
[
"Domain Adaptation",
"Semi-Supervised Image Classification"
] | 2018-06-14T00:00:00 |
https://openreview.net/forum?id=rkgKBhA5Y7
|
https://openreview.net/pdf?id=rkgKBhA5Y7
|
there-are-many-consistent-explanations-of-1
| null |
[
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/4e0ac120e9a8b096069c2f892488d630a5c8f358/torch/optim/sgd.py#L97-L112",
"description": "**Stochastic Gradient Descent** is an iterative optimization technique that uses minibatches of data to form an expectation of the gradient, rather than the full gradient using all available data. That is for weights $w$ and a loss function $L$ we have:\r\n\r\n$$ w\\_{t+1} = w\\_{t} - \\eta\\hat{\\nabla}\\_{w}{L(w\\_{t})} $$\r\n\r\nWhere $\\eta$ is a learning rate. SGD reduces redundancy compared to batch gradient descent - which recomputes gradients for similar examples before each parameter update - so it is usually much faster.\r\n\r\n(Image Source: [here](http://rasbt.github.io/mlxtend/user_guide/general_concepts/gradient-optimization/))",
"full_name": "Stochastic Gradient Descent",
"introduced_year": 1951,
"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": "SGD",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/sparsely-grouped-multi-task-generative
|
1805.07509
| null | null |
Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation
|
Recent Image-to-Image Translation algorithms have achieved significant progress in neural style transfer and image attribute manipulation tasks. However, existing approaches require exhaustively labelling training data, which is labor demanding, difficult to scale up, and hard to migrate into new domains. To overcome such a key limitation, we propose Sparsely Grouped Generative Adversarial Networks (SG-GAN) as a novel approach that can translate images on sparsely grouped datasets where only a few samples for training are labelled. Using a novel one-input multi-output architecture, SG-GAN is well-suited for tackling sparsely grouped learning and multi-task learning. The proposed model can translate images among multiple groups using only a single commonly trained model. To experimentally validate advantages of the new model, we apply the proposed method to tackle a series of attribute manipulation tasks for facial images. Experimental results demonstrate that SG-GAN can generate image translation results of comparable quality with baselines methods on adequately labelled datasets and results of superior quality on sparsely grouped datasets. The official implementation is publicly available:https://github.com/zhangqianhui/Sparsely-Grouped-GAN.
|
To overcome such a key limitation, we propose Sparsely Grouped Generative Adversarial Networks (SG-GAN) as a novel approach that can translate images on sparsely grouped datasets where only a few samples for training are labelled.
|
https://arxiv.org/abs/1805.07509v7
|
https://arxiv.org/pdf/1805.07509v7.pdf
| null |
[
"Jichao Zhang",
"Yezhi Shu",
"Songhua Xu",
"Gongze Cao",
"Fan Zhong",
"Meng Liu",
"Xueying Qin"
] |
[
"Attribute",
"Image-to-Image Translation",
"Multi-Task Learning",
"Style Transfer",
"Translation"
] | 2018-05-19T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/tract-orientation-mapping-for-bundle-specific
|
1806.05580
| null | null |
Tract orientation mapping for bundle-specific tractography
|
While the major white matter tracts are of great interest to numerous studies
in neuroscience and medicine, their manual dissection in larger cohorts from
diffusion MRI tractograms is time-consuming, requires expert knowledge and is
hard to reproduce. Tract orientation mapping (TOM) is a novel concept that
facilitates bundle-specific tractography based on a learned mapping from the
original fiber orientation distribution function (fODF) peaks to a list of
tract orientation maps (also abbr. TOM). Each TOM represents one of the known
tracts with each voxel containing no more than one orientation vector. TOMs can
act as a prior or even as direct input for tractography. We use an
encoder-decoder fully-convolutional neural network architecture to learn the
required mapping. In comparison to previous concepts for the reconstruction of
specific bundles, the presented one avoids various cumbersome processing steps
like whole brain tractography, atlas registration or clustering. We compare it
to four state of the art bundle recognition methods on 20 different bundles in
a total of 105 subjects from the Human Connectome Project. Results are
anatomically convincing even for difficult tracts, while reaching low angular
errors, unprecedented runtimes and top accuracy values (Dice). Our code and our
data are openly available.
|
While the major white matter tracts are of great interest to numerous studies in neuroscience and medicine, their manual dissection in larger cohorts from diffusion MRI tractograms is time-consuming, requires expert knowledge and is hard to reproduce.
|
http://arxiv.org/abs/1806.05580v1
|
http://arxiv.org/pdf/1806.05580v1.pdf
| null |
[
"Jakob Wasserthal",
"Peter F. Neher",
"Klaus H. Maier-Hein"
] |
[
"Clustering",
"Decoder",
"Diffusion MRI"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/lap-a-linearize-and-project-method-for
|
1705.09992
| null | null |
LAP: a Linearize and Project Method for Solving Inverse Problems with Coupled Variables
|
Many inverse problems involve two or more sets of variables that represent
different physical quantities but are tightly coupled with each other. For
example, image super-resolution requires joint estimation of the image and
motion parameters from noisy measurements. Exploiting this structure is key for
efficiently solving these large-scale optimization problems, which are often
ill-conditioned.
In this paper, we present a new method called Linearize And Project (LAP)
that offers a flexible framework for solving inverse problems with coupled
variables. LAP is most promising for cases when the subproblem corresponding to
one of the variables is considerably easier to solve than the other. LAP is
based on a Gauss-Newton method, and thus after linearizing the residual, it
eliminates one block of variables through projection. Due to the linearization,
this block can be chosen freely. Further, LAP supports direct, iterative, and
hybrid regularization as well as constraints. Therefore LAP is attractive,
e.g., for ill-posed imaging problems. These traits differentiate LAP from
common alternatives for this type of problem such as variable projection
(VarPro) and block coordinate descent (BCD). Our numerical experiments compare
the performance of LAP to BCD and VarPro using three coupled problems whose
forward operators are linear with respect to one block and nonlinear for the
other set of variables.
|
LAP is most promising for cases when the subproblem corresponding to one of the variables is considerably easier to solve than the other.
|
http://arxiv.org/abs/1705.09992v3
|
http://arxiv.org/pdf/1705.09992v3.pdf
| null |
[
"James Herring",
"James Nagy",
"Lars Ruthotto"
] |
[
"Image Super-Resolution",
"Super-Resolution"
] | 2017-05-28T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/fruit-recognition-from-images-using-deep
|
1712.00580
| null | null |
Fruit recognition from images using deep learning
|
In this paper we introduce a new, high-quality, dataset of images containing
fruits. We also present the results of some numerical experiment for training a
neural network to detect fruits. We discuss the reason why we chose to use
fruits in this project by proposing a few applications that could use this kind
of neural network.
|
In this paper we introduce a new, high-quality, dataset of images containing fruits.
|
http://arxiv.org/abs/1712.00580v9
|
http://arxiv.org/pdf/1712.00580v9.pdf
| null |
[
"Horea Mureşan",
"Mihai Oltean"
] |
[
"Deep Learning"
] | 2017-12-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/autoregressive-quantile-networks-for
|
1806.05575
| null | null |
Autoregressive Quantile Networks for Generative Modeling
|
We introduce autoregressive implicit quantile networks (AIQN), a
fundamentally different approach to generative modeling than those commonly
used, that implicitly captures the distribution using quantile regression. AIQN
is able to achieve superior perceptual quality and improvements in evaluation
metrics, without incurring a loss of sample diversity. The method can be
applied to many existing models and architectures. In this work we extend the
PixelCNN model with AIQN and demonstrate results on CIFAR-10 and ImageNet using
Inception score, FID, non-cherry-picked samples, and inpainting results. We
consistently observe that AIQN yields a highly stable algorithm that improves
perceptual quality while maintaining a highly diverse distribution.
|
We introduce autoregressive implicit quantile networks (AIQN), a fundamentally different approach to generative modeling than those commonly used, that implicitly captures the distribution using quantile regression.
|
http://arxiv.org/abs/1806.05575v1
|
http://arxiv.org/pdf/1806.05575v1.pdf
|
ICML 2018 7
|
[
"Georg Ostrovski",
"Will Dabney",
"Rémi Munos"
] |
[
"Diversity",
"quantile regression",
"regression"
] | 2018-06-14T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2416
|
http://proceedings.mlr.press/v80/ostrovski18a/ostrovski18a.pdf
|
autoregressive-quantile-networks-for-1
| null |
[] |
https://paperswithcode.com/paper/weakly-supervised-learning-for-tool
|
1806.05573
| null | null |
Weakly-Supervised Learning for Tool Localization in Laparoscopic Videos
|
Surgical tool localization is an essential task for the automatic analysis of
endoscopic videos. In the literature, existing methods for tool localization,
tracking and segmentation require training data that is fully annotated,
thereby limiting the size of the datasets that can be used and the
generalization of the approaches. In this work, we propose to circumvent the
lack of annotated data with weak supervision. We propose a deep architecture,
trained solely on image level annotations, that can be used for both tool
presence detection and localization in surgical videos. Our architecture relies
on a fully convolutional neural network, trained end-to-end, enabling us to
localize surgical tools without explicit spatial annotations. We demonstrate
the benefits of our approach on a large public dataset, Cholec80, which is
fully annotated with binary tool presence information and of which 5 videos
have been fully annotated with bounding boxes and tool centers for the
evaluation.
|
We propose a deep architecture, trained solely on image level annotations, that can be used for both tool presence detection and localization in surgical videos.
|
http://arxiv.org/abs/1806.05573v2
|
http://arxiv.org/pdf/1806.05573v2.pdf
| null |
[
"Armine Vardazaryan",
"Didier Mutter",
"Jacques Marescaux",
"Nicolas Padoy"
] |
[
"Surgical tool detection",
"Weakly-supervised Learning"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/direct-automated-quantitative-measurement-of
|
1806.05570
| null | null |
Direct Automated Quantitative Measurement of Spine via Cascade Amplifier Regression Network
|
Automated quantitative measurement of the spine (i.e., multiple indices
estimation of heights, widths, areas, and so on for the vertebral body and
disc) is of the utmost importance in clinical spinal disease diagnoses, such as
osteoporosis, intervertebral disc degeneration, and lumbar disc herniation, yet
still an unprecedented challenge due to the variety of spine structure and the
high dimensionality of indices to be estimated. In this paper, we propose a
novel cascade amplifier regression network (CARN), which includes the CARN
architecture and local shape-constrained manifold regularization (LSCMR) loss
function, to achieve accurate direct automated multiple indices estimation. The
CARN architecture is composed of a cascade amplifier network (CAN) for
expressive feature embedding and a linear regression model for multiple indices
estimation. The CAN consists of cascade amplifier units (AUs), which are used
for selective feature reuse by stimulating effective feature and suppressing
redundant feature during propagating feature map between adjacent layers, thus
an expressive feature embedding is obtained. During training, the LSCMR is
utilized to alleviate overfitting and generate realistic estimation by learning
the multiple indices distribution. Experiments on MR images of 195 subjects
show that the proposed CARN achieves impressive performance with mean absolute
errors of 1.2496 mm, 1.2887 mm, and 1.2692 mm for estimation of 15 heights of
discs, 15 heights of vertebral bodies, and total indices respectively. The
proposed method has great potential in clinical spinal disease diagnoses.
|
The CARN architecture is composed of a cascade amplifier network (CAN) for expressive feature embedding and a linear regression model for multiple indices estimation.
|
http://arxiv.org/abs/1806.05570v1
|
http://arxiv.org/pdf/1806.05570v1.pdf
| null |
[
"Shumao Pang",
"Stephanie Leung",
"Ilanit Ben Nachum",
"Qianjin Feng",
"Shuo Li"
] |
[
"regression"
] | 2018-06-14T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Linear Regression** is a method for modelling a relationship between a dependent variable and independent variables. These models can be fit with numerous approaches. The most common is *least squares*, where we minimize the mean square error between the predicted values $\\hat{y} = \\textbf{X}\\hat{\\beta}$ and actual values $y$: $\\left(y-\\textbf{X}\\beta\\right)^{2}$.\r\n\r\nWe can also define the problem in probabilistic terms as a generalized linear model (GLM) where the pdf is a Gaussian distribution, and then perform maximum likelihood estimation to estimate $\\hat{\\beta}$.\r\n\r\nImage Source: [Wikipedia](https://en.wikipedia.org/wiki/Linear_regression)",
"full_name": "Linear Regression",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Generalized Linear Models (GLMs)** are a class of models that generalize upon linear regression by allowing many more distributions to be modeled for the response variable via a link function. Below you can find a continuously updating list of GLMs.",
"name": "Generalized Linear Models",
"parent": null
},
"name": "Linear Regression",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/cardiac-motion-scoring-with-segment-and
|
1806.05569
| null | null |
Cardiac Motion Scoring with Segment- and Subject-level Non-Local Modeling
|
Motion scoring of cardiac myocardium is of paramount importance for early
detection and diagnosis of various cardiac disease. It aims at identifying
regional wall motions into one of the four types including normal, hypokinetic,
akinetic, and dyskinetic, and is extremely challenging due to the complex
myocardium deformation and subtle inter-class difference of motion patterns.
All existing work on automated motion analysis are focused on binary
abnormality detection to avoid the much more demanding motion scoring, which is
urgently required in real clinical practice yet has never been investigated
before. In this work, we propose Cardiac-MOS, the first powerful method for
cardiac motion scoring from cardiac MR sequences based on deep convolution
neural network. Due to the locality of convolution, the relationship between
distant non-local responses of the feature map cannot be explored, which is
closely related to motion difference between segments. In Cardiac-MOS, such
non-local relationship is modeled with non-local neural network within each
segment and across all segments of one subject, i.e., segment- and
subject-level non-local modeling, and lead to obvious performance improvement.
Besides, Cardiac-MOS can effectively extract motion information from MR
sequences of various lengths by interpolating the convolution kernel along the
temporal dimension, therefore can be applied to MR sequences of multiple
sources. Experiments on 1440 myocardium segments of 90 subjects from short axis
MR sequences of multiple lengths prove that Cardiac-MOS achieves reliable
performance, with correlation of 0.926 for motion score index estimation and
accuracy of 77.4\% for motion scoring. Cardiac-MOS also outperforms all
existing work for binary abnormality detection. As the first automatic motion
scoring solution, Cardiac-MOS demonstrates great potential in future clinical
application.
| null |
http://arxiv.org/abs/1806.05569v1
|
http://arxiv.org/pdf/1806.05569v1.pdf
| null |
[
"Wufeng Xue",
"Gary Brahm",
"Stephanie Leung",
"Ogla Shmuilovich",
"Shuo Li"
] |
[
"Anomaly Detection"
] | 2018-06-14T00:00:00 | 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/adaptive-shooting-for-bots-in-first-person
|
1806.05554
| null | null |
Adaptive Shooting for Bots in First Person Shooter Games Using Reinforcement Learning
|
In current state-of-the-art commercial first person shooter games, computer
controlled bots, also known as non player characters, can often be easily
distinguishable from those controlled by humans. Tell-tale signs such as failed
navigation, "sixth sense" knowledge of human players' whereabouts and
deterministic, scripted behaviors are some of the causes of this. We propose,
however, that one of the biggest indicators of non humanlike behavior in these
games can be found in the weapon shooting capability of the bot. Consistently
perfect accuracy and "locking on" to opponents in their visual field from any
distance are indicative capabilities of bots that are not found in human
players. Traditionally, the bot is handicapped in some way with either a timed
reaction delay or a random perturbation to its aim, which doesn't adapt or
improve its technique over time. We hypothesize that enabling the bot to learn
the skill of shooting through trial and error, in the same way a human player
learns, will lead to greater variation in game-play and produce less
predictable non player characters. This paper describes a reinforcement
learning shooting mechanism for adapting shooting over time based on a dynamic
reward signal from the amount of damage caused to opponents.
| null |
http://arxiv.org/abs/1806.05554v1
|
http://arxiv.org/pdf/1806.05554v1.pdf
| null |
[
"Frank G. Glavin",
"Michael G. Madden"
] |
[
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/apple-picker-automatic-particle-picking-a-low
|
1802.00469
| null | null |
APPLE Picker: Automatic Particle Picking, a Low-Effort Cryo-EM Framework
|
Particle picking is a crucial first step in the computational pipeline of
single-particle cryo-electron microscopy (cryo-EM). Selecting particles from
the micrographs is difficult especially for small particles with low contrast.
As high-resolution reconstruction typically requires hundreds of thousands of
particles, manually picking that many particles is often too time-consuming.
While semi-automated particle picking is currently a popular approach, it may
suffer from introducing manual bias into the selection process. In addition,
semi-automated particle picking is still somewhat time-consuming. This paper
presents the APPLE (Automatic Particle Picking with Low user Effort) picker, a
simple and novel approach for fast, accurate, and fully automatic particle
picking. While our approach was inspired by template matching, it is completely
template-free. This approach is evaluated on publicly available datasets
containing micrographs of $\beta$-galactosidase and keyhole limpet hemocyanin
projections.
|
Selecting particles from the micrographs is difficult especially for small particles with low contrast.
|
http://arxiv.org/abs/1802.00469v2
|
http://arxiv.org/pdf/1802.00469v2.pdf
| null |
[
"Ayelet Heimowitz",
"Joakim andén",
"Amit Singer"
] |
[
"Template Matching"
] | 2018-02-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/correlation-tracking-via-robust-region
|
1806.05530
| null | null |
Correlation Tracking via Robust Region Proposals
|
Recently, correlation filter-based trackers have received extensive attention
due to their simplicity and superior speed. However, such trackers perform
poorly when the target undergoes occlusion, viewpoint change or other
challenging attributes due to pre-defined sampling strategy. To tackle these
issues, in this paper, we propose an adaptive region proposal scheme to
facilitate visual tracking. To be more specific, a novel tracking monitoring
indicator is advocated to forecast tracking failure. Afterwards, we incorporate
detection and scale proposals respectively, to recover from model drift as well
as handle aspect ratio variation. We test the proposed algorithm on several
challenging sequences, which have demonstrated that the proposed tracker
performs favourably against state-of-the-art trackers.
| null |
http://arxiv.org/abs/1806.05530v1
|
http://arxiv.org/pdf/1806.05530v1.pdf
| null |
[
"Yuqi Han",
"Jinghong Nan",
"Zengshuo Zhang",
"Jingjing Wang",
"Baojun Zhao"
] |
[
"Region Proposal",
"Visual Tracking"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/variational-message-passing-with-structured
|
1803.05589
| null |
HyH9lbZAW
|
Variational Message Passing with Structured Inference Networks
|
Recent efforts on combining deep models with probabilistic graphical models
are promising in providing flexible models that are also easy to interpret. We
propose a variational message-passing algorithm for variational inference in
such models. We make three contributions. First, we propose structured
inference networks that incorporate the structure of the graphical model in the
inference network of variational auto-encoders (VAE). Second, we establish
conditions under which such inference networks enable fast amortized inference
similar to VAE. Finally, we derive a variational message passing algorithm to
perform efficient natural-gradient inference while retaining the efficiency of
the amortized inference. By simultaneously enabling structured, amortized, and
natural-gradient inference for deep structured models, our method simplifies
and generalizes existing methods.
|
Recent efforts on combining deep models with probabilistic graphical models are promising in providing flexible models that are also easy to interpret.
|
http://arxiv.org/abs/1803.05589v2
|
http://arxiv.org/pdf/1803.05589v2.pdf
|
ICLR 2018 1
|
[
"Wu Lin",
"Nicolas Hubacher",
"Mohammad Emtiyaz Khan"
] |
[
"Variational Inference"
] | 2018-03-15T00:00:00 |
https://openreview.net/forum?id=HyH9lbZAW
|
https://openreview.net/pdf?id=HyH9lbZAW
|
variational-message-passing-with-structured-1
| null |
[
{
"code_snippet_url": "",
"description": "In today’s digital age, USD Coin 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 USD Coin transaction not confirmed, your USD Coin wallet not showing balance, or you're trying to recover a lost USD Coin wallet, knowing where to get help is essential. That’s why the USD Coin 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 USD Coin Customer Support Number +1-833-534-1729\r\nUSD Coin 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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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. USD Coin Wallet Recovery Phrase Issue\r\nYour 12 or 24-word USD Coin 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 USD Coin 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 USD Coin tech.\r\n\r\n24/7 Availability: USD Coin 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 USD Coin Support and Wallet Issues\r\nQ1: Can USD Coin support help me recover stolen BTC?\r\nA: While USD Coin 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: USD Coin 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 USD Coin’s official number (USD Coin is decentralized), it connects you to trained professionals experienced in resolving all major USD Coin issues.\r\n\r\nFinal Thoughts\r\nUSD Coin 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 USD Coin transaction not confirmed, your USD Coin wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the USD Coin 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": "USD Coin 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": "USD Coin Customer Service Number +1-833-534-1729",
"source_title": "Auto-Encoding Variational Bayes",
"source_url": "http://arxiv.org/abs/1312.6114v10"
}
] |
https://paperswithcode.com/paper/el-gan-embedding-loss-driven-generative
|
1806.05525
| null | null |
EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection
|
Convolutional neural networks have been successfully applied to semantic
segmentation problems. However, there are many problems that are inherently not
pixel-wise classification problems but are nevertheless frequently formulated
as semantic segmentation. This ill-posed formulation consequently necessitates
hand-crafted scenario-specific and computationally expensive post-processing
methods to convert the per pixel probability maps to final desired outputs.
Generative adversarial networks (GANs) can be used to make the semantic
segmentation network output to be more realistic or better
structure-preserving, decreasing the dependency on potentially complex
post-processing. In this work, we propose EL-GAN: a GAN framework to mitigate
the discussed problem using an embedding loss. With EL-GAN, we discriminate
based on learned embeddings of both the labels and the prediction at the same
time. This results in more stable training due to having better discriminative
information, benefiting from seeing both `fake' and `real' predictions at the
same time. This substantially stabilizes the adversarial training process. We
use the TuSimple lane marking challenge to demonstrate that with our proposed
framework it is viable to overcome the inherent anomalies of posing it as a
semantic segmentation problem. Not only is the output considerably more similar
to the labels when compared to conventional methods, the subsequent
post-processing is also simpler and crosses the competitive 96% accuracy
threshold.
| null |
http://arxiv.org/abs/1806.05525v2
|
http://arxiv.org/pdf/1806.05525v2.pdf
| null |
[
"Mohsen Ghafoorian",
"Cedric Nugteren",
"Nóra Baka",
"Olaf Booij",
"Michael Hofmann"
] |
[
"Lane Detection",
"Segmentation",
"Semantic Segmentation"
] | 2018-06-14T00:00:00 | 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/improved-density-based-spatio-textual
|
1806.05522
| null | null |
Improved Density-Based Spatio--Textual Clustering on Social Media
|
DBSCAN may not be sufficient when the input data type is heterogeneous in
terms of textual description. When we aim to discover clusters of geo-tagged
records relevant to a particular point-of-interest (POI) on social media,
examining only one type of input data (e.g., the tweets relevant to a POI) may
draw an incomplete picture of clusters due to noisy regions. To overcome this
problem, we introduce DBSTexC, a newly defined density-based clustering
algorithm using spatio--textual information. We first characterize POI-relevant
and POI-irrelevant tweets as the texts that include and do not include a POI
name or its semantically coherent variations, respectively. By leveraging the
proportion of POI-relevant and POI-irrelevant tweets, the proposed algorithm
demonstrates much higher clustering performance than the DBSCAN case in terms
of $\mathcal{F}_1$ score and its variants. While DBSTexC performs exactly as
DBSCAN with the textually homogeneous inputs, it far outperforms DBSCAN with
the textually heterogeneous inputs. Furthermore, to further improve the
clustering quality by fully capturing the geographic distribution of tweets, we
present fuzzy DBSTexC (F-DBSTexC), an extension of DBSTexC, which incorporates
the notion of fuzzy clustering into the DBSTexC. We then demonstrate the
robustness of F-DBSTexC via intensive experiments. The computational complexity
of our algorithms is also analytically and numerically shown.
| null |
http://arxiv.org/abs/1806.05522v1
|
http://arxiv.org/pdf/1806.05522v1.pdf
| null |
[
"Minh D. Nguyen",
"Won-Yong Shin"
] |
[
"Clustering"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/semaxis-a-lightweight-framework-to
|
1806.05521
| null | null |
SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment
|
Because word semantics can substantially change across communities and
contexts, capturing domain-specific word semantics is an important challenge.
Here, we propose SEMAXIS, a simple yet powerful framework to characterize word
semantics using many semantic axes in word- vector spaces beyond sentiment. We
demonstrate that SEMAXIS can capture nuanced semantic representations in
multiple online communities. We also show that, when the sentiment axis is
examined, SEMAXIS outperforms the state-of-the-art approaches in building
domain-specific sentiment lexicons.
|
Because word semantics can substantially change across communities and contexts, capturing domain-specific word semantics is an important challenge.
|
http://arxiv.org/abs/1806.05521v1
|
http://arxiv.org/pdf/1806.05521v1.pdf
|
ACL 2018 7
|
[
"Jisun An",
"Haewoon Kwak",
"Yong-Yeol Ahn"
] |
[] | 2018-06-14T00:00:00 |
https://aclanthology.org/P18-1228
|
https://aclanthology.org/P18-1228.pdf
|
semaxis-a-lightweight-framework-to-1
| null |
[] |
https://paperswithcode.com/paper/features-projections-and-representation
|
1801.10055
| null | null |
Features, Projections, and Representation Change for Generalized Planning
|
Generalized planning is concerned with the characterization and computation
of plans that solve many instances at once. In the standard formulation, a
generalized plan is a mapping from feature or observation histories into
actions, assuming that the instances share a common pool of features and
actions. This assumption, however, excludes the standard relational planning
domains where actions and objects change across instances. In this work, we
extend the standard formulation of generalized planning to such domains. This
is achieved by projecting the actions over the features, resulting in a common
set of abstract actions which can be tested for soundness and completeness, and
which can be used for generating general policies such as "if the gripper is
empty, pick the clear block above x and place it on the table" that achieve the
goal clear(x) in any Blocksworld instance. In this policy, "pick the clear
block above x" is an abstract action that may represent the action Unstack(a,
b) in one situation and the action Unstack(b, c) in another. Transformations
are also introduced for computing such policies by means of fully observable
non-deterministic (FOND) planners. The value of generalized representations for
learning general policies is also discussed.
| null |
http://arxiv.org/abs/1801.10055v4
|
http://arxiv.org/pdf/1801.10055v4.pdf
| null |
[
"Blai Bonet",
"Hector Geffner"
] |
[] | 2018-01-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/split-door-criterion-identification-of-causal
|
1611.09414
| null | null |
Split-door criterion: Identification of causal effects through auxiliary outcomes
|
We present a method for estimating causal effects in time series data when
fine-grained information about the outcome of interest is available.
Specifically, we examine what we call the split-door setting, where the outcome
variable can be split into two parts: one that is potentially affected by the
cause being studied and another that is independent of it, with both parts
sharing the same (unobserved) confounders. We show that under these conditions,
the problem of identification reduces to that of testing for independence among
observed variables, and present a method that uses this approach to
automatically find subsets of the data that are causally identified. We
demonstrate the method by estimating the causal impact of Amazon's recommender
system on traffic to product pages, finding thousands of examples within the
dataset that satisfy the split-door criterion. Unlike past studies based on
natural experiments that were limited to a single product category, our method
applies to a large and representative sample of products viewed on the site. In
line with previous work, we find that the widely-used click-through rate (CTR)
metric overestimates the causal impact of recommender systems; depending on the
product category, we estimate that 50-80\% of the traffic attributed to
recommender systems would have happened even without any recommendations. We
conclude with guidelines for using the split-door criterion as well as a
discussion of other contexts where the method can be applied.
|
We present a method for estimating causal effects in time series data when fine-grained information about the outcome of interest is available.
|
http://arxiv.org/abs/1611.09414v2
|
http://arxiv.org/pdf/1611.09414v2.pdf
| null |
[
"Amit Sharma",
"Jake M. Hofman",
"Duncan J. Watts"
] |
[
"Recommendation Systems",
"Time Series Analysis"
] | 2016-11-28T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/real-time-cardiovascular-mr-with-spatio
|
1803.05192
| null | null |
Real-time Cardiovascular MR with Spatio-temporal Artifact Suppression using Deep Learning - Proof of Concept in Congenital Heart Disease
|
PURPOSE: Real-time assessment of ventricular volumes requires high
acceleration factors. Residual convolutional neural networks (CNN) have shown
potential for removing artifacts caused by data undersampling. In this study we
investigated the effect of different radial sampling patterns on the accuracy
of a CNN. We also acquired actual real-time undersampled radial data in
patients with congenital heart disease (CHD), and compare CNN reconstruction to
Compressed Sensing (CS).
METHODS: A 3D (2D plus time) CNN architecture was developed, and trained
using 2276 gold-standard paired 3D data sets, with 14x radial undersampling.
Four sampling schemes were tested, using 169 previously unseen 3D 'synthetic'
test data sets. Actual real-time tiny Golden Angle (tGA) radial SSFP data was
acquired in 10 new patients (122 3D data sets), and reconstructed using the 3D
CNN as well as a CS algorithm; GRASP.
RESULTS: Sampling pattern was shown to be important for image quality, and
accurate visualisation of cardiac structures. For actual real-time data,
overall reconstruction time with CNN (including creation of aliased images) was
shown to be more than 5x faster than GRASP. Additionally, CNN image quality and
accuracy of biventricular volumes was observed to be superior to GRASP for the
same raw data.
CONCLUSION: This paper has demonstrated the potential for the use of a 3D CNN
for deep de-aliasing of real-time radial data, within the clinical setting.
Clinical measures of ventricular volumes using real-time data with CNN
reconstruction are not statistically significantly different from the
gold-standard, cardiac gated, BH techniques.
| null |
http://arxiv.org/abs/1803.05192v3
|
http://arxiv.org/pdf/1803.05192v3.pdf
| null |
[
"Andreas Hauptmann",
"Simon Arridge",
"Felix Lucka",
"Vivek Muthurangu",
"Jennifer A. Steeden"
] |
[
"compressed sensing",
"De-aliasing"
] | 2018-03-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/translations-as-additional-contexts-for
|
1806.05516
| null | null |
Translations as Additional Contexts for Sentence Classification
|
In sentence classification tasks, additional contexts, such as the
neighboring sentences, may improve the accuracy of the classifier. However,
such contexts are domain-dependent and thus cannot be used for another
classification task with an inappropriate domain. In contrast, we propose the
use of translated sentences as context that is always available regardless of
the domain. We find that naive feature expansion of translations gains only
marginal improvements and may decrease the performance of the classifier, due
to possible inaccurate translations thus producing noisy sentence vectors. To
this end, we present multiple context fixing attachment (MCFA), a series of
modules attached to multiple sentence vectors to fix the noise in the vectors
using the other sentence vectors as context. We show that our method performs
competitively compared to previous models, achieving best classification
performance on multiple data sets. We are the first to use translations as
domain-free contexts for sentence classification.
|
We are the first to use translations as domain-free contexts for sentence classification.
|
http://arxiv.org/abs/1806.05516v1
|
http://arxiv.org/pdf/1806.05516v1.pdf
| null |
[
"Reinald Kim Amplayo",
"Kyungjae Lee",
"Jinyeong Yeo",
"Seung-won Hwang"
] |
[
"Classification",
"General Classification",
"Sentence",
"Sentence Classification",
"Subjectivity Analysis",
"Text Classification"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/the-exact-equivalence-of-distance-and-kernel
|
1806.05514
| null | null |
The Exact Equivalence of Distance and Kernel Methods for Hypothesis Testing
|
Distance-based tests, also called "energy statistics", are leading methods for two-sample and independence tests from the statistics community. Kernel-based tests, developed from "kernel mean embeddings", are leading methods for two-sample and independence tests from the machine learning community. A fixed-point transformation was previously proposed to connect the distance methods and kernel methods for the population statistics. In this paper, we propose a new bijective transformation between metrics and kernels. It simplifies the fixed-point transformation, inherits similar theoretical properties, allows distance methods to be exactly the same as kernel methods for sample statistics and p-value, and better preserves the data structure upon transformation. Our results further advance the understanding in distance and kernel-based tests, streamline the code base for implementing these tests, and enable a rich literature of distance-based and kernel-based methodologies to directly communicate with each other.
| null |
https://arxiv.org/abs/1806.05514v5
|
https://arxiv.org/pdf/1806.05514v5.pdf
| null |
[
"Cencheng Shen",
"Joshua T. Vogelstein"
] |
[
"Two-sample testing"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/humor-detection-in-english-hindi-code-mixed
|
1806.05513
| null | null |
Humor Detection in English-Hindi Code-Mixed Social Media Content : Corpus and Baseline System
|
The tremendous amount of user generated data through social networking sites
led to the gaining popularity of automatic text classification in the field of
computational linguistics over the past decade. Within this domain, one problem
that has drawn the attention of many researchers is automatic humor detection
in texts. In depth semantic understanding of the text is required to detect
humor which makes the problem difficult to automate. With increase in the
number of social media users, many multilingual speakers often interchange
between languages while posting on social media which is called code-mixing. It
introduces some challenges in the field of linguistic analysis of social media
content (Barman et al., 2014), like spelling variations and non-grammatical
structures in a sentence. Past researches include detecting puns in texts (Kao
et al., 2016) and humor in one-lines (Mihalcea et al., 2010) in a single
language, but with the tremendous amount of code-mixed data available online,
there is a need to develop techniques which detects humor in code-mixed tweets.
In this paper, we analyze the task of humor detection in texts and describe a
freely available corpus containing English-Hindi code-mixed tweets annotated
with humorous(H) or non-humorous(N) tags. We also tagged the words in the
tweets with Language tags (English/Hindi/Others). Moreover, we describe the
experiments carried out on the corpus and provide a baseline classification
system which distinguishes between humorous and non-humorous texts.
| null |
http://arxiv.org/abs/1806.05513v1
|
http://arxiv.org/pdf/1806.05513v1.pdf
|
LREC 2018 5
|
[
"Ankush Khandelwal",
"Sahil Swami",
"Syed S. Akhtar",
"Manish Shrivastava"
] |
[
"General Classification",
"Humor Detection",
"Sentence",
"text-classification",
"Text Classification"
] | 2018-06-14T00:00:00 |
https://aclanthology.org/L18-1193
|
https://aclanthology.org/L18-1193.pdf
|
humor-detection-in-english-hindi-code-mixed-1
| null |
[] |
https://paperswithcode.com/paper/netscore-towards-universal-metrics-for-large
|
1806.05512
| null |
Hyzq4ZKa97
|
NetScore: Towards Universal Metrics for Large-scale Performance Analysis of Deep Neural Networks for Practical On-Device Edge Usage
|
Much of the focus in the design of deep neural networks has been on improving
accuracy, leading to more powerful yet highly complex network architectures
that are difficult to deploy in practical scenarios, particularly on edge
devices such as mobile and other consumer devices given their high
computational and memory requirements. As a result, there has been a recent
interest in the design of quantitative metrics for evaluating deep neural
networks that accounts for more than just model accuracy as the sole indicator
of network performance. In this study, we continue the conversation towards
universal metrics for evaluating the performance of deep neural networks for
practical on-device edge usage. In particular, we propose a new balanced metric
called NetScore, which is designed specifically to provide a quantitative
assessment of the balance between accuracy, computational complexity, and
network architecture complexity of a deep neural network, which is important
for on-device edge operation. In what is one of the largest comparative
analysis between deep neural networks in literature, the NetScore metric, the
top-1 accuracy metric, and the popular information density metric were compared
across a diverse set of 60 different deep convolutional neural networks for
image classification on the ImageNet Large Scale Visual Recognition Challenge
(ILSVRC 2012) dataset. The evaluation results across these three metrics for
this diverse set of networks are presented in this study to act as a reference
guide for practitioners in the field. The proposed NetScore metric, along with
the other tested metrics, are by no means perfect, but the hope is to push the
conversation towards better universal metrics for evaluating deep neural
networks for use in practical on-device edge scenarios to help guide
practitioners in model design for such scenarios.
| null |
http://arxiv.org/abs/1806.05512v2
|
http://arxiv.org/pdf/1806.05512v2.pdf
| null |
[
"Alexander Wong"
] |
[
"image-classification",
"Image Classification",
"Object Recognition"
] | 2018-06-14T00:00:00 |
https://openreview.net/forum?id=Hyzq4ZKa97
|
https://openreview.net/pdf?id=Hyzq4ZKa97
| null | null |
[] |
https://paperswithcode.com/paper/cold-start-aware-user-and-product-attention
|
1806.05507
| null | null |
Cold-Start Aware User and Product Attention for Sentiment Classification
|
The use of user/product information in sentiment analysis is important,
especially for cold-start users/products, whose number of reviews are very
limited. However, current models do not deal with the cold-start problem which
is typical in review websites. In this paper, we present Hybrid Contextualized
Sentiment Classifier (HCSC), which contains two modules: (1) a fast word
encoder that returns word vectors embedded with short and long range dependency
features; and (2) Cold-Start Aware Attention (CSAA), an attention mechanism
that considers the existence of cold-start problem when attentively pooling the
encoded word vectors. HCSC introduces shared vectors that are constructed from
similar users/products, and are used when the original distinct vectors do not
have sufficient information (i.e. cold-start). This is decided by a
frequency-guided selective gate vector. Our experiments show that in terms of
RMSE, HCSC performs significantly better when compared with on famous datasets,
despite having less complexity, and thus can be trained much faster. More
importantly, our model performs significantly better than previous models when
the training data is sparse and has cold-start problems.
|
The use of user/product information in sentiment analysis is important, especially for cold-start users/products, whose number of reviews are very limited.
|
http://arxiv.org/abs/1806.05507v1
|
http://arxiv.org/pdf/1806.05507v1.pdf
|
ACL 2018 7
|
[
"Reinald Kim Amplayo",
"Jihyeok Kim",
"Sua Sung",
"Seung-won Hwang"
] |
[
"Classification",
"General Classification",
"Sentiment Analysis",
"Sentiment Classification"
] | 2018-06-14T00:00:00 |
https://aclanthology.org/P18-1236
|
https://aclanthology.org/P18-1236.pdf
|
cold-start-aware-user-and-product-attention-1
| null |
[] |
https://paperswithcode.com/paper/dense-light-field-reconstruction-from-sparse
|
1806.05506
| null | null |
Dense Light Field Reconstruction From Sparse Sampling Using Residual Network
|
A light field records numerous light rays from a real-world scene. However,
capturing a dense light field by existing devices is a time-consuming process.
Besides, reconstructing a large amount of light rays equivalent to multiple
light fields using sparse sampling arises a severe challenge for existing
methods. In this paper, we present a learning based method to reconstruct
multiple novel light fields between two mutually independent light fields. We
indicate that light rays distributed in different light fields have the same
consistent constraints under a certain condition. The most significant
constraint is a depth related correlation between angular and spatial
dimensions. Our method avoids working out the error-sensitive constraint by
employing a deep neural network. We solve residual values of pixels on epipolar
plane image (EPI) to reconstruct novel light fields. Our method is able to
reconstruct 2 to 4 novel light fields between two mutually independent input
light fields. We also compare our results with those yielded by a number of
alternatives elsewhere in the literature, which shows our reconstructed light
fields have better structure similarity and occlusion relationship.
| null |
http://arxiv.org/abs/1806.05506v2
|
http://arxiv.org/pdf/1806.05506v2.pdf
| null |
[
"Mantang Guo",
"Hao Zhu",
"Guoqing Zhou",
"Qing Wang"
] |
[] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/entity-commonsense-representation-for-neural
|
1806.05504
| null | null |
Entity Commonsense Representation for Neural Abstractive Summarization
|
A major proportion of a text summary includes important entities found in the
original text. These entities build up the topic of the summary. Moreover, they
hold commonsense information once they are linked to a knowledge base. Based on
these observations, this paper investigates the usage of linked entities to
guide the decoder of a neural text summarizer to generate concise and better
summaries. To this end, we leverage on an off-the-shelf entity linking system
(ELS) to extract linked entities and propose Entity2Topic (E2T), a module
easily attachable to a sequence-to-sequence model that transforms a list of
entities into a vector representation of the topic of the summary. Current
available ELS's are still not sufficiently effective, possibly introducing
unresolved ambiguities and irrelevant entities. We resolve the imperfections of
the ELS by (a) encoding entities with selective disambiguation, and (b) pooling
entity vectors using firm attention. By applying E2T to a simple
sequence-to-sequence model with attention mechanism as base model, we see
significant improvements of the performance in the Gigaword (sentence to title)
and CNN (long document to multi-sentence highlights) summarization datasets by
at least 2 ROUGE points.
|
To this end, we leverage on an off-the-shelf entity linking system (ELS) to extract linked entities and propose Entity2Topic (E2T), a module easily attachable to a sequence-to-sequence model that transforms a list of entities into a vector representation of the topic of the summary.
|
http://arxiv.org/abs/1806.05504v1
|
http://arxiv.org/pdf/1806.05504v1.pdf
|
NAACL 2018 6
|
[
"Reinald Kim Amplayo",
"Seonjae Lim",
"Seung-won Hwang"
] |
[
"Abstractive Text Summarization",
"Decoder",
"Entity Linking",
"Sentence"
] | 2018-06-14T00:00:00 |
https://aclanthology.org/N18-1064
|
https://aclanthology.org/N18-1064.pdf
|
entity-commonsense-representation-for-neural-1
| null |
[] |
https://paperswithcode.com/paper/gradient-based-meta-learning-with-learned
|
1801.05558
| null | null |
Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace
|
Gradient-based meta-learning methods leverage gradient descent to learn the
commonalities among various tasks. While previous such methods have been
successful in meta-learning tasks, they resort to simple gradient descent
during meta-testing. Our primary contribution is the {\em MT-net}, which
enables the meta-learner to learn on each layer's activation space a subspace
that the task-specific learner performs gradient descent on. Additionally, a
task-specific learner of an {\em MT-net} performs gradient descent with respect
to a meta-learned distance metric, which warps the activation space to be more
sensitive to task identity. We demonstrate that the dimension of this learned
subspace reflects the complexity of the task-specific learner's adaptation
task, and also that our model is less sensitive to the choice of initial
learning rates than previous gradient-based meta-learning methods. Our method
achieves state-of-the-art or comparable performance on few-shot classification
and regression tasks.
|
Our primary contribution is the {\em MT-net}, which enables the meta-learner to learn on each layer's activation space a subspace that the task-specific learner performs gradient descent on.
|
http://arxiv.org/abs/1801.05558v3
|
http://arxiv.org/pdf/1801.05558v3.pdf
|
ICML 2018 7
|
[
"Yoonho Lee",
"Seungjin Choi"
] |
[
"Few-Shot Image Classification",
"Meta-Learning"
] | 2018-01-17T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2202
|
http://proceedings.mlr.press/v80/lee18a/lee18a.pdf
|
gradient-based-meta-learning-with-learned-1
| null |
[] |
https://paperswithcode.com/paper/aspect-sentiment-model-for-micro-reviews
|
1806.05499
| null | null |
Aspect Sentiment Model for Micro Reviews
|
This paper aims at an aspect sentiment model for aspect-based sentiment
analysis (ABSA) focused on micro reviews. This task is important in order to
understand short reviews majority of the users write, while existing topic
models are targeted for expert-level long reviews with sufficient co-occurrence
patterns to observe. Current methods on aggregating micro reviews using
metadata information may not be effective as well due to metadata absence,
topical heterogeneity, and cold start problems. To this end, we propose a model
called Micro Aspect Sentiment Model (MicroASM). MicroASM is based on the
observation that short reviews 1) are viewed with sentiment-aspect word pairs
as building blocks of information, and 2) can be clustered into larger reviews.
When compared to the current state-of-the-art aspect sentiment models,
experiments show that our model provides better performance on aspect-level
tasks such as aspect term extraction and document-level tasks such as sentiment
classification.
|
This paper aims at an aspect sentiment model for aspect-based sentiment analysis (ABSA) focused on micro reviews.
|
http://arxiv.org/abs/1806.05499v1
|
http://arxiv.org/pdf/1806.05499v1.pdf
| null |
[
"Reinald Kim Amplayo",
"Seung-won Hwang"
] |
[
"Aspect-Based Sentiment Analysis",
"Aspect-Based Sentiment Analysis (ABSA)",
"model",
"Sentiment Analysis",
"Sentiment Classification",
"Term Extraction",
"Topic Models"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/on-accurate-evaluation-of-gans-for-language
|
1806.04936
| null |
rJMcdsA5FX
|
On Accurate Evaluation of GANs for Language Generation
|
Generative Adversarial Networks (GANs) are a promising approach to language generation. The latest works introducing novel GAN models for language generation use n-gram based metrics for evaluation and only report single scores of the best run. In this paper, we argue that this often misrepresents the true picture and does not tell the full story, as GAN models can be extremely sensitive to the random initialization and small deviations from the best hyperparameter choice. In particular, we demonstrate that the previously used BLEU score is not sensitive to semantic deterioration of generated texts and propose alternative metrics that better capture the quality and diversity of the generated samples. We also conduct a set of experiments comparing a number of GAN models for text with a conventional Language Model (LM) and find that neither of the considered models performs convincingly better than the LM.
| null |
https://arxiv.org/abs/1806.04936v3
|
https://arxiv.org/pdf/1806.04936v3.pdf
| null |
[
"Stanislau Semeniuta",
"Aliaksei Severyn",
"Sylvain Gelly"
] |
[
"Diversity",
"Language Modeling",
"Language Modelling",
"Text Generation"
] | 2018-06-13T00:00:00 |
https://openreview.net/forum?id=rJMcdsA5FX
|
https://openreview.net/pdf?id=rJMcdsA5FX
|
on-accurate-evaluation-of-gans-for-language-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/dynamic-video-segmentation-network
|
1804.00931
| null | null |
Dynamic Video Segmentation Network
|
In this paper, we present a detailed design of dynamic video segmentation
network (DVSNet) for fast and efficient semantic video segmentation. DVSNet
consists of two convolutional neural networks: a segmentation network and a
flow network. The former generates highly accurate semantic segmentations, but
is deeper and slower. The latter is much faster than the former, but its output
requires further processing to generate less accurate semantic segmentations.
We explore the use of a decision network to adaptively assign different frame
regions to different networks based on a metric called expected confidence
score. Frame regions with a higher expected confidence score traverse the flow
network. Frame regions with a lower expected confidence score have to pass
through the segmentation network. We have extensively performed experiments on
various configurations of DVSNet, and investigated a number of variants for the
proposed decision network. The experimental results show that our DVSNet is
able to achieve up to 70.4% mIoU at 19.8 fps on the Cityscape dataset. A high
speed version of DVSNet is able to deliver an fps of 30.4 with 63.2% mIoU on
the same dataset. DVSNet is also able to reduce up to 95% of the computational
workloads.
| null |
http://arxiv.org/abs/1804.00931v2
|
http://arxiv.org/pdf/1804.00931v2.pdf
|
CVPR 2018 6
|
[
"Yu-Syuan Xu",
"Tsu-Jui Fu",
"Hsuan-Kung Yang",
"Chun-Yi Lee"
] |
[
"Segmentation",
"Video Segmentation",
"Video Semantic Segmentation"
] | 2018-04-03T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Xu_Dynamic_Video_Segmentation_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_Dynamic_Video_Segmentation_CVPR_2018_paper.pdf
|
dynamic-video-segmentation-network-1
| null |
[] |
https://paperswithcode.com/paper/learning-a-tree-structured-ising-model-in
|
1604.06749
| null | null |
Learning a Tree-Structured Ising Model in Order to Make Predictions
|
We study the problem of learning a tree Ising model from samples such that
subsequent predictions made using the model are accurate. The prediction task
considered in this paper is that of predicting the values of a subset of
variables given values of some other subset of variables. Virtually all
previous work on graphical model learning has focused on recovering the true
underlying graph. We define a distance ("small set TV" or ssTV) between
distributions $P$ and $Q$ by taking the maximum, over all subsets $\mathcal{S}$
of a given size, of the total variation between the marginals of $P$ and $Q$ on
$\mathcal{S}$; this distance captures the accuracy of the prediction task of
interest. We derive non-asymptotic bounds on the number of samples needed to
get a distribution (from the same class) with small ssTV relative to the one
generating the samples. One of the main messages of this paper is that far
fewer samples are needed than for recovering the underlying tree, which means
that accurate predictions are possible using the wrong tree.
| null |
http://arxiv.org/abs/1604.06749v3
|
http://arxiv.org/pdf/1604.06749v3.pdf
| null |
[
"Guy Bresler",
"Mina Karzand"
] |
[] | 2016-04-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/inference-in-deep-gaussian-processes-using
|
1806.05490
| null | null |
Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo
|
Deep Gaussian Processes (DGPs) are hierarchical generalizations of Gaussian
Processes that combine well calibrated uncertainty estimates with the high
flexibility of multilayer models. One of the biggest challenges with these
models is that exact inference is intractable. The current state-of-the-art
inference method, Variational Inference (VI), employs a Gaussian approximation
to the posterior distribution. This can be a potentially poor unimodal
approximation of the generally multimodal posterior. In this work, we provide
evidence for the non-Gaussian nature of the posterior and we apply the
Stochastic Gradient Hamiltonian Monte Carlo method to generate samples. To
efficiently optimize the hyperparameters, we introduce the Moving Window MCEM
algorithm. This results in significantly better predictions at a lower
computational cost than its VI counterpart. Thus our method establishes a new
state-of-the-art for inference in DGPs.
|
The current state-of-the-art inference method, Variational Inference (VI), employs a Gaussian approximation to the posterior distribution.
|
http://arxiv.org/abs/1806.05490v3
|
http://arxiv.org/pdf/1806.05490v3.pdf
|
NeurIPS 2018 12
|
[
"Marton Havasi",
"José Miguel Hernández-Lobato",
"Juan José Murillo-Fuentes"
] |
[
"Gaussian Processes",
"Variational Inference"
] | 2018-06-14T00:00:00 |
http://papers.nips.cc/paper/7979-inference-in-deep-gaussian-processes-using-stochastic-gradient-hamiltonian-monte-carlo
|
http://papers.nips.cc/paper/7979-inference-in-deep-gaussian-processes-using-stochastic-gradient-hamiltonian-monte-carlo.pdf
|
inference-in-deep-gaussian-processes-using-1
| null |
[] |
https://paperswithcode.com/paper/nearly-zero-shot-learning-for-semantic
|
1806.05484
| null | null |
Nearly Zero-Shot Learning for Semantic Decoding in Spoken Dialogue Systems
|
This paper presents two ways of dealing with scarce data in semantic decoding
using N-Best speech recognition hypotheses. First, we learn features by using a
deep learning architecture in which the weights for the unknown and known
categories are jointly optimised. Second, an unsupervised method is used for
further tuning the weights. Sharing weights injects prior knowledge to unknown
categories. The unsupervised tuning (i.e. the risk minimisation) improves the
F-Measure when recognising nearly zero-shot data on the DSTC3 corpus. This
unsupervised method can be applied subject to two assumptions: the rank of the
class marginal is assumed to be known and the class-conditional scores of the
classifier are assumed to follow a Gaussian distribution.
| null |
http://arxiv.org/abs/1806.05484v2
|
http://arxiv.org/pdf/1806.05484v2.pdf
| null |
[
"Lina M. Rojas-Barahona",
"Stefan Ultes",
"Pawel Budzianowski",
"Iñigo Casanueva",
"Milica Gasic",
"Bo-Hsiang Tseng",
"Steve Young"
] |
[
"speech-recognition",
"Speech Recognition",
"Spoken Dialogue Systems",
"Zero-Shot Learning"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/morphological-and-language-agnostic-word
|
1806.05482
| null | null |
Morphological and Language-Agnostic Word Segmentation for NMT
|
The state of the art of handling rich morphology in neural machine
translation (NMT) is to break word forms into subword units, so that the
overall vocabulary size of these units fits the practical limits given by the
NMT model and GPU memory capacity. In this paper, we compare two common but
linguistically uninformed methods of subword construction (BPE and STE, the
method implemented in Tensor2Tensor toolkit) and two linguistically-motivated
methods: Morfessor and one novel method, based on a derivational dictionary.
Our experiments with German-to-Czech translation, both morphologically rich,
document that so far, the non-motivated methods perform better. Furthermore, we
iden- tify a critical difference between BPE and STE and show a simple pre-
processing step for BPE that considerably increases translation quality as
evaluated by automatic measures.
|
The state of the art of handling rich morphology in neural machine translation (NMT) is to break word forms into subword units, so that the overall vocabulary size of these units fits the practical limits given by the NMT model and GPU memory capacity.
|
http://arxiv.org/abs/1806.05482v1
|
http://arxiv.org/pdf/1806.05482v1.pdf
| null |
[
"Dominik Macháček",
"Jonáš Vidra",
"Ondřej Bojar"
] |
[
"GPU",
"Machine Translation",
"NMT",
"Translation"
] | 2018-06-14T00:00:00 | null | null | null | null |
[
{
"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"
}
] |
https://paperswithcode.com/paper/automatic-language-identification-for-romance
|
1806.05480
| null | null |
Automatic Language Identification for Romance Languages using Stop Words and Diacritics
|
Automatic language identification is a natural language processing problem
that tries to determine the natural language of a given content. In this paper
we present a statistical method for automatic language identification of
written text using dictionaries containing stop words and diacritics. We
propose different approaches that combine the two dictionaries to accurately
determine the language of textual corpora. This method was chosen because stop
words and diacritics are very specific to a language, although some languages
have some similar words and special characters they are not all common. The
languages taken into account were romance languages because they are very
similar and usually it is hard to distinguish between them from a computational
point of view. We have tested our method using a Twitter corpus and a news
article corpus. Both corpora consists of UTF-8 encoded text, so the diacritics
could be taken into account, in the case that the text has no diacritics only
the stop words are used to determine the language of the text. The experimental
results show that the proposed method has an accuracy of over 90% for small
texts and over 99.8% for
| null |
http://arxiv.org/abs/1806.05480v1
|
http://arxiv.org/pdf/1806.05480v1.pdf
| null |
[
"Ciprian-Octavian Truică",
"Julien Velcin",
"Alexandru Boicea"
] |
[
"Language Identification"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/copycat-cnn-stealing-knowledge-by-persuading
|
1806.05476
| null | null |
Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data
|
In the past few years, Convolutional Neural Networks (CNNs) have been
achieving state-of-the-art performance on a variety of problems. Many companies
employ resources and money to generate these models and provide them as an API,
therefore it is in their best interest to protect them, i.e., to avoid that
someone else copies them. Recent studies revealed that state-of-the-art CNNs
are vulnerable to adversarial examples attacks, and this weakness indicates
that CNNs do not need to operate in the problem domain (PD). Therefore, we
hypothesize that they also do not need to be trained with examples of the PD in
order to operate in it.
Given these facts, in this paper, we investigate if a target black-box CNN
can be copied by persuading it to confess its knowledge through random
non-labeled data. The copy is two-fold: i) the target network is queried with
random data and its predictions are used to create a fake dataset with the
knowledge of the network; and ii) a copycat network is trained with the fake
dataset and should be able to achieve similar performance as the target
network.
This hypothesis was evaluated locally in three problems (facial expression,
object, and crosswalk classification) and against a cloud-based API. In the
copy attacks, images from both non-problem domain and PD were used. All copycat
networks achieved at least 93.7% of the performance of the original models with
non-problem domain data, and at least 98.6% using additional data from the PD.
Additionally, the copycat CNN successfully copied at least 97.3% of the
performance of the Microsoft Azure Emotion API. Our results show that it is
possible to create a copycat CNN by simply querying a target network as
black-box with random non-labeled data.
|
The copy is two-fold: i) the target network is queried with random data and its predictions are used to create a fake dataset with the knowledge of the network; and ii) a copycat network is trained with the fake dataset and should be able to achieve similar performance as the target network.
|
http://arxiv.org/abs/1806.05476v1
|
http://arxiv.org/pdf/1806.05476v1.pdf
| null |
[
"Jacson Rodrigues Correia-Silva",
"Rodrigo F. Berriel",
"Claudine Badue",
"Alberto F. de Souza",
"Thiago Oliveira-Santos"
] |
[] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/efficient-active-learning-for-image
|
1806.05473
| null | null |
Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network
|
Training robust deep learning (DL) systems for medical image classification or segmentation is challenging due to limited images covering different disease types and severity. We propose an active learning (AL) framework to select most informative samples and add to the training data. We use conditional generative adversarial networks (cGANs) to generate realistic chest xray images with different disease characteristics by conditioning its generation on a real image sample. Informative samples to add to the training set are identified using a Bayesian neural network. Experiments show our proposed AL framework is able to achieve state of the art performance by using about 35% of the full dataset, thus saving significant time and effort over conventional methods.
| null |
https://arxiv.org/abs/1806.05473v4
|
https://arxiv.org/pdf/1806.05473v4.pdf
| null |
[
"Dwarikanath Mahapatra",
"Behzad Bozorgtabar",
"Jean-Philippe Thiran",
"Mauricio Reyes"
] |
[
"Active Learning",
"General Classification",
"Generative Adversarial Network",
"image-classification",
"Image Classification",
"Medical Image Classification"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/relation-networks-for-object-detection
|
1711.11575
| null | null |
Relation Networks for Object Detection
|
Although it is well believed for years that modeling relations between
objects would help object recognition, there has not been evidence that the
idea is working in the deep learning era. All state-of-the-art object detection
systems still rely on recognizing object instances individually, without
exploiting their relations during learning.
This work proposes an object relation module. It processes a set of objects
simultaneously through interaction between their appearance feature and
geometry, thus allowing modeling of their relations. It is lightweight and
in-place. It does not require additional supervision and is easy to embed in
existing networks. It is shown effective on improving object recognition and
duplicate removal steps in the modern object detection pipeline. It verifies
the efficacy of modeling object relations in CNN based detection. It gives rise
to the first fully end-to-end object detector.
|
Although it is well believed for years that modeling relations between objects would help object recognition, there has not been evidence that the idea is working in the deep learning era.
|
http://arxiv.org/abs/1711.11575v2
|
http://arxiv.org/pdf/1711.11575v2.pdf
|
CVPR 2018 6
|
[
"Han Hu",
"Jiayuan Gu",
"Zheng Zhang",
"Jifeng Dai",
"Yichen Wei"
] |
[
"Object",
"object-detection",
"Object Detection",
"Object Recognition",
"Relation"
] | 2017-11-30T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Hu_Relation_Networks_for_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Hu_Relation_Networks_for_CVPR_2018_paper.pdf
|
relation-networks-for-object-detection-1
| null |
[] |
https://paperswithcode.com/paper/dice-the-infinitely-differentiable-monte
|
1802.05098
| null | null |
DiCE: The Infinitely Differentiable Monte-Carlo Estimator
|
The score function estimator is widely used for estimating gradients of
stochastic objectives in stochastic computation graphs (SCG), eg, in
reinforcement learning and meta-learning. While deriving the first-order
gradient estimators by differentiating a surrogate loss (SL) objective is
computationally and conceptually simple, using the same approach for
higher-order derivatives is more challenging. Firstly, analytically deriving
and implementing such estimators is laborious and not compliant with automatic
differentiation. Secondly, repeatedly applying SL to construct new objectives
for each order derivative involves increasingly cumbersome graph manipulations.
Lastly, to match the first-order gradient under differentiation, SL treats part
of the cost as a fixed sample, which we show leads to missing and wrong terms
for estimators of higher-order derivatives. To address all these shortcomings
in a unified way, we introduce DiCE, which provides a single objective that can
be differentiated repeatedly, generating correct estimators of derivatives of
any order in SCGs. Unlike SL, DiCE relies on automatic differentiation for
performing the requisite graph manipulations. We verify the correctness of DiCE
both through a proof and numerical evaluation of the DiCE derivative estimates.
We also use DiCE to propose and evaluate a novel approach for multi-agent
learning. Our code is available at https://www.github.com/alshedivat/lola.
|
Lastly, to match the first-order gradient under differentiation, SL treats part of the cost as a fixed sample, which we show leads to missing and wrong terms for estimators of higher-order derivatives.
|
http://arxiv.org/abs/1802.05098v3
|
http://arxiv.org/pdf/1802.05098v3.pdf
| null |
[
"Jakob Foerster",
"Gregory Farquhar",
"Maruan Al-Shedivat",
"Tim Rocktäschel",
"Eric P. Xing",
"Shimon Whiteson"
] |
[
"Meta-Learning",
"Reinforcement Learning"
] | 2018-02-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-cross-lingual-distributed-logical
|
1806.05461
| null | null |
Learning Cross-lingual Distributed Logical Representations for Semantic Parsing
|
With the development of several multilingual datasets used for semantic
parsing, recent research efforts have looked into the problem of learning
semantic parsers in a multilingual setup. However, how to improve the
performance of a monolingual semantic parser for a specific language by
leveraging data annotated in different languages remains a research question
that is under-explored. In this work, we present a study to show how learning
distributed representations of the logical forms from data annotated in
different languages can be used for improving the performance of a monolingual
semantic parser. We extend two existing monolingual semantic parsers to
incorporate such cross-lingual distributed logical representations as features.
Experiments show that our proposed approach is able to yield improved semantic
parsing results on the standard multilingual GeoQuery dataset.
| null |
http://arxiv.org/abs/1806.05461v1
|
http://arxiv.org/pdf/1806.05461v1.pdf
|
ACL 2018 7
|
[
"Yanyan Zou",
"Wei Lu"
] |
[
"Semantic Parsing"
] | 2018-06-14T00:00:00 |
https://aclanthology.org/P18-2107
|
https://aclanthology.org/P18-2107.pdf
|
learning-cross-lingual-distributed-logical-1
| null |
[] |
https://paperswithcode.com/paper/gradient-layer-enhancing-the-convergence-of
|
1801.02227
| null | null |
Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models
|
We propose a new technique that boosts the convergence of training generative
adversarial networks. Generally, the rate of training deep models reduces
severely after multiple iterations. A key reason for this phenomenon is that a
deep network is expressed using a highly non-convex finite-dimensional model,
and thus the parameter gets stuck in a local optimum. Because of this, methods
often suffer not only from degeneration of the convergence speed but also from
limitations in the representational power of the trained network. To overcome
this issue, we propose an additional layer called the gradient layer to seek a
descent direction in an infinite-dimensional space. Because the layer is
constructed in the infinite-dimensional space, we are not restricted by the
specific model structure of finite-dimensional models. As a result, we can get
out of the local optima in finite-dimensional models and move towards the
global optimal function more directly. In this paper, this phenomenon is
explained from the functional gradient method perspective of the gradient
layer. Interestingly, the optimization procedure using the gradient layer
naturally constructs the deep structure of the network. Moreover, we
demonstrate that this procedure can be regarded as a discretization method of
the gradient flow that naturally reduces the objective function. Finally, the
method is tested using several numerical experiments, which show its fast
convergence.
| null |
http://arxiv.org/abs/1801.02227v2
|
http://arxiv.org/pdf/1801.02227v2.pdf
| null |
[
"Atsushi Nitanda",
"Taiji Suzuki"
] |
[] | 2018-01-07T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/lorenzopapa5/SPEED",
"description": "The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key components in autonomous and robotic systems. \r\nApproaches based on the state of the art vision transformer architectures are extremely deep and complex not suitable for real-time inference operations on edge and autonomous systems equipped with low resources (i.e. robot indoor navigation and surveillance). This paper presents SPEED, a Separable Pyramidal pooling EncodEr-Decoder architecture designed to achieve real-time frequency performances on multiple hardware platforms. The proposed model is a fast-throughput deep architecture for MDE able to obtain depth estimations with high accuracy from low resolution images using minimum hardware resources (i.e. edge devices). Our encoder-decoder model exploits two depthwise separable pyramidal pooling layers, which allow to increase the inference frequency while reducing the overall computational complexity. The proposed method performs better than other fast-throughput architectures in terms of both accuracy and frame rates, achieving real-time performances over cloud CPU, TPU and the NVIDIA Jetson TX1 on two indoor benchmarks: the NYU Depth v2 and the DIML Kinect v2 datasets.",
"full_name": "SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings",
"introduced_year": 2000,
"main_collection": null,
"name": "SPEED",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/analysis-of-the-effect-of-unexpected-outliers
|
1806.05455
| null | null |
Analysis of the Effect of Unexpected Outliers in the Classification of Spectroscopy Data
|
Multi-class classification algorithms are very widely used, but we argue that
they are not always ideal from a theoretical perspective, because they assume
all classes are characterized by the data, whereas in many applications,
training data for some classes may be entirely absent, rare, or statistically
unrepresentative. We evaluate one-sided classifiers as an alternative, since
they assume that only one class (the target) is well characterized. We consider
a task of identifying whether a substance contains a chlorinated solvent, based
on its chemical spectrum. For this application, it is not really feasible to
collect a statistically representative set of outliers, since that group may
contain \emph{anything} apart from the target chlorinated solvents. Using a new
one-sided classification toolkit, we compare a One-Sided k-NN algorithm with
two well-known binary classification algorithms, and conclude that the
one-sided classifier is more robust to unexpected outliers.
| null |
http://arxiv.org/abs/1806.05455v1
|
http://arxiv.org/pdf/1806.05455v1.pdf
| null |
[
"Frank G. Glavin",
"Michael G. Madden"
] |
[
"Binary Classification",
"Classification",
"General Classification",
"Multi-class Classification"
] | 2018-06-14T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**$k$-Nearest Neighbors** is a clustering-based algorithm for classification and regression. It is a a type of instance-based learning as it does not attempt to construct a general internal model, but simply stores instances of the training data. Prediction is computed from a simple majority vote of the nearest neighbors of each point: a query point is assigned the data class which has the most representatives within the nearest neighbors of the point.\r\n\r\nSource of Description and Image: [scikit-learn](https://scikit-learn.org/stable/modules/neighbors.html#classification)",
"full_name": "k-Nearest Neighbors",
"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": "k-NN",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/low-rank-geometric-mean-metric-learning
|
1806.05454
| null | null |
Low-rank geometric mean metric learning
|
We propose a low-rank approach to learning a Mahalanobis metric from data.
Inspired by the recent geometric mean metric learning (GMML) algorithm, we
propose a low-rank variant of the algorithm. This allows to jointly learn a
low-dimensional subspace where the data reside and the Mahalanobis metric that
appropriately fits the data. Our results show that we compete effectively with
GMML at lower ranks.
|
We propose a low-rank approach to learning a Mahalanobis metric from data.
|
http://arxiv.org/abs/1806.05454v1
|
http://arxiv.org/pdf/1806.05454v1.pdf
| null |
[
"Mukul Bhutani",
"Pratik Jawanpuria",
"Hiroyuki Kasai",
"Bamdev Mishra"
] |
[
"Metric Learning"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/4dfab-a-large-scale-4d-facial-expression
|
1712.01443
| null | null |
4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications
|
The progress we are currently witnessing in many computer vision
applications, including automatic face analysis, would not be made possible
without tremendous efforts in collecting and annotating large scale visual
databases. To this end, we propose 4DFAB, a new large scale database of dynamic
high-resolution 3D faces (over 1,800,000 3D meshes). 4DFAB contains recordings
of 180 subjects captured in four different sessions spanning over a five-year
period. It contains 4D videos of subjects displaying both spontaneous and posed
facial behaviours. The database can be used for both face and facial expression
recognition, as well as behavioural biometrics. It can also be used to learn
very powerful blendshapes for parametrising facial behaviour. In this paper, we
conduct several experiments and demonstrate the usefulness of the database for
various applications. The database will be made publicly available for research
purposes.
|
4DFAB contains recordings of 180 subjects captured in four different sessions spanning over a five-year period.
|
http://arxiv.org/abs/1712.01443v2
|
http://arxiv.org/pdf/1712.01443v2.pdf
| null |
[
"Shiyang Cheng",
"Irene Kotsia",
"Maja Pantic",
"Stefanos Zafeiriou"
] |
[
"Facial Expression Recognition",
"Facial Expression Recognition (FER)"
] | 2017-12-05T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/masked-autoregressive-flow-for-density
|
1705.07057
| null | null |
Masked Autoregressive Flow for Density Estimation
|
Autoregressive models are among the best performing neural density
estimators. We describe an approach for increasing the flexibility of an
autoregressive model, based on modelling the random numbers that the model uses
internally when generating data. By constructing a stack of autoregressive
models, each modelling the random numbers of the next model in the stack, we
obtain a type of normalizing flow suitable for density estimation, which we
call Masked Autoregressive Flow. This type of flow is closely related to
Inverse Autoregressive Flow and is a generalization of Real NVP. Masked
Autoregressive Flow achieves state-of-the-art performance in a range of
general-purpose density estimation tasks.
|
By constructing a stack of autoregressive models, each modelling the random numbers of the next model in the stack, we obtain a type of normalizing flow suitable for density estimation, which we call Masked Autoregressive Flow.
|
http://arxiv.org/abs/1705.07057v4
|
http://arxiv.org/pdf/1705.07057v4.pdf
|
NeurIPS 2017 12
|
[
"George Papamakarios",
"Theo Pavlakou",
"Iain Murray"
] |
[
"Density Estimation"
] | 2017-05-19T00:00:00 |
http://papers.nips.cc/paper/6828-masked-autoregressive-flow-for-density-estimation
|
http://papers.nips.cc/paper/6828-masked-autoregressive-flow-for-density-estimation.pdf
|
masked-autoregressive-flow-for-density-1
| null |
[] |
https://paperswithcode.com/paper/deep-generative-models-in-the-real-world-an
|
1806.05452
| null | null |
Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging
|
Recent advances in deep learning led to novel generative modeling techniques
that achieve unprecedented quality in generated samples and performance in
learning complex distributions in imaging data. These new models in medical
image computing have important applications that form clinically relevant and
very challenging unsupervised learning problems. In this paper, we explore the
feasibility of using state-of-the-art auto-encoder-based deep generative
models, such as variational and adversarial auto-encoders, for one such task:
abnormality detection in medical imaging. We utilize typical, publicly
available datasets with brain scans from healthy subjects and patients with
stroke lesions and brain tumors. We use the data from healthy subjects to train
different auto-encoder based models to learn the distribution of healthy images
and detect pathologies as outliers. Models that can better learn the data
distribution should be able to detect outliers more accurately. We evaluate the
detection performance of deep generative models and compare them with non-deep
learning based approaches to provide a benchmark of the current state of
research. We conclude that abnormality detection is a challenging task for deep
generative models and large room exists for improvement. In order to facilitate
further research, we aim to provide carefully pre-processed imaging data
available to the research community.
| null |
http://arxiv.org/abs/1806.05452v1
|
http://arxiv.org/pdf/1806.05452v1.pdf
| null |
[
"Xiaoran Chen",
"Nick Pawlowski",
"Martin Rajchl",
"Ben Glocker",
"Ender Konukoglu"
] |
[
"Anomaly Detection"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/the-committee-machine-computational-to
|
1806.05451
| null | null |
The committee machine: Computational to statistical gaps in learning a two-layers neural network
|
Heuristic tools from statistical physics have been used in the past to locate the phase transitions and compute the optimal learning and generalization errors in the teacher-student scenario in multi-layer neural networks. In this contribution, we provide a rigorous justification of these approaches for a two-layers neural network model called the committee machine. We also introduce a version of the approximate message passing (AMP) algorithm for the committee machine that allows to perform optimal learning in polynomial time for a large set of parameters. We find that there are regimes in which a low generalization error is information-theoretically achievable while the AMP algorithm fails to deliver it, strongly suggesting that no efficient algorithm exists for those cases, and unveiling a large computational gap.
|
Heuristic tools from statistical physics have been used in the past to locate the phase transitions and compute the optimal learning and generalization errors in the teacher-student scenario in multi-layer neural networks.
|
https://arxiv.org/abs/1806.05451v3
|
https://arxiv.org/pdf/1806.05451v3.pdf
|
NeurIPS 2018 12
|
[
"Benjamin Aubin",
"Antoine Maillard",
"Jean Barbier",
"Florent Krzakala",
"Nicolas Macris",
"Lenka Zdeborová"
] |
[] | 2018-06-14T00:00:00 |
http://papers.nips.cc/paper/7584-the-committee-machine-computational-to-statistical-gaps-in-learning-a-two-layers-neural-network
|
http://papers.nips.cc/paper/7584-the-committee-machine-computational-to-statistical-gaps-in-learning-a-two-layers-neural-network.pdf
|
the-committee-machine-computational-to-1
| null |
[] |
https://paperswithcode.com/paper/stochastic-gradient-descent-with-exponential
|
1806.05438
| null | null |
Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors
|
We consider stochastic gradient descent and its averaging variant for binary classification problems in a reproducing kernel Hilbert space. In the traditional analysis using a consistency property of loss functions, it is known that the expected classification error converges more slowly than the expected risk even when assuming a low-noise condition on the conditional label probabilities. Consequently, the resulting rate is sublinear. Therefore, it is important to consider whether much faster convergence of the expected classification error can be achieved. In recent research, an exponential convergence rate for stochastic gradient descent was shown under a strong low-noise condition but provided theoretical analysis was limited to the squared loss function, which is somewhat inadequate for binary classification tasks. In this paper, we show an exponential convergence of the expected classification error in the final phase of the stochastic gradient descent for a wide class of differentiable convex loss functions under similar assumptions. As for the averaged stochastic gradient descent, we show that the same convergence rate holds from the early phase of training. In experiments, we verify our analyses on the $L_2$-regularized logistic regression.
| null |
https://arxiv.org/abs/1806.05438v4
|
https://arxiv.org/pdf/1806.05438v4.pdf
| null |
[
"Atsushi Nitanda",
"Taiji Suzuki"
] |
[
"Binary Classification",
"Classification",
"General Classification"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/servenet-a-deep-neural-network-for-web
|
1806.05437
| null | null |
ServeNet: A Deep Neural Network for Web Services Classification
|
Automated service classification plays a crucial role in service discovery, selection, and composition. Machine learning has been widely used for service classification in recent years. However, the performance of conventional machine learning methods highly depends on the quality of manual feature engineering. In this paper, we present a novel deep neural network to automatically abstract low-level representation of both service name and service description to high-level merged features without feature engineering and the length limitation, and then predict service classification on 50 service categories. To demonstrate the effectiveness of our approach, we conduct a comprehensive experimental study by comparing 10 machine learning methods on 10,000 real-world web services. The result shows that the proposed deep neural network can achieve higher accuracy in classification and more robust than other machine learning methods.
|
Automated service classification plays a crucial role in service discovery, selection, and composition.
|
https://arxiv.org/abs/1806.05437v3
|
https://arxiv.org/pdf/1806.05437v3.pdf
| null |
[
"Yilong Yang",
"Nafees Qamar",
"Peng Liu",
"Katarina Grolinger",
"Weiru Wang",
"Zhi Li",
"Zhifang Liao"
] |
[
"BIG-bench Machine Learning",
"Classification",
"Feature Engineering",
"General Classification"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/transfer-learning-for-context-aware-question
|
1806.05434
| null | null |
Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce
|
Building multi-turn information-seeking conversation systems is an important
and challenging research topic. Although several advanced neural text matching
models have been proposed for this task, they are generally not efficient for
industrial applications. Furthermore, they rely on a large amount of labeled
data, which may not be available in real-world applications. To alleviate these
problems, we study transfer learning for multi-turn information seeking
conversations in this paper. We first propose an efficient and effective
multi-turn conversation model based on convolutional neural networks. After
that, we extend our model to adapt the knowledge learned from a resource-rich
domain to enhance the performance. Finally, we deployed our model in an
industrial chatbot called AliMe Assist
(https://consumerservice.taobao.com/online-help) and observed a significant
improvement over the existing online model.
| null |
http://arxiv.org/abs/1806.05434v1
|
http://arxiv.org/pdf/1806.05434v1.pdf
|
ACL 2018 7
|
[
"Minghui Qiu",
"Liu Yang",
"Feng Ji",
"Weipeng Zhao",
"Wei Zhou",
"Jun Huang",
"Haiqing Chen",
"W. Bruce Croft",
"Wei. Lin"
] |
[
"Chatbot",
"Text Matching",
"Transfer Learning"
] | 2018-06-14T00:00:00 |
https://aclanthology.org/P18-2034
|
https://aclanthology.org/P18-2034.pdf
|
transfer-learning-for-context-aware-question-1
| null |
[] |
https://paperswithcode.com/paper/urdu-word-segmentation-using-conditional
|
1806.05432
| null | null |
Urdu Word Segmentation using Conditional Random Fields (CRFs)
|
State-of-the-art Natural Language Processing algorithms rely heavily on
efficient word segmentation. Urdu is amongst languages for which word
segmentation is a complex task as it exhibits space omission as well as space
insertion issues. This is partly due to the Arabic script which although
cursive in nature, consists of characters that have inherent joining and
non-joining attributes regardless of word boundary. This paper presents a word
segmentation system for Urdu which uses a Conditional Random Field sequence
modeler with orthographic, linguistic and morphological features. Our proposed
model automatically learns to predict white space as word boundary as well as
Zero Width Non-Joiner (ZWNJ) as sub-word boundary. Using a manually annotated
corpus, our model achieves F1 score of 0.97 for word boundary identification
and 0.85 for sub-word boundary identification tasks. We have made our code and
corpus publicly available to make our results reproducible.
|
State-of-the-art Natural Language Processing algorithms rely heavily on efficient word segmentation.
|
http://arxiv.org/abs/1806.05432v1
|
http://arxiv.org/pdf/1806.05432v1.pdf
|
COLING 2018 8
|
[
"Haris Bin Zia",
"Agha Ali Raza",
"Awais Athar"
] |
[
"Segmentation"
] | 2018-06-14T00:00:00 |
https://aclanthology.org/C18-1217
|
https://aclanthology.org/C18-1217.pdf
|
urdu-word-segmentation-using-conditional-1
| null |
[] |
https://paperswithcode.com/paper/only-bayes-should-learn-a-manifold-on-the
|
1806.04994
| null | null |
Only Bayes should learn a manifold (on the estimation of differential geometric structure from data)
|
We investigate learning of the differential geometric structure of a data manifold embedded in a high-dimensional Euclidean space. We first analyze kernel-based algorithms and show that under the usual regularizations, non-probabilistic methods cannot recover the differential geometric structure, but instead find mostly linear manifolds or spaces equipped with teleports. To properly learn the differential geometric structure, non-probabilistic methods must apply regularizations that enforce large gradients, which go against common wisdom. We repeat the analysis for probabilistic methods and find that under reasonable priors, the geometric structure can be recovered. Fully exploiting the recovered structure, however, requires the development of stochastic extensions to classic Riemannian geometry. We take early steps in that regard. Finally, we partly extend the analysis to modern models based on neural networks, thereby highlighting geometric and probabilistic shortcomings of current deep generative models.
| null |
https://arxiv.org/abs/1806.04994v3
|
https://arxiv.org/pdf/1806.04994v3.pdf
| null |
[
"Søren Hauberg"
] |
[] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/modeling-coherence-for-neural-machine
|
1711.11221
| null | null |
Modeling Coherence for Neural Machine Translation with Dynamic and Topic Caches
|
Sentences in a well-formed text are connected to each other via various links
to form the cohesive structure of the text. Current neural machine translation
(NMT) systems translate a text in a conventional sentence-by-sentence fashion,
ignoring such cross-sentence links and dependencies. This may lead to generate
an incoherent target text for a coherent source text. In order to handle this
issue, we propose a cache-based approach to modeling coherence for neural
machine translation by capturing contextual information either from recently
translated sentences or the entire document. Particularly, we explore two types
of caches: a dynamic cache, which stores words from the best translation
hypotheses of preceding sentences, and a topic cache, which maintains a set of
target-side topical words that are semantically related to the document to be
translated. On this basis, we build a new layer to score target words in these
two caches with a cache-based neural model. Here the estimated probabilities
from the cache-based neural model are combined with NMT probabilities into the
final word prediction probabilities via a gating mechanism. Finally, the
proposed cache-based neural model is trained jointly with NMT system in an
end-to-end manner. Experiments and analysis presented in this paper demonstrate
that the proposed cache-based model achieves substantial improvements over
several state-of-the-art SMT and NMT baselines.
| null |
http://arxiv.org/abs/1711.11221v3
|
http://arxiv.org/pdf/1711.11221v3.pdf
|
COLING 2018 8
|
[
"Shaohui Kuang",
"Deyi Xiong",
"Weihua Luo",
"Guodong Zhou"
] |
[
"Machine Translation",
"NMT",
"Sentence",
"Translation"
] | 2017-11-30T00:00:00 |
https://aclanthology.org/C18-1050
|
https://aclanthology.org/C18-1050.pdf
|
modeling-coherence-for-neural-machine-2
| null |
[] |
https://paperswithcode.com/paper/cross-modal-hallucination-for-few-shot-fine
|
1806.05147
| null | null |
Cross-modal Hallucination for Few-shot Fine-grained Recognition
|
State-of-the-art deep learning algorithms generally require large amounts of
data for model training. Lack thereof can severely deteriorate the performance,
particularly in scenarios with fine-grained boundaries between categories. To
this end, we propose a multimodal approach that facilitates bridging the
information gap by means of meaningful joint embeddings. Specifically, we
present a benchmark that is multimodal during training (i.e. images and texts)
and single-modal in testing time (i.e. images), with the associated task to
utilize multimodal data in base classes (with many samples), to learn explicit
visual classifiers for novel classes (with few samples). Next, we propose a
framework built upon the idea of cross-modal data hallucination. In this
regard, we introduce a discriminative text-conditional GAN for sample
generation with a simple self-paced strategy for sample selection. We show the
results of our proposed discriminative hallucinated method for 1-, 2-, and 5-
shot learning on the CUB dataset, where the accuracy is improved by employing
multimodal data.
| null |
http://arxiv.org/abs/1806.05147v2
|
http://arxiv.org/pdf/1806.05147v2.pdf
| null |
[
"Frederik Pahde",
"Patrick Jähnichen",
"Tassilo Klein",
"Moin Nabi"
] |
[
"Hallucination"
] | 2018-06-13T00:00:00 | 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. 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Knowing how to recover a lost Dogecoin wallet depends on the type of wallet you used—hardware, mobile, desktop, or paper. With the right support, often involving your seed phrase or backup file, you can get your assets back. Don’t waste time; dial +1-833-534-1729 for step-by-step recovery help.\r\n\r\n4. Dogecoin Deposit Not Received\r\nIf someone has sent you Dogecoin but it’s not showing up in your wallet, it could be a delay in network confirmation or a mistake in the receiving address. A Dogecoin deposit not received needs quick attention. Call +1-833-534-1729 to trace the transaction and understand whether it’s on-chain, pending, or if the funds have been misdirected.\r\n\r\n5. Dogecoin Transaction Stuck or Pending\r\nSometimes your Dogecoin transaction is stuck or pending due to low gas fees or heavy blockchain traffic. While this can resolve itself, in some cases it doesn't. Don’t stay in the dark. A quick call to +1-833-534-1729 can give you clarity and guidance on whether to wait, rebroadcast, or use a transaction accelerator.\r\n\r\n6. Dogecoin Wallet Recovery Phrase Issue\r\nYour 12 or 24-word Dogecoin wallet recovery phrase is the key to your funds. But what if it’s not working? If you’re seeing errors or your wallet can’t be restored, something might have gone wrong during the backup. Experts at +1-833-534-1729 can help verify the phrase, troubleshoot format issues, and guide you on next steps.\r\n\r\nHow the Dogecoin Support Number +1-833-534-1729 Helps You\r\nWhen you’re dealing with cryptocurrency issues, every second counts. Here’s why users trust +1-833-534-1729:\r\n\r\nLive Experts: Talk to real people who understand wallets, blockchain, and Dogecoin tech.\r\n\r\n24/7 Availability: Dogecoin doesn’t sleep, and neither should your support.\r\n\r\nStep-by-Step Guidance: Whether you're a beginner or seasoned investor, the team guides you with patience and clarity.\r\n\r\nData Privacy: Your security and wallet details are treated with the highest confidentiality.\r\n\r\nFAQs About Dogecoin Support and Wallet Issues\r\nQ1: Can Dogecoin support help me recover stolen BTC?\r\nA: While Dogecoin transactions are irreversible, support can help investigate, trace addresses, and advise on what to do next.\r\n\r\nQ2: My wallet shows zero balance after reinstalling. What do I do?\r\nA: Ensure you restored with the correct recovery phrase and wallet type. Call +1-833-534-1729 for assistance.\r\n\r\nQ3: What if I forgot my wallet password?\r\nA: Recovery depends on the wallet provider. Support can check if recovery options or tools are available.\r\n\r\nQ4: I sent BTC to the wrong address. Can support help?\r\nA: Dogecoin transactions are final. If the address is invalid, the transaction may fail. If it’s valid but unintended, unfortunately, it’s not reversible. Still, call +1-833-534-1729 to explore all possible solutions.\r\n\r\nQ5: Is this number official?\r\nA: While +1-833-534-1729 is not Dogecoin’s official number (Dogecoin is decentralized), it connects you to trained professionals experienced in resolving all major Dogecoin issues.\r\n\r\nFinal Thoughts\r\nDogecoin is a powerful tool for financial freedom—but only when everything works as expected. When things go sideways, you need someone to rely on. Whether it's a Dogecoin transaction not confirmed, your Dogecoin wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Dogecoin customer support number +1-833-534-1729 can be your fastest path to peace of mind.\r\n\r\nNo matter what the issue, you don’t have to face it alone. Expert help is just a call away—+1-833-534-1729.",
"full_name": "Dogecoin Customer Service Number +1-833-534-1729",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Generative Models** aim to model data generatively (rather than discriminatively), that is they aim to approximate the probability distribution of the data. Below you can find a continuously updating list of generative models for computer vision.",
"name": "Generative Models",
"parent": null
},
"name": "Dogecoin Customer Service Number +1-833-534-1729",
"source_title": "Generative Adversarial Networks",
"source_url": "https://arxiv.org/abs/1406.2661v1"
}
] |
https://paperswithcode.com/paper/ranking-recovery-from-limited-comparisons
|
1806.05419
| null | null |
Ranking Recovery from Limited Comparisons using Low-Rank Matrix Completion
|
This paper proposes a new method for solving the well-known rank aggregation
problem from pairwise comparisons using the method of low-rank matrix
completion. The partial and noisy data of pairwise comparisons is transformed
into a matrix form. We then use tools from matrix completion, which has served
as a major component in the low-rank completion solution of the Netflix
challenge, to construct the preference of the different objects. In our
approach, the data of multiple comparisons is used to create an estimate of the
probability of object i to win (or be chosen) over object j, where only a
partial set of comparisons between N objects is known. The data is then
transformed into a matrix form for which the noiseless solution has a known
rank of one. An alternating minimization algorithm, in which the target matrix
takes a bilinear form, is then used in combination with maximum likelihood
estimation for both factors. The reconstructed matrix is used to obtain the
true underlying preference intensity. This work demonstrates the improvement of
our proposed algorithm over the current state-of-the-art in both simulated
scenarios and real data.
| null |
http://arxiv.org/abs/1806.05419v1
|
http://arxiv.org/pdf/1806.05419v1.pdf
| null |
[
"Tal Levy",
"Alireza Vahid",
"Raja Giryes"
] |
[
"Form",
"Low-Rank Matrix Completion",
"Matrix Completion"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/configurable-markov-decision-processes
|
1806.05415
| null | null |
Configurable Markov Decision Processes
|
In many real-world problems, there is the possibility to configure, to a
limited extent, some environmental parameters to improve the performance of a
learning agent. In this paper, we propose a novel framework, Configurable
Markov Decision Processes (Conf-MDPs), to model this new type of interaction
with the environment. Furthermore, we provide a new learning algorithm, Safe
Policy-Model Iteration (SPMI), to jointly and adaptively optimize the policy
and the environment configuration. After having introduced our approach and
derived some theoretical results, we present the experimental evaluation in two
explicative problems to show the benefits of the environment configurability on
the performance of the learned policy.
| null |
http://arxiv.org/abs/1806.05415v1
|
http://arxiv.org/pdf/1806.05415v1.pdf
|
ICML 2018 7
|
[
"Alberto Maria Metelli",
"Mirco Mutti",
"Marcello Restelli"
] |
[] | 2018-06-14T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2137
|
http://proceedings.mlr.press/v80/metelli18a/metelli18a.pdf
|
configurable-markov-decision-processes-1
| null |
[] |
https://paperswithcode.com/paper/learning-dynamics-of-linear-denoising
|
1806.05413
| null | null |
Learning Dynamics of Linear Denoising Autoencoders
|
Denoising autoencoders (DAEs) have proven useful for unsupervised
representation learning, but a thorough theoretical understanding is still
lacking of how the input noise influences learning. Here we develop theory for
how noise influences learning in DAEs. By focusing on linear DAEs, we are able
to derive analytic expressions that exactly describe their learning dynamics.
We verify our theoretical predictions with simulations as well as experiments
on MNIST and CIFAR-10. The theory illustrates how, when tuned correctly, noise
allows DAEs to ignore low variance directions in the inputs while learning to
reconstruct them. Furthermore, in a comparison of the learning dynamics of DAEs
to standard regularised autoencoders, we show that noise has a similar
regularisation effect to weight decay, but with faster training dynamics. We
also show that our theoretical predictions approximate learning dynamics on
real-world data and qualitatively match observed dynamics in nonlinear DAEs.
|
Here we develop theory for how noise influences learning in DAEs.
|
http://arxiv.org/abs/1806.05413v2
|
http://arxiv.org/pdf/1806.05413v2.pdf
|
ICML 2018 7
|
[
"Arnu Pretorius",
"Steve Kroon",
"Herman Kamper"
] |
[
"Denoising",
"Representation Learning"
] | 2018-06-14T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2013
|
http://proceedings.mlr.press/v80/pretorius18a/pretorius18a.pdf
|
learning-dynamics-of-linear-denoising-1
| null |
[] |
https://paperswithcode.com/paper/an-approach-to-vehicle-trajectory-prediction
|
1802.08632
| null | null |
An Approach to Vehicle Trajectory Prediction Using Automatically Generated Traffic Maps
|
Trajectory and intention prediction of traffic participants is an important
task in automated driving and crucial for safe interaction with the
environment. In this paper, we present a new approach to vehicle trajectory
prediction based on automatically generated maps containing statistical
information about the behavior of traffic participants in a given area. These
maps are generated based on trajectory observations using image processing and
map matching techniques and contain all typical vehicle movements and
probabilities in the considered area. Our prediction approach matches an
observed trajectory to a behavior contained in the map and uses this
information to generate a prediction. We evaluated our approach on a dataset
containing over 14000 trajectories and found that it produces significantly
more precise mid-term predictions compared to motion model-based prediction
approaches.
| null |
http://arxiv.org/abs/1802.08632v2
|
http://arxiv.org/pdf/1802.08632v2.pdf
| null |
[
"Jannik Quehl",
"Haohao Hu",
"Sascha Wirges",
"Martin Lauer"
] |
[
"Prediction",
"Trajectory Prediction"
] | 2018-02-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/incremental-natural-language-processing-1
|
1805.12518
| null | null |
Incremental Natural Language Processing: Challenges, Strategies, and Evaluation
|
Incrementality is ubiquitous in human-human interaction and beneficial for
human-computer interaction. It has been a topic of research in different parts
of the NLP community, mostly with focus on the specific topic at hand even
though incremental systems have to deal with similar challenges regardless of
domain. In this survey, I consolidate and categorize the approaches,
identifying similarities and differences in the computation and data, and show
trade-offs that have to be considered. A focus lies on evaluating incremental
systems because the standard metrics often fail to capture the incremental
properties of a system and coming up with a suitable evaluation scheme is
non-trivial.
| null |
http://arxiv.org/abs/1805.12518v2
|
http://arxiv.org/pdf/1805.12518v2.pdf
| null |
[
"Arne Köhn"
] |
[] | 2018-05-31T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/on-the-perceptrons-compression
|
1806.05403
| null | null |
On the Perceptron's Compression
|
We study and provide exposition to several phenomena that are related to the
perceptron's compression. One theme concerns modifications of the perceptron
algorithm that yield better guarantees on the margin of the hyperplane it
outputs. These modifications can be useful in training neural networks as well,
and we demonstrate them with some experimental data. In a second theme, we
deduce conclusions from the perceptron's compression in various contexts.
| null |
http://arxiv.org/abs/1806.05403v1
|
http://arxiv.org/pdf/1806.05403v1.pdf
| null |
[
"Shay Moran",
"Ido Nachum",
"Itai Panasoff",
"Amir Yehudayoff"
] |
[] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/aggregating-predictions-on-multiple-non
|
1806.04000
| null | null |
Aggregating Predictions on Multiple Non-disclosed Datasets using Conformal Prediction
|
Conformal Prediction is a machine learning methodology that produces valid
prediction regions under mild conditions. In this paper, we explore the
application of making predictions over multiple data sources of different sizes
without disclosing data between the sources. We propose that each data source
applies a transductive conformal predictor independently using the local data,
and that the individual predictions are then aggregated to form a combined
prediction region. We demonstrate the method on several data sets, and show
that the proposed method produces conservatively valid predictions and reduces
the variance in the aggregated predictions. We also study the effect that the
number of data sources and size of each source has on aggregated predictions,
as compared with equally sized sources and pooled data.
| null |
http://arxiv.org/abs/1806.04000v2
|
http://arxiv.org/pdf/1806.04000v2.pdf
| null |
[
"Ola Spjuth",
"Lars Carlsson",
"Niharika Gauraha"
] |
[
"Conformal Prediction",
"Prediction",
"valid"
] | 2018-06-11T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/dynamical-isometry-and-a-mean-field-theory-of
|
1806.05394
| null | null |
Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks
|
Recurrent neural networks have gained widespread use in modeling sequence
data across various domains. While many successful recurrent architectures
employ a notion of gating, the exact mechanism that enables such remarkable
performance is not well understood. We develop a theory for signal propagation
in recurrent networks after random initialization using a combination of mean
field theory and random matrix theory. To simplify our discussion, we introduce
a new RNN cell with a simple gating mechanism that we call the minimalRNN and
compare it with vanilla RNNs. Our theory allows us to define a maximum
timescale over which RNNs can remember an input. We show that this theory
predicts trainability for both recurrent architectures. We show that gated
recurrent networks feature a much broader, more robust, trainable region than
vanilla RNNs, which corroborates recent experimental findings. Finally, we
develop a closed-form critical initialization scheme that achieves dynamical
isometry in both vanilla RNNs and minimalRNNs. We show that this results in
significantly improvement in training dynamics. Finally, we demonstrate that
the minimalRNN achieves comparable performance to its more complex
counterparts, such as LSTMs or GRUs, on a language modeling task.
| null |
http://arxiv.org/abs/1806.05394v2
|
http://arxiv.org/pdf/1806.05394v2.pdf
|
ICML 2018 7
|
[
"Minmin Chen",
"Jeffrey Pennington",
"Samuel S. Schoenholz"
] |
[
"Language Modeling",
"Language Modelling"
] | 2018-06-14T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2464
|
http://proceedings.mlr.press/v80/chen18i/chen18i.pdf
|
dynamical-isometry-and-a-mean-field-theory-of-4
| null |
[] |
https://paperswithcode.com/paper/dynamical-isometry-and-a-mean-field-theory-of-2
|
1806.05393
| null | null |
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks
|
In recent years, state-of-the-art methods in computer vision have utilized
increasingly deep convolutional neural network architectures (CNNs), with some
of the most successful models employing hundreds or even thousands of layers. A
variety of pathologies such as vanishing/exploding gradients make training such
deep networks challenging. While residual connections and batch normalization
do enable training at these depths, it has remained unclear whether such
specialized architecture designs are truly necessary to train deep CNNs. In
this work, we demonstrate that it is possible to train vanilla CNNs with ten
thousand layers or more simply by using an appropriate initialization scheme.
We derive this initialization scheme theoretically by developing a mean field
theory for signal propagation and by characterizing the conditions for
dynamical isometry, the equilibration of singular values of the input-output
Jacobian matrix. These conditions require that the convolution operator be an
orthogonal transformation in the sense that it is norm-preserving. We present
an algorithm for generating such random initial orthogonal convolution kernels
and demonstrate empirically that they enable efficient training of extremely
deep architectures.
|
In this work, we demonstrate that it is possible to train vanilla CNNs with ten thousand layers or more simply by using an appropriate initialization scheme.
|
http://arxiv.org/abs/1806.05393v2
|
http://arxiv.org/pdf/1806.05393v2.pdf
|
ICML 2018 7
|
[
"Lechao Xiao",
"Yasaman Bahri",
"Jascha Sohl-Dickstein",
"Samuel S. Schoenholz",
"Jeffrey Pennington"
] |
[] | 2018-06-14T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2421
|
http://proceedings.mlr.press/v80/xiao18a/xiao18a.pdf
|
dynamical-isometry-and-a-mean-field-theory-of-3
| 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
}
] |
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