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https://paperswithcode.com/paper/group-equivariant-capsule-networks
|
1806.05086
| null | null |
Group Equivariant Capsule Networks
|
We present group equivariant capsule networks, a framework to introduce
guaranteed equivariance and invariance properties to the capsule network idea.
Our work can be divided into two contributions. First, we present a generic
routing by agreement algorithm defined on elements of a group and prove that
equivariance of output pose vectors, as well as invariance of output
activations, hold under certain conditions. Second, we connect the resulting
equivariant capsule networks with work from the field of group convolutional
networks. Through this connection, we provide intuitions of how both methods
relate and are able to combine the strengths of both approaches in one deep
neural network architecture. The resulting framework allows sparse evaluation
of the group convolution operator, provides control over specific equivariance
and invariance properties, and can use routing by agreement instead of pooling
operations. In addition, it is able to provide interpretable and equivariant
representation vectors as output capsules, which disentangle evidence of object
existence from its pose.
|
We present group equivariant capsule networks, a framework to introduce guaranteed equivariance and invariance properties to the capsule network idea.
|
http://arxiv.org/abs/1806.05086v2
|
http://arxiv.org/pdf/1806.05086v2.pdf
|
NeurIPS 2018 12
|
[
"Jan Eric Lenssen",
"Matthias Fey",
"Pascal Libuschewski"
] |
[] | 2018-06-13T00:00:00 |
http://papers.nips.cc/paper/8100-group-equivariant-capsule-networks
|
http://papers.nips.cc/paper/8100-group-equivariant-capsule-networks.pdf
|
group-equivariant-capsule-networks-1
| null |
[
{
"code_snippet_url": null,
"description": "A capsule is an activation vector that basically executes on its inputs some complex internal\r\ncomputations. Length of these activation vectors signifies the\r\nprobability of availability of a feature. Furthermore, the condition\r\nof the recognized element is encoded as the direction in which\r\nthe vector is pointing. In traditional, CNN uses Max pooling for\r\ninvariance activities of neurons, which is nothing except a minor\r\nchange in input and the neurons of output signal will remains\r\nsame.",
"full_name": "Capsule Network",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Neural Architecture Search** methods are search methods that seek to learn architectures for machine learning tasks, including the underlying build blocks. Below you can find a continuously updating list of neural architecture search algorithms. ",
"name": "Neural Architecture Search",
"parent": null
},
"name": "Capsule Network",
"source_title": "Dynamic Routing Between Capsules",
"source_url": "http://arxiv.org/abs/1710.09829v2"
},
{
"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/your-2-is-my-1-your-3-is-my-9-handling
|
1806.05085
| null | null |
Your 2 is My 1, Your 3 is My 9: Handling Arbitrary Miscalibrations in Ratings
|
Cardinal scores (numeric ratings) collected from people are well known to
suffer from miscalibrations. A popular approach to address this issue is to
assume simplistic models of miscalibration (such as linear biases) to de-bias
the scores. This approach, however, often fares poorly because people's
miscalibrations are typically far more complex and not well understood. In the
absence of simplifying assumptions on the miscalibration, it is widely believed
by the crowdsourcing community that the only useful information in the cardinal
scores is the induced ranking. In this paper, inspired by the framework of
Stein's shrinkage, empirical Bayes, and the classic two-envelope problem, we
contest this widespread belief. Specifically, we consider cardinal scores with
arbitrary (or even adversarially chosen) miscalibrations which are only
required to be consistent with the induced ranking. We design estimators which
despite making no assumptions on the miscalibration, strictly and uniformly
outperform all possible estimators that rely on only the ranking. Our
estimators are flexible in that they can be used as a plug-in for a variety of
applications, and we provide a proof-of-concept for A/B testing and ranking.
Our results thus provide novel insights in the eternal debate between cardinal
and ordinal data.
| null |
http://arxiv.org/abs/1806.05085v2
|
http://arxiv.org/pdf/1806.05085v2.pdf
| null |
[
"Jing-Yan Wang",
"Nihar B. Shah"
] |
[] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/multiple-instance-learning-for-heterogeneous
|
1806.05083
| null | null |
Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology
|
Multiple instance (MI) learning with a convolutional neural network enables
end-to-end training in the presence of weak image-level labels. We propose a
new method for aggregating predictions from smaller regions of the image into
an image-level classification by using the quantile function. The quantile
function provides a more complete description of the heterogeneity within each
image, improving image-level classification. We also adapt image augmentation
to the MI framework by randomly selecting cropped regions on which to apply MI
aggregation during each epoch of training. This provides a mechanism to study
the importance of MI learning. We validate our method on five different
classification tasks for breast tumor histology and provide a visualization
method for interpreting local image classifications that could lead to future
insights into tumor heterogeneity.
| null |
http://arxiv.org/abs/1806.05083v1
|
http://arxiv.org/pdf/1806.05083v1.pdf
| null |
[
"Heather D. Couture",
"J. S. Marron",
"Charles M. Perou",
"Melissa A. Troester",
"Marc Niethammer"
] |
[
"Classification",
"General Classification",
"Image Augmentation",
"Multiple Instance Learning"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/minimizing-regret-in-bandit-online
|
1806.05069
| null | null |
Minimizing Regret of Bandit Online Optimization in Unconstrained Action Spaces
|
We consider online convex optimization with a zero-order oracle feedback. In particular, the decision maker does not know the explicit representation of the time-varying cost functions, or their gradients. At each time step, she observes the value of the corresponding cost function evaluated at her chosen action (zero-order oracle). The objective is to minimize the regret, that is, the difference between the sum of the costs she accumulates and that of a static optimal action had she known the sequence of cost functions a priori. We present a novel algorithm to minimize regret in unconstrained action spaces. Our algorithm hinges on a classical idea of one-point estimation of the gradients of the cost functions based on their observed values. The algorithm is independent of problem parameters. Letting $T$ denote the number of queries of the zero-order oracle and $n$ the problem dimension, the regret rate achieved is $O(n^{2/3}T^{2/3})$. Moreover, we adapt the presented algorithm to the setting with two-point feedback and demonstrate that the adapted procedure achieves the theoretical lower bound on the regret of $(n^{1/2}T^{1/2})$.
| null |
https://arxiv.org/abs/1806.05069v3
|
https://arxiv.org/pdf/1806.05069v3.pdf
| null |
[
"Tatiana Tatarenko",
"Maryam Kamgarpour"
] |
[] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/error-bounds-for-piecewise-smooth-and
|
1707.07938
| null | null |
Error Bounds for Piecewise Smooth and Switching Regression
|
The paper deals with regression problems, in which the nonsmooth target is
assumed to switch between different operating modes. Specifically, piecewise
smooth (PWS) regression considers target functions switching deterministically
via a partition of the input space, while switching regression considers
arbitrary switching laws. The paper derives generalization error bounds in
these two settings by following the approach based on Rademacher complexities.
For PWS regression, our derivation involves a chaining argument and a
decomposition of the covering numbers of PWS classes in terms of the ones of
their component functions and the capacity of the classifier partitioning the
input space. This yields error bounds with a radical dependency on the number
of modes. For switching regression, the decomposition can be performed directly
at the level of the Rademacher complexities, which yields bounds with a linear
dependency on the number of modes. By using once more chaining and a
decomposition at the level of covering numbers, we show how to recover a
radical dependency. Examples of applications are given in particular for PWS
and swichting regression with linear and kernel-based component functions.
| null |
http://arxiv.org/abs/1707.07938v2
|
http://arxiv.org/pdf/1707.07938v2.pdf
| null |
[
"Fabien Lauer"
] |
[
"regression"
] | 2017-07-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/map-inference-via-block-coordinate-frank
|
1806.05049
| null | null |
MAP inference via Block-Coordinate Frank-Wolfe Algorithm
|
We present a new proximal bundle method for Maximum-A-Posteriori (MAP)
inference in structured energy minimization problems. The method optimizes a
Lagrangean relaxation of the original energy minimization problem using a multi
plane block-coordinate Frank-Wolfe method that takes advantage of the specific
structure of the Lagrangean decomposition. We show empirically that our method
outperforms state-of-the-art Lagrangean decomposition based algorithms on some
challenging Markov Random Field, multi-label discrete tomography and graph
matching problems.
|
We present a new proximal bundle method for Maximum-A-Posteriori (MAP) inference in structured energy minimization problems.
|
http://arxiv.org/abs/1806.05049v2
|
http://arxiv.org/pdf/1806.05049v2.pdf
|
CVPR 2019 6
|
[
"Paul Swoboda",
"Vladimir Kolmogorov"
] |
[
"Graph Matching"
] | 2018-06-13T00:00:00 |
http://openaccess.thecvf.com/content_CVPR_2019/html/Swoboda_MAP_Inference_via_Block-Coordinate_Frank-Wolfe_Algorithm_CVPR_2019_paper.html
|
http://openaccess.thecvf.com/content_CVPR_2019/papers/Swoboda_MAP_Inference_via_Block-Coordinate_Frank-Wolfe_Algorithm_CVPR_2019_paper.pdf
|
map-inference-via-block-coordinate-frank-1
| null |
[] |
https://paperswithcode.com/paper/beyond-counting-comparisons-of-density-maps
|
1705.10118
| null | null |
Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks - Counting, Detection, and Tracking
|
For crowded scenes, the accuracy of object-based computer vision methods
declines when the images are low-resolution and objects have severe occlusions.
Taking counting methods for example, almost all the recent state-of-the-art
counting methods bypass explicit detection and adopt regression-based methods
to directly count the objects of interest. Among regression-based methods,
density map estimation, where the number of objects inside a subregion is the
integral of the density map over that subregion, is especially promising
because it preserves spatial information, which makes it useful for both
counting and localization (detection and tracking). With the power of deep
convolutional neural networks (CNNs) the counting performance has improved
steadily. The goal of this paper is to evaluate density maps generated by
density estimation methods on a variety of crowd analysis tasks, including
counting, detection, and tracking. Most existing CNN methods produce density
maps with resolution that is smaller than the original images, due to the
downsample strides in the convolution/pooling operations. To produce an
original-resolution density map, we also evaluate a classical CNN that uses a
sliding window regressor to predict the density for every pixel in the image.
We also consider a fully convolutional (FCNN) adaptation, with skip connections
from lower convolutional layers to compensate for loss in spatial information
during upsampling. In our experiments, we found that the lower-resolution
density maps sometimes have better counting performance. In contrast, the
original-resolution density maps improved localization tasks, such as detection
and tracking, compared to bilinear upsampling the lower-resolution density
maps. Finally, we also propose several metrics for measuring the quality of a
density map, and relate them to experiment results on counting and
localization.
|
The goal of this paper is to evaluate density maps generated by density estimation methods on a variety of crowd analysis tasks, including counting, detection, and tracking.
|
http://arxiv.org/abs/1705.10118v2
|
http://arxiv.org/pdf/1705.10118v2.pdf
| null |
[
"Di Kang",
"Zheng Ma",
"Antoni B. Chan"
] |
[
"Density Estimation",
"regression"
] | 2017-05-29T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/extracting-parallel-sentences-with
|
1806.05559
| null | null |
Extracting Parallel Sentences with Bidirectional Recurrent Neural Networks to Improve Machine Translation
|
Parallel sentence extraction is a task addressing the data sparsity problem
found in multilingual natural language processing applications. We propose a
bidirectional recurrent neural network based approach to extract parallel
sentences from collections of multilingual texts. Our experiments with noisy
parallel corpora show that we can achieve promising results against a
competitive baseline by removing the need of specific feature engineering or
additional external resources. To justify the utility of our approach, we
extract sentence pairs from Wikipedia articles to train machine translation
systems and show significant improvements in translation performance.
|
Parallel sentence extraction is a task addressing the data sparsity problem found in multilingual natural language processing applications.
|
http://arxiv.org/abs/1806.05559v2
|
http://arxiv.org/pdf/1806.05559v2.pdf
|
COLING 2018 8
|
[
"Francis Grégoire",
"Philippe Langlais"
] |
[
"Articles",
"Feature Engineering",
"Machine Translation",
"Sentence",
"Translation"
] | 2018-06-13T00:00:00 |
https://aclanthology.org/C18-1122
|
https://aclanthology.org/C18-1122.pdf
|
extracting-parallel-sentences-with-2
| null |
[] |
https://paperswithcode.com/paper/a-probabilistic-u-net-for-segmentation-of
|
1806.05034
| null | null |
A Probabilistic U-Net for Segmentation of Ambiguous Images
|
Many real-world vision problems suffer from inherent ambiguities. In clinical
applications for example, it might not be clear from a CT scan alone which
particular region is cancer tissue. Therefore a group of graders typically
produces a set of diverse but plausible segmentations. We consider the task of
learning a distribution over segmentations given an input. To this end we
propose a generative segmentation model based on a combination of a U-Net with
a conditional variational autoencoder that is capable of efficiently producing
an unlimited number of plausible hypotheses. We show on a lung abnormalities
segmentation task and on a Cityscapes segmentation task that our model
reproduces the possible segmentation variants as well as the frequencies with
which they occur, doing so significantly better than published approaches.
These models could have a high impact in real-world applications, such as being
used as clinical decision-making algorithms accounting for multiple plausible
semantic segmentation hypotheses to provide possible diagnoses and recommend
further actions to resolve the present ambiguities.
|
To this end we propose a generative segmentation model based on a combination of a U-Net with a conditional variational autoencoder that is capable of efficiently producing an unlimited number of plausible hypotheses.
|
http://arxiv.org/abs/1806.05034v4
|
http://arxiv.org/pdf/1806.05034v4.pdf
|
NeurIPS 2018 12
|
[
"Simon A. A. Kohl",
"Bernardino Romera-Paredes",
"Clemens Meyer",
"Jeffrey De Fauw",
"Joseph R. Ledsam",
"Klaus H. Maier-Hein",
"S. M. Ali Eslami",
"Danilo Jimenez Rezende",
"Olaf Ronneberger"
] |
[
"Decision Making",
"Segmentation",
"Semantic Segmentation"
] | 2018-06-13T00:00:00 |
http://papers.nips.cc/paper/7928-a-probabilistic-u-net-for-segmentation-of-ambiguous-images
|
http://papers.nips.cc/paper/7928-a-probabilistic-u-net-for-segmentation-of-ambiguous-images.pdf
|
a-probabilistic-u-net-for-segmentation-of-1
| null |
[
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/densenet.py#L113",
"description": "A **Concatenated Skip Connection** is a type of skip connection that seeks to reuse features by concatenating them to new layers, allowing more information to be retained from previous layers of the network. This contrasts with say, residual connections, where element-wise summation is used instead to incorporate information from previous layers. This type of skip connection is prominently used in DenseNets (and also Inception networks), which the Figure to the right illustrates.",
"full_name": "Concatenated Skip Connection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connections** allow layers to skip layers and connect to layers further up the network, allowing for information to flow more easily up the network. Below you can find a continuously updating list of skip connection methods.",
"name": "Skip Connections",
"parent": null
},
"name": "Concatenated Skip Connection",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!\r\n\r\n\r\nHow Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!",
"full_name": "*Communicated@Fast*How Do I Communicate to Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**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/milesial/Pytorch-UNet/blob/67bf11b4db4c5f2891bd7e8e7f58bcde8ee2d2db/unet/unet_model.py#L8",
"description": "**U-Net** is an architecture for semantic segmentation. It consists of a contracting path and an expansive path. The contracting path follows the typical architecture of a convolutional network. It consists of the repeated application of two 3x3 convolutions (unpadded convolutions), each followed by a rectified linear unit ([ReLU](https://paperswithcode.com/method/relu)) and a 2x2 [max pooling](https://paperswithcode.com/method/max-pooling) operation with stride 2 for downsampling. At each downsampling step we double the number of feature channels. Every step in the expansive path consists of an upsampling of the feature map followed by a 2x2 [convolution](https://paperswithcode.com/method/convolution) (“up-convolution”) that halves the number of feature channels, a concatenation with the correspondingly cropped feature map from the contracting path, and two 3x3 convolutions, each followed by a ReLU. The cropping is necessary due to the loss of border pixels in every convolution. At the final layer a [1x1 convolution](https://paperswithcode.com/method/1x1-convolution) is used to map each 64-component feature vector to the desired number of classes. In total the network has 23 convolutional layers.\r\n\r\n[Original MATLAB Code](https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/u-net-release-2015-10-02.tar.gz)",
"full_name": "U-Net",
"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": "U-Net",
"source_title": "U-Net: Convolutional Networks for Biomedical Image Segmentation",
"source_url": "http://arxiv.org/abs/1505.04597v1"
},
{
"code_snippet_url": "",
"description": "In today’s digital age, Solana 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 Solana transaction not confirmed, your Solana wallet not showing balance, or you're trying to recover a lost Solana wallet, knowing where to get help is essential. That’s why the Solana 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 Solana Customer Support Number +1-833-534-1729\r\nSolana 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 Solana 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. Solana Deposit Not Received\r\nIf someone has sent you Solana 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 Solana 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. Solana Transaction Stuck or Pending\r\nSometimes your Solana 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. Solana Wallet Recovery Phrase Issue\r\nYour 12 or 24-word Solana 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 Solana 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 Solana tech.\r\n\r\n24/7 Availability: Solana 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 Solana Support and Wallet Issues\r\nQ1: Can Solana support help me recover stolen BTC?\r\nA: While Solana 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: Solana 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 Solana’s official number (Solana is decentralized), it connects you to trained professionals experienced in resolving all major Solana issues.\r\n\r\nFinal Thoughts\r\nSolana 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 Solana transaction not confirmed, your Solana wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Solana 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": "Solana 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": "Solana Customer Service Number +1-833-534-1729",
"source_title": "Reducing the Dimensionality of Data with Neural Networks",
"source_url": "https://science.sciencemag.org/content/313/5786/504"
}
] |
https://paperswithcode.com/paper/informative-gene-selection-for-microarray
|
1806.01466
| null | null |
Informative Gene Selection for Microarray Classification via Adaptive Elastic Net with Conditional Mutual Information
|
Due to the advantage of achieving a better performance under weak
regularization, elastic net has attracted wide attention in statistics, machine
learning, bioinformatics, and other fields. In particular, a variation of the
elastic net, adaptive elastic net (AEN), integrates the adaptive grouping
effect. In this paper, we aim to develop a new algorithm: Adaptive Elastic Net
with Conditional Mutual Information (AEN-CMI) that further improves AEN by
incorporating conditional mutual information into the gene selection process.
We apply this new algorithm to screen significant genes for two kinds of
cancers: colon cancer and leukemia. Compared with other algorithms including
Support Vector Machine, Classic Elastic Net and Adaptive Elastic Net, the
proposed algorithm, AEN-CMI, obtains the best classification performance using
the least number of genes.
| null |
http://arxiv.org/abs/1806.01466v3
|
http://arxiv.org/pdf/1806.01466v3.pdf
| null |
[
"Xin-Guang Yang",
"Yongjin Lu"
] |
[
"General Classification",
"Microarray Classification"
] | 2018-06-05T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/visually-grounded-cross-lingual-keyword
|
1806.05030
| null | null |
Visually grounded cross-lingual keyword spotting in speech
|
Recent work considered how images paired with speech can be used as
supervision for building speech systems when transcriptions are not available.
We ask whether visual grounding can be used for cross-lingual keyword spotting:
given a text keyword in one language, the task is to retrieve spoken utterances
containing that keyword in another language. This could enable searching
through speech in a low-resource language using text queries in a high-resource
language. As a proof-of-concept, we use English speech with German queries: we
use a German visual tagger to add keyword labels to each training image, and
then train a neural network to map English speech to German keywords. Without
seeing parallel speech-transcriptions or translations, the model achieves a
precision at ten of 58%. We show that most erroneous retrievals contain
equivalent or semantically relevant keywords; excluding these would improve
P@10 to 91%.
| null |
http://arxiv.org/abs/1806.05030v1
|
http://arxiv.org/pdf/1806.05030v1.pdf
| null |
[
"Herman Kamper",
"Michael Roth"
] |
[
"Keyword Spotting",
"Visual Grounding"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/hyperdrive-a-systolically-scalable-binary
|
1804.00623
| null | null |
Hyperdrive: A Multi-Chip Systolically Scalable Binary-Weight CNN Inference Engine
|
Deep neural networks have achieved impressive results in computer vision and
machine learning. Unfortunately, state-of-the-art networks are extremely
compute and memory intensive which makes them unsuitable for mW-devices such as
IoT end-nodes. Aggressive quantization of these networks dramatically reduces
the computation and memory footprint. Binary-weight neural networks (BWNs)
follow this trend, pushing weight quantization to the limit. Hardware
accelerators for BWNs presented up to now have focused on core efficiency,
disregarding I/O bandwidth and system-level efficiency that are crucial for
deployment of accelerators in ultra-low power devices. We present Hyperdrive: a
BWN accelerator dramatically reducing the I/O bandwidth exploiting a novel
binary-weight streaming approach, which can be used for arbitrarily sized
convolutional neural network architecture and input resolution by exploiting
the natural scalability of the compute units both at chip-level and
system-level by arranging Hyperdrive chips systolically in a 2D mesh while
processing the entire feature map together in parallel. Hyperdrive achieves 4.3
TOp/s/W system-level efficiency (i.e., including I/Os)---3.1x higher than
state-of-the-art BWN accelerators, even if its core uses resource-intensive
FP16 arithmetic for increased robustness.
| null |
http://arxiv.org/abs/1804.00623v3
|
http://arxiv.org/pdf/1804.00623v3.pdf
| null |
[
"Renzo Andri",
"Lukas Cavigelli",
"Davide Rossi",
"Luca Benini"
] |
[
"Quantization"
] | 2018-03-05T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/self-supervised-feature-learning-by-learning
|
1806.05024
| null | null |
Self-Supervised Feature Learning by Learning to Spot Artifacts
|
We introduce a novel self-supervised learning method based on adversarial
training. Our objective is to train a discriminator network to distinguish real
images from images with synthetic artifacts, and then to extract features from
its intermediate layers that can be transferred to other data domains and
tasks. To generate images with artifacts, we pre-train a high-capacity
autoencoder and then we use a damage and repair strategy: First, we freeze the
autoencoder and damage the output of the encoder by randomly dropping its
entries. Second, we augment the decoder with a repair network, and train it in
an adversarial manner against the discriminator. The repair network helps
generate more realistic images by inpainting the dropped feature entries. To
make the discriminator focus on the artifacts, we also make it predict what
entries in the feature were dropped. We demonstrate experimentally that
features learned by creating and spotting artifacts achieve state of the art
performance in several benchmarks.
| null |
http://arxiv.org/abs/1806.05024v1
|
http://arxiv.org/pdf/1806.05024v1.pdf
|
CVPR 2018 6
|
[
"Simon Jenni",
"Paolo Favaro"
] |
[
"Decoder",
"Self-Supervised Learning"
] | 2018-06-13T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Jenni_Self-Supervised_Feature_Learning_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Jenni_Self-Supervised_Feature_Learning_CVPR_2018_paper.pdf
|
self-supervised-feature-learning-by-learning-1
| null |
[] |
https://paperswithcode.com/paper/brain-computer-interface-with-corrupted-eeg
|
1806.05017
| null | null |
Brain-Computer Interface with Corrupted EEG Data: A Tensor Completion Approach
|
One of the current issues in Brain-Computer Interface is how to deal with
noisy Electroencephalography measurements organized as multidimensional
datasets. On the other hand, recently, significant advances have been made in
multidimensional signal completion algorithms that exploit tensor decomposition
models to capture the intricate relationship among entries in a
multidimensional signal. We propose to use tensor completion applied to EEG
data for improving the classification performance in a motor imagery BCI system
with corrupted measurements. Noisy measurements are considered as unknowns that
are inferred from a tensor decomposition model. We evaluate the performance of
four recently proposed tensor completion algorithms plus a simple interpolation
strategy, first with random missing entries and then with missing samples
constrained to have a specific structure (random missing channels), which is a
more realistic assumption in BCI Applications. We measured the ability of these
algorithms to reconstruct the tensor from observed data. Then, we tested the
classification accuracy of imagined movement in a BCI experiment with missing
samples. We show that for random missing entries, all tensor completion
algorithms can recover missing samples increasing the classification
performance compared to a simple interpolation approach. For the random missing
channels case, we show that tensor completion algorithms help to reconstruct
missing channels, significantly improving the accuracy in the classification of
motor imagery, however, not at the same level as clean data. Tensor completion
algorithms are useful in real BCI applications. The proposed strategy could
allow using motor imagery BCI systems even when EEG data is highly affected by
missing channels and/or samples, avoiding the need of new acquisitions in the
calibration stage.
| null |
http://arxiv.org/abs/1806.05017v2
|
http://arxiv.org/pdf/1806.05017v2.pdf
| null |
[
"Jordi Sole-Casals",
"Cesar F. Caiafa",
"Qibin Zhao",
"Adrzej Cichocki"
] |
[
"Brain Computer Interface",
"Classification",
"EEG",
"Electroencephalogram (EEG)",
"General Classification",
"Motor Imagery",
"Tensor Decomposition"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/safe-learning-based-optimal-motion-planning
|
1805.09994
| null | null |
Safe learning-based optimal motion planning for automated driving
|
This paper presents preliminary work on learning the search heuristic for the
optimal motion planning for automated driving in urban traffic. Previous work
considered search-based optimal motion planning framework (SBOMP) that utilized
numerical or model-based heuristics that did not consider dynamic obstacles.
Optimal solution was still guaranteed since dynamic obstacles can only increase
the cost. However, significant variations in the search efficiency are observed
depending whether dynamic obstacles are present or not. This paper introduces
machine learning (ML) based heuristic that takes into account dynamic
obstacles, thus adding to the performance consistency for achieving real-time
implementation.
| null |
http://arxiv.org/abs/1805.09994v2
|
http://arxiv.org/pdf/1805.09994v2.pdf
| null |
[
"Zlatan Ajanovic",
"Bakir Lacevic",
"Georg Stettinger",
"Daniel Watzenig",
"Martin Horn"
] |
[
"BIG-bench Machine Learning",
"Motion Planning",
"Optimal Motion Planning"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/gradient-based-camera-exposure-control-for
|
1708.07338
| null | null |
Gradient-based Camera Exposure Control for Outdoor Mobile Platforms
|
We introduce a novel method to automatically adjust camera exposure for image
processing and computer vision applications on mobile robot platforms. Because
most image processing algorithms rely heavily on low-level image features that
are based mainly on local gradient information, we consider that gradient
quantity can determine the proper exposure level, allowing a camera to capture
the important image features in a manner robust to illumination conditions. We
then extend this concept to a multi-camera system and present a new control
algorithm to achieve both brightness consistency between adjacent cameras and a
proper exposure level for each camera. We implement our prototype system with
off-the-shelf machine-vision cameras and demonstrate the effectiveness of the
proposed algorithms on practical applications, including pedestrian detection,
visual odometry, surround-view imaging, panoramic imaging and stereo matching.
| null |
http://arxiv.org/abs/1708.07338v3
|
http://arxiv.org/pdf/1708.07338v3.pdf
| null |
[
"Inwook Shim",
"Tae-Hyun Oh",
"Joon-Young Lee",
"Jinwook Choi",
"Dong-Geol Choi",
"In So Kweon"
] |
[
"Pedestrian Detection",
"Stereo Matching",
"Stereo Matching Hand",
"Visual Odometry"
] | 2017-08-24T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/fast-model-identification-via-physics-engines
|
1710.08893
| null | null |
Fast Model Identification via Physics Engines for Data-Efficient Policy Search
|
This paper presents a method for identifying mechanical parameters of robots
or objects, such as their mass and friction coefficients. Key features are the
use of off-the-shelf physics engines and the adaptation of a Bayesian
optimization technique towards minimizing the number of real-world experiments
needed for model-based reinforcement learning. The proposed framework
reproduces in a physics engine experiments performed on a real robot and
optimizes the model's mechanical parameters so as to match real-world
trajectories. The optimized model is then used for learning a policy in
simulation, before real-world deployment. It is well understood, however, that
it is hard to exactly reproduce real trajectories in simulation. Moreover, a
near-optimal policy can be frequently found with an imperfect model. Therefore,
this work proposes a strategy for identifying a model that is just good enough
to approximate the value of a locally optimal policy with a certain confidence,
instead of wasting effort on identifying the most accurate model. Evaluations,
performed both in simulation and on a real robotic manipulation task, indicate
that the proposed strategy results in an overall time-efficient, integrated
model identification and learning solution, which significantly improves the
data-efficiency of existing policy search algorithms.
| null |
http://arxiv.org/abs/1710.08893v3
|
http://arxiv.org/pdf/1710.08893v3.pdf
| null |
[
"Shaojun Zhu",
"Andrew Kimmel",
"Kostas E. Bekris",
"Abdeslam Boularias"
] |
[
"Bayesian Optimization",
"Friction",
"Model-based Reinforcement Learning",
"Reinforcement Learning"
] | 2017-10-24T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/generating-sentences-using-a-dynamic-canvas
|
1806.05178
| null | null |
Generating Sentences Using a Dynamic Canvas
|
We introduce the Attentive Unsupervised Text (W)riter (AUTR), which is a word
level generative model for natural language. It uses a recurrent neural network
with a dynamic attention and canvas memory mechanism to iteratively construct
sentences. By viewing the state of the memory at intermediate stages and where
the model is placing its attention, we gain insight into how it constructs
sentences. We demonstrate that AUTR learns a meaningful latent representation
for each sentence, and achieves competitive log-likelihood lower bounds whilst
being computationally efficient. It is effective at generating and
reconstructing sentences, as well as imputing missing words.
| null |
http://arxiv.org/abs/1806.05178v1
|
http://arxiv.org/pdf/1806.05178v1.pdf
| null |
[
"Harshil Shah",
"Bowen Zheng",
"David Barber"
] |
[
"Sentence"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/co-evolutionary-multi-task-learning-for
|
1703.01887
| null | null |
Co-evolutionary multi-task learning for dynamic time series prediction
|
Time series prediction typically consists of a data reconstruction phase
where the time series is broken into overlapping windows known as the timespan.
The size of the timespan can be seen as a way of determining the extent of past
information required for an effective prediction. In certain applications such
as the prediction of wind-intensity of storms and cyclones, prediction models
need to be dynamic in accommodating different values of the timespan. These
applications require robust prediction as soon as the event takes place. We
identify a new category of problem called dynamic time series prediction that
requires a model to give prediction when presented with varying lengths of the
timespan. In this paper, we propose a co-evolutionary multi-task learning
method that provides a synergy between multi-task learning and co-evolutionary
algorithms to address dynamic time series prediction. The method features
effective use of building blocks of knowledge inspired by dynamic programming
and multi-task learning. It enables neural networks to retain modularity during
training for making a decision in situations even when certain inputs are
missing. The effectiveness of the method is demonstrated using one-step-ahead
chaotic time series and tropical cyclone wind-intensity prediction.
|
In this paper, we propose a co-evolutionary multi-task learning method that provides a synergy between multi-task learning and co-evolutionary algorithms to address dynamic time series prediction.
|
http://arxiv.org/abs/1703.01887v2
|
http://arxiv.org/pdf/1703.01887v2.pdf
| null |
[
"Rohitash Chandra",
"Yew-Soon Ong",
"Chi-Keong Goh"
] |
[
"Evolutionary Algorithms",
"Multi-Task Learning",
"Prediction",
"Time Series",
"Time Series Analysis",
"Time Series Prediction"
] | 2017-02-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/plug-and-play-unplugged-optimization-free
|
1705.08983
| null | null |
Plug-and-Play Unplugged: Optimization Free Reconstruction using Consensus Equilibrium
|
Regularized inversion methods for image reconstruction are used widely due to
their tractability and ability to combine complex physical sensor models with
useful regularity criteria. Such methods motivated the recently developed
Plug-and-Play prior method, which provides a framework to use advanced
denoising algorithms as regularizers in inversion. However, the need to
formulate regularized inversion as the solution to an optimization problem
limits the possible regularity conditions and physical sensor models.
In this paper, we introduce Consensus Equilibrium (CE), which generalizes
regularized inversion to include a much wider variety of both forward
components and prior components without the need for either to be expressed
with a cost function. CE is based on the solution of a set of equilibrium
equations that balance data fit and regularity. In this framework, the problem
of MAP estimation in regularized inversion is replaced by the problem of
solving these equilibrium equations, which can be approached in multiple ways.
The key contribution of CE is to provide a novel framework for fusing
multiple heterogeneous models of physical sensors or models learned from data.
We describe the derivation of the CE equations and prove that the solution of
the CE equations generalizes the standard MAP estimate under appropriate
circumstances.
We also discuss algorithms for solving the CE equations, including ADMM with
a novel form of preconditioning and Newton's method. We give examples to
illustrate consensus equilibrium and the convergence properties of these
algorithms and demonstrate this method on some toy problems and on a denoising
example in which we use an array of convolutional neural network denoisers,
none of which is tuned to match the noise level in a noisy image but which in
consensus can achieve a better result than any of them individually.
| null |
http://arxiv.org/abs/1705.08983v3
|
http://arxiv.org/pdf/1705.08983v3.pdf
| null |
[
"Gregery T. Buzzard",
"Stanley H. Chan",
"Suhas Sreehari",
"Charles A. Bouman"
] |
[
"Denoising",
"Image Reconstruction"
] | 2017-05-24T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "The **alternating direction method of multipliers** (**ADMM**) is an algorithm that solves convex optimization problems by breaking them into smaller pieces, each of which are then easier to handle. It takes the form of a decomposition-coordination procedure, in which the solutions to small\r\nlocal subproblems are coordinated to find a solution to a large global problem. ADMM can be viewed as an attempt to blend the benefits of dual decomposition and augmented Lagrangian methods for constrained optimization. It turns out to be equivalent or closely related to many other algorithms\r\nas well, such as Douglas-Rachford splitting from numerical analysis, Spingarn’s method of partial inverses, Dykstra’s alternating projections method, Bregman iterative algorithms for l1 problems in signal processing, proximal methods, and many others.\r\n\r\nText Source: [https://stanford.edu/~boyd/papers/pdf/admm_distr_stats.pdf](https://stanford.edu/~boyd/papers/pdf/admm_distr_stats.pdf)\r\n\r\nImage Source: [here](https://www.slideshare.net/derekcypang/alternating-direction)",
"full_name": "Alternating Direction Method of Multipliers",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Optimization",
"parent": null
},
"name": "ADMM",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/openedgar-open-source-software-for-sec-edgar
|
1806.04973
| null | null |
OpenEDGAR: Open Source Software for SEC EDGAR Analysis
|
OpenEDGAR is an open source Python framework designed to rapidly construct
research databases based on the Electronic Data Gathering, Analysis, and
Retrieval (EDGAR) system operated by the US Securities and Exchange Commission
(SEC). OpenEDGAR is built on the Django application framework, supports
distributed compute across one or more servers, and includes functionality to
(i) retrieve and parse index and filing data from EDGAR, (ii) build tables for
key metadata like form type and filer, (iii) retrieve, parse, and update CIK to
ticker and industry mappings, (iv) extract content and metadata from filing
documents, and (v) search filing document contents. OpenEDGAR is designed for
use in both academic research and industrial applications, and is distributed
under MIT License at https://github.com/LexPredict/openedgar.
|
OpenEDGAR is an open source Python framework designed to rapidly construct research databases based on the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system operated by the US Securities and Exchange Commission (SEC).
|
http://arxiv.org/abs/1806.04973v1
|
http://arxiv.org/pdf/1806.04973v1.pdf
| null |
[
"Michael J Bommarito II",
"Daniel Martin Katz",
"Eric M Detterman"
] |
[
"Retrieval"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/unsupervised-detection-of-lesions-in-brain
|
1806.04972
| null | null |
Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders
|
Lesion detection in brain Magnetic Resonance Images (MRI) remains a
challenging task. State-of-the-art approaches are mostly based on supervised
learning making use of large annotated datasets. Human beings, on the other
hand, even non-experts, can detect most abnormal lesions after seeing a handful
of healthy brain images. Replicating this capability of using prior information
on the appearance of healthy brain structure to detect lesions can help
computers achieve human level abnormality detection, specifically reducing the
need for numerous labeled examples and bettering generalization of previously
unseen lesions. To this end, we study detection of lesion regions in an
unsupervised manner by learning data distribution of brain MRI of healthy
subjects using auto-encoder based methods. We hypothesize that one of the main
limitations of the current models is the lack of consistency in latent
representation. We propose a simple yet effective constraint that helps mapping
of an image bearing lesion close to its corresponding healthy image in the
latent space. We use the Human Connectome Project dataset to learn distribution
of healthy-appearing brain MRI and report improved detection, in terms of AUC,
of the lesions in the BRATS challenge dataset.
|
Lesion detection in brain Magnetic Resonance Images (MRI) remains a challenging task.
|
http://arxiv.org/abs/1806.04972v1
|
http://arxiv.org/pdf/1806.04972v1.pdf
| null |
[
"Xiaoran Chen",
"Ender Konukoglu"
] |
[
"Anomaly Detection",
"Lesion Detection"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/crowd-powered-data-mining
|
1806.04968
| null | null |
Crowd-Powered Data Mining
|
Many data mining tasks cannot be completely addressed by auto- mated
processes, such as sentiment analysis and image classification. Crowdsourcing
is an effective way to harness the human cognitive ability to process these
machine-hard tasks. Thanks to public crowdsourcing platforms, e.g., Amazon
Mechanical Turk and Crowd- Flower, we can easily involve hundreds of thousands
of ordinary workers (i.e., the crowd) to address these machine-hard tasks. In
this tutorial, we will survey and synthesize a wide spectrum of existing
studies on crowd-powered data mining. We first give an overview of
crowdsourcing, and then summarize the fundamental techniques, including quality
control, cost control, and latency control, which must be considered in
crowdsourced data mining. Next we review crowd-powered data mining operations,
including classification, clustering, pattern mining, machine learning using
the crowd (including deep learning, transfer learning and semi-supervised
learning) and knowledge discovery. Finally, we provide the emerging challenges
in crowdsourced data mining.
| null |
http://arxiv.org/abs/1806.04968v2
|
http://arxiv.org/pdf/1806.04968v2.pdf
| null |
[
"Chengliang Chai",
"Ju Fan",
"Guoliang Li",
"Jiannan Wang",
"Yudian Zheng"
] |
[
"Clustering",
"General Classification",
"image-classification",
"Image Classification",
"Sentiment Analysis",
"Transfer Learning"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/one-shot-segmentation-in-clutter
|
1803.09597
| null | null |
One-Shot Segmentation in Clutter
|
We tackle the problem of one-shot segmentation: finding and segmenting a
previously unseen object in a cluttered scene based on a single instruction
example. We propose a novel dataset, which we call $\textit{cluttered
Omniglot}$. Using a baseline architecture combining a Siamese embedding for
detection with a U-net for segmentation we show that increasing levels of
clutter make the task progressively harder. Using oracle models with access to
various amounts of ground-truth information, we evaluate different aspects of
the problem and show that in this kind of visual search task, detection and
segmentation are two intertwined problems, the solution to each of which helps
solving the other. We therefore introduce $\textit{MaskNet}$, an improved model
that attends to multiple candidate locations, generates segmentation proposals
to mask out background clutter and selects among the segmented objects. Our
findings suggest that such image recognition models based on an iterative
refinement of object detection and foreground segmentation may provide a way to
deal with highly cluttered scenes.
|
We tackle the problem of one-shot segmentation: finding and segmenting a previously unseen object in a cluttered scene based on a single instruction example.
|
http://arxiv.org/abs/1803.09597v2
|
http://arxiv.org/pdf/1803.09597v2.pdf
|
ICML 2018 7
|
[
"Claudio Michaelis",
"Matthias Bethge",
"Alexander S. Ecker"
] |
[
"Foreground Segmentation",
"object-detection",
"One-Shot Segmentation",
"Segmentation"
] | 2018-03-26T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2303
|
http://proceedings.mlr.press/v80/michaelis18a/michaelis18a.pdf
|
one-shot-segmentation-in-clutter-1
| null |
[
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/densenet.py#L113",
"description": "A **Concatenated Skip Connection** is a type of skip connection that seeks to reuse features by concatenating them to new layers, allowing more information to be retained from previous layers of the network. This contrasts with say, residual connections, where element-wise summation is used instead to incorporate information from previous layers. This type of skip connection is prominently used in DenseNets (and also Inception networks), which the Figure to the right illustrates.",
"full_name": "Concatenated Skip Connection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connections** allow layers to skip layers and connect to layers further up the network, allowing for information to flow more easily up the network. Below you can find a continuously updating list of skip connection methods.",
"name": "Skip Connections",
"parent": null
},
"name": "Concatenated Skip Connection",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!\r\n\r\n\r\nHow Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!",
"full_name": "*Communicated@Fast*How Do I Communicate to Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**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/milesial/Pytorch-UNet/blob/67bf11b4db4c5f2891bd7e8e7f58bcde8ee2d2db/unet/unet_model.py#L8",
"description": "**U-Net** is an architecture for semantic segmentation. It consists of a contracting path and an expansive path. The contracting path follows the typical architecture of a convolutional network. It consists of the repeated application of two 3x3 convolutions (unpadded convolutions), each followed by a rectified linear unit ([ReLU](https://paperswithcode.com/method/relu)) and a 2x2 [max pooling](https://paperswithcode.com/method/max-pooling) operation with stride 2 for downsampling. At each downsampling step we double the number of feature channels. Every step in the expansive path consists of an upsampling of the feature map followed by a 2x2 [convolution](https://paperswithcode.com/method/convolution) (“up-convolution”) that halves the number of feature channels, a concatenation with the correspondingly cropped feature map from the contracting path, and two 3x3 convolutions, each followed by a ReLU. The cropping is necessary due to the loss of border pixels in every convolution. At the final layer a [1x1 convolution](https://paperswithcode.com/method/1x1-convolution) is used to map each 64-component feature vector to the desired number of classes. In total the network has 23 convolutional layers.\r\n\r\n[Original MATLAB Code](https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/u-net-release-2015-10-02.tar.gz)",
"full_name": "U-Net",
"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": "U-Net",
"source_title": "U-Net: Convolutional Networks for Biomedical Image Segmentation",
"source_url": "http://arxiv.org/abs/1505.04597v1"
}
] |
https://paperswithcode.com/paper/the-streaming-rollout-of-deep-networks
|
1806.04965
| null | null |
The streaming rollout of deep networks - towards fully model-parallel execution
|
Deep neural networks, and in particular recurrent networks, are promising
candidates to control autonomous agents that interact in real-time with the
physical world. However, this requires a seamless integration of temporal
features into the network's architecture. For the training of and inference
with recurrent neural networks, they are usually rolled out over time, and
different rollouts exist. Conventionally during inference, the layers of a
network are computed in a sequential manner resulting in sparse temporal
integration of information and long response times. In this study, we present a
theoretical framework to describe rollouts, the level of model-parallelization
they induce, and demonstrate differences in solving specific tasks. We prove
that certain rollouts, also for networks with only skip and no recurrent
connections, enable earlier and more frequent responses, and show empirically
that these early responses have better performance. The streaming rollout
maximizes these properties and enables a fully parallel execution of the
network reducing runtime on massively parallel devices. Finally, we provide an
open-source toolbox to design, train, evaluate, and interact with streaming
rollouts.
|
Deep neural networks, and in particular recurrent networks, are promising candidates to control autonomous agents that interact in real-time with the physical world.
|
http://arxiv.org/abs/1806.04965v2
|
http://arxiv.org/pdf/1806.04965v2.pdf
|
NeurIPS 2018 12
|
[
"Volker Fischer",
"Jan Köhler",
"Thomas Pfeil"
] |
[] | 2018-06-13T00:00:00 |
http://papers.nips.cc/paper/7659-the-streaming-rollout-of-deep-networks-towards-fully-model-parallel-execution
|
http://papers.nips.cc/paper/7659-the-streaming-rollout-of-deep-networks-towards-fully-model-parallel-execution.pdf
|
the-streaming-rollout-of-deep-networks-1
| null |
[] |
https://paperswithcode.com/paper/fairness-behind-a-veil-of-ignorance-a-welfare
|
1806.04959
| null | null |
Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making
|
We draw attention to an important, yet largely overlooked aspect of
evaluating fairness for automated decision making systems---namely risk and
welfare considerations. Our proposed family of measures corresponds to the
long-established formulations of cardinal social welfare in economics, and is
justified by the Rawlsian conception of fairness behind a veil of ignorance.
The convex formulation of our welfare-based measures of fairness allows us to
integrate them as a constraint into any convex loss minimization pipeline. Our
empirical analysis reveals interesting trade-offs between our proposal and (a)
prediction accuracy, (b) group discrimination, and (c) Dwork et al.'s notion of
individual fairness. Furthermore and perhaps most importantly, our work
provides both heuristic justification and empirical evidence suggesting that a
lower-bound on our measures often leads to bounded inequality in algorithmic
outcomes; hence presenting the first computationally feasible mechanism for
bounding individual-level inequality.
| null |
http://arxiv.org/abs/1806.04959v4
|
http://arxiv.org/pdf/1806.04959v4.pdf
|
NeurIPS 2018 12
|
[
"Hoda Heidari",
"Claudio Ferrari",
"Krishna P. Gummadi",
"Andreas Krause"
] |
[
"Decision Making",
"Fairness"
] | 2018-06-13T00:00:00 |
http://papers.nips.cc/paper/7402-fairness-behind-a-veil-of-ignorance-a-welfare-analysis-for-automated-decision-making
|
http://papers.nips.cc/paper/7402-fairness-behind-a-veil-of-ignorance-a-welfare-analysis-for-automated-decision-making.pdf
|
fairness-behind-a-veil-of-ignorance-a-welfare-1
| null |
[] |
https://paperswithcode.com/paper/expression-empowered-residen-network-for
|
1806.04957
| null | null |
Expression Empowered ResiDen Network for Facial Action Unit Detection
|
The paper explores the topic of Facial Action Unit (FAU) detection in the
wild. In particular, we are interested in answering the following questions:
(1) how useful are residual connections across dense blocks for face analysis?
(2) how useful is the information from a network trained for categorical Facial
Expression Recognition (FER) for the task of FAU detection? The proposed
network (ResiDen) exploits dense blocks along with residual connections and
uses auxiliary information from a FER network. The experiments are performed on
the EmotionNet and DISFA datasets. The experiments show the usefulness of
facial expression information for AU detection. The proposed network achieves
state-of-art results on the two databases. Analysis of the results for cross
database protocol shows the effectiveness of the network.
| null |
http://arxiv.org/abs/1806.04957v1
|
http://arxiv.org/pdf/1806.04957v1.pdf
| null |
[
"Shreyank Jyoti",
"Abhinav Dhall"
] |
[
"Action Unit Detection",
"Facial Action Unit Detection",
"Facial Expression Recognition",
"Facial Expression Recognition (FER)"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/higher-order-of-motion-magnification-for
|
1806.04955
| null | null |
Higher Order of Motion Magnification for Vessel Localisation in Surgical Video
|
Locating vessels during surgery is critical for avoiding inadvertent damage,
yet vasculature can be difficult to identify. Video motion magnification can
potentially highlight vessels by exaggerating subtle motion embedded within the
video to become perceivable to the surgeon. In this paper, we explore a
physiological model of artery distension to extend motion magnification to
incorporate higher orders of motion, leveraging the difference in acceleration
over time (jerk) in pulsatile motion to highlight the vascular pulse wave. Our
method is compared to first and second order motion based Eulerian video
magnification algorithms. Using data from a surgical video retrieved during a
robotic prostatectomy, we show that our method can accentuate
cardio-physiological features and produce a more succinct and clearer video for
motion magnification, with more similarities in areas without motion to the
source video at large magnifications. We validate the approach with a Structure
Similarity (SSIM) and Peak Signal to Noise Ratio (PSNR) assessment of three
videos at an increasing working distance, using three different levels of
optical magnification. Spatio-temporal cross sections are presented to show the
effectiveness of our proposal and video samples are provided to demonstrates
qualitatively our results.
| null |
http://arxiv.org/abs/1806.04955v1
|
http://arxiv.org/pdf/1806.04955v1.pdf
| null |
[
"Mirek Janatka",
"Ashwin Sridhar",
"John Kelly",
"Danail Stoyanov"
] |
[
"Motion Magnification",
"SSIM"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/towards-semantically-enhanced-data
|
1806.04952
| null | null |
Towards Semantically Enhanced Data Understanding
|
In the field of machine learning, data understanding is the practice of
getting initial insights in unknown datasets. Such knowledge-intensive tasks
require a lot of documentation, which is necessary for data scientists to grasp
the meaning of the data. Usually, documentation is separate from the data in
various external documents, diagrams, spreadsheets and tools which causes
considerable look up overhead. Moreover, other supporting applications are not
able to consume and utilize such unstructured data. That is why we propose a
methodology that uses a single semantic model that interlinks data with its
documentation. Hence, data scientists are able to directly look up the
connected information about the data by simply following links. Equally, they
can browse the documentation which always refers to the data. Furthermore, the
model can be used by other approaches providing additional support, like
searching, comparing, integrating or visualizing data. To showcase our approach
we also demonstrate an early prototype.
| null |
http://arxiv.org/abs/1806.04952v1
|
http://arxiv.org/pdf/1806.04952v1.pdf
| null |
[
"Markus Schröder",
"Christian Jilek",
"Jörn Hees",
"Andreas Dengel"
] |
[] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/diachronic-word-embeddings-and-semantic
|
1806.03537
| null | null |
Diachronic word embeddings and semantic shifts: a survey
|
Recent years have witnessed a surge of publications aimed at tracing temporal
changes in lexical semantics using distributional methods, particularly
prediction-based word embedding models. However, this vein of research lacks
the cohesion, common terminology and shared practices of more established areas
of natural language processing. In this paper, we survey the current state of
academic research related to diachronic word embeddings and semantic shifts
detection. We start with discussing the notion of semantic shifts, and then
continue with an overview of the existing methods for tracing such time-related
shifts with word embedding models. We propose several axes along which these
methods can be compared, and outline the main challenges before this emerging
subfield of NLP, as well as prospects and possible applications.
| null |
http://arxiv.org/abs/1806.03537v2
|
http://arxiv.org/pdf/1806.03537v2.pdf
|
COLING 2018 8
|
[
"Andrey Kutuzov",
"Lilja Øvrelid",
"Terrence Szymanski",
"Erik Velldal"
] |
[
"Diachronic Word Embeddings",
"Survey",
"Word Embeddings"
] | 2018-06-09T00:00:00 |
https://aclanthology.org/C18-1117
|
https://aclanthology.org/C18-1117.pdf
|
diachronic-word-embeddings-and-semantic-1
| null |
[] |
https://paperswithcode.com/paper/fmri-semantic-category-decoding-using
|
1806.05177
| null | null |
fMRI Semantic Category Decoding using Linguistic Encoding of Word Embeddings
|
The dispute of how the human brain represents conceptual knowledge has been
argued in many scientific fields. Brain imaging studies have shown that the
spatial patterns of neural activation in the brain are correlated with thinking
about different semantic categories of words (for example, tools, animals, and
buildings) or when viewing the related pictures. In this paper, we present a
computational model that learns to predict the neural activation captured in
functional magnetic resonance imaging (fMRI) data of test words. Unlike the
models with hand-crafted features that have been used in the literature, in
this paper we propose a novel approach wherein decoding models are built with
features extracted from popular linguistic encodings of Word2Vec, GloVe,
Meta-Embeddings in conjunction with the empirical fMRI data associated with
viewing several dozen concrete nouns. We compared these models with several
other models that use word features extracted from FastText, Randomly-generated
features, Mitchell's 25 features [1]. The experimental results show that the
predicted fMRI images using Meta-Embeddings meet the state-of-the-art
performance. Although models with features from GloVe and Word2Vec predict fMRI
images similar to the state-of-the-art model, model with features from
Meta-Embeddings predicts significantly better. The proposed scheme that uses
popular linguistic encoding offers a simple and easy approach for semantic
decoding from fMRI experiments.
| null |
http://arxiv.org/abs/1806.05177v1
|
http://arxiv.org/pdf/1806.05177v1.pdf
| null |
[
"Subba Reddy Oota",
"Naresh Manwani",
"Bapi Raju S"
] |
[
"Word Embeddings"
] | 2018-06-13T00:00:00 | 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/convolutional-sparse-coding-for-high-dynamic
|
1806.04942
| null | null |
Convolutional Sparse Coding for High Dynamic Range Imaging
|
Current HDR acquisition techniques are based on either (i) fusing
multibracketed, low dynamic range (LDR) images, (ii) modifying existing
hardware and capturing different exposures simultaneously with multiple
sensors, or (iii) reconstructing a single image with spatially-varying pixel
exposures. In this paper, we propose a novel algorithm to recover high-quality
HDRI images from a single, coded exposure. The proposed reconstruction method
builds on recently-introduced ideas of convolutional sparse coding (CSC); this
paper demonstrates how to make CSC practical for HDR imaging. We demonstrate
that the proposed algorithm achieves higher-quality reconstructions than
alternative methods, we evaluate optical coding schemes, analyze algorithmic
parameters, and build a prototype coded HDR camera that demonstrates the
utility of convolutional sparse HDRI coding with a custom hardware platform.
| null |
http://arxiv.org/abs/1806.04942v1
|
http://arxiv.org/pdf/1806.04942v1.pdf
| null |
[
"Ana Serrano",
"Felix Heide",
"Diego Gutierrez",
"Gordon Wetzstein",
"Belen Masia"
] |
[
"Vocal Bursts Intensity Prediction"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/far-ho-a-bilevel-programming-package-for
|
1806.04941
| null | null |
Far-HO: A Bilevel Programming Package for Hyperparameter Optimization and Meta-Learning
|
In (Franceschi et al., 2018) we proposed a unified mathematical framework,
grounded on bilevel programming, that encompasses gradient-based hyperparameter
optimization and meta-learning. We formulated an approximate version of the
problem where the inner objective is solved iteratively, and gave sufficient
conditions ensuring convergence to the exact problem. In this work we show how
to optimize learning rates, automatically weight the loss of single examples
and learn hyper-representations with Far-HO, a software package based on the
popular deep learning framework TensorFlow that allows to seamlessly tackle
both HO and ML problems.
|
In (Franceschi et al., 2018) we proposed a unified mathematical framework, grounded on bilevel programming, that encompasses gradient-based hyperparameter optimization and meta-learning.
|
http://arxiv.org/abs/1806.04941v1
|
http://arxiv.org/pdf/1806.04941v1.pdf
| null |
[
"Luca Franceschi",
"Riccardo Grazzi",
"Massimiliano Pontil",
"Saverio Salzo",
"Paolo Frasconi"
] |
[
"Hyperparameter Optimization",
"Meta-Learning"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/convolutional-sparse-coding-for-capturing
|
1806.04935
| null | null |
Convolutional sparse coding for capturing high speed video content
|
Video capture is limited by the trade-off between spatial and temporal
resolution: when capturing videos of high temporal resolution, the spatial
resolution decreases due to bandwidth limitations in the capture system.
Achieving both high spatial and temporal resolution is only possible with
highly specialized and very expensive hardware, and even then the same basic
trade-off remains. The recent introduction of compressive sensing and sparse
reconstruction techniques allows for the capture of single-shot high-speed
video, by coding the temporal information in a single frame, and then
reconstructing the full video sequence from this single coded image and a
trained dictionary of image patches. In this paper, we first analyze this
approach, and find insights that help improve the quality of the reconstructed
videos. We then introduce a novel technique, based on convolutional sparse
coding (CSC), and show how it outperforms the state-of-the-art, patch-based
approach in terms of flexibility and efficiency, due to the convolutional
nature of its filter banks. The key idea for CSC high-speed video acquisition
is extending the basic formulation by imposing an additional constraint in the
temporal dimension, which enforces sparsity of the first-order derivatives over
time.
| null |
http://arxiv.org/abs/1806.04935v1
|
http://arxiv.org/pdf/1806.04935v1.pdf
| null |
[
"Ana Serrano",
"Elena Garces",
"Diego Gutierrez",
"Belen Masia"
] |
[
"Compressive Sensing",
"Vocal Bursts Intensity Prediction"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/reservoir-computing-hardware-with-cellular
|
1806.04932
| null | null |
Reservoir Computing Hardware with Cellular Automata
|
Elementary cellular automata (ECA) is a widely studied one-dimensional processing methodology where the successive iteration of the automaton may lead to the recreation of a rich pattern dynamic. Recently, cellular automata have been proposed as a feasible way to implement Reservoir Computing (RC) systems in which the automata rule is fixed and the training is performed using a linear regression. In this work we perform an exhaustive study of the performance of the different ECA rules when applied to pattern recognition of time-independent input signals using a RC scheme. Once the different ECA rules have been tested, the most accurate one (rule 90) is selected to implement a digital circuit. Rule 90 is easily reproduced using a reduced set of XOR gates and shift-registers, thus representing a high-performance alternative for RC hardware implementation in terms of processing time, circuit area, power dissipation and system accuracy. The model (both in software and its hardware implementation) has been tested using a pattern recognition task of handwritten numbers (the MNIST database) for which we obtained competitive results in terms of accuracy, speed and power dissipation. The proposed model can be considered to be a low-cost method to implement fast pattern recognition digital circuits.
| null |
https://arxiv.org/abs/1806.04932v2
|
https://arxiv.org/pdf/1806.04932v2.pdf
| null |
[
"Alejandro Morán",
"Christiam F. Frasser",
"Josep L. Rosselló"
] |
[] | 2018-06-13T00:00:00 | 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/an-image-representation-based-convolutional
|
1806.04931
| null |
HJvvRoe0W
|
An image representation based convolutional network for DNA classification
|
The folding structure of the DNA molecule combined with helper molecules,
also referred to as the chromatin, is highly relevant for the functional
properties of DNA. The chromatin structure is largely determined by the
underlying primary DNA sequence, though the interaction is not yet fully
understood. In this paper we develop a convolutional neural network that takes
an image-representation of primary DNA sequence as its input, and predicts key
determinants of chromatin structure. The method is developed such that it is
capable of detecting interactions between distal elements in the DNA sequence,
which are known to be highly relevant. Our experiments show that the method
outperforms several existing methods both in terms of prediction accuracy and
training time.
|
The folding structure of the DNA molecule combined with helper molecules, also referred to as the chromatin, is highly relevant for the functional properties of DNA.
|
http://arxiv.org/abs/1806.04931v1
|
http://arxiv.org/pdf/1806.04931v1.pdf
|
ICLR 2018 1
|
[
"Bojian Yin",
"Marleen Balvert",
"Davide Zambrano",
"Alexander Schönhuth",
"Sander Bohte"
] |
[
"Classification",
"General Classification"
] | 2018-06-13T00:00:00 |
https://openreview.net/forum?id=HJvvRoe0W
|
https://openreview.net/pdf?id=HJvvRoe0W
|
an-image-representation-based-convolutional-1
| null |
[] |
https://paperswithcode.com/paper/probabilistic-feature-selection-and
|
1609.05486
| null | null |
Probabilistic Feature Selection and Classification Vector Machine
|
Sparse Bayesian learning is a state-of-the-art supervised learning algorithm
that can choose a subset of relevant samples from the input data and make
reliable probabilistic predictions. However, in the presence of
high-dimensional data with irrelevant features, traditional sparse Bayesian
classifiers suffer from performance degradation and low efficiency by failing
to eliminate irrelevant features. To tackle this problem, we propose a novel
sparse Bayesian embedded feature selection method that adopts truncated
Gaussian distributions as both sample and feature priors. The proposed method,
called probabilistic feature selection and classification vector machine
(PFCVMLP ), is able to simultaneously select relevant features and samples for
classification tasks. In order to derive the analytical solutions, Laplace
approximation is applied to compute approximate posteriors and marginal
likelihoods. Finally, parameters and hyperparameters are optimized by the
type-II maximum likelihood method. Experiments on three datasets validate the
performance of PFCVMLP along two dimensions: classification performance and
effectiveness for feature selection. Finally, we analyze the generalization
performance and derive a generalization error bound for PFCVMLP . By tightening
the bound, the importance of feature selection is demonstrated.
| null |
http://arxiv.org/abs/1609.05486v3
|
http://arxiv.org/pdf/1609.05486v3.pdf
| null |
[
"Bingbing Jiang",
"Chang Li",
"Maarten de Rijke",
"Xin Yao",
"Huanhuan Chen"
] |
[
"Classification",
"feature selection",
"General Classification"
] | 2016-09-18T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/the-iq-of-artificial-intelligence
|
1806.04915
| null | null |
The IQ of Artificial Intelligence
|
All it takes to identify the computer programs which are Artificial
Intelligence is to give them a test and award AI to those that pass the test.
Let us say that the scores they earn at the test will be called IQ. We cannot
pinpoint a minimum IQ threshold that a program has to cover in order to be AI,
however, we will choose a certain value. Thus, our definition for AI will be
any program the IQ of which is above the chosen value. While this idea has
already been implemented in [3], here we will revisit this construct in order
to introduce certain improvements.
| null |
http://arxiv.org/abs/1806.04915v1
|
http://arxiv.org/pdf/1806.04915v1.pdf
| null |
[
"Dimiter Dobrev"
] |
[] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/bilevel-programming-for-hyperparameter
|
1806.04910
| null | null |
Bilevel Programming for Hyperparameter Optimization and Meta-Learning
|
We introduce a framework based on bilevel programming that unifies
gradient-based hyperparameter optimization and meta-learning. We show that an
approximate version of the bilevel problem can be solved by taking into
explicit account the optimization dynamics for the inner objective. Depending
on the specific setting, the outer variables take either the meaning of
hyperparameters in a supervised learning problem or parameters of a
meta-learner. We provide sufficient conditions under which solutions of the
approximate problem converge to those of the exact problem. We instantiate our
approach for meta-learning in the case of deep learning where representation
layers are treated as hyperparameters shared across a set of training episodes.
In experiments, we confirm our theoretical findings, present encouraging
results for few-shot learning and contrast the bilevel approach against
classical approaches for learning-to-learn.
| null |
http://arxiv.org/abs/1806.04910v2
|
http://arxiv.org/pdf/1806.04910v2.pdf
|
ICML 2018 7
|
[
"Luca Franceschi",
"Paolo Frasconi",
"Saverio Salzo",
"Riccardo Grazzi",
"Massimilano Pontil"
] |
[
"Few-Shot Learning",
"Hyperparameter Optimization",
"Meta-Learning"
] | 2018-06-13T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2248
|
http://proceedings.mlr.press/v80/franceschi18a/franceschi18a.pdf
|
bilevel-programming-for-hyperparameter-1
| null |
[] |
https://paperswithcode.com/paper/a-machine-learning-item-recommendation-system
|
1806.04900
| null | null |
A Machine-Learning Item Recommendation System for Video Games
|
Video-game players generate huge amounts of data, as everything they do
within a game is recorded. In particular, among all the stored actions and
behaviors, there is information on the in-game purchases of virtual products.
Such information is of critical importance in modern free-to-play titles, where
gamers can select or buy a profusion of items during the game in order to
progress and fully enjoy their experience.
To try to maximize these kind of purchases, one can use a recommendation
system so as to present players with items that might be interesting for them.
Such systems can better achieve their goal by employing machine learning
algorithms that are able to predict the rating of an item or product by a
particular user. In this paper we evaluate and compare two of these algorithms,
an ensemble-based model (extremely randomized trees) and a deep neural network,
both of which are promising candidates for operational video-game recommender
engines.
Item recommenders can help developers improve the game. But, more
importantly, it should be possible to integrate them into the game, so that
users automatically get personalized recommendations while playing. The
presented models are not only able to meet this challenge, providing accurate
predictions of the items that a particular player will find attractive, but
also sufficiently fast and robust to be used in operational settings.
| null |
http://arxiv.org/abs/1806.04900v2
|
http://arxiv.org/pdf/1806.04900v2.pdf
| null |
[
"Paul Bertens",
"Anna Guitart",
"Pei Pei Chen",
"África Periáñez"
] |
[
"BIG-bench Machine Learning"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/look-imagine-and-match-improving-textual
|
1711.06420
| null | null |
Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with Generative Models
|
Textual-visual cross-modal retrieval has been a hot research topic in both
computer vision and natural language processing communities. Learning
appropriate representations for multi-modal data is crucial for the cross-modal
retrieval performance. Unlike existing image-text retrieval approaches that
embed image-text pairs as single feature vectors in a common representational
space, we propose to incorporate generative processes into the cross-modal
feature embedding, through which we are able to learn not only the global
abstract features but also the local grounded features. Extensive experiments
show that our framework can well match images and sentences with complex
content, and achieve the state-of-the-art cross-modal retrieval results on
MSCOCO dataset.
| null |
http://arxiv.org/abs/1711.06420v2
|
http://arxiv.org/pdf/1711.06420v2.pdf
|
CVPR 2018 6
|
[
"Jiuxiang Gu",
"Jianfei Cai",
"Shafiq Joty",
"Li Niu",
"Gang Wang"
] |
[
"Cross-Modal Retrieval",
"Image-text Retrieval",
"Retrieval",
"Text Retrieval"
] | 2017-11-17T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Gu_Look_Imagine_and_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Gu_Look_Imagine_and_CVPR_2018_paper.pdf
|
look-imagine-and-match-improving-textual-1
| null |
[] |
https://paperswithcode.com/paper/learning-longer-term-dependencies-in-rnns
|
1803.00144
| null | null |
Learning Longer-term Dependencies in RNNs with Auxiliary Losses
|
Despite recent advances in training recurrent neural networks (RNNs),
capturing long-term dependencies in sequences remains a fundamental challenge.
Most approaches use backpropagation through time (BPTT), which is difficult to
scale to very long sequences. This paper proposes a simple method that improves
the ability to capture long term dependencies in RNNs by adding an unsupervised
auxiliary loss to the original objective. This auxiliary loss forces RNNs to
either reconstruct previous events or predict next events in a sequence, making
truncated backpropagation feasible for long sequences and also improving full
BPTT. We evaluate our method on a variety of settings, including pixel-by-pixel
image classification with sequence lengths up to 16\,000, and a real document
classification benchmark. Our results highlight good performance and resource
efficiency of this approach over competitive baselines, including other
recurrent models and a comparable sized Transformer. Further analyses reveal
beneficial effects of the auxiliary loss on optimization and regularization, as
well as extreme cases where there is little to no backpropagation.
|
Despite recent advances in training recurrent neural networks (RNNs), capturing long-term dependencies in sequences remains a fundamental challenge.
|
http://arxiv.org/abs/1803.00144v3
|
http://arxiv.org/pdf/1803.00144v3.pdf
|
ICML 2018
|
[
"Trieu H. Trinh",
"Andrew M. Dai",
"Minh-Thang Luong",
"Quoc V. Le"
] |
[
"Document Classification",
"General Classification",
"image-classification",
"Image Classification"
] | 2018-03-01T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "A **Linear Layer** is a projection $\\mathbf{XW + b}$.",
"full_name": "Linear Layer",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Linear Layer",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "**Absolute Position Encodings** are a type of position embeddings for [[Transformer](https://paperswithcode.com/method/transformer)-based models] where positional encodings are added to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension $d\\_{model}$ as the embeddings, so that the two can be summed. In the original implementation, sine and cosine functions of different frequencies are used:\r\n\r\n$$ \\text{PE}\\left(pos, 2i\\right) = \\sin\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\n$$ \\text{PE}\\left(pos, 2i+1\\right) = \\cos\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\nwhere $pos$ is the position and $i$ is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from $2\\pi$ to $10000 \\dot 2\\pi$. This function was chosen because the authors hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset $k$, $\\text{PE}\\_{pos+k}$ can be represented as a linear function of $\\text{PE}\\_{pos}$.\r\n\r\nImage Source: [D2L.ai](https://d2l.ai/chapter_attention-mechanisms/self-attention-and-positional-encoding.html)",
"full_name": "Absolute Position Encodings",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Position Embeddings",
"parent": null
},
"name": "Absolute Position Encodings",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": null,
"description": "**Position-Wise Feed-Forward Layer** is a type of [feedforward layer](https://www.paperswithcode.com/method/category/feedforwad-networks) consisting of two [dense layers](https://www.paperswithcode.com/method/dense-connections) that applies to the last dimension, which means the same dense layers are used for each position item in the sequence, so called position-wise.",
"full_name": "Position-Wise Feed-Forward Layer",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Position-Wise Feed-Forward Layer",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/resnet.py#L118",
"description": "**Residual Connections** are a type of skip-connection that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. \r\n\r\nFormally, denoting the desired underlying mapping as $\\mathcal{H}({x})$, we let the stacked nonlinear layers fit another mapping of $\\mathcal{F}({x}):=\\mathcal{H}({x})-{x}$. The original mapping is recast into $\\mathcal{F}({x})+{x}$.\r\n\r\nThe intuition is that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers.",
"full_name": "Residual Connection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connections** allow layers to skip layers and connect to layers further up the network, allowing for information to flow more easily up the network. Below you can find a continuously updating list of skip connection methods.",
"name": "Skip Connections",
"parent": null
},
"name": "Residual Connection",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/b7bda236d18815052378c88081f64935427d7716/torch/optim/adam.py#L6",
"description": "**Adam** is an adaptive learning rate optimization algorithm that utilises both momentum and scaling, combining the benefits of [RMSProp](https://paperswithcode.com/method/rmsprop) and [SGD w/th Momentum](https://paperswithcode.com/method/sgd-with-momentum). The optimizer is designed to be appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. \r\n\r\nThe weight updates are performed as:\r\n\r\n$$ w_{t} = w_{t-1} - \\eta\\frac{\\hat{m}\\_{t}}{\\sqrt{\\hat{v}\\_{t}} + \\epsilon} $$\r\n\r\nwith\r\n\r\n$$ \\hat{m}\\_{t} = \\frac{m_{t}}{1-\\beta^{t}_{1}} $$\r\n\r\n$$ \\hat{v}\\_{t} = \\frac{v_{t}}{1-\\beta^{t}_{2}} $$\r\n\r\n$$ m_{t} = \\beta_{1}m_{t-1} + (1-\\beta_{1})g_{t} $$\r\n\r\n$$ v_{t} = \\beta_{2}v_{t-1} + (1-\\beta_{2})g_{t}^{2} $$\r\n\r\n\r\n$ \\eta $ is the step size/learning rate, around 1e-3 in the original paper. $ \\epsilon $ is a small number, typically 1e-8 or 1e-10, to prevent dividing by zero. $ \\beta_{1} $ and $ \\beta_{2} $ are forgetting parameters, with typical values 0.9 and 0.999, respectively.",
"full_name": "Adam",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Stochastic Optimization** methods are used to optimize neural networks. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. Below you can find a continuously updating list of stochastic optimization algorithms.",
"name": "Stochastic Optimization",
"parent": "Optimization"
},
"name": "Adam",
"source_title": "Adam: A Method for Stochastic Optimization",
"source_url": "http://arxiv.org/abs/1412.6980v9"
},
{
"code_snippet_url": "",
"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": "https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/fec78a687210851f055f792d45300d27cc60ae41/transformer/SubLayers.py#L9",
"description": "**Multi-head Attention** is a module for attention mechanisms which runs through an attention mechanism several times in parallel. The independent attention outputs are then concatenated and linearly transformed into the expected dimension. Intuitively, multiple attention heads allows for attending to parts of the sequence differently (e.g. longer-term dependencies versus shorter-term dependencies). \r\n\r\n$$ \\text{MultiHead}\\left(\\textbf{Q}, \\textbf{K}, \\textbf{V}\\right) = \\left[\\text{head}\\_{1},\\dots,\\text{head}\\_{h}\\right]\\textbf{W}_{0}$$\r\n\r\n$$\\text{where} \\text{ head}\\_{i} = \\text{Attention} \\left(\\textbf{Q}\\textbf{W}\\_{i}^{Q}, \\textbf{K}\\textbf{W}\\_{i}^{K}, \\textbf{V}\\textbf{W}\\_{i}^{V} \\right) $$\r\n\r\nAbove $\\textbf{W}$ are all learnable parameter matrices.\r\n\r\nNote that [scaled dot-product attention](https://paperswithcode.com/method/scaled) is most commonly used in this module, although in principle it can be swapped out for other types of attention mechanism.\r\n\r\nSource: [Lilian Weng](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html#a-family-of-attention-mechanisms)",
"full_name": "Multi-Head Attention",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Attention Modules** refer to modules that incorporate attention mechanisms. For example, multi-head attention is a module that incorporates multiple attention heads. Below you can find a continuously updating list of attention modules.",
"name": "Attention Modules",
"parent": "Attention"
},
"name": "Multi-Head Attention",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": null,
"description": "**Byte Pair Encoding**, or **BPE**, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations).\r\n\r\n[Lei Mao](https://leimao.github.io/blog/Byte-Pair-Encoding/) has a detailed blog post that explains how this works.",
"full_name": "Byte Pair Encoding",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Subword Segmentation",
"parent": null
},
"name": "BPE",
"source_title": "Neural Machine Translation of Rare Words with Subword Units",
"source_url": "http://arxiv.org/abs/1508.07909v5"
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "**Label Smoothing** is a regularization technique that introduces noise for the labels. This accounts for the fact that datasets may have mistakes in them, so maximizing the likelihood of $\\log{p}\\left(y\\mid{x}\\right)$ directly can be harmful. Assume for a small constant $\\epsilon$, the training set label $y$ is correct with probability $1-\\epsilon$ and incorrect otherwise. Label Smoothing regularizes a model based on a [softmax](https://paperswithcode.com/method/softmax) with $k$ output values by replacing the hard $0$ and $1$ classification targets with targets of $\\frac{\\epsilon}{k}$ and $1-\\frac{k-1}{k}\\epsilon$ respectively.\r\n\r\nSource: Deep Learning, Goodfellow et al\r\n\r\nImage Source: [When Does Label Smoothing Help?](https://arxiv.org/abs/1906.02629)",
"full_name": "Label Smoothing",
"introduced_year": 1985,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Label Smoothing",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": 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/CyberZHG/torch-layer-normalization/blob/89f405b60f53f85da6f03fe685c190ef394ce50c/torch_layer_normalization/layer_normalization.py#L8",
"description": "Unlike [batch normalization](https://paperswithcode.com/method/batch-normalization), **Layer Normalization** directly estimates the normalization statistics from the summed inputs to the neurons within a hidden layer so the normalization does not introduce any new dependencies between training cases. It works well for [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and improves both the training time and the generalization performance of several existing RNN models. More recently, it has been used with [Transformer](https://paperswithcode.com/methods/category/transformers) models.\r\n\r\nWe compute the layer normalization statistics over all the hidden units in the same layer as follows:\r\n\r\n$$ \\mu^{l} = \\frac{1}{H}\\sum^{H}\\_{i=1}a\\_{i}^{l} $$\r\n\r\n$$ \\sigma^{l} = \\sqrt{\\frac{1}{H}\\sum^{H}\\_{i=1}\\left(a\\_{i}^{l}-\\mu^{l}\\right)^{2}} $$\r\n\r\nwhere $H$ denotes the number of hidden units in a layer. Under layer normalization, all the hidden units in a layer share the same normalization terms $\\mu$ and $\\sigma$, but different training cases have different normalization terms. Unlike batch normalization, layer normalization does not impose any constraint on the size of the mini-batch and it can be used in the pure online regime with batch size 1.",
"full_name": "Layer Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Layer Normalization",
"source_title": "Layer Normalization",
"source_url": "http://arxiv.org/abs/1607.06450v1"
},
{
"code_snippet_url": "",
"description": "",
"full_name": "Attention Is All You Need",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "If you're looking to get in touch with American Airlines fast, ☎️+1-801-(855)-(5905)or +1-804-853-9001✅ there are\r\nseveral efficient ways to reach their customer service team. The quickest method is to dial ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. American’s phone service ensures that you can speak with a live\r\nrepresentative promptly to resolve any issues or queries regarding your booking, reservation,\r\nor any changes, such as name corrections or ticket cancellations.",
"name": "Attention Mechanisms",
"parent": "Attention"
},
"name": "Attention",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": "https://github.com/tunz/transformer-pytorch/blob/e7266679f0b32fd99135ea617213f986ceede056/model/transformer.py#L201",
"description": "A **Transformer** is a model architecture that eschews recurrence and instead relies entirely on an [attention mechanism](https://paperswithcode.com/methods/category/attention-mechanisms-1) to draw global dependencies between input and output. Before Transformers, the dominant sequence transduction models were based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The Transformer also employs an encoder and decoder, but removing recurrence in favor of [attention mechanisms](https://paperswithcode.com/methods/category/attention-mechanisms-1) allows for significantly more parallelization than methods like [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and [CNNs](https://paperswithcode.com/methods/category/convolutional-neural-networks).",
"full_name": "Transformer",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Transformer",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
}
] |
https://paperswithcode.com/paper/a-multi-task-deep-learning-architecture-for
|
1806.03972
| null | null |
A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data Streams
|
In a world of global trading, maritime safety, security and efficiency are
crucial issues. We propose a multi-task deep learning framework for vessel
monitoring using Automatic Identification System (AIS) data streams. We combine
recurrent neural networks with latent variable modeling and an embedding of AIS
messages to a new representation space to jointly address key issues to be
dealt with when considering AIS data streams: massive amount of streaming data,
noisy data and irregular timesampling. We demonstrate the relevance of the
proposed deep learning framework on real AIS datasets for a three-task setting,
namely trajectory reconstruction, anomaly detection and vessel type
identification.
|
In a world of global trading, maritime safety, security and efficiency are crucial issues.
|
http://arxiv.org/abs/1806.03972v3
|
http://arxiv.org/pdf/1806.03972v3.pdf
| null |
[
"Duong Nguyen",
"Rodolphe Vadaine",
"Guillaume Hajduch",
"René Garello",
"Ronan Fablet"
] |
[
"Anomaly Detection",
"Deep Learning"
] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/weight-initialization-without-local-minima-in
|
1806.04884
| null | null |
Spurious Local Minima of Deep ReLU Neural Networks in the Neural Tangent Kernel Regime
|
In this paper, we theoretically prove that the deep ReLU neural networks do not lie in spurious local minima in the loss landscape under the Neural Tangent Kernel (NTK) regime, that is, in the gradient descent training dynamics of the deep ReLU neural networks whose parameters are initialized by a normal distribution in the limit as the widths of the hidden layers tend to infinity.
| null |
https://arxiv.org/abs/1806.04884v3
|
https://arxiv.org/pdf/1806.04884v3.pdf
| null |
[
"Tohru Nitta"
] |
[] | 2018-06-13T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!\r\n\r\n\r\nHow Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!",
"full_name": "*Communicated@Fast*How Do I Communicate to Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/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"
}
] |
https://paperswithcode.com/paper/visual-speech-enhancement
|
1711.08789
| null | null |
Visual Speech Enhancement
|
When video is shot in noisy environment, the voice of a speaker seen in the
video can be enhanced using the visible mouth movements, reducing background
noise. While most existing methods use audio-only inputs, improved performance
is obtained with our visual speech enhancement, based on an audio-visual neural
network. We include in the training data videos to which we added the voice of
the target speaker as background noise. Since the audio input is not sufficient
to separate the voice of a speaker from his own voice, the trained model better
exploits the visual input and generalizes well to different noise types. The
proposed model outperforms prior audio visual methods on two public lipreading
datasets. It is also the first to be demonstrated on a dataset not designed for
lipreading, such as the weekly addresses of Barack Obama.
|
When video is shot in noisy environment, the voice of a speaker seen in the video can be enhanced using the visible mouth movements, reducing background noise.
|
http://arxiv.org/abs/1711.08789v3
|
http://arxiv.org/pdf/1711.08789v3.pdf
| null |
[
"Aviv Gabbay",
"Asaph Shamir",
"Shmuel Peleg"
] |
[
"Lipreading",
"Speech Enhancement"
] | 2017-11-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/unsupervised-adaptation-with-interpretable
|
1806.04872
| null | null |
Unsupervised Adaptation with Interpretable Disentangled Representations for Distant Conversational Speech Recognition
|
The current trend in automatic speech recognition is to leverage large
amounts of labeled data to train supervised neural network models.
Unfortunately, obtaining data for a wide range of domains to train robust
models can be costly. However, it is relatively inexpensive to collect large
amounts of unlabeled data from domains that we want the models to generalize
to. In this paper, we propose a novel unsupervised adaptation method that
learns to synthesize labeled data for the target domain from unlabeled
in-domain data and labeled out-of-domain data. We first learn without
supervision an interpretable latent representation of speech that encodes
linguistic and nuisance factors (e.g., speaker and channel) using different
latent variables. To transform a labeled out-of-domain utterance without
altering its transcript, we transform the latent nuisance variables while
maintaining the linguistic variables. To demonstrate our approach, we focus on
a channel mismatch setting, where the domain of interest is distant
conversational speech, and labels are only available for close-talking speech.
Our proposed method is evaluated on the AMI dataset, outperforming all
baselines and bridging the gap between unadapted and in-domain models by over
77% without using any parallel data.
| null |
http://arxiv.org/abs/1806.04872v1
|
http://arxiv.org/pdf/1806.04872v1.pdf
| null |
[
"Wei-Ning Hsu",
"Hao Tang",
"James Glass"
] |
[
"Automatic Speech Recognition",
"Automatic Speech Recognition (ASR)",
"speech-recognition",
"Speech Recognition"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/when-simple-exploration-is-sample-efficient
|
1805.09045
| null | null |
When Simple Exploration is Sample Efficient: Identifying Sufficient Conditions for Random Exploration to Yield PAC RL Algorithms
|
Efficient exploration is one of the key challenges for reinforcement learning
(RL) algorithms. Most traditional sample efficiency bounds require strategic
exploration. Recently many deep RL algorithms with simple heuristic exploration
strategies that have few formal guarantees, achieve surprising success in many
domains. These results pose an important question about understanding these
exploration strategies such as $e$-greedy, as well as understanding what
characterize the difficulty of exploration in MDPs. In this work we propose
problem specific sample complexity bounds of $Q$ learning with random walk
exploration that rely on several structural properties. We also link our
theoretical results to some empirical benchmark domains, to illustrate if our
bound gives polynomial sample complexity in these domains and how that is
related with the empirical performance.
| null |
http://arxiv.org/abs/1805.09045v4
|
http://arxiv.org/pdf/1805.09045v4.pdf
| null |
[
"Yao Liu",
"Emma Brunskill"
] |
[
"Efficient Exploration",
"Q-Learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/mqapviz-a-divide-and-conquer-multi-objective
|
1804.00656
| null | null |
mQAPViz: A divide-and-conquer multi-objective optimization algorithm to compute large data visualizations
|
Algorithms for data visualizations are essential tools for transforming data
into useful narratives. Unfortunately, very few visualization algorithms can
handle the large datasets of many real-world scenarios. In this study, we
address the visualization of these datasets as a Multi-Objective Optimization
Problem. We propose mQAPViz, a divide-and-conquer multi-objective optimization
algorithm to compute large-scale data visualizations. Our method employs the
Multi-Objective Quadratic Assignment Problem (mQAP) as the mathematical
foundation to solve the visualization task at hand. The algorithm applies
advanced sampling techniques originating from the field of machine learning and
efficient data structures to scale to millions of data objects. The algorithm
allocates objects onto a 2D grid layout. Experimental results on real-world and
large datasets demonstrate that mQAPViz is a competitive alternative to
existing techniques.
| null |
http://arxiv.org/abs/1804.00656v3
|
http://arxiv.org/pdf/1804.00656v3.pdf
| null |
[
"Claudio Sanhueza",
"Francia Jiménez",
"Regina Berretta",
"Pablo Moscato"
] |
[] | 2018-04-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/convolutional-dictionary-learning-a
|
1709.02893
| null | null |
Convolutional Dictionary Learning: A Comparative Review and New Algorithms
|
Convolutional sparse representations are a form of sparse representation with
a dictionary that has a structure that is equivalent to convolution with a set
of linear filters. While effective algorithms have recently been developed for
the convolutional sparse coding problem, the corresponding dictionary learning
problem is substantially more challenging. Furthermore, although a number of
different approaches have been proposed, the absence of thorough comparisons
between them makes it difficult to determine which of them represents the
current state of the art. The present work both addresses this deficiency and
proposes some new approaches that outperform existing ones in certain contexts.
A thorough set of performance comparisons indicates a very wide range of
performance differences among the existing and proposed methods, and clearly
identifies those that are the most effective.
| null |
http://arxiv.org/abs/1709.02893v5
|
http://arxiv.org/pdf/1709.02893v5.pdf
| null |
[
"Cristina Garcia-Cardona",
"Brendt Wohlberg"
] |
[
"Dictionary Learning"
] | 2017-09-09T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/cell-identity-codes-understanding-cell
|
1806.04863
| null | null |
Cell Identity Codes: Understanding Cell Identity from Gene Expression Profiles using Deep Neural Networks
|
Understanding cell identity is an important task in many biomedical areas.
Expression patterns of specific marker genes have been used to characterize
some limited cell types, but exclusive markers are not available for many cell
types. A second approach is to use machine learning to discriminate cell types
based on the whole gene expression profiles (GEPs). The accuracies of simple
classification algorithms such as linear discriminators or support vector
machines are limited due to the complexity of biological systems. We used deep
neural networks to analyze 1040 GEPs from 16 different human tissues and cell
types. After comparing different architectures, we identified a specific
structure of deep autoencoders that can encode a GEP into a vector of 30
numeric values, which we call the cell identity code (CIC). The original GEP
can be reproduced from the CIC with an accuracy comparable to technical
replicates of the same experiment. Although we use an unsupervised approach to
train the autoencoder, we show different values of the CIC are connected to
different biological aspects of the cell, such as different pathways or
biological processes. This network can use CIC to reproduce the GEP of the cell
types it has never seen during the training. It also can resist some noise in
the measurement of the GEP. Furthermore, we introduce classifier autoencoder,
an architecture that can accurately identify cell type based on the GEP or the
CIC.
| null |
http://arxiv.org/abs/1806.04863v1
|
http://arxiv.org/pdf/1806.04863v1.pdf
| null |
[
"Farzad Abdolhosseini",
"Behrooz Azarkhalili",
"Abbas Maazallahi",
"Aryan Kamal",
"Seyed Abolfazl Motahari",
"Ali Sharifi-Zarchi",
"Hamidreza Chitsaz"
] |
[] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-visual-knowledge-memory-networks-for
|
1806.04860
| null | null |
Learning Visual Knowledge Memory Networks for Visual Question Answering
|
Visual question answering (VQA) requires joint comprehension of images and
natural language questions, where many questions can't be directly or clearly
answered from visual content but require reasoning from structured human
knowledge with confirmation from visual content. This paper proposes visual
knowledge memory network (VKMN) to address this issue, which seamlessly
incorporates structured human knowledge and deep visual features into memory
networks in an end-to-end learning framework. Comparing to existing methods for
leveraging external knowledge for supporting VQA, this paper stresses more on
two missing mechanisms. First is the mechanism for integrating visual contents
with knowledge facts. VKMN handles this issue by embedding knowledge triples
(subject, relation, target) and deep visual features jointly into the visual
knowledge features. Second is the mechanism for handling multiple knowledge
facts expanding from question and answer pairs. VKMN stores joint embedding
using key-value pair structure in the memory networks so that it is easy to
handle multiple facts. Experiments show that the proposed method achieves
promising results on both VQA v1.0 and v2.0 benchmarks, while outperforms
state-of-the-art methods on the knowledge-reasoning related questions.
| null |
http://arxiv.org/abs/1806.04860v1
|
http://arxiv.org/pdf/1806.04860v1.pdf
|
CVPR 2018 6
|
[
"Zhou Su",
"Chen Zhu",
"Yinpeng Dong",
"Dongqi Cai",
"Yurong Chen",
"Jianguo Li"
] |
[
"Question Answering",
"Visual Question Answering",
"Visual Question Answering (VQA)"
] | 2018-06-13T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Su_Learning_Visual_Knowledge_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Su_Learning_Visual_Knowledge_CVPR_2018_paper.pdf
|
learning-visual-knowledge-memory-networks-for-1
| null |
[
{
"code_snippet_url": "https://github.com/aykutaaykut/Memory-Networks",
"description": "A **Memory Network** provides a memory component that can be read from and written to with the inference capabilities of a neural network model. The motivation is that many neural networks lack a long-term memory component, and their existing memory component encoded by states and weights is too small and not compartmentalized enough to accurately remember facts from the past (RNNs for example, have difficult memorizing and doing tasks like copying). \r\n\r\nA memory network consists of a memory $\\textbf{m}$ (an array of objects indexed by $\\textbf{m}\\_{i}$ and four potentially learned components:\r\n\r\n- Input feature map $I$ - feature representation of the data input.\r\n- Generalization $G$ - updates old memories given the new input.\r\n- Output feature map $O$ - produces new feature map given $I$ and $G$.\r\n- Response $R$ - converts output into the desired response. \r\n\r\nGiven an input $x$ (e.g., an input character, word or sentence depending on the granularity chosen, an image or an audio signal) the flow of the model is as follows:\r\n\r\n1. Convert $x$ to an internal feature representation $I\\left(x\\right)$.\r\n2. Update memories $m\\_{i}$ given the new input: $m\\_{i} = G\\left(m\\_{i}, I\\left(x\\right), m\\right)$, $\\forall{i}$.\r\n3. Compute output features $o$ given the new input and the memory: $o = O\\left(I\\left(x\\right), m\\right)$.\r\n4. Finally, decode output features $o$ to give the final response: $r = R\\left(o\\right)$.\r\n\r\nThis process is applied at both train and test time, if there is a distinction between such phases, that\r\nis, memories are also stored at test time, but the model parameters of $I$, $G$, $O$ and $R$ are not updated. Memory networks cover a wide class of possible implementations. The components $I$, $G$, $O$ and $R$ can potentially use any existing ideas from the machine learning literature.\r\n\r\nImage Source: [Adrian Colyer](https://blog.acolyer.org/2016/03/10/memory-networks/)",
"full_name": "Memory Network",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Working Memory Models** aim to supplement neural networks with a memory module to increase their capability for memorization and allowing them to more easily perform tasks such as retrieving and copying information. Below you can find a continuously updating list of working memory models.",
"name": "Working Memory Models",
"parent": null
},
"name": "Memory Network",
"source_title": "Memory Networks",
"source_url": "http://arxiv.org/abs/1410.3916v11"
}
] |
https://paperswithcode.com/paper/deepcas-a-deep-reinforcement-learning
|
1803.02998
| null | null |
DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling
|
We consider networked control systems consisting of multiple independent
controlled subsystems, operating over a shared communication network. Such
systems are ubiquitous in cyber-physical systems, Internet of Things, and
large-scale industrial systems. In many large-scale settings, the size of the
communication network is smaller than the size of the system. In consequence,
scheduling issues arise. The main contribution of this paper is to develop a
deep reinforcement learning-based \emph{control-aware} scheduling
(\textsc{DeepCAS}) algorithm to tackle these issues. We use the following
(optimal) design strategy: First, we synthesize an optimal controller for each
subsystem; next, we design a learning algorithm that adapts to the chosen
subsystems (plants) and controllers. As a consequence of this adaptation, our
algorithm finds a schedule that minimizes the \emph{control loss}. We present
empirical results to show that \textsc{DeepCAS} finds schedules with better
performance than periodic ones.
| null |
http://arxiv.org/abs/1803.02998v2
|
http://arxiv.org/pdf/1803.02998v2.pdf
| null |
[
"Burak Demirel",
"Arunselvan Ramaswamy",
"Daniel E. Quevedo",
"Holger Karl"
] |
[
"Deep Reinforcement Learning",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)",
"Scheduling"
] | 2018-03-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/curve-reconstruction-via-the-global
|
1711.03172
| null | null |
Curve Reconstruction via the Global Statistics of Natural Curves
|
Reconstructing the missing parts of a curve has been the subject of much
computational research, with applications in image inpainting, object
synthesis, etc. Different approaches for solving that problem are typically
based on processes that seek visually pleasing or perceptually plausible
completions. In this work we focus on reconstructing the underlying physically
likely shape by utilizing the global statistics of natural curves. More
specifically, we develop a reconstruction model that seeks the mean physical
curve for a given inducer configuration. This simple model is both
straightforward to compute and it is receptive to diverse additional
information, but it requires enough samples for all curve configurations, a
practical requirement that limits its effective utilization. To address this
practical issue we explore and exploit statistical geometrical properties of
natural curves, and in particular, we show that in many cases the mean curve is
scale invariant and oftentimes it is extensible. This, in turn, allows to boost
the number of examples and thus the robustness of the statistics and its
applicability. The reconstruction results are not only more physically
plausible but they also lead to important insights on the reconstruction
problem, including an elegant explanation why certain inducer configurations
are more likely to yield consistent perceptual completions than others.
| null |
http://arxiv.org/abs/1711.03172v3
|
http://arxiv.org/pdf/1711.03172v3.pdf
|
CVPR 2018 6
|
[
"Ehud Barnea",
"Ohad Ben-Shahar"
] |
[
"Image Inpainting"
] | 2017-11-08T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Barnea_Curve_Reconstruction_via_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Barnea_Curve_Reconstruction_via_CVPR_2018_paper.pdf
|
curve-reconstruction-via-the-global-1
| null |
[] |
https://paperswithcode.com/paper/double-path-networks-for-sequence-to-sequence
|
1806.04856
| null | null |
Double Path Networks for Sequence to Sequence Learning
|
Encoder-decoder based Sequence to Sequence learning (S2S) has made remarkable
progress in recent years. Different network architectures have been used in the
encoder/decoder. Among them, Convolutional Neural Networks (CNN) and Self
Attention Networks (SAN) are the prominent ones. The two architectures achieve
similar performances but use very different ways to encode and decode context:
CNN use convolutional layers to focus on the local connectivity of the
sequence, while SAN uses self-attention layers to focus on global semantics. In
this work we propose Double Path Networks for Sequence to Sequence learning
(DPN-S2S), which leverage the advantages of both models by using double path
information fusion. During the encoding step, we develop a double path
architecture to maintain the information coming from different paths with
convolutional layers and self-attention layers separately. To effectively use
the encoded context, we develop a cross attention module with gating and use it
to automatically pick up the information needed during the decoding step. By
deeply integrating the two paths with cross attention, both types of
information are combined and well exploited. Experiments show that our proposed
method can significantly improve the performance of sequence to sequence
learning over state-of-the-art systems.
|
In this work we propose Double Path Networks for Sequence to Sequence learning (DPN-S2S), which leverage the advantages of both models by using double path information fusion.
|
http://arxiv.org/abs/1806.04856v2
|
http://arxiv.org/pdf/1806.04856v2.pdf
|
COLING 2018 8
|
[
"Kaitao Song",
"Xu Tan",
"Di He",
"Jianfeng Lu",
"Tao Qin",
"Tie-Yan Liu"
] |
[
"Decoder"
] | 2018-06-13T00:00:00 |
https://aclanthology.org/C18-1259
|
https://aclanthology.org/C18-1259.pdf
|
double-path-networks-for-sequence-to-sequence-2
| null |
[] |
https://paperswithcode.com/paper/fast-and-scalable-bayesian-deep-learning-by
|
1806.04854
| null | null |
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
|
Uncertainty computation in deep learning is essential to design robust and
reliable systems. Variational inference (VI) is a promising approach for such
computation, but requires more effort to implement and execute compared to
maximum-likelihood methods. In this paper, we propose new natural-gradient
algorithms to reduce such efforts for Gaussian mean-field VI. Our algorithms
can be implemented within the Adam optimizer by perturbing the network weights
during gradient evaluations, and uncertainty estimates can be cheaply obtained
by using the vector that adapts the learning rate. This requires lower memory,
computation, and implementation effort than existing VI methods, while
obtaining uncertainty estimates of comparable quality. Our empirical results
confirm this and further suggest that the weight-perturbation in our algorithm
could be useful for exploration in reinforcement learning and stochastic
optimization.
|
Uncertainty computation in deep learning is essential to design robust and reliable systems.
|
http://arxiv.org/abs/1806.04854v3
|
http://arxiv.org/pdf/1806.04854v3.pdf
|
ICML 2018 7
|
[
"Mohammad Emtiyaz Khan",
"Didrik Nielsen",
"Voot Tangkaratt",
"Wu Lin",
"Yarin Gal",
"Akash Srivastava"
] |
[
"Reinforcement Learning",
"Stochastic Optimization",
"Variational Inference"
] | 2018-06-13T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2261
|
http://proceedings.mlr.press/v80/khan18a/khan18a.pdf
|
fast-and-scalable-bayesian-deep-learning-by-1
| null |
[
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/b7bda236d18815052378c88081f64935427d7716/torch/optim/adam.py#L6",
"description": "**Adam** is an adaptive learning rate optimization algorithm that utilises both momentum and scaling, combining the benefits of [RMSProp](https://paperswithcode.com/method/rmsprop) and [SGD w/th Momentum](https://paperswithcode.com/method/sgd-with-momentum). The optimizer is designed to be appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. \r\n\r\nThe weight updates are performed as:\r\n\r\n$$ w_{t} = w_{t-1} - \\eta\\frac{\\hat{m}\\_{t}}{\\sqrt{\\hat{v}\\_{t}} + \\epsilon} $$\r\n\r\nwith\r\n\r\n$$ \\hat{m}\\_{t} = \\frac{m_{t}}{1-\\beta^{t}_{1}} $$\r\n\r\n$$ \\hat{v}\\_{t} = \\frac{v_{t}}{1-\\beta^{t}_{2}} $$\r\n\r\n$$ m_{t} = \\beta_{1}m_{t-1} + (1-\\beta_{1})g_{t} $$\r\n\r\n$$ v_{t} = \\beta_{2}v_{t-1} + (1-\\beta_{2})g_{t}^{2} $$\r\n\r\n\r\n$ \\eta $ is the step size/learning rate, around 1e-3 in the original paper. $ \\epsilon $ is a small number, typically 1e-8 or 1e-10, to prevent dividing by zero. $ \\beta_{1} $ and $ \\beta_{2} $ are forgetting parameters, with typical values 0.9 and 0.999, respectively.",
"full_name": "Adam",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Stochastic Optimization** methods are used to optimize neural networks. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. Below you can find a continuously updating list of stochastic optimization algorithms.",
"name": "Stochastic Optimization",
"parent": "Optimization"
},
"name": "Adam",
"source_title": "Adam: A Method for Stochastic Optimization",
"source_url": "http://arxiv.org/abs/1412.6980v9"
}
] |
https://paperswithcode.com/paper/learning-structural-node-embeddings-via
|
1710.10321
| null | null |
Learning Structural Node Embeddings Via Diffusion Wavelets
|
Nodes residing in different parts of a graph can have similar structural
roles within their local network topology. The identification of such roles
provides key insight into the organization of networks and can be used for a
variety of machine learning tasks. However, learning structural representations
of nodes is a challenging problem, and it has typically involved manually
specifying and tailoring topological features for each node. In this paper, we
develop GraphWave, a method that represents each node's network neighborhood
via a low-dimensional embedding by leveraging heat wavelet diffusion patterns.
Instead of training on hand-selected features, GraphWave learns these
embeddings in an unsupervised way. We mathematically prove that nodes with
similar network neighborhoods will have similar GraphWave embeddings even
though these nodes may reside in very different parts of the network, and our
method scales linearly with the number of edges. Experiments in a variety of
different settings demonstrate GraphWave's real-world potential for capturing
structural roles in networks, and our approach outperforms existing
state-of-the-art baselines in every experiment, by as much as 137%.
|
Nodes residing in different parts of a graph can have similar structural roles within their local network topology.
|
http://arxiv.org/abs/1710.10321v4
|
http://arxiv.org/pdf/1710.10321v4.pdf
|
KDD 2018 6
|
[
"Claire Donnat",
"Marinka Zitnik",
"David Hallac",
"Jure Leskovec"
] |
[] | 2017-10-27T00:00:00 | null | null |
learning-structural-node-embeddings-via-1
| null |
[] |
https://paperswithcode.com/paper/analyzing-the-robustness-of-nearest-neighbors
|
1706.03922
| null | null |
Analyzing the Robustness of Nearest Neighbors to Adversarial Examples
|
Motivated by safety-critical applications, test-time attacks on classifiers via adversarial examples has recently received a great deal of attention. However, there is a general lack of understanding on why adversarial examples arise; whether they originate due to inherent properties of data or due to lack of training samples remains ill-understood. In this work, we introduce a theoretical framework analogous to bias-variance theory for understanding these effects. We use our framework to analyze the robustness of a canonical non-parametric classifier - the k-nearest neighbors. Our analysis shows that its robustness properties depend critically on the value of k - the classifier may be inherently non-robust for small k, but its robustness approaches that of the Bayes Optimal classifier for fast-growing k. We propose a novel modified 1-nearest neighbor classifier, and guarantee its robustness in the large sample limit. Our experiments suggest that this classifier may have good robustness properties even for reasonable data set sizes.
|
Our analysis shows that its robustness properties depend critically on the value of k - the classifier may be inherently non-robust for small k, but its robustness approaches that of the Bayes Optimal classifier for fast-growing k. We propose a novel modified 1-nearest neighbor classifier, and guarantee its robustness in the large sample limit.
|
https://arxiv.org/abs/1706.03922v6
|
https://arxiv.org/pdf/1706.03922v6.pdf
|
ICML 2018 7
|
[
"Yizhen Wang",
"Somesh Jha",
"Kamalika Chaudhuri"
] |
[] | 2017-06-13T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2076
|
http://proceedings.mlr.press/v80/wang18c/wang18c.pdf
|
analyzing-the-robustness-of-nearest-neighbors-1
| null |
[] |
https://paperswithcode.com/paper/polynomial-regression-as-an-alternative-to
|
1806.06850
| null | null |
Polynomial Regression As an Alternative to Neural Nets
|
Despite the success of neural networks (NNs), there is still a concern among
many over their "black box" nature. Why do they work? Here we present a simple
analytic argument that NNs are in fact essentially polynomial regression
models. This view will have various implications for NNs, e.g. providing an
explanation for why convergence problems arise in NNs, and it gives rough
guidance on avoiding overfitting. In addition, we use this phenomenon to
predict and confirm a multicollinearity property of NNs not previously reported
in the literature. Most importantly, given this loose correspondence, one may
choose to routinely use polynomial models instead of NNs, thus avoiding some
major problems of the latter, such as having to set many tuning parameters and
dealing with convergence issues. We present a number of empirical results; in
each case, the accuracy of the polynomial approach matches or exceeds that of
NN approaches. A many-featured, open-source software package, polyreg, is
available.
|
Despite the success of neural networks (NNs), there is still a concern among many over their "black box" nature.
|
http://arxiv.org/abs/1806.06850v3
|
http://arxiv.org/pdf/1806.06850v3.pdf
| null |
[
"Xi Cheng",
"Bohdan Khomtchouk",
"Norman Matloff",
"Pete Mohanty"
] |
[
"regression"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-from-mutants-using-code-mutation-to
|
1801.00903
| null | null |
Learning from Mutants: Using Code Mutation to Learn and Monitor Invariants of a Cyber-Physical System
|
Cyber-physical systems (CPS) consist of sensors, actuators, and controllers
all communicating over a network; if any subset becomes compromised, an
attacker could cause significant damage. With access to data logs and a model
of the CPS, the physical effects of an attack could potentially be detected
before any damage is done. Manually building a model that is accurate enough in
practice, however, is extremely difficult. In this paper, we propose a novel
approach for constructing models of CPS automatically, by applying supervised
machine learning to data traces obtained after systematically seeding their
software components with faults ("mutants"). We demonstrate the efficacy of
this approach on the simulator of a real-world water purification plant,
presenting a framework that automatically generates mutants, collects data
traces, and learns an SVM-based model. Using cross-validation and statistical
model checking, we show that the learnt model characterises an invariant
physical property of the system. Furthermore, we demonstrate the usefulness of
the invariant by subjecting the system to 55 network and code-modification
attacks, and showing that it can detect 85% of them from the data logs
generated at runtime.
| null |
http://arxiv.org/abs/1801.00903v2
|
http://arxiv.org/pdf/1801.00903v2.pdf
| null |
[
"Yuqi Chen",
"Christopher M. Poskitt",
"Jun Sun"
] |
[] | 2018-01-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-study-of-enhancement-augmentation-and
|
1806.04841
| null | null |
A Study of Enhancement, Augmentation, and Autoencoder Methods for Domain Adaptation in Distant Speech Recognition
|
Speech recognizers trained on close-talking speech do not generalize to
distant speech and the word error rate degradation can be as large as 40%
absolute. Most studies focus on tackling distant speech recognition as a
separate problem, leaving little effort to adapting close-talking speech
recognizers to distant speech. In this work, we review several approaches from
a domain adaptation perspective. These approaches, including speech
enhancement, multi-condition training, data augmentation, and autoencoders, all
involve a transformation of the data between domains. We conduct experiments on
the AMI data set, where these approaches can be realized under the same
controlled setting. These approaches lead to different amounts of improvement
under their respective assumptions. The purpose of this paper is to quantify
and characterize the performance gap between the two domains, setting up the
basis for studying adaptation of speech recognizers from close-talking speech
to distant speech. Our results also have implications for improving distant
speech recognition.
| null |
http://arxiv.org/abs/1806.04841v1
|
http://arxiv.org/pdf/1806.04841v1.pdf
| null |
[
"Hao Tang",
"Wei-Ning Hsu",
"Francois Grondin",
"James Glass"
] |
[
"Data Augmentation",
"Distant Speech Recognition",
"Domain Adaptation",
"Speech Enhancement",
"speech-recognition",
"Speech Recognition"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/sequence-prediction-with-neural-segmental
|
1709.01572
| null | null |
Sequence Prediction with Neural Segmental Models
|
Segments that span contiguous parts of inputs, such as phonemes in speech,
named entities in sentences, actions in videos, occur frequently in sequence
prediction problems. Segmental models, a class of models that explicitly
hypothesizes segments, have allowed the exploration of rich segment features
for sequence prediction. However, segmental models suffer from slow decoding,
hampering the use of computationally expensive features.
In this thesis, we introduce discriminative segmental cascades, a multi-pass
inference framework that allows us to improve accuracy by adding higher-order
features and neural segmental features while maintaining efficiency. We also
show that instead of including more features to obtain better accuracy,
segmental cascades can be used to speed up training and decoding.
Segmental models, similarly to conventional speech recognizers, are typically
trained in multiple stages. In the first stage, a frame classifier is trained
with manual alignments, and then in the second stage, segmental models are
trained with manual alignments and the out- puts of the frame classifier.
However, obtaining manual alignments are time-consuming and expensive. We
explore end-to-end training for segmental models with various loss functions,
and show how end-to-end training with marginal log loss can eliminate the need
for detailed manual alignments.
We draw the connections between the marginal log loss and a popular
end-to-end training approach called connectionist temporal classification. We
present a unifying framework for various end-to-end graph search-based models,
such as hidden Markov models, connectionist temporal classification, and
segmental models. Finally, we discuss possible extensions of segmental models
to large-vocabulary sequence prediction tasks.
| null |
http://arxiv.org/abs/1709.01572v3
|
http://arxiv.org/pdf/1709.01572v3.pdf
| null |
[
"Hao Tang"
] |
[
"General Classification",
"Prediction"
] | 2017-09-05T00:00:00 | 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/partial-auc-maximization-via-nonlinear
|
1806.04838
| null | null |
Partial AUC Maximization via Nonlinear Scoring Functions
|
We propose a method for maximizing a partial area under a receiver operating
characteristic (ROC) curve (pAUC) for binary classification tasks. In binary
classification tasks, accuracy is the most commonly used as a measure of
classifier performance. In some applications such as anomaly detection and
diagnostic testing, accuracy is not an appropriate measure since prior
probabilties are often greatly biased. Although in such cases the pAUC has been
utilized as a performance measure, few methods have been proposed for directly
maximizing the pAUC. This optimization is achieved by using a scoring function.
The conventional approach utilizes a linear function as the scoring function.
In contrast we newly introduce nonlinear scoring functions for this purpose.
Specifically, we present two types of nonlinear scoring functions based on
generative models and deep neural networks. We show experimentally that
nonlinear scoring fucntions improve the conventional methods through the
application of a binary classification of real and bogus objects obtained with
the Hyper Suprime-Cam on the Subaru telescope.
| null |
http://arxiv.org/abs/1806.04838v1
|
http://arxiv.org/pdf/1806.04838v1.pdf
| null |
[
"Naonori Ueda",
"Akinori Fujino"
] |
[
"Anomaly Detection",
"Binary Classification",
"Classification",
"Diagnostic",
"General Classification"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/3d-pose-estimation-for-fine-grained-object
|
1806.04314
| null | null |
3D Pose Estimation for Fine-Grained Object Categories
|
Existing object pose estimation datasets are related to generic object types
and there is so far no dataset for fine-grained object categories. In this
work, we introduce a new large dataset to benchmark pose estimation for
fine-grained objects, thanks to the availability of both 2D and 3D fine-grained
data recently. Specifically, we augment two popular fine-grained recognition
datasets (StanfordCars and CompCars) by finding a fine-grained 3D CAD model for
each sub-category and manually annotating each object in images with 3D pose.
We show that, with enough training data, a full perspective model with
continuous parameters can be estimated using 2D appearance information alone.
We achieve this via a framework based on Faster/Mask R-CNN. This goes beyond
previous works on category-level pose estimation, which only estimate
discrete/continuous viewpoint angles or recover rotation matrices often with
the help of key points. Furthermore, with fine-grained 3D models available, we
incorporate a dense 3D representation named as location field into the
CNN-based pose estimation framework to further improve the performance. The new
dataset is available at www.umiacs.umd.edu/~wym/3dpose.html
|
The new dataset is available at www. umiacs. umd. edu/~wym/3dpose. html
|
http://arxiv.org/abs/1806.04314v3
|
http://arxiv.org/pdf/1806.04314v3.pdf
| null |
[
"Yaming Wang",
"Xiao Tan",
"Yi Yang",
"Xiao Liu",
"Errui Ding",
"Feng Zhou",
"Larry S. Davis"
] |
[
"3D Pose Estimation",
"Object",
"Pose Estimation"
] | 2018-06-12T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/state-space-representations-of-deep-neural
|
1806.03751
| null | null |
State Space Representations of Deep Neural Networks
|
This paper deals with neural networks as dynamical systems governed by
differential or difference equations. It shows that the introduction of skip
connections into network architectures, such as residual networks and dense
networks, turns a system of static equations into a system of dynamical
equations with varying levels of smoothness on the layer-wise transformations.
Closed form solutions for the state space representations of general dense
networks, as well as $k^{th}$ order smooth networks, are found in general
settings. Furthermore, it is shown that imposing $k^{th}$ order smoothness on a
network architecture with $d$-many nodes per layer increases the state space
dimension by a multiple of $k$, and so the effective embedding dimension of the
data manifold is $k \cdot d$-many dimensions. It follows that network
architectures of these types reduce the number of parameters needed to maintain
the same embedding dimension by a factor of $k^2$ when compared to an
equivalent first-order, residual network, significantly motivating the
development of network architectures of these types. Numerical simulations were
run to validate parts of the developed theory.
| null |
http://arxiv.org/abs/1806.03751v3
|
http://arxiv.org/pdf/1806.03751v3.pdf
| null |
[
"Michael Hauser",
"Sean Gunn",
"Samer Saab Jr",
"Asok Ray"
] |
[] | 2018-06-11T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/deep-multiscale-model-learning
|
1806.04830
| null | null |
Deep Multiscale Model Learning
|
The objective of this paper is to design novel multi-layer neural network
architectures for multiscale simulations of flows taking into account the
observed data and physical modeling concepts. Our approaches use deep learning
concepts combined with local multiscale model reduction methodologies to
predict flow dynamics. Using reduced-order model concepts is important for
constructing robust deep learning architectures since the reduced-order models
provide fewer degrees of freedom. Flow dynamics can be thought of as
multi-layer networks. More precisely, the solution (e.g., pressures and
saturations) at the time instant $n+1$ depends on the solution at the time
instant $n$ and input parameters, such as permeability fields, forcing terms,
and initial conditions. One can regard the solution as a multi-layer network,
where each layer, in general, is a nonlinear forward map and the number of
layers relates to the internal time steps. We will rely on rigorous model
reduction concepts to define unknowns and connections for each layer. In each
layer, our reduced-order models will provide a forward map, which will be
modified ("trained") using available data. It is critical to use reduced-order
models for this purpose, which will identify the regions of influence and the
appropriate number of variables. Because of the lack of available data, the
training will be supplemented with computational data as needed and the
interpolation between data-rich and data-deficient models. We will also use
deep learning algorithms to train the elements of the reduced model discrete
system. We will present main ingredients of our approach and numerical results.
Numerical results show that using deep learning and multiscale models, we can
improve the forward models, which are conditioned to the available data.
| null |
http://arxiv.org/abs/1806.04830v1
|
http://arxiv.org/pdf/1806.04830v1.pdf
| null |
[
"Yating Wang",
"Siu Wun Cheung",
"Eric T. Chung",
"Yalchin Efendiev",
"Min Wang"
] |
[
"Deep Learning",
"model"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/position-detection-and-direction-prediction
|
1806.04828
| null | null |
Position Detection and Direction Prediction for Arbitrary-Oriented Ships via Multitask Rotation Region Convolutional Neural Network
|
Ship detection is of great importance and full of challenges in the field of
remote sensing. The complexity of application scenarios, the redundancy of
detection region, and the difficulty of dense ship detection are all the main
obstacles that limit the successful operation of traditional methods in ship
detection. In this paper, we propose a brand new detection model based on
multitask rotational region convolutional neural network to solve the problems
above. This model is mainly consist of five consecutive parts: Dense Feature
Pyramid Network (DFPN), adaptive region of interest (ROI) Align, rotational
bounding box regression, prow direction prediction and rotational nonmaximum
suppression (R-NMS). First of all, the low-level location information and
high-level semantic information are fully utilized through multiscale feature
networks. Then, we design Adaptive ROI Align to obtain high quality proposals
which remain complete spatial and semantic information. Unlike most previous
approaches, the prediction obtained by our method is the minimum bounding
rectangle of the object with less redundant regions. Therefore, rotational
region detection framework is more suitable to detect the dense object than
traditional detection model. Additionally, we can find the berthing and sailing
direction of ship through prediction. A detailed evaluation based on SRSS for
rotation detection shows that our detection method has a competitive
performance.
|
The complexity of application scenarios, the redundancy of detection region, and the difficulty of dense ship detection are all the main obstacles that limit the successful operation of traditional methods in ship detection.
|
http://arxiv.org/abs/1806.04828v2
|
http://arxiv.org/pdf/1806.04828v2.pdf
| null |
[
"Xue Yang",
"Hao Sun",
"Xian Sun",
"Menglong Yan",
"Zhi Guo",
"Kun fu"
] |
[
"Position"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/detection-of-premature-ventricular
|
1806.04564
| null | null |
Detection of Premature Ventricular Contractions Using Densely Connected Deep Convolutional Neural Network with Spatial Pyramid Pooling Layer
|
Premature ventricular contraction(PVC) is a type of premature ectopic beat originating from the ventricles. Automatic method for accurate and robust detection of PVC is highly clinically desired.Currently, most of these methods are developed and tested using the same database divided into training and testing set and their generalization performance across databases has not been fully validated. In this paper, a method based on densely connected convolutional neural network and spatial pyramid pooling is proposed for PVC detection which can take arbitrarily-sized QRS complexes as input both in training and testing. With a much less complicated and more straightforward architecture,the proposed network achieves comparable results to current state-of-the-art deep learning based method with regard to accuracy,sensitivity and specificity by training and testing using the MIT-BIH arrhythmia database as benchmark.Besides the benchmark database,QRS complexes are extracted from four more open databases namely the St-Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database,The MIT-BIH Normal Sinus Rhythm Database,The MIT-BIH Long Term Database and European ST-T Database. The extracted QRS complexes are different in length and sampling rate among the five databases.Cross-database training and testing is also experimented.The performance of the network shows an improvement on the benchmark database according to the result demonstrating the advantage of using multiple databases for training over using only a single database.The network also achieves satisfactory scores on the other four databases showing good generalization capability.
| null |
https://arxiv.org/abs/1806.04564v7
|
https://arxiv.org/pdf/1806.04564v7.pdf
| null |
[
"Jianning Li"
] |
[
"Rhythm",
"Specificity"
] | 2018-06-12T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/yueruchen/sppnet-pytorch/blob/270529337baa5211538bf553bda222b9140838b3/spp_layer.py#L2",
"description": "** Spatial Pyramid Pooling (SPP)** is a pooling layer that removes the fixed-size constraint of the network, i.e. a CNN does not require a fixed-size input image. Specifically, we add an SPP layer on top of the last convolutional layer. The SPP layer pools the features and generates fixed-length outputs, which are then fed into the fully-connected layers (or other classifiers). In other words, we perform some information aggregation at a deeper stage of the network hierarchy (between convolutional layers and fully-connected layers) to avoid the need for cropping or warping at the beginning.",
"full_name": "Spatial Pyramid Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Spatial Pyramid Pooling",
"source_title": "Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition",
"source_url": "http://arxiv.org/abs/1406.4729v4"
}
] |
https://paperswithcode.com/paper/plug-in-regularized-estimation-of-high
|
1806.04823
| null | null |
Regularized Orthogonal Machine Learning for Nonlinear Semiparametric Models
|
This paper proposes a Lasso-type estimator for a high-dimensional sparse parameter identified by a single index conditional moment restriction (CMR). In addition to this parameter, the moment function can also depend on a nuisance function, such as the propensity score or the conditional choice probability, which we estimate by modern machine learning tools. We first adjust the moment function so that the gradient of the future loss function is insensitive (formally, Neyman-orthogonal) with respect to the first-stage regularization bias, preserving the single index property. We then take the loss function to be an indefinite integral of the adjusted moment function with respect to the single index. The proposed Lasso estimator converges at the oracle rate, where the oracle knows the nuisance function and solves only the parametric problem. We demonstrate our method by estimating the short-term heterogeneous impact of Connecticut's Jobs First welfare reform experiment on women's welfare participation decision.
|
This paper proposes a Lasso-type estimator for a high-dimensional sparse parameter identified by a single index conditional moment restriction (CMR).
|
https://arxiv.org/abs/1806.04823v8
|
https://arxiv.org/pdf/1806.04823v8.pdf
| null |
[
"Denis Nekipelov",
"Vira Semenova",
"Vasilis Syrgkanis"
] |
[
"BIG-bench Machine Learning"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/does-higher-order-lstm-have-better-accuracy
|
1711.08231
| null | null |
Does Higher Order LSTM Have Better Accuracy for Segmenting and Labeling Sequence Data?
|
Existing neural models usually predict the tag of the current token
independent of the neighboring tags. The popular LSTM-CRF model considers the
tag dependencies between every two consecutive tags. However, it is hard for
existing neural models to take longer distance dependencies of tags into
consideration. The scalability is mainly limited by the complex model
structures and the cost of dynamic programming during training. In our work, we
first design a new model called "high order LSTM" to predict multiple tags for
the current token which contains not only the current tag but also the previous
several tags. We call the number of tags in one prediction as "order". Then we
propose a new method called Multi-Order BiLSTM (MO-BiLSTM) which combines low
order and high order LSTMs together. MO-BiLSTM keeps the scalability to high
order models with a pruning technique. We evaluate MO-BiLSTM on all-phrase
chunking and NER datasets. Experiment results show that MO-BiLSTM achieves the
state-of-the-art result in chunking and highly competitive results in two NER
datasets.
|
In our work, we first design a new model called "high order LSTM" to predict multiple tags for the current token which contains not only the current tag but also the previous several tags.
|
http://arxiv.org/abs/1711.08231v3
|
http://arxiv.org/pdf/1711.08231v3.pdf
|
COLING 2018 8
|
[
"Yi Zhang",
"Xu sun",
"Shuming Ma",
"Yang Yang",
"Xuancheng Ren"
] |
[
"Chunking",
"NER",
"TAG"
] | 2017-11-22T00:00:00 |
https://aclanthology.org/C18-1061
|
https://aclanthology.org/C18-1061.pdf
|
does-higher-order-lstm-have-better-accuracy-2
| null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Pruning",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Model Compression",
"parent": null
},
"name": "Pruning",
"source_title": "Pruning Filters for Efficient ConvNets",
"source_url": "http://arxiv.org/abs/1608.08710v3"
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L277",
"description": "**Sigmoid Activations** are a type of activation function for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{1}{\\left(1+\\exp\\left(-x\\right)\\right)}$$\r\n\r\nSome drawbacks of this activation that have been noted in the literature are: sharp damp gradients during backpropagation from deeper hidden layers to inputs, gradient saturation, and slow convergence.",
"full_name": "Sigmoid Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Sigmoid Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L329",
"description": "**Tanh Activation** is an activation function used for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$\r\n\r\nHistorically, the tanh function became preferred over the [sigmoid function](https://paperswithcode.com/method/sigmoid-activation) as it gave better performance for multi-layer neural networks. But it did not solve the vanishing gradient problem that sigmoids suffered, which was tackled more effectively with the introduction of [ReLU](https://paperswithcode.com/method/relu) activations.\r\n\r\nImage Source: [Junxi Feng](https://www.researchgate.net/profile/Junxi_Feng)",
"full_name": "Tanh Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Tanh Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "An **LSTM** is a type of [recurrent neural network](https://paperswithcode.com/methods/category/recurrent-neural-networks) that addresses the vanishing gradient problem in vanilla RNNs through additional cells, input and output gates. Intuitively, vanishing gradients are solved through additional *additive* components, and forget gate activations, that allow the gradients to flow through the network without vanishing as quickly.\r\n\r\n(Image Source [here](https://medium.com/datadriveninvestor/how-do-lstm-networks-solve-the-problem-of-vanishing-gradients-a6784971a577))\r\n\r\n(Introduced by Hochreiter and Schmidhuber)",
"full_name": "Long Short-Term Memory",
"introduced_year": 1997,
"main_collection": {
"area": "Sequential",
"description": "",
"name": "Recurrent Neural Networks",
"parent": null
},
"name": "LSTM",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://www.ebi.ac.uk/intact/search?query=id:P00722*#interactor",
"description": "A **Bidirectional LSTM**, or **biLSTM**, is a sequence processing model that consists of two LSTMs: one taking the input in a forward direction, and the other in a backwards direction. BiLSTMs effectively increase the amount of information available to the network, improving the context available to the algorithm (e.g. knowing what words immediately follow *and* precede a word in a sentence).\r\n\r\nImage Source: Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks, Cornegruta et al",
"full_name": "Bidirectional LSTM",
"introduced_year": 2001,
"main_collection": {
"area": "General",
"description": "Consists of tabular data learning approaches that use deep learning architectures for learning on tabular data. According to the taxonomy in [V.Borisov et al. (2021)](https://paperswithcode.com/paper/deep-neural-networks-and-tabular-data-a), deep learning approaches for tabular data can be categorized into:\r\n\r\n- **Regularization models**\r\n- **Transformer-based models**: [TabNet](/method/tabnet), [TabTransformer](/method/tabtransformer), [SAINT](/method/saint), [ARM-Net](/method/arm-net),...\r\n- **Hybrid models** (fully differentiable and partly differentiable): [Wide&Deep](/method/wide-deep), [TabNN](/method/tabnn), [NON](/method/non), [Boost-GNN](/method/boost-gnn), [NODE](/method/node),...\r\n- **Data encoding methods** (single-dimensional encoding and multi-dimensional encoding): [VIME](/method/vime), [SCARF](/method/scarf),...",
"name": "Deep Tabular Learning",
"parent": null
},
"name": "BiLSTM",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/integral-privacy-for-sampling-from-mollifier
|
1806.04819
| null | null |
Integral Privacy for Sampling
|
Differential privacy is a leading protection setting, focused by design on individual privacy. Many applications, in medical / pharmaceutical domains or social networks, rather posit privacy at a group level, a setting we call integral privacy. We aim for the strongest form of privacy: the group size is in particular not known in advance. We study a problem with related applications in domains cited above that have recently met with substantial recent press: sampling. Keeping correct utility levels in such a strong model of statistical indistinguishability looks difficult to be achieved with the usual differential privacy toolbox because it would typically scale in the worst case the sensitivity by the sample size and so the noise variance by up to its square. We introduce a trick specific to sampling that bypasses the sensitivity analysis. Privacy enforces an information theoretic barrier on approximation, and we show how to reach this barrier with guarantees on the approximation of the target non private density. We do so using a recent approach to non private density estimation relying on the original boosting theory, learning the sufficient statistics of an exponential family with classifiers. Approximation guarantees cover the mode capture problem. In the context of learning, the sampling problem is particularly important: because integral privacy enjoys the same closure under post-processing as differential privacy does, any algorithm using integrally privacy sampled data would result in an output equally integrally private. We also show that this brings fairness guarantees on post-processing that would eventually elude classical differential privacy: any decision process has bounded data-dependent bias when the data is integrally privately sampled. Experimental results against private kernel density estimation and private GANs displays the quality of our results.
|
Privacy enforces an information theoretic barrier on approximation, and we show how to reach this barrier with guarantees on the approximation of the target non private density.
|
https://arxiv.org/abs/1806.04819v5
|
https://arxiv.org/pdf/1806.04819v5.pdf
| null |
[
"Hisham Husain",
"Zac Cranko",
"Richard Nock"
] |
[
"Density Estimation",
"Fairness"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/disintegration-and-bayesian-inversion-via
|
1709.00322
| null | null |
Disintegration and Bayesian Inversion via String Diagrams
|
The notions of disintegration and Bayesian inversion are fundamental in
conditional probability theory. They produce channels, as conditional
probabilities, from a joint state, or from an already given channel (in
opposite direction). These notions exist in the literature, in concrete
situations, but are presented here in abstract graphical formulations. The
resulting abstract descriptions are used for proving basic results in
conditional probability theory. The existence of disintegration and Bayesian
inversion is discussed for discrete probability, and also for measure-theoretic
probability --- via standard Borel spaces and via likelihoods. Finally, the
usefulness of disintegration and Bayesian inversion is illustrated in several
examples.
| null |
http://arxiv.org/abs/1709.00322v3
|
http://arxiv.org/pdf/1709.00322v3.pdf
| null |
[
"Kenta Cho",
"Bart Jacobs"
] |
[] | 2017-08-29T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-unified-framework-for-generalizable-style
|
1806.05173
| null | null |
A Unified Framework for Generalizable Style Transfer: Style and Content Separation
|
Image style transfer has drawn broad attention in recent years. However, most
existing methods aim to explicitly model the transformation between different
styles, and the learned model is thus not generalizable to new styles. We here
propose a unified style transfer framework for both character typeface transfer
and neural style transfer tasks leveraging style and content separation. A key
merit of such framework is its generalizability to new styles and contents. The
overall framework consists of style encoder, content encoder, mixer and
decoder. The style encoder and content encoder are used to extract the style
and content representations from the corresponding reference images. The mixer
integrates the above two representations and feeds it into the decoder to
generate images with the target style and content. During training, the encoder
networks learn to extract styles and contents from limited size of
style/content reference images. This learning framework allows simultaneous
style transfer among multiple styles and can be deemed as a special
`multi-task' learning scenario. The encoders are expected to capture the
underlying features for different styles and contents which is generalizable to
new styles and contents. Under this framework, we design two individual
networks for character typeface transfer and neural style transfer,
respectively. For character typeface transfer, to separate the style features
and content features, we leverage the conditional dependence of styles and
contents given an image. For neural style transfer, we leverage the statistical
information of feature maps in certain layers to represent style. Extensive
experimental results have demonstrated the effectiveness and robustness of the
proposed methods.
|
The encoders are expected to capture the underlying features for different styles and contents which is generalizable to new styles and contents.
|
http://arxiv.org/abs/1806.05173v1
|
http://arxiv.org/pdf/1806.05173v1.pdf
| null |
[
"Yexun Zhang",
"Ya zhang",
"Wenbin Cai"
] |
[
"Decoder",
"Multi-Task Learning",
"Style Transfer"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/deep-learning-for-forecasting-stock-returns
|
1801.01777
| null | null |
Deep Learning for Forecasting Stock Returns in the Cross-Section
|
Many studies have been undertaken by using machine learning techniques,
including neural networks, to predict stock returns. Recently, a method known
as deep learning, which achieves high performance mainly in image recognition
and speech recognition, has attracted attention in the machine learning field.
This paper implements deep learning to predict one-month-ahead stock returns in
the cross-section in the Japanese stock market and investigates the performance
of the method. Our results show that deep neural networks generally outperform
shallow neural networks, and the best networks also outperform representative
machine learning models. These results indicate that deep learning shows
promise as a skillful machine learning method to predict stock returns in the
cross-section.
| null |
http://arxiv.org/abs/1801.01777v4
|
http://arxiv.org/pdf/1801.01777v4.pdf
| null |
[
"Masaya Abe",
"Hideki Nakayama"
] |
[
"BIG-bench Machine Learning",
"Deep Learning",
"speech-recognition",
"Speech Recognition"
] | 2018-01-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-representations-of-ultrahigh
|
1806.04808
| null | null |
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection
|
Learning expressive low-dimensional representations of ultrahigh-dimensional
data, e.g., data with thousands/millions of features, has been a major way to
enable learning methods to address the curse of dimensionality. However,
existing unsupervised representation learning methods mainly focus on
preserving the data regularity information and learning the representations
independently of subsequent outlier detection methods, which can result in
suboptimal and unstable performance of detecting irregularities (i.e.,
outliers).
This paper introduces a ranking model-based framework, called RAMODO, to
address this issue. RAMODO unifies representation learning and outlier
detection to learn low-dimensional representations that are tailored for a
state-of-the-art outlier detection approach - the random distance-based
approach. This customized learning yields more optimal and stable
representations for the targeted outlier detectors. Additionally, RAMODO can
leverage little labeled data as prior knowledge to learn more expressive and
application-relevant representations. We instantiate RAMODO to an efficient
method called REPEN to demonstrate the performance of RAMODO.
Extensive empirical results on eight real-world ultrahigh dimensional data
sets show that REPEN (i) enables a random distance-based detector to obtain
significantly better AUC performance and two orders of magnitude speedup; (ii)
performs substantially better and more stably than four state-of-the-art
representation learning methods; and (iii) leverages less than 1% labeled data
to achieve up to 32% AUC improvement.
|
However, existing unsupervised representation learning methods mainly focus on preserving the data regularity information and learning the representations independently of subsequent outlier detection methods, which can result in suboptimal and unstable performance of detecting irregularities (i. e., outliers).
|
http://arxiv.org/abs/1806.04808v1
|
http://arxiv.org/pdf/1806.04808v1.pdf
| null |
[
"Guansong Pang",
"Longbing Cao",
"Ling Chen",
"Huan Liu"
] |
[
"Anomaly Detection",
"Disease Prediction",
"Network Intrusion Detection",
"Outlier Detection",
"Representation Learning"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/ba-net-dense-bundle-adjustment-network
|
1806.04807
| null | null |
BA-Net: Dense Bundle Adjustment Network
|
This paper introduces a network architecture to solve the structure-from-motion (SfM) problem via feature-metric bundle adjustment (BA), which explicitly enforces multi-view geometry constraints in the form of feature-metric error. The whole pipeline is differentiable so that the network can learn suitable features that make the BA problem more tractable. Furthermore, this work introduces a novel depth parameterization to recover dense per-pixel depth. The network first generates several basis depth maps according to the input image and optimizes the final depth as a linear combination of these basis depth maps via feature-metric BA. The basis depth maps generator is also learned via end-to-end training. The whole system nicely combines domain knowledge (i.e. hard-coded multi-view geometry constraints) and deep learning (i.e. feature learning and basis depth maps learning) to address the challenging dense SfM problem. Experiments on large scale real data prove the success of the proposed method.
|
The network first generates several basis depth maps according to the input image and optimizes the final depth as a linear combination of these basis depth maps via feature-metric BA.
|
https://arxiv.org/abs/1806.04807v3
|
https://arxiv.org/pdf/1806.04807v3.pdf
| null |
[
"Chengzhou Tang",
"Ping Tan"
] |
[
"Depth And Camera Motion"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/meta-learning-transferable-active-learning
|
1806.04798
| null |
HJ4IhxZAb
|
Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning
|
Active learning (AL) aims to enable training high performance classifiers
with low annotation cost by predicting which subset of unlabelled instances
would be most beneficial to label. The importance of AL has motivated extensive
research, proposing a wide variety of manually designed AL algorithms with
diverse theoretical and intuitive motivations. In contrast to this body of
research, we propose to treat active learning algorithm design as a
meta-learning problem and learn the best criterion from data. We model an
active learning algorithm as a deep neural network that inputs the base learner
state and the unlabelled point set and predicts the best point to annotate
next. Training this active query policy network with reinforcement learning,
produces the best non-myopic policy for a given dataset. The key challenge in
achieving a general solution to AL then becomes that of learner generalisation,
particularly across heterogeneous datasets. We propose a multi-task
dataset-embedding approach that allows dataset-agnostic active learners to be
trained. Our evaluation shows that AL algorithms trained in this way can
directly generalise across diverse problems.
| null |
http://arxiv.org/abs/1806.04798v1
|
http://arxiv.org/pdf/1806.04798v1.pdf
|
ICLR 2018 1
|
[
"Kunkun Pang",
"Mingzhi Dong",
"Yang Wu",
"Timothy Hospedales"
] |
[
"Active Learning",
"Deep Reinforcement Learning",
"Meta-Learning",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-06-12T00:00:00 |
https://openreview.net/forum?id=HJ4IhxZAb
|
https://openreview.net/pdf?id=HJ4IhxZAb
|
meta-learning-transferable-active-learning-1
| null |
[] |
https://paperswithcode.com/paper/drive2vec-multiscale-state-space-embedding-of
|
1806.04795
| null | null |
Drive2Vec: Multiscale State-Space Embedding of Vehicular Sensor Data
|
With automobiles becoming increasingly reliant on sensors to perform various
driving tasks, it is important to encode the relevant CAN bus sensor data in a
way that captures the general state of the vehicle in a compact form. In this
paper, we develop a deep learning-based method, called Drive2Vec, for embedding
such sensor data in a low-dimensional yet actionable form. Our method is based
on stacked gated recurrent units (GRUs). It accepts a short interval of
automobile sensor data as input and computes a low-dimensional representation
of that data, which can then be used to accurately solve a range of tasks. With
this representation, we (1) predict the exact values of the sensors in the
short term (up to three seconds in the future), (2) forecast the long-term
average values of these same sensors, (3) infer additional contextual
information that is not encoded in the data, including the identity of the
driver behind the wheel, and (4) build a knowledge base that can be used to
auto-label data and identify risky states. We evaluate our approach on a
dataset collected by Audi, which equipped a fleet of test vehicles with data
loggers to store all sensor readings on 2,098 hours of driving on real roads.
We show in several experiments that our method outperforms other baselines by
up to 90%, and we further demonstrate how these embeddings of sensor data can
be used to solve a variety of real-world automotive applications.
| null |
http://arxiv.org/abs/1806.04795v1
|
http://arxiv.org/pdf/1806.04795v1.pdf
| null |
[
"David Hallac",
"Suvrat Bhooshan",
"Michael Chen",
"Kacem Abida",
"Rok Sosic",
"Jure Leskovec"
] |
[] | 2018-06-12T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-connectome-based-hexagonal-lattice
|
1806.04793
| null | null |
A Connectome Based Hexagonal Lattice Convolutional Network Model of the Drosophila Visual System
|
What can we learn from a connectome? We constructed a simplified model of the
first two stages of the fly visual system, the lamina and medulla. The
resulting hexagonal lattice convolutional network was trained using
backpropagation through time to perform object tracking in natural scene
videos. Networks initialized with weights from connectome reconstructions
automatically discovered well-known orientation and direction selectivity
properties in T4 neurons and their inputs, while networks initialized at random
did not. Our work is the first demonstration, that knowledge of the connectome
can enable in silico predictions of the functional properties of individual
neurons in a circuit, leading to an understanding of circuit function from
structure alone.
| null |
http://arxiv.org/abs/1806.04793v2
|
http://arxiv.org/pdf/1806.04793v2.pdf
| null |
[
"Fabian David Tschopp",
"Michael B. Reiser",
"Srinivas C. Turaga"
] |
[
"Object Tracking"
] | 2018-06-12T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/aspa-fast-adversarial-attack-example
|
1802.05763
| null | null |
ASP:A Fast Adversarial Attack Example Generation Framework based on Adversarial Saliency Prediction
|
With the excellent accuracy and feasibility, the Neural Networks have been
widely applied into the novel intelligent applications and systems. However,
with the appearance of the Adversarial Attack, the NN based system performance
becomes extremely vulnerable:the image classification results can be
arbitrarily misled by the adversarial examples, which are crafted images with
human unperceivable pixel-level perturbation. As this raised a significant
system security issue, we implemented a series of investigations on the
adversarial attack in this work: We first identify an image's pixel
vulnerability to the adversarial attack based on the adversarial saliency
analysis. By comparing the analyzed saliency map and the adversarial
perturbation distribution, we proposed a new evaluation scheme to
comprehensively assess the adversarial attack precision and efficiency. Then,
with a novel adversarial saliency prediction method, a fast adversarial example
generation framework, namely "ASP", is proposed with significant attack
efficiency improvement and dramatic computation cost reduction. Compared to the
previous methods, experiments show that ASP has at most 12 times speed-up for
adversarial example generation, 2 times lower perturbation rate, and high
attack success rate of 87% on both MNIST and Cifar10. ASP can be also well
utilized to support the data-hungry NN adversarial training. By reducing the
attack success rate as much as 90%, ASP can quickly and effectively enhance the
defense capability of NN based system to the adversarial attacks.
| null |
http://arxiv.org/abs/1802.05763v3
|
http://arxiv.org/pdf/1802.05763v3.pdf
| null |
[
"Fuxun Yu",
"Qide Dong",
"Xiang Chen"
] |
[
"Adversarial Attack",
"image-classification",
"Image Classification",
"Saliency Prediction"
] | 2018-02-15T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/global-locally-self-attentive-dialogue-state
|
1805.09655
| null | null |
Global-Locally Self-Attentive Dialogue State Tracker
|
Dialogue state tracking, which estimates user goals and requests given the
dialogue context, is an essential part of task-oriented dialogue systems. In
this paper, we propose the Global-Locally Self-Attentive Dialogue State Tracker
(GLAD), which learns representations of the user utterance and previous system
actions with global-local modules. Our model uses global modules to share
parameters between estimators for different types (called slots) of dialogue
states, and uses local modules to learn slot-specific features. We show that
this significantly improves tracking of rare states and achieves
state-of-the-art performance on the WoZ and DSTC2 state tracking tasks. GLAD
obtains 88.1% joint goal accuracy and 97.1% request accuracy on WoZ,
outperforming prior work by 3.7% and 5.5%. On DSTC2, our model obtains 74.5%
joint goal accuracy and 97.5% request accuracy, outperforming prior work by
1.1% and 1.0%.
|
Dialogue state tracking, which estimates user goals and requests given the dialogue context, is an essential part of task-oriented dialogue systems.
|
http://arxiv.org/abs/1805.09655v3
|
http://arxiv.org/pdf/1805.09655v3.pdf
| null |
[
"Victor Zhong",
"Caiming Xiong",
"Richard Socher"
] |
[
"Dialogue State Tracking",
"Multi-domain Dialogue State Tracking",
"Task-Oriented Dialogue Systems"
] | 2018-05-19T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/distribution-matching-in-variational
|
1802.06847
| null | null |
Distribution Matching in Variational Inference
|
With the increasingly widespread deployment of generative models, there is a mounting need for a deeper understanding of their behaviors and limitations. In this paper, we expose the limitations of Variational Autoencoders (VAEs), which consistently fail to learn marginal distributions in both latent and visible spaces. We show this to be a consequence of learning by matching conditional distributions, and the limitations of explicit model and posterior distributions. It is popular to consider Generative Adversarial Networks (GANs) as a means of overcoming these limitations, leading to hybrids of VAEs and GANs. We perform a large-scale evaluation of several VAE-GAN hybrids and analyze the implications of class probability estimation for learning distributions. While promising, we conclude that at present, VAE-GAN hybrids have limited applicability: they are harder to scale, evaluate, and use for inference compared to VAEs; and they do not improve over the generation quality of GANs.
| null |
https://arxiv.org/abs/1802.06847v4
|
https://arxiv.org/pdf/1802.06847v4.pdf
| null |
[
"Mihaela Rosca",
"Balaji Lakshminarayanan",
"Shakir Mohamed"
] |
[
"Variational Inference"
] | 2018-02-19T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-fast-algorithm-with-minimax-optimal
|
1805.06837
| null | null |
A fast algorithm with minimax optimal guarantees for topic models with an unknown number of topics
|
We propose a new method of estimation in topic models, that is not a variation on the existing simplex finding algorithms, and that estimates the number of topics K from the observed data. We derive new finite sample minimax lower bounds for the estimation of A, as well as new upper bounds for our proposed estimator. We describe the scenarios where our estimator is minimax adaptive. Our finite sample analysis is valid for any number of documents (n), individual document length (N_i), dictionary size (p) and number of topics (K), and both p and K are allowed to increase with n, a situation not handled well by previous analyses. We complement our theoretical results with a detailed simulation study. We illustrate that the new algorithm is faster and more accurate than the current ones, although we start out with a computational and theoretical disadvantage of not knowing the correct number of topics K, while we provide the competing methods with the correct value in our simulations.
|
We propose a new method of estimation in topic models, that is not a variation on the existing simplex finding algorithms, and that estimates the number of topics K from the observed data.
|
https://arxiv.org/abs/1805.06837v3
|
https://arxiv.org/pdf/1805.06837v3.pdf
| null |
[
"Xin Bing",
"Florentina Bunea",
"Marten Wegkamp"
] |
[
"Topic Models",
"valid"
] | 2018-05-17T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/viton-an-image-based-virtual-try-on-network
|
1711.08447
| null | null |
VITON: An Image-based Virtual Try-on Network
|
We present an image-based VIirtual Try-On Network (VITON) without using 3D
information in any form, which seamlessly transfers a desired clothing item
onto the corresponding region of a person using a coarse-to-fine strategy.
Conditioned upon a new clothing-agnostic yet descriptive person representation,
our framework first generates a coarse synthesized image with the target
clothing item overlaid on that same person in the same pose. We further enhance
the initial blurry clothing area with a refinement network. The network is
trained to learn how much detail to utilize from the target clothing item, and
where to apply to the person in order to synthesize a photo-realistic image in
which the target item deforms naturally with clear visual patterns. Experiments
on our newly collected Zalando dataset demonstrate its promise in the
image-based virtual try-on task over state-of-the-art generative models.
|
We present an image-based VIirtual Try-On Network (VITON) without using 3D information in any form, which seamlessly transfers a desired clothing item onto the corresponding region of a person using a coarse-to-fine strategy.
|
http://arxiv.org/abs/1711.08447v4
|
http://arxiv.org/pdf/1711.08447v4.pdf
|
CVPR 2018 6
|
[
"Xintong Han",
"Zuxuan Wu",
"Zhe Wu",
"Ruichi Yu",
"Larry S. Davis"
] |
[
"Descriptive",
"Virtual Try-on"
] | 2017-11-22T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Han_VITON_An_Image-Based_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Han_VITON_An_Image-Based_CVPR_2018_paper.pdf
|
viton-an-image-based-virtual-try-on-network-1
| null |
[] |
https://paperswithcode.com/paper/adaptive-three-operator-splitting
|
1804.02339
| null | null |
Adaptive Three Operator Splitting
|
We propose and analyze an adaptive step-size variant of the Davis-Yin three
operator splitting. This method can solve optimization problems composed by a
sum of a smooth term for which we have access to its gradient and an arbitrary
number of potentially non-smooth terms for which we have access to their
proximal operator. The proposed method sets the step-size based on local
information of the objective --hence allowing for larger step-sizes--, only
requires two extra function evaluations per iteration and does not depend on
any step-size hyperparameter besides an initial estimate. We provide an
iteration complexity analysis that matches the best known results for the
non-adaptive variant: sublinear convergence for general convex functions and
linear convergence under strong convexity of the smooth term and smoothness of
one of the proximal terms. Finally, an empirical comparison with related
methods on 6 different problems illustrates the computational advantage of the
proposed method.
| null |
http://arxiv.org/abs/1804.02339v3
|
http://arxiv.org/pdf/1804.02339v3.pdf
|
ICML 2018 7
|
[
"Fabian Pedregosa",
"Gauthier Gidel"
] |
[] | 2018-04-06T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=1871
|
http://proceedings.mlr.press/v80/pedregosa18a/pedregosa18a.pdf
|
adaptive-three-operator-splitting-1
| null |
[] |
https://paperswithcode.com/paper/convolutional-neural-networks-for-aircraft
|
1806.04779
| null | null |
Convolutional Neural Networks for Aircraft Noise Monitoring
|
Air travel is one of the fastest growing modes of transportation, however,
the effects of aircraft noise on populations surrounding airports is hindering
its growth. In an effort to study and ultimately mitigate the impact that this
noise has, many airports continuously monitor the aircraft noise in their
surrounding communities. Noise monitoring and analysis is complicated by the
fact that aircraft are not the only source of noise. In this work, we show that
a Convolutional Neural Network is well-suited for the task of identifying noise
events which are not caused by aircraft. Our system achieves an accuracy of
0.970 when trained on 900 manually labeled noise events. Our training data and
a TensorFlow implementation of our model are available at
https://github.com/neheller/aircraftnoise.
|
Air travel is one of the fastest growing modes of transportation, however, the effects of aircraft noise on populations surrounding airports is hindering its growth.
|
http://arxiv.org/abs/1806.04779v1
|
http://arxiv.org/pdf/1806.04779v1.pdf
| null |
[
"Nicholas Heller",
"Derek Anderson",
"Matt Baker",
"Brad Juffer",
"Nikolaos Papanikolopoulos"
] |
[] | 2018-06-12T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-to-ask-good-questions-ranking
|
1805.04655
| null | null |
Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information
|
Inquiry is fundamental to communication, and machines cannot effectively
collaborate with humans unless they can ask questions. In this work, we build a
neural network model for the task of ranking clarification questions. Our model
is inspired by the idea of expected value of perfect information: a good
question is one whose expected answer will be useful. We study this problem
using data from StackExchange, a plentiful online resource in which people
routinely ask clarifying questions to posts so that they can better offer
assistance to the original poster. We create a dataset of clarification
questions consisting of ~77K posts paired with a clarification question (and
answer) from three domains of StackExchange: askubuntu, unix and superuser. We
evaluate our model on 500 samples of this dataset against expert human
judgments and demonstrate significant improvements over controlled baselines.
|
Inquiry is fundamental to communication, and machines cannot effectively collaborate with humans unless they can ask questions.
|
http://arxiv.org/abs/1805.04655v2
|
http://arxiv.org/pdf/1805.04655v2.pdf
|
ACL 2018 7
|
[
"Sudha Rao",
"Hal Daumé III"
] |
[] | 2018-05-12T00:00:00 |
https://aclanthology.org/P18-1255
|
https://aclanthology.org/P18-1255.pdf
|
learning-to-ask-good-questions-ranking-1
| null |
[] |
https://paperswithcode.com/paper/early-stopping-for-nonparametric-testing
|
1805.09950
| null | null |
Early Stopping for Nonparametric Testing
|
Early stopping of iterative algorithms is an algorithmic regularization
method to avoid over-fitting in estimation and classification. In this paper,
we show that early stopping can also be applied to obtain the minimax optimal
testing in a general non-parametric setup. Specifically, a Wald-type test
statistic is obtained based on an iterated estimate produced by functional
gradient descent algorithms in a reproducing kernel Hilbert space. A notable
contribution is to establish a "sharp" stopping rule: when the number of
iterations achieves an optimal order, testing optimality is achievable;
otherwise, testing optimality becomes impossible. As a by-product, a similar
sharpness result is also derived for minimax optimal estimation under early
stopping studied in [11] and [19]. All obtained results hold for various kernel
classes, including Sobolev smoothness classes and Gaussian kernel classes.
| null |
http://arxiv.org/abs/1805.09950v3
|
http://arxiv.org/pdf/1805.09950v3.pdf
|
NeurIPS 2018 12
|
[
"Meimei Liu",
"Guang Cheng"
] |
[
"General Classification"
] | 2018-05-25T00:00:00 |
http://papers.nips.cc/paper/7654-early-stopping-for-nonparametric-testing
|
http://papers.nips.cc/paper/7654-early-stopping-for-nonparametric-testing.pdf
|
early-stopping-for-nonparametric-testing-1
| null |
[
{
"code_snippet_url": "",
"description": "**Early Stopping** is a regularization technique for deep neural networks that stops training when parameter updates no longer begin to yield improves on a validation set. In essence, we store and update the current best parameters during training, and when parameter updates no longer yield an improvement (after a set number of iterations) we stop training and use the last best parameters. It works as a regularizer by restricting the optimization procedure to a smaller volume of parameter space.\r\n\r\nImage Source: [Ramazan Gençay](https://www.researchgate.net/figure/Early-stopping-based-on-cross-validation_fig1_3302948)",
"full_name": "Early Stopping",
"introduced_year": 1995,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Early Stopping",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/static-malware-detection-subterfuge
|
1806.04773
| null | null |
Static Malware Detection & Subterfuge: Quantifying the Robustness of Machine Learning and Current Anti-Virus
|
As machine-learning (ML) based systems for malware detection become more
prevalent, it becomes necessary to quantify the benefits compared to the more
traditional anti-virus (AV) systems widely used today. It is not practical to
build an agreed upon test set to benchmark malware detection systems on pure
classification performance. Instead we tackle the problem by creating a new
testing methodology, where we evaluate the change in performance on a set of
known benign & malicious files as adversarial modifications are performed. The
change in performance combined with the evasion techniques then quantifies a
system's robustness against that approach. Through these experiments we are
able to show in a quantifiable way how purely ML based systems can be more
robust than AV products at detecting malware that attempts evasion through
modification, but may be slower to adapt in the face of significantly novel
attacks.
| null |
http://arxiv.org/abs/1806.04773v1
|
http://arxiv.org/pdf/1806.04773v1.pdf
| null |
[
"William Fleshman",
"Edward Raff",
"Richard Zak",
"Mark McLean",
"Charles Nicholas"
] |
[
"BIG-bench Machine Learning",
"Malware Detection"
] | 2018-06-12T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/deep-sparse-coding-for-invariant-multimodal
|
1711.07998
| null | null |
Deep Sparse Coding for Invariant Multimodal Halle Berry Neurons
|
Deep feed-forward convolutional neural networks (CNNs) have become ubiquitous
in virtually all machine learning and computer vision challenges; however,
advancements in CNNs have arguably reached an engineering saturation point
where incremental novelty results in minor performance gains. Although there is
evidence that object classification has reached human levels on narrowly
defined tasks, for general applications, the biological visual system is far
superior to that of any computer. Research reveals there are numerous missing
components in feed-forward deep neural networks that are critical in mammalian
vision. The brain does not work solely in a feed-forward fashion, but rather
all of the neurons are in competition with each other; neurons are integrating
information in a bottom up and top down fashion and incorporating expectation
and feedback in the modeling process. Furthermore, our visual cortex is working
in tandem with our parietal lobe, integrating sensory information from various
modalities.
In our work, we sought to improve upon the standard feed-forward deep
learning model by augmenting them with biologically inspired concepts of
sparsity, top-down feedback, and lateral inhibition. We define our model as a
sparse coding problem using hierarchical layers. We solve the sparse coding
problem with an additional top-down feedback error driving the dynamics of the
neural network. While building and observing the behavior of our model, we were
fascinated that multimodal, invariant neurons naturally emerged that mimicked,
"Halle Berry neurons" found in the human brain. Furthermore, our sparse
representation of multimodal signals demonstrates qualitative and quantitative
superiority to the standard feed-forward joint embedding in common vision and
machine learning tasks.
| null |
http://arxiv.org/abs/1711.07998v2
|
http://arxiv.org/pdf/1711.07998v2.pdf
|
CVPR 2018 6
|
[
"Edward Kim",
"Darryl Hannan",
"Garrett Kenyon"
] |
[
"BIG-bench Machine Learning"
] | 2017-11-21T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Kim_Deep_Sparse_Coding_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Kim_Deep_Sparse_Coding_CVPR_2018_paper.pdf
|
deep-sparse-coding-for-invariant-multimodal-1
| null |
[] |
https://paperswithcode.com/paper/improving-optimization-for-models-with
|
1803.03234
| null | null |
Improving Optimization for Models With Continuous Symmetry Breaking
|
Many loss functions in representation learning are invariant under a
continuous symmetry transformation. For example, the loss function of word
embeddings (Mikolov et al., 2013) remains unchanged if we simultaneously rotate
all word and context embedding vectors. We show that representation learning
models for time series possess an approximate continuous symmetry that leads to
slow convergence of gradient descent. We propose a new optimization algorithm
that speeds up convergence using ideas from gauge theory in physics. Our
algorithm leads to orders of magnitude faster convergence and to more
interpretable representations, as we show for dynamic extensions of matrix
factorization and word embedding models. We further present an example
application of our proposed algorithm that translates modern words into their
historic equivalents.
| null |
http://arxiv.org/abs/1803.03234v3
|
http://arxiv.org/pdf/1803.03234v3.pdf
| null |
[
"Robert Bamler",
"Stephan Mandt"
] |
[
"Representation Learning",
"Time Series",
"Time Series Analysis",
"Word Embeddings"
] | 2018-03-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/hierarchical-long-term-video-prediction
|
1806.04768
| null | null |
Hierarchical Long-term Video Prediction without Supervision
|
Much of recent research has been devoted to video prediction and generation,
yet most of the previous works have demonstrated only limited success in
generating videos on short-term horizons. The hierarchical video prediction
method by Villegas et al. (2017) is an example of a state-of-the-art method for
long-term video prediction, but their method is limited because it requires
ground truth annotation of high-level structures (e.g., human joint landmarks)
at training time. Our network encodes the input frame, predicts a high-level
encoding into the future, and then a decoder with access to the first frame
produces the predicted image from the predicted encoding. The decoder also
produces a mask that outlines the predicted foreground object (e.g., person) as
a by-product. Unlike Villegas et al. (2017), we develop a novel training method
that jointly trains the encoder, the predictor, and the decoder together
without highlevel supervision; we further improve upon this by using an
adversarial loss in the feature space to train the predictor. Our method can
predict about 20 seconds into the future and provides better results compared
to Denton and Fergus (2018) and Finn et al. (2016) on the Human 3.6M dataset.
| null |
http://arxiv.org/abs/1806.04768v1
|
http://arxiv.org/pdf/1806.04768v1.pdf
|
ICML 2018 7
|
[
"Nevan Wichers",
"Ruben Villegas",
"Dumitru Erhan",
"Honglak Lee"
] |
[
"Decoder",
"Prediction",
"Video Prediction"
] | 2018-06-12T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2381
|
http://proceedings.mlr.press/v80/wichers18a/wichers18a.pdf
|
hierarchical-long-term-video-prediction-1
| null |
[] |
https://paperswithcode.com/paper/learning-mixtures-of-linear-regressions-with
|
1802.07895
| null | null |
Learning Mixtures of Linear Regressions with Nearly Optimal Complexity
|
Mixtures of Linear Regressions (MLR) is an important mixture model with many applications. In this model, each observation is generated from one of the several unknown linear regression components, where the identity of the generated component is also unknown. Previous works either assume strong assumptions on the data distribution or have high complexity. This paper proposes a fixed parameter tractable algorithm for the problem under general conditions, which achieves global convergence and the sample complexity scales nearly linearly in the dimension. In particular, different from previous works that require the data to be from the standard Gaussian, the algorithm allows the data from Gaussians with different covariances. When the conditional number of the covariances and the number of components are fixed, the algorithm has nearly optimal sample complexity $N = \tilde{O}(d)$ as well as nearly optimal computational complexity $\tilde{O}(Nd)$, where $d$ is the dimension of the data space. To the best of our knowledge, this approach provides the first such recovery guarantee for this general setting.
| null |
https://arxiv.org/abs/1802.07895v3
|
https://arxiv.org/pdf/1802.07895v3.pdf
| null |
[
"Yuanzhi Li",
"YIngyu Liang"
] |
[] | 2018-02-22T00:00:00 | 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/fully-convolutional-network-for-melanoma
|
1806.04765
| null | null |
Fully Convolutional Network for Melanoma Diagnostics
|
This work seeks to determine how modern machine learning techniques may be
applied to the previously unexplored topic of melanoma diagnostics using
digital pathology. We curated a new dataset of 50 patient cases of cutaneous
melanoma using digital pathology. We provide gold standard annotations for
three tissue types (tumour, epidermis, and dermis) which are important for the
prognostic measurements known as Breslow thickness and Clark level. Then, we
devised a novel multi-stride fully convolutional network (FCN) architecture
that outperformed other networks trained and evaluated using the same data
according to standard metrics. Finally, we trained a model to detect and
localize the target tissue types. When processing previously unseen cases, our
model's output is qualitatively very similar to the gold standard. In addition
to the standard metrics computed as a baseline for our approach, we asked three
additional pathologists to measure the Breslow thickness on the network's
output. Their responses were diagnostically equivalent to the ground truth
measurements, and when removing cases where a measurement was not appropriate,
inter-rater reliability (IRR) between the four pathologists was 75.0%. Given
the qualitative and quantitative results, it is possible to overcome the
discriminative challenges of the skin and tumour anatomy for segmentation using
modern machine learning techniques, though more work is required to improve the
network's performance on dermis segmentation. Further, we show that it is
possible to achieve a level of accuracy required to manually perform the
Breslow thickness measurement.
| null |
http://arxiv.org/abs/1806.04765v1
|
http://arxiv.org/pdf/1806.04765v1.pdf
| null |
[
"Adon Phillips",
"Iris Teo",
"Jochen Lang"
] |
[
"Anatomy",
"BIG-bench Machine Learning"
] | 2018-06-12T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/inferno-inference-aware-neural-optimisation
|
1806.04743
| null | null |
INFERNO: Inference-Aware Neural Optimisation
|
Complex computer simulations are commonly required for accurate data
modelling in many scientific disciplines, making statistical inference
challenging due to the intractability of the likelihood evaluation for the
observed data. Furthermore, sometimes one is interested on inference drawn over
a subset of the generative model parameters while taking into account model
uncertainty or misspecification on the remaining nuisance parameters. In this
work, we show how non-linear summary statistics can be constructed by
minimising inference-motivated losses via stochastic gradient descent such they
provided the smallest uncertainty for the parameters of interest. As a use
case, the problem of confidence interval estimation for the mixture coefficient
in a multi-dimensional two-component mixture model (i.e. signal vs background)
is considered, where the proposed technique clearly outperforms summary
statistics based on probabilistic classification, which are a commonly used
alternative but do not account for the presence of nuisance parameters.
|
Complex computer simulations are commonly required for accurate data modelling in many scientific disciplines, making statistical inference challenging due to the intractability of the likelihood evaluation for the observed data.
|
http://arxiv.org/abs/1806.04743v2
|
http://arxiv.org/pdf/1806.04743v2.pdf
| null |
[
"Pablo de Castro",
"Tommaso Dorigo"
] |
[] | 2018-06-12T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/pricing-engine-estimating-causal-impacts-in
|
1806.03285
| null | null |
Pricing Engine: Estimating Causal Impacts in Real World Business Settings
|
We introduce the Pricing Engine package to enable the use of Double ML
estimation techniques in general panel data settings. Customization allows the
user to specify first-stage models, first-stage featurization, second stage
treatment selection and second stage causal-modeling. We also introduce a
DynamicDML class that allows the user to generate dynamic treatment-aware
forecasts at a range of leads and to understand how the forecasts will vary as
a function of causally estimated treatment parameters. The Pricing Engine is
built on Python 3.5 and can be run on an Azure ML Workbench environment with
the addition of only a few Python packages. This note provides high-level
discussion of the Double ML method, describes the packages intended use and
includes an example Jupyter notebook demonstrating application to some publicly
available data. Installation of the package and additional technical
documentation is available at
$\href{https://github.com/bquistorff/pricingengine}{github.com/bquistorff/pricingengine}$.
|
We introduce the Pricing Engine package to enable the use of Double ML estimation techniques in general panel data settings.
|
http://arxiv.org/abs/1806.03285v2
|
http://arxiv.org/pdf/1806.03285v2.pdf
| null |
[
"Matt Goldman",
"Brian Quistorff"
] |
[] | 2018-06-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/delta-encoder-an-effective-sample-synthesis
|
1806.04734
| null | null |
Delta-encoder: an effective sample synthesis method for few-shot object recognition
|
Learning to classify new categories based on just one or a few examples is a
long-standing challenge in modern computer vision. In this work, we proposes a
simple yet effective method for few-shot (and one-shot) object recognition. Our
approach is based on a modified auto-encoder, denoted Delta-encoder, that
learns to synthesize new samples for an unseen category just by seeing few
examples from it. The synthesized samples are then used to train a classifier.
The proposed approach learns to both extract transferable intra-class
deformations, or "deltas", between same-class pairs of training examples, and
to apply those deltas to the few provided examples of a novel class (unseen
during training) in order to efficiently synthesize samples from that new
class. The proposed method improves over the state-of-the-art in one-shot
object-recognition and compares favorably in the few-shot case. Upon acceptance
code will be made available.
|
Our approach is based on a modified auto-encoder, denoted Delta-encoder, that learns to synthesize new samples for an unseen category just by seeing few examples from it.
|
http://arxiv.org/abs/1806.04734v3
|
http://arxiv.org/pdf/1806.04734v3.pdf
|
NeurIPS 2018 12
|
[
"Eli Schwartz",
"Leonid Karlinsky",
"Joseph Shtok",
"Sivan Harary",
"Mattias Marder",
"Rogerio Feris",
"Abhishek Kumar",
"Raja Giryes",
"Alex M. Bronstein"
] |
[
"Few-Shot Image Classification",
"Few-Shot Learning",
"Object Recognition"
] | 2018-06-12T00:00:00 |
http://papers.nips.cc/paper/7549-delta-encoder-an-effective-sample-synthesis-method-for-few-shot-object-recognition
|
http://papers.nips.cc/paper/7549-delta-encoder-an-effective-sample-synthesis-method-for-few-shot-object-recognition.pdf
|
delta-encoder-an-effective-sample-synthesis-1
| null |
[] |
https://paperswithcode.com/paper/some-variations-on-ensembled-random-survival
|
1709.05515
| null | null |
Some variations on Ensembled Random Survival Forest with application to Cancer Research
|
In this paper we describe a novel implementation of adaboost for prediction
of survival function. We take different variations of the algorithm and compare
the algorithms based on system run time and root mean square error. Our
construction includes right censoring data and competing risk data too. We take
different data set to illustrate the performance of the algorithms.
| null |
http://arxiv.org/abs/1709.05515v2
|
http://arxiv.org/pdf/1709.05515v2.pdf
| null |
[
"Arabin Kumar Dey",
"Suhas N.",
"Talasila Sai Teja",
"Anshul Juneja"
] |
[] | 2017-09-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/accurate-detection-of-inner-ears-in-head-cts
|
1806.04725
| null | null |
Accurate Detection of Inner Ears in Head CTs Using a Deep Volume-to-Volume Regression Network with False Positive Suppression and a Shape-Based Constraint
|
Cochlear implants (CIs) are neural prosthetics which are used to treat
patients with hearing loss. CIs use an array of electrodes which are surgically
inserted into the cochlea to stimulate the auditory nerve endings. After
surgery, CIs need to be programmed. Studies have shown that the spatial
relationship between the intra-cochlear anatomy and electrodes derived from
medical images can guide CI programming and lead to significant improvement in
hearing outcomes. However, clinical head CT images are usually obtained from
scanners of different brands with different protocols. The field of view thus
varies greatly and visual inspection is needed to document their content prior
to applying algorithms for electrode localization and intra-cochlear anatomy
segmentation. In this work, to determine the presence/absence of inner ears and
to accurately localize them in head CTs, we use a volume-to-volume
convolutional neural network which can be trained end-to-end to map a raw CT
volume to probability maps which indicate inner ear positions. We incorporate a
false positive suppression strategy in training and apply a shape-based
constraint. We achieve a labeling accuracy of 98.59% and a localization error
of 2.45mm. The localization error is significantly smaller than a random
forest-based approach that has been proposed recently to perform the same task.
| null |
http://arxiv.org/abs/1806.04725v1
|
http://arxiv.org/pdf/1806.04725v1.pdf
| null |
[
"Dongqing Zhang",
"Jianing Wang",
"Jack H. Noble",
"Benoit M. Dawant"
] |
[
"Anatomy"
] | 2018-06-12T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/improving-exploration-in-evolution-strategies
|
1712.06560
| null | null |
Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents
|
Evolution strategies (ES) are a family of black-box optimization algorithms
able to train deep neural networks roughly as well as Q-learning and policy
gradient methods on challenging deep reinforcement learning (RL) problems, but
are much faster (e.g. hours vs. days) because they parallelize better. However,
many RL problems require directed exploration because they have reward
functions that are sparse or deceptive (i.e. contain local optima), and it is
unknown how to encourage such exploration with ES. Here we show that algorithms
that have been invented to promote directed exploration in small-scale evolved
neural networks via populations of exploring agents, specifically novelty
search (NS) and quality diversity (QD) algorithms, can be hybridized with ES to
improve its performance on sparse or deceptive deep RL tasks, while retaining
scalability. Our experiments confirm that the resultant new algorithms, NS-ES
and two QD algorithms, NSR-ES and NSRA-ES, avoid local optima encountered by ES
to achieve higher performance on Atari and simulated robots learning to walk
around a deceptive trap. This paper thus introduces a family of fast, scalable
algorithms for reinforcement learning that are capable of directed exploration.
It also adds this new family of exploration algorithms to the RL toolbox and
raises the interesting possibility that analogous algorithms with multiple
simultaneous paths of exploration might also combine well with existing RL
algorithms outside ES.
|
Evolution strategies (ES) are a family of black-box optimization algorithms able to train deep neural networks roughly as well as Q-learning and policy gradient methods on challenging deep reinforcement learning (RL) problems, but are much faster (e. g. hours vs. days) because they parallelize better.
|
http://arxiv.org/abs/1712.06560v3
|
http://arxiv.org/pdf/1712.06560v3.pdf
|
NeurIPS 2018 12
|
[
"Edoardo Conti",
"Vashisht Madhavan",
"Felipe Petroski Such",
"Joel Lehman",
"Kenneth O. Stanley",
"Jeff Clune"
] |
[
"Deep Reinforcement Learning",
"Policy Gradient Methods",
"Q-Learning",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2017-12-18T00:00:00 |
http://papers.nips.cc/paper/7750-improving-exploration-in-evolution-strategies-for-deep-reinforcement-learning-via-a-population-of-novelty-seeking-agents
|
http://papers.nips.cc/paper/7750-improving-exploration-in-evolution-strategies-for-deep-reinforcement-learning-via-a-population-of-novelty-seeking-agents.pdf
|
improving-exploration-in-evolution-strategies-1
| null |
[
{
"code_snippet_url": null,
"description": "**Q-Learning** is an off-policy temporal difference control algorithm:\r\n\r\n$$Q\\left(S\\_{t}, A\\_{t}\\right) \\leftarrow Q\\left(S\\_{t}, A\\_{t}\\right) + \\alpha\\left[R_{t+1} + \\gamma\\max\\_{a}Q\\left(S\\_{t+1}, a\\right) - Q\\left(S\\_{t}, A\\_{t}\\right)\\right] $$\r\n\r\nThe learned action-value function $Q$ directly approximates $q\\_{*}$, the optimal action-value function, independent of the policy being followed.\r\n\r\nSource: Sutton and Barto, Reinforcement Learning, 2nd Edition",
"full_name": "Q-Learning",
"introduced_year": 1984,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Off-Policy TD Control",
"parent": null
},
"name": "Q-Learning",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/weakly-supervised-semantic-segmentation-by
|
1806.04659
| null | null |
Weakly-Supervised Semantic Segmentation by Iteratively Mining Common Object Features
|
Weakly-supervised semantic segmentation under image tags supervision is a
challenging task as it directly associates high-level semantic to low-level
appearance. To bridge this gap, in this paper, we propose an iterative
bottom-up and top-down framework which alternatively expands object regions and
optimizes segmentation network. We start from initial localization produced by
classification networks. While classification networks are only responsive to
small and coarse discriminative object regions, we argue that, these regions
contain significant common features about objects. So in the bottom-up step, we
mine common object features from the initial localization and expand object
regions with the mined features. To supplement non-discriminative regions,
saliency maps are then considered under Bayesian framework to refine the object
regions. Then in the top-down step, the refined object regions are used as
supervision to train the segmentation network and to predict object masks.
These object masks provide more accurate localization and contain more regions
of object. Further, we take these object masks as initial localization and mine
common object features from them. These processes are conducted iteratively to
progressively produce fine object masks and optimize segmentation networks.
Experimental results on Pascal VOC 2012 dataset demonstrate that the proposed
method outperforms previous state-of-the-art methods by a large margin.
| null |
http://arxiv.org/abs/1806.04659v1
|
http://arxiv.org/pdf/1806.04659v1.pdf
|
CVPR 2018 6
|
[
"Xiang Wang",
"ShaoDi You",
"Xi Li",
"Huimin Ma"
] |
[
"General Classification",
"Object",
"Segmentation",
"Semantic Segmentation",
"Weakly supervised Semantic Segmentation",
"Weakly-Supervised Semantic Segmentation"
] | 2018-06-12T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Wang_Weakly-Supervised_Semantic_Segmentation_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_Weakly-Supervised_Semantic_Segmentation_CVPR_2018_paper.pdf
|
weakly-supervised-semantic-segmentation-by-1
| null |
[] |
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