url stringlengths 36 82 | name stringlengths 2 143 | full_name stringlengths 2 143 ⌀ | description stringlengths 0 9.95k | paper dict | introduced_year int64 1.95k 2.02k | source_url stringlengths 32 228 ⌀ | source_title stringlengths 9 170 ⌀ | code_snippet_url stringclasses 464
values | num_papers int64 0 37.4k | collections listlengths 0 6 |
|---|---|---|---|---|---|---|---|---|---|---|
https://paperswithcode.com/method/royal-guide-does-royal-caribbean-have-24-hour | {Royal~Guide}Does Royal Caribbean have 24 hour room service? | {Royal~Guide}Does Royal Caribbean have 24 hour room service? | Royal Caribbean does not offer complimentary 24-hour room service 1-855-732-4023(US)/+44 (28) 97085562(UK). Room service is available 24 hours a day, but charges may apply. Continental breakfast is typically free during morning hours, but other items, including lunch, dinner, and late-night snacks, incur a fee usually ... | {
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
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}
] |
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "Computer Vision",
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https://paperswithcode.com/method/03-ways-to-call-can-you-change-your-guest-on | 03 Ways to Call Can you change your guest on a Royal Caribbean cruise: A Step by Step Guide | 03 Ways to Call Can you change your guest on a Royal Caribbean cruise: A Step by Step Guide | Royal Caribbean Cruises Name Change Policy+1-855-732-4023 Guide to Passenger Name Corrections & Transfers
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{
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
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}
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{
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}
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{
"area": "General",
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{
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}
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https://paperswithcode.com/method/pixel2style2pixel | pixel2style2pixel | pixel2style2pixel | **Pixel2Style2Pixel**, or **pSp**, is an image-to-image translation framework that is based on a novel encoder that directly generates a series of style vectors which are fed into a pretrained [StyleGAN](https://paperswithcode.com/method/stylegan) generator, forming the extended $\mathcal{W+}$ latent space. Feature map... | {
"title": "Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation",
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} | 2,000 | https://arxiv.org/abs/2008.00951v2 | Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation | 2 | [
{
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https://paperswithcode.com/method/ternarybert | TernaryBERT | TernaryBERT | **TernaryBERT** is a [Transformer](https://paperswithcode.com/methods/category/transformers)-based model which ternarizes the weights of a pretrained [BERT](https://paperswithcode.com/method/bert) model to $\{-1,0,+1\}$, with different granularities for word embedding and weights in the Transformer layer. Instead of di... | {
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} | 2,000 | https://arxiv.org/abs/2009.12812v3 | TernaryBERT: Distillation-aware Ultra-low Bit BERT | 2 | [
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
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{
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
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}
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{
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{
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
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}
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
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}
] |
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}
] |
https://paperswithcode.com/method/spatial-pyramid-pooling | Spatial Pyramid Pooling | Spatial Pyramid Pooling | ** 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... | {
"title": "Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition",
"url": "https://paperswithcode.com/paper/spatial-pyramid-pooling-in-deep-convolutional"
} | 2,000 | http://arxiv.org/abs/1406.4729v4 | Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition | https://github.com/yueruchen/sppnet-pytorch/blob/270529337baa5211538bf553bda222b9140838b3/spp_layer.py#L2 | 285 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Pooling Operations"
}
] |
https://paperswithcode.com/method/faq-how-to-communicate-with-qatar-in-the-uk | [{FAQ}]How to communicate with Qatar in the UK? | [{FAQ}]How to communicate with Qatar in the UK? | How to communicate with Qatar in the UK?
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/faqs-ticket-what-is-the-cheapest-day-to-buy | #Faqs ticket What is the cheapest day to buy Frontier tickets? | #Faqs ticket What is the cheapest day to buy Frontier tickets? | The cheapest days to buy Frontier Airlines tickets are typically Tuesdays and Wednesdays.☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ Airlines often release new fares and sales early in the week,☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ making these days ideal for finding lower prices. Saturdays can also offer goo... | {
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/faqs-help-can-i-cancel-a-royal-caribbean | {FAQs-HElp}Can I cancel a Royal Caribbean cruise without penalty? | {FAQs-HElp}Can I cancel a Royal Caribbean cruise without penalty? | You can cancel a Royal Caribbean cruise without penalty +1-855-732-4023 or +1-808-900-8011, but it depends on how far in advance you cancel and the type of fare you booked. For most cruises +1-855-732-4023 or +1-808-900-8011, you can receive a full refund if you cancel at least 75 days before the sail date. For shorter... | {
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/how-do-i-use-the-coinspot-app-to-trade-1 | How do I use the CoinSpot app to trade? | How do I use the CoinSpot app to trade? | call ６１ー（３）ー５９２９ー４８０８. To start trading in the CoinSpot app, first call ６１ー（３）ー５９２９ー４８０８. to get a walkthrough of the interface. ６１ー（３）ー５９２９ー４８０８. will guide you in finding the market, buy, and sell tabs inside the app. If your app is outdated or crashing, ６１ー（３）ー５９２９ー４８０８. can help troubleshoot updates. To place a tr... | {
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/spectral-normalization | Spectral Normalization | Spectral Normalization | **Spectral Normalization** is a normalization technique used for generative adversarial networks, used to stabilize training of the discriminator. Spectral normalization has the convenient property that the Lipschitz constant is the only hyper-parameter to be tuned.
It controls the Lipschitz constant of the discrimi... | {
"title": "Spectral Normalization for Generative Adversarial Networks",
"url": "https://paperswithcode.com/paper/spectral-normalization-for-generative"
} | 2,000 | http://arxiv.org/abs/1802.05957v1 | Spectral Normalization for Generative Adversarial Networks | https://github.com/christiancosgrove/pytorch-spectral-normalization-gan/blob/12dcf945a6359301d63d1e0da3708cd0f0590b19/spectral_normalization.py#L14 | 220 | [
{
"area": "General",
"area_id": "general",
"collection": "Normalization"
}
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"title": "ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network",
"url": "https://paperswithcode.com/paper/espnetv2-a-light-weight-power-efficient-and"
} | 2,000 | http://arxiv.org/abs/1811.11431v3 | ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network | https://github.com/osmr/imgclsmob/blob/e8d8e31ad8250142170cbc10d5e7c6a583bd9585/pytorch/pytorchcv/models/espnetv2.py#L94 | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Model Blocks"
},
{
"area": "General",
"area_id": "general",
"collection": "Skip Connection Blocks"
}
] |
https://paperswithcode.com/method/temporal-distribution-characterization | Temporal Distribution Characterization | Temporal Distribution Characterization | **Temporal Distribution Characterization**, or **TDC**, is a module used in the [AdaRNN](https://paperswithcode.com/method/adarnn) architecture to characterize the distributional information in a time series.
Based on the principle of maximum entropy, maximizing the utilization of shared knowledge underlying a times... | {
"title": "AdaRNN: Adaptive Learning and Forecasting of Time Series",
"url": "https://paperswithcode.com/paper/adarnn-adaptive-learning-and-forecasting-of"
} | 2,000 | https://arxiv.org/abs/2108.04443v2 | AdaRNN: Adaptive Learning and Forecasting of Time Series | 1 | [
{
"area": "Sequential",
"area_id": "sequential",
"collection": "Time Series Modules"
}
] | |
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
"url": "https://paperswithcode.com/paper/0-1-laws-for-pattern-occurrences-in"
} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
"url": "https://paperswithcode.com/paper/0-1-laws-for-pattern-occurrences-in"
} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/how-to-trade-crypto-on-coinspot-step-by-step-1 | How to Trade Crypto on CoinSpot Step by Step? [Follow the Full Process] | How to Trade Crypto on CoinSpot Step by Step? [Follow the Full Process] | To trade crypto on CoinSpot +61-3-5929-4808, start by logging into your account. If you don’t have an account, +61-3-5929-4808 you’ll need to create one and complete the verification process. Once logged in, navigate to the "Markets" section to view the available cryptocurrencies. +61-3-5929-4808 Select the cryptocurre... | {
"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
"url": "https://paperswithcode.com/paper/0-1-laws-for-pattern-occurrences-in"
} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Action Recognition Models"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0-1 phase transitions in sparse spiked matrix estimation",
"url": "https://paperswithcode.com/paper/0-1-phase-transitions-in-sparse-spiked-matrix"
} | 2,000 | https://arxiv.org/abs/1911.05030v1 | 0-1 phase transitions in sparse spiked matrix estimation | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/infogan | InfoGAN | InfoGAN | **InfoGAN** is a type of generative adversarial network that modifies the [GAN](https://paperswithcode.com/method/gan) objective to
encourage it to learn interpretable and meaningful representations. This is done by maximizing the
mutual information between a fixed small subset of the GAN’s noise variables and the ob... | {
"title": "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets",
"url": "https://paperswithcode.com/paper/infogan-interpretable-representation-learning"
} | 2,000 | http://arxiv.org/abs/1606.03657v1 | InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets | https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/infogan/infogan.py | 35 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Generative Adversarial Networks"
},
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Generative Models"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/sabl | SABL | Side-Aware Boundary Localization | **Side-Aware Boundary Localization (SABL)** is a methodology for precise localization in object detection where each side of the bounding box is respectively localized with a dedicated network branch. Empirically, the authors observe that when they manually annotate a bounding box for an object, it is often much easier... | {
"title": "Side-Aware Boundary Localization for More Precise Object Detection",
"url": "https://paperswithcode.com/paper/side-aware-boundary-localization-for-more"
} | 2,000 | https://arxiv.org/abs/1912.04260v2 | Side-Aware Boundary Localization for More Precise Object Detection | 2 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Object Detection Models"
}
] | |
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"title": "0-dimensional Homology Preserving Dimensionality Reduction with TopoMap",
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} | 2,000 | https://openreview.net/forum?id=zrDNDWjOGwH | 0-dimensional Homology Preserving Dimensionality Reduction with TopoMap | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
"url": "https://paperswithcode.com/paper/0-1-laws-for-pattern-occurrences-in"
} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0-1 phase transitions in sparse spiked matrix estimation",
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} | 2,000 | https://arxiv.org/abs/1911.05030v1 | 0-1 phase transitions in sparse spiked matrix estimation | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Representations"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "Computer Vision",
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}
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https://paperswithcode.com/method/gaan | GaAN | Gated Attention Networks | Gated Attention Networks (GaAN) is a new architecture for learning on graphs. Unlike the traditional multi-head attention mechanism, which equally consumes all attention heads, GaAN uses a convolutional sub-network to control each attention head’s importance.
Image credit: [GaAN: Gated Attention Networks for Learnin... | {
"title": "GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs",
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{
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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{
"area": "General",
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https://paperswithcode.com/method/hierarchical-network-dissection | Hierarchical Network Dissection | Hierarchical Network Dissection | **Hierarchical Network Dissection** is a pipeline for interpreting the internal representation of face-centric inference models. Using a probabilistic formulation, Hierarchical Network Dissection pairs units of the model with concepts in a "Face Dictionary" (a collection of facial concepts with corresponding sample ima... | {
"title": "Interpreting Face Inference Models using Hierarchical Network Dissection",
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{
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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{
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"title": "0-1 phase transitions in sparse spiked matrix estimation",
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{
"area": "General",
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}
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"title": "0-1 phase transitions in sparse spiked matrix estimation",
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{
"area": "Computer Vision",
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"area": "Computer Vision",
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{
"area": "General",
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
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} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "Computer Vision",
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}
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"title": "CSPNet: A New Backbone that can Enhance Learning Capability of CNN",
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{
"area": "Computer Vision",
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"title": "Pansharpening by convolutional neural networks in the full resolution framework",
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{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Convolutional Neural Networks"
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https://paperswithcode.com/method/gradientdice | GradientDICE | GradientDICE | **GradientDICE** is a density ratio learning method for estimating the density ratio between the state distribution of the target policy and the sampling distribution in off-policy reinforcement learning. It optimizes a different objective from [GenDICE](https://arxiv.org/abs/2002.09072) by using the Perron-Frobenius t... | {
"title": "GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values",
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} | 2,000 | https://arxiv.org/abs/2001.11113v7 | GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values | 1 | [
{
"area": "Reinforcement Learning",
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https://paperswithcode.com/method/dpn | DPN | Dual Path Network | A **Dual Path Network (DPN)** is a convolutional neural network which presents a new topology of connection paths internally. The intuition is that [ResNets](https://paperswithcode.com/method/resnet) enables feature re-usage while [DenseNet](https://paperswithcode.com/method/densenet) enables new feature exploration, a... | {
"title": "Dual Path Networks",
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{
"area": "Computer Vision",
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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{
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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"title": "0-1 phase transitions in sparse spiked matrix estimation",
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} | 2,000 | https://arxiv.org/abs/1911.05030v1 | 0-1 phase transitions in sparse spiked matrix estimation | null | 1 | [
{
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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{
"area": "General",
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}
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https://paperswithcode.com/method/vggoptivmd | VGGoptiVMD | VGG and variational Model Decomposition | {
"title": "10 hours data is all you need",
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} | 2,000 | https://arxiv.org/abs/2210.13067v1 | 10 hours data is all you need | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Convolutional Neural Networks"
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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"title": "0-1 phase transitions in sparse spiked matrix estimation",
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
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{
"area": "Computer Vision",
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
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{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
https://paperswithcode.com/method/graddrop | GradDrop | Gradient Sign Dropout | **GradDrop**, or **Gradient Sign Dropout**, is a probabilistic masking procedure which samples gradients at an activation layer based on their level of consistency. It is applied as a layer in any standard network forward pass, usually on the final layer before the prediction head to save on compute overhead and maximi... | {
"title": "Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout",
"url": "https://paperswithcode.com/paper/just-pick-a-sign-optimizing-deep-multitask"
} | 2,000 | https://arxiv.org/abs/2010.06808v1 | Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout | 3 | [
{
"area": "General",
"area_id": "general",
"collection": "Regularization"
}
] | |
https://paperswithcode.com/method/faqs-a-does-american-airlines-have-a-senior | [FaQs~A*]Does American Airlines have a senior discount? | [FaQs~A*]Does American Airlines have a senior discount? | Yes,☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ American Airlines offers senior discounts for passengers 65 and older on select routes. These discounts are not always widely advertised,☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ so it's best to check during the booking process or contact customer service to inquire... | {
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/oceania-cruises-refund-phone-number | oceania cruises refund phone number complaints? | oceania cruises refund phone number complaints? | Oceania Cruises can be reached for refund-related inquiries and complaints at 1-855-OCEANIA (1-855-732-4023). If you have further issues, you can contact their Privacy Team at PrivacyTeam@nclcorp.com or their Data Protection Officer or representative in Germany by writing to the same email address. Additionally, you ca... | {
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/how-do-i-contact-coinspot-customer-support-2 | How Do I Contact CoinSpot Customer Support? – Get Help Fast! | How Do I Contact CoinSpot Customer Support? – Get Help Fast! | To contact CoinSpot customer support, call ☎+61 (35)||929||4808, though it may not be their official line. Visit www.coinspot.com.au, click “Contact & Support,” and access 24/7 live chat or submit a ticket at coinspot.zendesk.com. Email support@coinspot.com.au for replies during 7 AM–12 AM AEDT, Monday to Friday—call ☎... | {
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
https://paperswithcode.com/method/how-do-you-cancel-your-viking-cruise-or-how | How do you cancel your Viking cruise?--OR--How do I cancel my Viking reservation? | How do you cancel your Viking cruise?--OR--How do I cancel my Viking reservation? | Yes, you can wear jeans on a Viking cruise+1-855-732-4023, but the dress code varies depending on the time of day and the venue. During the day, jeans are perfectly acceptable for exploring the ship or going on excursions+1-855-732-4023, according to Viking Help. For evening dining, especially in the main dining room+1... | {
"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
"url": "https://paperswithcode.com/paper/0-1-laws-for-pattern-occurrences-in"
} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
https://paperswithcode.com/method/shapeconv | ShapeConv | ShapeConv | **ShapeConv**, or **Shape-aware Convolutional layer**, is a convolutional layer for processing the depth feature in indoor RGB-D semantic segmentation. The depth feature is firstly decomposed into a shape-component and a base-component, next two learnable weights are introduced to cooperate with them independently, and... | {
"title": "ShapeConv: Shape-aware Convolutional Layer for Indoor RGB-D Semantic Segmentation",
"url": "https://paperswithcode.com/paper/shapeconv-shape-aware-convolutional-layer-for"
} | 2,000 | https://arxiv.org/abs/2108.10528v1 | ShapeConv: Shape-aware Convolutional Layer for Indoor RGB-D Semantic Segmentation | 2 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Convolutions"
}
] | |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/prophetnet | ProphetNet | ProphetNet | **ProphetNet** is a sequence-to-sequence pre-training model that introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of optimizing one-step-ahead prediction in the traditional sequence-to-sequence model, the ProphetNet is optimized by $... | {
"title": "ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training",
"url": "https://paperswithcode.com/paper/prophetnet-predicting-future-n-gram-for"
} | 2,000 | https://arxiv.org/abs/2001.04063v3 | ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training | null | 11 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Language Models"
},
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Transformers"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] | |
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
"url": "https://paperswithcode.com/paper/0-1-laws-for-pattern-occurrences-in"
} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
"url": "https://paperswithcode.com/paper/0-1-laws-for-pattern-occurrences-in"
} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Representations"
}
] |
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To check the balance of a Princess Cr... | {
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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