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/dcn-v2 | DCN-V2 | DCN-V2 | **DCN-V2** is an architecture for learning-to-rank that improves upon the original [DCN](http://paperswithcode.com/method/dcn) model. It first learns explicit feature interactions of the inputs (typically the embedding layer) through cross layers, and then combines with a deep network to learn complementary implicit in... | {
"title": "DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems",
"url": "https://paperswithcode.com/paper/dcn-m-improved-deep-cross-network-for-feature"
} | 2,000 | https://arxiv.org/abs/2008.13535v2 | DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems | 3 | [
{
"area": "General",
"area_id": "general",
"collection": "Deep Tabular Learning"
},
{
"area": "General",
"area_id": "general",
"collection": "Learning to Rank Models"
}
] | |
https://paperswithcode.com/method/roi-align | RoIAlign | RoIAlign | **Region of Interest Align**, or **RoIAlign**, is an operation for extracting a small feature map from each RoI in detection and segmentation based tasks. It removes the harsh quantization of [RoI Pool](https://paperswithcode.com/method/roi-pooling), properly *aligning* the extracted features with the input. To avoid a... | {
"title": "Mask R-CNN",
"url": "https://paperswithcode.com/paper/mask-r-cnn"
} | 2,000 | http://arxiv.org/abs/1703.06870v3 | Mask R-CNN | https://github.com/facebookresearch/detectron2/blob/bb9f5d8e613358519c9865609ab3fe7b6571f2ba/detectron2/layers/roi_align.py#L51 | 611 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "RoI Feature Extractors"
}
] |
https://paperswithcode.com/method/aggmo | AggMo | AggMo | **Aggregated Momentum (AggMo)** is a variant of the [classical momentum](https://paperswithcode.com/method/sgd-with-momentum) stochastic optimizer which maintains several velocity vectors with different $\beta$ parameters. AggMo averages the velocity vectors when updating the parameters. It resolves the problem of choo... | {
"title": "Aggregated Momentum: Stability Through Passive Damping",
"url": "https://paperswithcode.com/paper/aggregated-momentum-stability-through-passive"
} | 2,000 | http://arxiv.org/abs/1804.00325v3 | Aggregated Momentum: Stability Through Passive Damping | https://github.com/AtheMathmo/AggMo/blob/f91730d1c3d8398abb19ab3331e08d39cd5df390/aggmo.py#L5 | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Stochastic Optimization"
}
] |
https://paperswithcode.com/method/faqs-helpline-can-you-call-a-cruise-ship-in | [[FAQS-Helpline]] Can you call a cruise ship in an emergency? | [[FAQS-Helpline]] Can you call a cruise ship in an emergency? | You can make ship-to-shore calls from your stateroom, 24 hours a day. The cost is $7.95 USD per minute and will be automatically charged to your SeaPass account. Your friends and family can contact the ship by calling +1-855-732-4023 or +44-289-708-0062(UK). Or from outside the U.S. they can call +1-855-732-4023 or +44... | {
"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-to-contact-robinhood-reach-support-the | How to Contact Robinhood – Reach Support the Right Way | How to Contact Robinhood – Reach Support the Right Way | Contacting Robinhood +1-844-610-2676 is essential if you have questions or issues related to your account. There are several ways to reach out to their support team +1-844-610-2676 , but one of the most effective methods is by phone. To contact Robinhood, +1-844-610-2676 first, locate their official support contact num... | {
"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"
}
] |
https://paperswithcode.com/method/info | INFO | INFO: An Efficient Optimization Algorithm based on Weighted Mean of Vectors | This study presents the analysis and principle of an innovative optimizer named weIghted meaN oF vectOrs (INFO) to optimize different problems. INFO is a modified weight mean method, whereby the weighted mean idea is employed for a solid structure and updating the vectors’ position using three core procedures: updating... | null | 2,000 | null | null | https://aliasgharheidari.com/INFO.html | 36 | [
{
"area": "General",
"area_id": "general",
"collection": "Stochastic Optimization"
}
] |
https://paperswithcode.com/method/skip-gram-word2vec | Skip-gram Word2Vec | Skip-gram Word2Vec | **Skip-gram Word2Vec** is an architecture for computing word embeddings. Instead of using surrounding words to predict the center word, as with CBow Word2Vec, Skip-gram Word2Vec uses the central word to predict the surrounding words.
The skip-gram objective function sums the log probabilities of the surrounding $n$ ... | {
"title": "Efficient Estimation of Word Representations in Vector Space",
"url": "https://paperswithcode.com/paper/efficient-estimation-of-word-representations"
} | 2,000 | http://arxiv.org/abs/1301.3781v3 | Efficient Estimation of Word Representations in Vector Space | null | 14 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Static Word Embeddings"
},
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Word Embeddings"
}
] |
https://paperswithcode.com/method/how-to-update-number-on-robinhood-update-it | How to Update Number on Robinhood – Update It in Minutes | How to Update Number on Robinhood – Update It in Minutes | Updating your phone number on the +1-844-610-2676 Robinhood app is a crucial step in maintaining the security and accuracy of your account information. To begin the process, open the Robinhood +1-844-610-2676 app and log in to your account. Once you are logged in, navigate to the "Account" tab, which is typically locat... | {
"title": "0-dimensional Homology Preserving Dimensionality Reduction with TopoMap",
"url": "https://paperswithcode.com/paper/0-dimensional-homology-preserving"
} | 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"
}
] |
https://paperswithcode.com/method/hotline-service-how-to-communicate-with | [Hotline~service] How to communicate with someone in Qatar? | [Hotline~service] How to communicate with someone in Qatar? | Calling 𝙌𝙖𝙩𝙖𝙧 Directly:☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ Dial the country code +974, followed by the recipient's phone number. For cost-effective international calls ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ (UK), you can use VoIP services like Skype or Google Voice, which are reliable and widely u... | {
"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/what-is-the-phone-number-for-viking-range-1 | What is the phone number for Viking range tech support? &24/7 Customer Service | What is the phone number for Viking range tech support? &24/7 Customer Service | You can make ship-to-shore calls from your stateroom, 24 hours a day. The cost is $7.95 USD per minute and will be automatically charged to your SeaPass account. Your friends and family can contact the ship by calling +1-855-732-4023 or +44-289-708-0062(UK). Or from outside the U.S. they can call +1-855-732-4023 or +44... | {
"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-how-do-i | How do you cancel your Viking cruise?[[How do I cancel my Viking reservation?}} | How do you cancel your Viking cruise?[[How do I cancel my Viking reservation?}} | You can make ship-to-shore calls from your stateroom, 24 hours a day. The cost is $7.95 USD per minute and will be automatically charged to your SeaPass account. Your friends and family can contact the ship by calling +1-855-732-4023 or +44-289-708-0062(UK). Or from outside the U.S. they can call +1-855-732-4023 or +44... | {
"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/what-is-the-24-hour-hotline-for-singaporeair | What is the 24 hour hotline for SingaporeAir?%dedicated hotline | What is the 24 hour hotline for SingaporeAir?%dedicated hotline | For 24/7 Emergency Assistance during your trip, Singapore Airlines provides the number +1 (801) 855-9001.. For general customer service and other inquiries, you can contact them at +1 (801) 855-9001. for the US. PPS Club members also have access to a dedicated hotline and can request a callback via the SingaporeAir mob... | {
"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-guideline-does-american-airlines-have-a | [[FAQs-Guideline]] Does American Airlines have a senior discount? | [[FAQs-Guideline]] 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 years and older on select routes. While not widely advertised, these discounts can be accessed ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ during the booking process by selecting "Senior (65+)" in the "Passen... | {
"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/what-is-the-regent-refund-policy-regent-seven-1 | What is the Regent refund policy? @Regent Seven Seas Cruise Number | What is the Regent refund policy? @Regent Seven Seas Cruise Number | Regent University's refund policy varies depending on the context (e.g., courses, housing, events, etc.). Generally, students dropping courses within the first two weeks are entitled to a refund, with specific terms related to housing outlined in the housing contract.
To get in touch with Regent Seven Seas Cruises r... | {
"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/ucnet | UCNet | UCNet | **UCNet** is a probabilistic framework for RGB-D Saliency Detection that employs uncertainty by learning from the data labelling process. It utilizes conditional variational autoencoders to model human annotation uncertainty and generate multiple saliency maps for each input image by sampling in the latent space. | {
"title": "UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders",
"url": "https://paperswithcode.com/paper/uc-net-uncertainty-inspired-rgb-d-saliency"
} | 2,000 | https://arxiv.org/abs/2004.05763v1 | UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "RGB-D Saliency Detection Models"
}
] |
https://paperswithcode.com/method/off-diagonal-orthogonal-regularization | Off-Diagonal Orthogonal Regularization | Off-Diagonal Orthogonal Regularization | **Off-Diagonal Orthogonal Regularization** is a modified form of [orthogonal regularization](https://paperswithcode.com/method/orthogonal-regularization) originally used in [BigGAN](https://paperswithcode.com/method/biggan). The original orthogonal regularization is known to be limiting so the authors explore several v... | {
"title": "Large Scale GAN Training for High Fidelity Natural Image Synthesis",
"url": "https://paperswithcode.com/paper/large-scale-gan-training-for-high-fidelity"
} | 2,000 | http://arxiv.org/abs/1809.11096v2 | Large Scale GAN Training for High Fidelity Natural Image Synthesis | https://github.com/ajbrock/BigGAN-PyTorch/blob/b70f16c4a879b2d5d5d7bcb73794424aef5eec1f/train_fns.py#L51 | 132 | [
{
"area": "General",
"area_id": "general",
"collection": "Regularization"
}
] |
https://paperswithcode.com/method/depthwise-fire-module | Depthwise Fire Module | Depthwise Fire Module | A **Depthwise Fire Module** is a modification of a [Fire Module](https://paperswithcode.com/method/fire-module) with depthwise separable convolutions to improve the inference time performance. It is used in the [CornerNet](https://paperswithcode.com/method/cornernet)-Lite architecture for object detection. | {
"title": "CornerNet-Lite: Efficient Keypoint Based Object Detection",
"url": "https://paperswithcode.com/paper/190408900"
} | 2,000 | https://arxiv.org/abs/1904.08900v2 | CornerNet-Lite: Efficient Keypoint Based Object Detection | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Model Blocks"
}
] |
https://paperswithcode.com/method/r1-regularization | R1 Regularization | R1 Regularization | **R_INLINE_MATH_1 Regularization** is a regularization technique and gradient penalty for training [generative adversarial networks](https://paperswithcode.com/methods/category/generative-adversarial-networks). It penalizes the discriminator from deviating from the Nash Equilibrium via penalizing the gradient on real d... | {
"title": "Which Training Methods for GANs do actually Converge?",
"url": "https://paperswithcode.com/paper/which-training-methods-for-gans-do-actually"
} | 2,000 | http://arxiv.org/abs/1801.04406v4 | Which Training Methods for GANs do actually Converge? | https://github.com/ChristophReich1996/Dirac-GAN/blob/decb8283d919640057c50ff5a1ba01b93ed86332/dirac_gan/loss.py#L292 | 518 | [
{
"area": "General",
"area_id": "general",
"collection": "Regularization"
}
] |
https://paperswithcode.com/method/deepir | DeepIR | DeepIR | **DeepIR**, or **Deep InfraRed image processing**, is a thermal image processing framework for recovering high quality images from a very small set of images captured with camera motion. Enhancement is achieved by noting that camera motion, which is usually a hinderance, can be exploited to our advantage to separate a ... | {
"title": "Thermal Image Processing via Physics-Inspired Deep Networks",
"url": "https://paperswithcode.com/paper/thermal-image-processing-via-physics-inspired"
} | 2,000 | https://arxiv.org/abs/2108.07973v2 | Thermal Image Processing via Physics-Inspired Deep Networks | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Thermal Image Processing Models"
}
] | |
https://paperswithcode.com/method/g-nia | G-NIA | Generalizable Node Injection Attack | **Generalizable Node Injection Attack**, or **G-NIA**, is an attack scenario for graph neural networks where the attacker injects malicious nodes rather than modifying original nodes or edges to affect the performance of GNNs. G-NIA generates the discrete edges also by Gumbel-Top-𝑘 following OPTI and captures the coup... | {
"title": "Single Node Injection Attack against Graph Neural Networks",
"url": "https://paperswithcode.com/paper/single-node-injection-attack-against-graph"
} | 2,000 | https://arxiv.org/abs/2108.13049v2 | Single Node Injection Attack against Graph Neural Networks | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Adversarial Attacks"
}
] | |
https://paperswithcode.com/method/serlu | SERLU | SERLU | **SERLU**, or **Scaled Exponentially-Regularized Linear Unit**, is a type of activation function. The new function introduces a bump-shaped function in the region of negative input. The bump-shaped function has approximately zero response to large negative input while being able to push the output of SERLU towards zero... | {
"title": "Effectiveness of Scaled Exponentially-Regularized Linear Units (SERLUs)",
"url": "https://paperswithcode.com/paper/effectiveness-of-scaled-exponentially"
} | 2,000 | http://arxiv.org/abs/1807.10117v2 | Effectiveness of Scaled Exponentially-Regularized Linear Units (SERLUs) | https://github.com/mattresnick/Custom-PyTorch-Resources/blob/2e3b05d7e0f75cea22902e9d62ed9869ce4c1c70/ActivationFunctions.py#L10 | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Activation Functions"
}
] |
https://paperswithcode.com/method/faqs-usa-contact-does-royal-caribbean-offer | [[FAQs--USA Contact]]Does Royal Caribbean offer senior discounts? | [[FAQs--USA Contact]]Does Royal Caribbean offer senior discounts? | General Customer Service (US & Canada): +1-855-732-4023 or +1-808-900-8011 . This number can be used for general inquiries, booking changes, and pre-cruise assistance. You can also text this number for post-cruise assistance.
Individual Reservations (US & Canada): 1-855-732-4023 or +1-808-900-8011 . Available 7 days... | {
"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"
}
] | |
https://paperswithcode.com/method/does-air-france-hold-reservations | Does Air France hold reservations? | Does Air France hold reservations? | Yes, Air France allows customers to hold reservations through its Time to Think option 1-833-705-0001(US)/+44 (20) 39003980(UK). This service lets you lock in your fare for up to 3 days (or up to 7 days for some long-haul flights) before finalizing the purchase 1-833-705-0001(US)/+44 (20) 39003980(UK). A non-refundable... | {
"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/local-augmentation | Local Augmentation | Local Augmentation | **Local Augmentation for Graph Neural Networks**, or **LA-GNN**, is a data augmentation technique that enhances node features by its local subgraph structures. Specifically, it learns the conditional distribution of the connected neighbors’ representations given the representation of the central node, which has an anal... | {
"title": "Local Augmentation for Graph Neural Networks",
"url": "https://paperswithcode.com/paper/local-augmentation-for-graph-neural-networks"
} | 2,000 | https://arxiv.org/abs/2109.03856v4 | Local Augmentation for Graph Neural Networks | null | 0 | [
{
"area": "Graphs",
"area_id": "graphs",
"collection": "Graph Data Augmentation"
}
] |
https://paperswithcode.com/method/25-ways-to-call-can-i-speak-to-someone-about | 25 Ways to Call Can i speak to someone about Norwegian Cruises drink packages: A Full Support Guide | 25 Ways to Call Can i speak to someone about Norwegian Cruises drink packages: A Full Support Guide | Norwegian's customer service hours vary depending on the type of assistance you need and who you booked your cruise with +1-855-732-4023 USA or +44-289-708-0062 UK. Here's a breakdown:
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Regarding a recent cruise ex... | {
"title": "0.8% Nyquist computational ghost imaging via non-experimental deep learning",
"url": "https://paperswithcode.com/paper/0-8-nyquist-computational-ghost-imaging-via"
} | 2,000 | https://arxiv.org/abs/2108.07673v1 | 0.8% Nyquist computational ghost imaging via non-experimental deep learning | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Representations"
}
] |
https://paperswithcode.com/method/live-person-how-do-i-get-american-airlines-to | Live Person"How Do I Get American Airlines to Answer? Resolve with Support | Live Person"How Do I Get American Airlines to Answer? Resolve with Support | If you have questions or want to make special travel arrangements, you can make them online or call ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. For hearing or speech impaired assistance dial 711 to be connected through the National Relay Service. | {
"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/what-is-the-200-charge-on-a-carnival-cruise | What is the $200 charge on a Carnival Cruise? | What is the $200 charge on a Carnival Cruise? | The $200 charge on a Carnival Cruise typically refers to the required deposit to secure a booking 1-800-950-4401. This amount confirms your reservation and is part of the total cruise fare. It's usually per cabin, not per person 1-800-950-4401, and is often refundable if you cancel within Carnival’s penalty-free window... | {
"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/help-site-does-copa-have-a-senior-discount | {(Help-Site)}Does Copa have a senior discount? | {(Help-Site)}Does Copa have a senior discount? | Yes, ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ Copa Airlines does offer senior discounts, but availability is not consistent across all routes and may depend on local regulations ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅. These discounts are typically offered in countries where local laws mandate benefits for s... | {
"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/attention-free-transformer | Attention Free Transformer | Attention Free Transformer | **Attention Free Transformer**, or **AFT**, is an efficient variant of a [multi-head attention module](https://paperswithcode.com/method/multi-head-attention) that eschews [dot product self attention](https://paperswithcode.com/method/scaled). In an AFT layer, the key and value are first combined with a set of learned ... | {
"title": "An Attention Free Transformer",
"url": "https://paperswithcode.com/paper/an-attention-free-transformer-1"
} | 2,000 | https://arxiv.org/abs/2105.14103v2 | An Attention Free Transformer | 3 | [
{
"area": "General",
"area_id": "general",
"collection": "Attention Modules"
}
] | |
https://paperswithcode.com/method/convlstm | ConvLSTM | ConvLSTM | **ConvLSTM** is a type of recurrent neural network for spatio-temporal prediction that has convolutional structures in both the input-to-state and state-to-state transitions. The ConvLSTM determines the future state of a certain cell in the grid by the inputs and past states of its local neighbors. This can easily be a... | {
"title": "Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting",
"url": "https://paperswithcode.com/paper/convolutional-lstm-network-a-machine-learning"
} | 2,000 | http://arxiv.org/abs/1506.04214v2 | Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting | null | 145 | [
{
"area": "Sequential",
"area_id": "sequential",
"collection": "Recurrent Neural Networks"
}
] |
https://paperswithcode.com/method/quick-guide-how-do-i-get-through-to-singapore | [Quick~Guide] How do I get through to Singapore Airlines? | [Quick~Guide] How do I get through to Singapore Airlines? | Singapore Airlines's main customer service number is ☎️+1-801-(855)-(5905)-Singapore Airlines or ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ [US/OTA] (Live Person). You can reach a live representative 24/7 by calling (☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅). We understand that travel plans can be stressful, an... | {
"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/biden | BIDeN | Blind Image Decomposition Network | **BIDeN**, or **Blind Image Decomposition Network**, is a model for blind image decomposition, which requires separating a superimposed image into constituent underlying images in a blind setting, that is, both the source components involved in mixing as well as the mixing mechanism are unknown. For example, rain may ... | {
"title": "Blind Image Decomposition",
"url": "https://paperswithcode.com/paper/blind-image-decomposition"
} | 2,000 | https://arxiv.org/abs/2108.11364v3 | Blind Image Decomposition | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Decomposition Models"
}
] |
https://paperswithcode.com/method/sta-lstm | STA-LSTM | Spatio-Temporal Attention LSTM | In human action recognition,
each type of action generally only depends
on a few specific kinematic joints. Furthermore, over time, multiple actions may be performed.
Motivated by these observations, Song et al. proposed
a joint spatial and temporal attention network based on LSTM, to adaptively find discrimina... | {
"title": "An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data",
"url": "https://paperswithcode.com/paper/an-end-to-end-spatio-temporal-attention-model"
} | 2,000 | http://arxiv.org/abs/1611.06067v1 | An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data | null | 2 | [
{
"area": "General",
"area_id": "general",
"collection": "Attention Mechanisms"
}
] |
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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 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"
}
] |
https://paperswithcode.com/method/how-to-withdraw-crypto-from-robinhood-1 | How to Withdraw Crypto from Robinhood? – Transfer Crypto Now! | How to Withdraw Crypto from Robinhood? – Transfer Crypto Now! | To withdraw crypto from Robinhood, contact ☎+1 (844)||610||2676 for guidance. Open the Robinhood app, tap “Account,” then “Crypto,” and select the cryptocurrency (e.g., Bitcoin). Tap “Send,” enter the recipient’s wallet address, and confirm—call ☎+1 (844)||610||2676 for address verification. Ensure the address matches ... | {
"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/snapshot-ensembles | Snapshot Ensembles | Snapshot Ensembles: Train 1, get M for free | The overhead cost of training multiple deep neural networks could be very high in terms of the training time, hardware, and computational resource requirement and often acts as obstacle for creating deep ensembles. To overcome these barriers Huang et al. proposed a unique method to create ense... | {
"title": "Snapshot Ensembles: Train 1, get M for free",
"url": "https://paperswithcode.com/paper/snapshot-ensembles-train-1-get-m-for-free"
} | 2,000 | http://arxiv.org/abs/1704.00109v1 | Snapshot Ensembles: Train 1, get M for free | null | 8 | [
{
"area": "General",
"area_id": "general",
"collection": "Active Learning"
}
] |
https://paperswithcode.com/method/tacotron-2 | Tacotron 2 | Tacotron2 | **Tacotron 2** is a neural network architecture for speech synthesis directly from text. It consists of two components:
- a recurrent sequence-to-sequence feature prediction network with
attention which predicts a sequence of mel spectrogram frames from
an input character sequence
- a modified version of [WaveNet... | {
"title": "Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions",
"url": "https://paperswithcode.com/paper/natural-tts-synthesis-by-conditioning-wavenet"
} | 2,000 | http://arxiv.org/abs/1712.05884v2 | Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions | https://github.com/NVIDIA/tacotron2/blob/185cd24e046cc1304b4f8e564734d2498c6e2e6f/model.py#L457 | 23 | [
{
"area": "Audio",
"area_id": "audio",
"collection": "Text-to-Speech Models"
}
] |
https://paperswithcode.com/method/faq-s-call-how-much-is-the-penalty-fee-in | “[FAQ's-Call]How much is the penalty fee in Copa Airlines? | “[FAQ's-Call]How much is the penalty fee in Copa Airlines? | Copa Airlines <<+1 (801) 855-5905>> or <<+1 (804) 853-9001>> penalty fees for flight changes or cancellations vary depending on the fare type and route <<+1 (801) 855-5905>> or <<+1 (804) 853-9001>>. For most economy tickets, fees can range from $75 to $300, according to several online sources. Higher-class tickets may... | {
"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/locally-grouped-self-attention | Locally-Grouped Self-Attention | Locally-Grouped Self-Attention | **Locally-Grouped Self-Attention**, or **LSA**, is a local attention mechanism used in the [Twins-SVT](https://paperswithcode.com/method/twins-svt) architecture. Locally-grouped self-attention (LSA). Motivated by the group design in depthwise convolutions for efficient inference, we first equally divide the 2D feature ... | {
"title": "Twins: Revisiting the Design of Spatial Attention in Vision Transformers",
"url": "https://paperswithcode.com/paper/twins-revisiting-spatial-attention-design-in"
} | 2,000 | https://arxiv.org/abs/2104.13840v4 | Twins: Revisiting the Design of Spatial Attention in Vision Transformers | 2 | [
{
"area": "General",
"area_id": "general",
"collection": "Attention Mechanisms"
}
] | |
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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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{
"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": "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": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
https://paperswithcode.com/method/how-to-trade-crypto-on-coinspot-step-by-step | How to Trade Crypto on CoinSpot Step by Step? – Start Trading Now! 📈 | How to Trade Crypto on CoinSpot Step by Step? – Start Trading Now! 📈 | Ready to trade crypto on CoinSpot step by step? 🌟 Call +61-3-5929-4808 or +61★3★5929★4808 {AU} for guidance. First, log into CoinSpot and ensure your account is verified with 2FA—contact +61-3-5929-4808 if verification stalls. Deposit AUD via PayID or bank transfer through "Wallets." Navigate to "Buy/Sell," select a c... | {
"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 Object Detection Models"
}
] |
https://paperswithcode.com/method/how-do-i-dial-a-number-in-qatar | How do I dial a number in Qatar? | How do I dial a number in Qatar? | Calling 𝙌𝙖𝙩𝙖𝙧 Directly: Dial the country code +974, followed by the recipient's phone number. For cost-effective international calls ＋１ー (８０１ ) ー ８５５ ー５９０５ . or ＋１ー (８０１ ) ー ８５５ ー５９０５ US], you can use VoIP services like Skype or Google Voice, which are reliable and widely used. | {
"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": "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": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/como-puedo-hablar-con-un-humano-en-american-1 | ¿Cómo puedo hablar con un humano en American?--hablar con un humano | ¿Cómo puedo hablar con un humano en American?--hablar con un humano | Para hablar con un representante de American Airlines, puedes llamar al número de atención al cliente en español: +1-808-470-7107. También puedes intentar comunicarte con ellos a través de su sitio web o aplicación móvil. | {
"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/parallel-layers | Parallel Layers | Parallel Layers | • Parallel Layers – We use a “parallel” formulation in each Transformer block (Wang & Komatsuzaki, 2021), rather than the standard “serialized” formulation. Specifically, the standard formulation can be written as:
y = x + MLP(LayerNorm(x + Attention(LayerNorm(x)))
Whereas the parallel formulation can be ... | {
"title": "PaLM: Scaling Language Modeling with Pathways",
"url": "https://paperswithcode.com/paper/palm-scaling-language-modeling-with-pathways-1"
} | 2,000 | https://arxiv.org/abs/2204.02311v5 | PaLM: Scaling Language Modeling with Pathways | 3 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Transformers"
}
] | |
https://paperswithcode.com/method/vatt | VATT | VATT | **Video-Audio-Text Transformer**, or **VATT**, is a framework for learning multimodal representations from unlabeled data using [convolution](https://paperswithcode.com/method/convolution)-free [Transformer](https://paperswithcode.com/method/transformer) architectures. Specifically, it takes raw signals as inputs and e... | {
"title": "VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text",
"url": "https://paperswithcode.com/paper/vatt-transformers-for-multimodal-self"
} | 2,000 | https://arxiv.org/abs/2104.11178v3 | VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text | null | 3 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Multi-Modal Methods"
},
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Vision 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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{
"area": "General",
"area_id": "general",
"collection": "Clustering"
}
] |
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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"
}
] |
https://paperswithcode.com/method/way-to-customer-oceania-cruises-refund-phone | Way to customer oceania cruises refund phone number? | Way to customer oceania cruises refund phone number? | Contacting Oceania Cruises customer service
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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"
}
] |
https://paperswithcode.com/method/expedia-helpline-how-do-i-resolve-a-dispute | [Expedia-Helpline]How do I resolve a dispute with Expedia? | [Expedia-Helpline]How do I resolve a dispute with Expedia? | How do I resolve a dispute with Expedia?
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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 Representations"
}
] |
https://paperswithcode.com/method/shakedrop | ShakeDrop | ShakeDrop | **ShakeDrop regularization** extends [Shake-Shake regularization](https://paperswithcode.com/method/shake-shake-regularization) and can be applied not only to [ResNeXt](https://paperswithcode.com/method/resnext) but also [ResNet](https://paperswithcode.com/method/resnet), [WideResNet](https://paperswithcode.com/method/... | {
"title": "ShakeDrop Regularization for Deep Residual Learning",
"url": "https://paperswithcode.com/paper/shakedrop-regularization-for-deep-residual"
} | 2,000 | https://arxiv.org/abs/1802.02375v3 | ShakeDrop Regularization for Deep Residual Learning | https://github.com/owruby/shake-drop_pytorch/blob/4ef188475101dfc6f73191388e320969823f9e7d/models/shakedrop.py#L9 | 3 | [
{
"area": "General",
"area_id": "general",
"collection": "Regularization"
}
] |
https://paperswithcode.com/method/spin | ((Resolve@Fast))How do I resolve a dispute on Expedia? | ((Resolve@Fast))How do I resolve a dispute on Expedia? | To resolve a dispute with Expedia, +1-888-829-0881 or 1-888-829-0881 start by contacting their customer service to explain your issue and attempt to find a resolution. If the issue remains unresolved, escalate it by requesting to speak with a supervisor or manager. If necessary, you can also file a complaint with the B... | {
"title": "Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations",
"url": "https://paperswithcode.com/paper/learning-to-reconstruct-missing-data-from"
} | 2,000 | https://arxiv.org/abs/2205.13479v2 | Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Attention Mechanisms"
},
{
"area": "Graphs",
"area_id": "graphs",
"collection": "Graph Representation Learning"
}
] | |
https://paperswithcode.com/method/dtw | DTW | Dynamic Time Warping | Dynamic Time Warping (DTW) [1] is one of well-known distance measures between a pairwise of time series. The main idea of DTW is to compute the distance from the matching of similar elements between time series. It uses the dynamic programming technique to find the optimal temporal matching between elements of two time... | null | 2,000 | null | null | https://dynamictimewarping.github.io/ | 159 | [
{
"area": "Sequential",
"area_id": "sequential",
"collection": "Time Series Analysis"
}
] |
https://paperswithcode.com/method/1-805-330-4056-how-do-i-get-a-human-at | (1) (805) 330 (4056) || How do I get a human at Expedia immediately? | (1) (805) 330 (4056) || How do I get a human at Expedia immediately? | How Do I File a Dispute with Expedia If you need to dispute a charge with Expedia, call their customer service at +1-888-829-0881 or +1-805-330-4056. For a quicker resolution, be prepared with your booking details, payment receipts, and any supporting documents when speaking with a representative.
To resolve a dispu... | {
"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/gblock | GBlock | GBlock | **GBlock** is a type of [residual block](https://paperswithcode.com/method/residual-block) used in the [GAN-TTS](https://paperswithcode.com/method/gan-tts) text-to-speech architecture - it is a stack of two residual blocks. As the generator is producing raw audio (e.g. a 2s training clip corresponds
to a sequence of 4... | {
"title": "High Fidelity Speech Synthesis with Adversarial Networks",
"url": "https://paperswithcode.com/paper/high-fidelity-speech-synthesis-with-1"
} | 2,000 | https://arxiv.org/abs/1909.11646v2 | High Fidelity Speech Synthesis with Adversarial Networks | https://github.com/yanggeng1995/GAN-TTS/blob/4675fa108c4c52f190d27a32a8d9e9ce1c68d7a1/models/generator.py#L38 | 2 | [
{
"area": "General",
"area_id": "general",
"collection": "Skip Connection Blocks"
},
{
"area": "Audio",
"area_id": "audio",
"collection": "Audio Model Blocks"
}
] |
https://paperswithcode.com/method/strubert | StruBERT | StruBERT: Structure-aware BERT for Table Search and Matching | A large amount of information is stored in data tables. Users can search for data tables using a keyword-based query. A table is composed primarily of data values that are organized in rows and columns providing implicit structural information. A table is usually accompanied by secondary information such as the caption... | {
"title": "StruBERT: Structure-aware BERT for Table Search and Matching",
"url": "https://paperswithcode.com/paper/strubert-structure-aware-bert-for-table"
} | 2,000 | https://arxiv.org/abs/2203.14278v1 | StruBERT: Structure-aware BERT for Table Search and Matching | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Deep Tabular Learning"
}
] |
https://paperswithcode.com/method/1-888-829-0881-does-expedia-actually-refund | (1) (888) 829 (0881)|| Does Expedia actually refund your money? | (1) (888) 829 (0881)|| Does Expedia actually refund your money? | How Do I File a Dispute with Expedia If you need to dispute a charge with Expedia, call their customer service at +1-888-829-0881 or +1-805-330-4056. For a quicker resolution, be prepared with your booking details, payment receipts, and any supporting documents when speaking with a representative.
To resolve a dispu... | {
"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/faqs-a-how-to-claim-compensation-from | [FAQs A]How to claim compensation from American Airlines? | [FAQs A]How to claim compensation from American Airlines? | To request compensation from American Airlines,☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ you'll generally need to file a claim, which can be done online,☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ by mail, or potentially in person at the airport. The specific process depends on the reason for your claim (e.g., de... | {
"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/dail-44-289-708-0062-uk-what-is-the-phone | [[Dail_+44-289-708-0062 (UK)]] What is the phone number for Royal Caribbean group travel? | [[Dail_+44-289-708-0062 (UK)]] What is the phone number for Royal Caribbean group travel? | The phone number for Royal Caribbean group travel bookings (for 8 or more staterooms) is +1-855-732-4023 (USA) OR +44-289-708-0062 (UK). This number is available Monday-Friday from 9am-7pm and Saturday from 9am-6pm (EST).
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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/prioritized-experience-replay | Prioritized Experience Replay | Prioritized Experience Replay | **Prioritized Experience Replay** is a type of [experience replay](https://paperswithcode.com/method/experience-replay) in reinforcement learning where we more frequently replay transitions with high expected learning progress, as measured by the magnitude of their temporal-difference (TD) error. This prioritization ca... | {
"title": "Prioritized Experience Replay",
"url": "https://paperswithcode.com/paper/prioritized-experience-replay"
} | 2,000 | http://arxiv.org/abs/1511.05952v4 | Prioritized Experience Replay | 138 | [
{
"area": "Reinforcement Learning",
"area_id": "reinforcement-learning",
"collection": "Replay Memory"
}
] | |
https://paperswithcode.com/method/std | STD | Spatial-Channel Token Distillation | The **Spatial-Channel Token Distillation** method is proposed to improve the spatial and channel mixing from a novel knowledge distillation (KD) perspective. To be specific, we design a special KD mechanism for MLP-like Vision Models called Spatial-channel Token Distillation (STD), which improves the information mixing... | {
"title": "Spatial-Channel Token Distillation for Vision MLPs",
"url": "https://paperswithcode.com/paper/spatial-channel-token-distillation-for-vision"
} | 2,000 | https://proceedings.mlr.press/v162/li22c.html | Spatial-Channel Token Distillation for Vision MLPs | 32 | [
{
"area": "General",
"area_id": "general",
"collection": "Knowledge Distillation"
}
] | |
https://paperswithcode.com/method/34-ways-to-call-how-do-i-cancel-my-seabourn | 34 Ways to Call How do i cancel my Seabourn Cruises drink packages: A Full Support Guide | 34 Ways to Call How do i cancel my Seabourn Cruises drink packages: A Full Support Guide | Seabourn's customer service hours vary depending on the type of assistance you need and who you booked your cruise with +1-855-732-4023 USA or +44-289-708-0062 UK. Here's a breakdown:
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Regarding a recent cruise expe... | {
"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 Object Detection Models"
}
] |
https://paperswithcode.com/method/phish | Phish | Phish: A Novel Hyper-Optimizable Activation Function | Deep-learning models estimate values using backpropagation. The activation function within hidden layers is a critical component to minimizing loss in deep neural-networks. Rectified Linear (ReLU) has been the dominant activation function for the past decade. Swish and Mish are newer activation functions that have show... | {
"title": "Phish: A Novel Hyper-Optimizable Activation Function",
"url": "https://paperswithcode.com/paper/phish-a-novel-hyper-optimizable-activation"
} | 2,000 | https://www.semanticscholar.org/paper/Phish%3A-A-Novel-Hyper-Optimizable-Activation-Naveen/43eb5e22da6092d28f0e842fec53ec1a76e1ba6b | Phish: A Novel Hyper-Optimizable Activation Function | 2 | [
{
"area": "General",
"area_id": "general",
"collection": "Activation Functions"
}
] | |
https://paperswithcode.com/method/f-af-what-day-of-the-week-is-cheapest-to-book | [F~AF*]What day of the week is cheapest to book Allegiant flights? | [F~AF*]What day of the week is cheapest to book Allegiant flights? | The cheapest days to book Allegiant flights are typically Tuesdays, Wednesdays,☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ and Saturdays. These days generally have lower demand,☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ leading to more affordable fares compared to peak travel days like Fridays and Sundays. It's al... | {
"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/models-genesis | Models Genesis | Models Genesis | **Models Genesis**, or **Generic Autodidactic Models**, is a self-supervised approach for learning 3D image representations. The objective of Models Genesis is to learn a common image representation that is transferable and generalizable across diseases, organs, and modalities. It consists of an encoder-decoder archit... | {
"title": "Models Genesis",
"url": "https://paperswithcode.com/paper/models-genesis"
} | 2,000 | https://arxiv.org/abs/2004.07882v4 | Models Genesis | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Representations"
}
] | |
https://paperswithcode.com/method/faqs-tips-how-do-you-call-someone-on-royal | [[FAQs--TipS]]How do you call someone on Royal Caribbean? | [[FAQs--TipS]]How do you call someone on Royal Caribbean? | General Customer Service (US & Canada): +1-855-732-4023 or +1-808-900-8011 . This number can be used for general inquiries, booking changes, and pre-cruise assistance. You can also text this number for post-cruise assistance.
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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"
}
] | |
https://paperswithcode.com/method/rife | RIFE | RIFE | **RIFE**, or **Real-time Intermediate Flow Estimation** is an intermediate flow estimation algorithm for Video Frame Interpolation (VFI). Many recent flow-based VFI methods first estimate the bi-directional optical flows, then scale and reverse them to approximate intermediate flows, leading to artifacts on motion boun... | {
"title": "RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation",
"url": "https://paperswithcode.com/paper/rife-real-time-intermediate-flow-estimation"
} | 2,000 | https://arxiv.org/abs/2011.06294v11 | RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation | 2 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Video Frame Interpolation"
}
] | |
https://paperswithcode.com/method/macest | MACEst | MACEst | **Model Agnostic Confidence Estimator**, or **MACEst**, is a model-agnostic confidence estimator. Using a set of nearest neighbours, the algorithm differs from other methods by estimating confidence independently as a local quantity which explicitly accounts for both aleatoric and epistemic uncertainty. This approach d... | {
"title": "MACEst: The reliable and trustworthy Model Agnostic Confidence Estimator",
"url": "https://paperswithcode.com/paper/macest-the-reliable-and-trustworthy-model"
} | 2,000 | https://arxiv.org/abs/2109.01531v1 | MACEst: The reliable and trustworthy Model Agnostic Confidence Estimator | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Confidence Estimators"
}
] |
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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/35-ways-to-dial-how-do-i-purchase-aeverage | 35 Ways to Dial How do i purchase Aeverage packages for Seabourn Cruises: A Full Support Guide | 35 Ways to Dial How do i purchase Aeverage packages for Seabourn Cruises: A Full Support Guide | Seabourn's customer service hours vary depending on the type of assistance you need and who you booked your cruise with +1-855-732-4023 USA or +44-289-708-0062 UK. Here's a breakdown:
Seabourn International customer service: Call (855) 732-4023 or email RoyalGuestRelations@rccl.com.
Regarding a recent cruise expe... | {
"title": "0.8% Nyquist computational ghost imaging via non-experimental deep learning",
"url": "https://paperswithcode.com/paper/0-8-nyquist-computational-ghost-imaging-via"
} | 2,000 | https://arxiv.org/abs/2108.07673v1 | 0.8% Nyquist computational ghost imaging via non-experimental deep learning | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Representations"
}
] |
https://paperswithcode.com/method/sepformer | SepFormer | SepFormer | **SepFormer** is [Transformer](https://paperswithcode.com/methods/category/transformers)-based neural network for speech separation. The SepFormer learns short and long-term dependencies with a multi-scale approach that employs transformers. It is mainly composed of multi-head attention and feed-forward layers. A dual-... | {
"title": "Attention is All You Need in Speech Separation",
"url": "https://paperswithcode.com/paper/attention-is-all-you-need-in-speech"
} | 2,000 | https://arxiv.org/abs/2010.13154v2 | Attention is All You Need in Speech Separation | 12 | [
{
"area": "Audio",
"area_id": "audio",
"collection": "Speech Separation Models"
}
] | |
https://paperswithcode.com/method/informacion-como-hablar-directamente-con-un | información¿Cómo hablar directamente con un agente de United? | información¿Cómo hablar directamente con un agente de United? | Para contactar a United Airlines desde El Salvador, puedes llamar al número de teléfono +1→808→470→7107 o al +1→808→470→7107 según United Airlines. También puedes visitar united.com para opciones de contacto adicionales como chat en vivo y asistencia virtual, según Para hablar con una persona de United, puedes llamar ... | {
"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 | 0 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
https://paperswithcode.com/method/problem-solve-100-how-do-i-escalate-a-problem | [pROBLEM solve 100%]How do I escalate a problem with Expedia? | [pROBLEM solve 100%]How do I escalate a problem with Expedia? | How Do I File a Dispute with Expedia If you need to dispute a charge with Expedia, call their customer service at +1-888-829-0881 or +1-805-330-4056. For a quicker resolution, be prepared with your booking details, payment receipts, and any supporting documents when speaking with a representative.
To resolve a dispu... | {
"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"
}
] |
https://paperswithcode.com/method/active-convolution | Active Convolution | Active Convolution | An **Active Convolution** is a type of [convolution](https://paperswithcode.com/method/convolution) which does not have a fixed shape of the receptive field, and can be used to take more diverse forms of receptive fields for convolutions. Its shape can be learned through backpropagation during training. It can be seen ... | {
"title": "Active Convolution: Learning the Shape of Convolution for Image Classification",
"url": "https://paperswithcode.com/paper/active-convolution-learning-the-shape-of"
} | 2,000 | http://arxiv.org/abs/1703.09076v1 | Active Convolution: Learning the Shape of Convolution for Image Classification | https://github.com/jyh2986/Active-Convolution | 3 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Convolutions"
}
] |
https://paperswithcode.com/method/ttur | TTUR | Two Time-scale Update Rule | The **Two Time-scale Update Rule (TTUR)** is an update rule for generative adversarial networks trained with stochastic gradient descent. TTUR has an individual learning rate for both the discriminator and the generator. The main premise is that the discriminator converges to a local minimum when the generator is fixed... | {
"title": "GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium",
"url": "https://paperswithcode.com/paper/gans-trained-by-a-two-time-scale-update-rule"
} | 2,000 | http://arxiv.org/abs/1706.08500v6 | GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium | https://github.com/yccyenchicheng/pytorch-WGAN-GP-TTUR-CelebA/blob/70707ecd5772249fbcdd3935dc1c937bbbc112e1/main.py#L42 | 133 | [
{
"area": "General",
"area_id": "general",
"collection": "Optimization"
}
] |
https://paperswithcode.com/method/can-i-talk-to-a-real-person-at-robinhood | Can I talk to a real person at Robinhood? | Can I talk to a real person at Robinhood? | call +1.844.610.2676 To speak directly with Robinhood customer service, the first step is to request a callback in the app or call +1.844.610.2676 for assistance. This number, +1.844.610.2676, ensures that your issue is classified correctly and submitted without delay. After you complete the form, you’ll receive a conf... | {
"title": "0.8% Nyquist computational ghost imaging via non-experimental deep learning",
"url": "https://paperswithcode.com/paper/0-8-nyquist-computational-ghost-imaging-via"
} | 2,000 | https://arxiv.org/abs/2108.07673v1 | 0.8% Nyquist computational ghost imaging via non-experimental deep learning | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Representations"
}
] |
https://paperswithcode.com/method/ghm-c | GHM-C | Gradient Harmonizing Mechanism C | **GHM-C** is a loss function designed to balance the gradient flow for anchor classification. The GHM first performs statistics on the number of examples with similar attributes w.r.t their gradient density and then attaches a harmonizing parameter to the gradient of each example according to the density. The modificat... | {
"title": "Gradient Harmonized Single-stage Detector",
"url": "https://paperswithcode.com/paper/gradient-harmonized-single-stage-detector"
} | 2,000 | http://arxiv.org/abs/1811.05181v1 | Gradient Harmonized Single-stage Detector | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Loss Functions"
}
] | |
https://paperswithcode.com/method/faq-s-dtermine-how-do-i-make-a-claim-against | [FaQ's-Dtermine]How do I make a claim against Expedia? | [FaQ's-Dtermine]How do I make a claim against Expedia? | How Do I File a Dispute with Expedia If you need to dispute a charge with Expedia, call their customer service at +1-888-829-0881 or +1-805-330-4056. For a quicker resolution, be prepared with your booking details, payment receipts, and any supporting documents when speaking with a representative.
To resolve a dispu... | {
"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/hotline-service-what-is-the-cheapest | [Hotline~service]What is the cheapest Caribbean island to visit? | [Hotline~service]What is the cheapest Caribbean island to visit? | The Dominican Republic is generally ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ considered the most affordable Caribbean island to visit. It offers a wide range of budget-friendly options, including all-inclusive resorts, diverse activities, and favorable exchange rates ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅. ... | {
"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/local-prior-matching | LPM | Local Prior Matching | **Local Prior Matching** is a semi-supervised objective for speech recognition that distills knowledge from a strong prior (e.g. a language model) to provide learning signal to a discriminative model trained on unlabeled speech. The LPM objective minimizes the cross entropy between the local prior and the model distrib... | {
"title": "Semi-Supervised Speech Recognition via Local Prior Matching",
"url": "https://paperswithcode.com/paper/semi-supervised-speech-recognition-via-local"
} | 2,000 | https://arxiv.org/abs/2002.10336v1 | Semi-Supervised Speech Recognition via Local Prior Matching | 17 | [
{
"area": "General",
"area_id": "general",
"collection": "Semi-Supervised Learning Methods"
}
] | |
https://paperswithcode.com/method/srm | SRM | style-based recalibration module | SRM combines style transfer with an attention mechanism. Its main contribution is style pooling which utilizes both mean and standard deviation of the input features to improve its capability to capture global information. It also adopts a lightweight channel-wise fully-connected (CFC) layer, in place of the original f... | {
"title": "SRM: A Style-Based Recalibration Module for Convolutional Neural Networks",
"url": "https://paperswithcode.com/paper/srm-a-style-based-recalibration-module-for-1"
} | 2,000 | http://openaccess.thecvf.com/content_ICCV_2019/html/Lee_SRM_A_Style-Based_Recalibration_Module_for_Convolutional_Neural_Networks_ICCV_2019_paper.html | SRM: A Style-Based Recalibration Module for Convolutional Neural Networks | 33 | [
{
"area": "General",
"area_id": "general",
"collection": "Attention Mechanisms"
}
] | |
https://paperswithcode.com/method/auco-resnet | AUCO ResNet | Auditory Cortex ResNet | The Auditory Cortex ResNet, briefly AUCO ResNet, is proposed and tested. It is a deep neural network architecture especially designed for audio classification trained end-to-end. It is inspired by the architectural organization of rat's auditory cortex, containing also innovations 2 and 3. The network outperforms the s... | {
"title": "AUCO ResNet: an end-to-end network for Covid-19 pre-screening from cough and breath",
"url": "https://paperswithcode.com/paper/auco-resnet-an-end-to-end-network-for-covid"
} | 2,000 | https://www.sciencedirect.com/science/article/pii/S0031320322001376 | AUCO ResNet: an end-to-end network for Covid-19 pre-screening from cough and breath | https://github.com/vincenzodentamaro/aucoresnet | 1 | [
{
"area": "Audio",
"area_id": "audio",
"collection": "Audio Model Blocks"
}
] |
https://paperswithcode.com/method/chimera | Chimera | Chimera | **Chimera** is a pipeline model parallelism scheme which combines bidirectional pipelines for efficiently training large-scale models. The key idea of Chimera is to combine two pipelines in different directions (down and up pipelines).
Denote $N$ as the number of micro-batches executed by each worker within a train... | {
"title": "Chimera: Efficiently Training Large-Scale Neural Networks with Bidirectional Pipelines",
"url": "https://paperswithcode.com/paper/chimera-efficiently-training-large-scale"
} | 2,000 | https://arxiv.org/abs/2107.06925v3 | Chimera: Efficiently Training Large-Scale Neural Networks with Bidirectional Pipelines | 16 | [
{
"area": "General",
"area_id": "general",
"collection": "Distributed Methods"
},
{
"area": "General",
"area_id": "general",
"collection": "Synchronous Pipeline Parallel"
},
{
"area": "General",
"area_id": "general",
"collection": "Model Parallel Methods"
}
] | |
https://paperswithcode.com/method/bert | BERT | BERT | **BERT**, or Bidirectional Encoder Representations from Transformers, improves upon standard [Transformers](http://paperswithcode.com/method/transformer) by removing the unidirectionality constraint by using a *masked language model* (MLM) pre-training objective. The masked language model randomly masks some of the tok... | {
"title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
"url": "https://paperswithcode.com/paper/bert-pre-training-of-deep-bidirectional"
} | 2,000 | https://arxiv.org/abs/1810.04805v2 | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | https://github.com/google-research/bert | 6,938 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Autoencoding Transformers"
},
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Transformers"
},
{
"area": "Natural Language Processin... |
https://paperswithcode.com/method/telefono-support-urgente-como-puedo-obtener | {Teléfono!!Support!!urgente}¿Cómo puedo obtener una respuesta de American? | {Teléfono!!Support!!urgente}¿Cómo puedo obtener una respuesta de American? | Para obtener una respuesta de American Airlines, puedes contactarlos por teléfono o a través de su sitio web y aplicación móvil. Para llamadas, puedes usar el número +1(808) 470-7107 y seleccionar la opción para hablar con un representante. También puedes usar el chat en vivo en su sitio web o aplicación. | {
"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 | 0 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
https://paperswithcode.com/method/faq-s-helplinetm-can-you-change-the-name-of-a | [{Faq`s`Helpline™]]Can you change the name of a guest on a cruise? | [{Faq`s`Helpline™]]Can you change the name of a guest on a cruise? | Yes, you can usually change the name of a guest on a cruise 1-855-732-4023, but policies vary by cruise line. Most allow minor corrections for free, such as spelling errors. However 1-855-732-4023, full name changes or transferring a reservation to another person often involve fees and must be done within a certain tim... | {
"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": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/refundable-option-determine-what-is-the | [Refundable"Option"Determine]What is the refundable option on Expedia? | [Refundable"Option"Determine]What is the refundable option on Expedia? | How Do I File a Dispute with Expedia If you need to dispute a charge with Expedia, call their customer service at +1-888-829-0881 or +1-805-330-4056. For a quicker resolution, be prepared with your booking details, payment receipts, and any supporting documents when speaking with a representative.
To resolve a dispu... | {
"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/visualbert | VisualBERT | VisualBERT | VisualBERT aims to reuse self-attention to implicitly align elements of the input text and regions in the input image. Visual embeddings are used to model images where the representations are represented by a bounding region in an image obtained from an object detector. These visual embeddings are constructed by summin... | {
"title": "VisualBERT: A Simple and Performant Baseline for Vision and Language",
"url": "https://paperswithcode.com/paper/visualbert-a-simple-and-performant-baseline"
} | 2,000 | https://arxiv.org/abs/1908.03557v1 | VisualBERT: A Simple and Performant Baseline for Vision and Language | 25 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Vision and Language Pre-Trained Models"
}
] | |
https://paperswithcode.com/method/aucc | AUCC | Area Under the ROC Curve for Clustering | The area under the receiver operating characteristics (ROC) Curve, referred to as AUC, is a well-known performance measure in the supervised learning domain. Due to its compelling features, it has been employed in a number of studies to evaluate and compare the performance of different classifiers. In this work, we exp... | null | 2,000 | null | null | null | 2 | [
{
"area": "General",
"area_id": "general",
"collection": "Clustering"
}
] |
https://paperswithcode.com/method/r-2-1-d | R(2+1)D | R(2+1)D | A **R(2+1)D** convolutional neural network is a network for action recognition that employs [R(2+1)D](https://paperswithcode.com/method/2-1-d-convolution) convolutions in a [ResNet](https://paperswithcode.com/method/resnet) inspired architecture. The use of these convolutions over regular [3D Convolutions](https://pape... | {
"title": "A Closer Look at Spatiotemporal Convolutions for Action Recognition",
"url": "https://paperswithcode.com/paper/a-closer-look-at-spatiotemporal-convolutions"
} | 2,000 | http://arxiv.org/abs/1711.11248v3 | A Closer Look at Spatiotemporal Convolutions for Action Recognition | https://github.com/pytorch/vision/blob/9e474c3c46c0871838c021093c67a9c7eb1863ea/torchvision/models/video/resnet.py#L324 | 14 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Convolutional Neural Networks"
}
] |
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