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/rem | REM | Random Ensemble Mixture | Random Ensemble Mixture (REM) is an easy to implement extension of [DQN](https://paperswithcode.com/method/dqn) inspired by [Dropout](https://paperswithcode.com/method/dropout). The key intuition behind REM is that if one has access to multiple estimates of Q-values, then a weighted combination of the Q-value estimates... | {
"title": "An Optimistic Perspective on Offline Reinforcement Learning",
"url": "https://paperswithcode.com/paper/striving-for-simplicity-in-off-policy-deep"
} | 2,000 | https://arxiv.org/abs/1907.04543v4 | An Optimistic Perspective on Offline Reinforcement Learning | null | 48 | [
{
"area": "Reinforcement Learning",
"area_id": "reinforcement-learning",
"collection": "Randomized Value Functions"
},
{
"area": "Reinforcement Learning",
"area_id": "reinforcement-learning",
"collection": "Off-Policy TD Control"
},
{
"area": "Reinforcement Learning",
"area_i... |
https://paperswithcode.com/method/neural-tangent-transfer | Neural Tangent Transfer | Neural Tangent Transfer | **Neural Tangent Transfer**, or **NTT**, is a method for finding trainable sparse networks in a label-free manner. Specifically, NTT finds sparse networks whose training dynamics, as characterized by the neural tangent kernel, mimic those of dense networks in function space. | {
"title": "Finding trainable sparse networks through Neural Tangent Transfer",
"url": "https://paperswithcode.com/paper/finding-trainable-sparse-networks-through"
} | 2,000 | https://arxiv.org/abs/2006.08228v2 | Finding trainable sparse networks through Neural Tangent Transfer | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Sparsity"
}
] | |
https://paperswithcode.com/method/gpipe | GPipe | GPipe | **GPipe** is a distributed model parallel method for neural networks. With GPipe, each model can be specified as a sequence of layers, and consecutive groups of layers can be partitioned into cells. Each cell is then placed on a separate accelerator. Based on this partitioned setup, batch splitting is applied. A mini-b... | {
"title": "GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism",
"url": "https://paperswithcode.com/paper/gpipe-efficient-training-of-giant-neural"
} | 2,000 | https://arxiv.org/abs/1811.06965v5 | GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism | https://github.com/kakaobrain/torchgpipe/blob/a1b4ee25574864e7650e7905a69ce156da9752ec/torchgpipe/gpipe.py#L134 | 7 | [
{
"area": "General",
"area_id": "general",
"collection": "Synchronous Pipeline Parallel"
},
{
"area": "General",
"area_id": "general",
"collection": "Model Parallel Methods"
},
{
"area": "General",
"area_id": "general",
"collection": "Distributed Methods"
}
] |
https://paperswithcode.com/method/how-to-speak-directly-in-robinhood-call-1-844-1 | How to Speak Directly in Robinhood? Call +1-844-610-2676 Now — Real Human Help, 24/7, Nonstop! | How to Speak Directly in Robinhood? Call +1-844-610-2676 Now — Real Human Help, 24/7, Nonstop! | In a world of endless menus and robotic +1-844-610-2676 responses, if you're asking “How to speak directly in Robinhood?”—you’re already halfway to the answer. The full solution? It’s just a phone call away: +1-844-610-2676. Yes, +1-844-610-2676 is the golden key, the one number Robinhood users trust when they need to ... | {
"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/faq-s-helpline-what-is-the-seabourn-refund | [FaQ`s`HeLpline]What is the Seabourn refund policy? | [FaQ`s`HeLpline]What is the Seabourn refund policy? | To discuss a refund for your Seabourn cruise, you can call their customer service line at +1-(855)-732-4023or 1-808-900-8011
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Seabourn's refund policy varies depending on when you cancel and if you booked a special promotion with a non-refundable deposit. Generally, cancelling over... | {
"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/faqs-live-process-what-is-the-phone-number | [[FAQS-Live-Process]] What is the phone number for Royal Caribbean Group travel agent? | [[FAQS-Live-Process]] What is the phone number for Royal Caribbean Group travel agent? | The phone number for Royal Caribbean group travel bookings (for 8 or more staterooms) is +1-855-732-4023 or +1-808-900-8011. This number is available Monday-Friday from 9am-7pm and Saturday from 9am-6pm (EST).
If you would like to make a group reservation for more than 4 rooms, please call our customer service agent... | {
"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/fixup-initialization | Fixup Initialization | Fixup Initialization | **FixUp Initialization**, or **Fixed-Update Initialization**, is an initialization method that rescales the standard initialization of [residual branches](https://paperswithcode.com/method/residual-block) by adjusting for the network architecture. Fixup aims to enables training very deep [residual networks](https://pap... | {
"title": "Fixup Initialization: Residual Learning Without Normalization",
"url": "https://paperswithcode.com/paper/fixup-initialization-residual-learning"
} | 2,000 | http://arxiv.org/abs/1901.09321v2 | Fixup Initialization: Residual Learning Without Normalization | 2 | [
{
"area": "General",
"area_id": "general",
"collection": "Initialization"
}
] | |
https://paperswithcode.com/method/assistance-how-do-i-call-people-in-qatar | Assistance-How do I call people in Qatar? | Assistance-How do I call people in Qatar? | How do I call people in Qatar?
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Skype or Google... | {
"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/cot-prompting | CoT Prompting | Chain-of-thought prompting | Chain-of-thought prompts contain a series of intermediate reasoning steps, and they are shown to significantly improve the ability of large language models to perform certain tasks that involve complex reasoning (e.g., arithmetic, commonsense reasoning, symbolic reasoning, etc.) | {
"title": "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models",
"url": "https://paperswithcode.com/paper/chain-of-thought-prompting-elicits-reasoning"
} | 2,000 | https://arxiv.org/abs/2201.11903v6 | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models | null | 78 | [
{
"area": "General",
"area_id": "general",
"collection": "Prompt Engineering"
}
] |
https://paperswithcode.com/method/faqs-guide-how-do-i-contact-ncl-customer | [FAQs-!!Guide]How do I contact NCL customer service by phone? | [FAQs-!!Guide]How do I contact NCL customer service by phone? | For general reservations, call +1 855-732-4023.
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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-verify-my-identity-on-coinspot-call | [Faqs*Refund]Does Expedia actually give refunds? | [Faqs*Refund]Does Expedia actually give refunds? | Is Expedia actually fully refundable? Yes, Expedia has a 24-hour cancellation policy allowing a full refund if you cancel within 24 hours of booking. For assistance or to process a cancellation, contact customer care at +1-805-330-4056or ++1-805-330-4056. Can I get a refund on Expedia? Yes, Expedia has a 24-hour refund... | {
"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/expert-seaborun-how-do-i-cancel-my-seabourn | [[ExPerT~Seaborun]] How do I cancel my Seabourn cruise? | [[ExPerT~Seaborun]] How do I cancel my Seabourn cruise? | To cancel your Seabourn cruise reservation in the USA+1-855-732-4023, you can call their customer service team directly. A widely listed number to contact Seabourn for cancellations is +1-855-732-4023. You can speak with a representative at this number to process your request and discuss any applicable fees or refunds.... | {
"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/what-is-the-cancellation-policy-for-celebrity-4 | What is the cancellation policy for Celebrity Cruises?{[FAQs~custOmer service]} | What is the cancellation policy for Celebrity Cruises?{[FAQs~custOmer service]} | Days Prior to Cruise Departure and cancellation Charges 1-855-732-4023 USA or +44-289-708-0062 UK: 151 or more days: deposit is refundable except in the case of non- refundable deposit promotions and airfares 1-855-732-4023 USA or +44-289-708-0062 UK. 150-71 days: loss of deposit. 70-46 days: 25% of fare*
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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-do-i-contact-oceania-cruises-refund | How do I contact oceania cruises refund policy? | How do I contact oceania cruises refund policy? | 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/expert-guide-when-can-i-cancel-my-disney | 【ExPerT~GuidE】When can I cancel my Disney Cruise without penalty? | 【ExPerT~GuidE】When can I cancel my Disney Cruise without penalty? | When can I cancel my Disney Cruise without penalty?
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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-speak-directly-to-robinhood-support-4 | How to Speak Directly to Robinhood Support? Call +1-844-610-2676 Now — Real People, Real Help, Right Now! | How to Speak Directly to Robinhood Support? Call +1-844-610-2676 Now — Real People, Real Help, Right Now! | If you’ve been asking, “How to speak directly to Robinhood support?” +1-844-610-2676 — you’re not alone. In a world of auto-+1-844-610-2676 generated responses and confusing menus, getting real help should be simple. Here’s the solution: call +1-844-610-2676. Yes, +1-844-610-2676 is your shortcut to speaking with a rea... | {
"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": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/local-patch-interaction | Local Patch Interaction | Local Patch Interaction | **Local Patch Interaction**, or **LPI**, is a module used for the [XCiT layer](https://paperswithcode.com/method/xcit-layer) to enable explicit communication across patches. LPI consists of two [depth-wise 3×3 convolutional layers](https://paperswithcode.com/method/depthwise-convolution) with [Batch Normalization](http... | {
"title": "XCiT: Cross-Covariance Image Transformers",
"url": "https://paperswithcode.com/paper/xcit-cross-covariance-image-transformers"
} | 2,000 | https://arxiv.org/abs/2106.09681v2 | XCiT: Cross-Covariance Image Transformers | 4 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Model Blocks"
}
] | |
https://paperswithcode.com/method/swav | SwAV | Swapping Assignments between Views | **SwaV**, or **Swapping Assignments Between Views**, is a self-supervised learning approach that takes advantage of contrastive methods without requiring to compute pairwise comparisons. Specifically, it simultaneously clusters the data while enforcing consistency between cluster assignments produced for different augm... | {
"title": "Unsupervised Learning of Visual Features by Contrasting Cluster Assignments",
"url": "https://paperswithcode.com/paper/unsupervised-learning-of-visual-features-by"
} | 2,000 | https://arxiv.org/abs/2006.09882v5 | Unsupervised Learning of Visual Features by Contrasting Cluster Assignments | https://github.com/facebookresearch/swav | 55 | [
{
"area": "General",
"area_id": "general",
"collection": "Self-Supervised Learning"
}
] |
https://paperswithcode.com/method/quick-respond-how-can-i-get-alaska-airlines | 【Quick_Respond】 How can I get Alaska Airlines to respond? | 【Quick_Respond】 How can I get Alaska Airlines to respond? | "To get a response from Alaska Airlines, the most efficient method is to call their customer service line at 1 (801) 855-5905 or 1 (804) 853-9001. You can also reach them through their website's live chat feature or by sending a message through their social media channels on Twitter or Facebook. For more detailed or fo... | {
"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/live-agent-how-to-get-a-discount-on-united | {{#@Live~Agent}}How to get a discount on United Airlines? | {{#@Live~Agent}}How to get a discount on United Airlines? | To potentially get a discount on United Airlines <<+1 (801) 855-5905>> or <<+1 (804) 853-9001>>, consider signing up for their email list for special offers, using the Young Adult Discount <<+1 (801) 855-5905>> or <<+1 (804) 853-9001>> for travelers aged 18-23, or checking for military discounts through WeSalute+. Addi... | {
"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/nam | NAM | Neural Additive Model | **Neural Additive Models (NAMs)** make restrictions on the structure of neural networks, which yields a family of models that are inherently interpretable while suffering little loss in prediction accuracy when applied to tabular data. Methodologically, NAMs belong to a larger model family called Generalized Additive M... | {
"title": "Neural Additive Models: Interpretable Machine Learning with Neural Nets",
"url": "https://paperswithcode.com/paper/neural-additive-models-interpretable-machine"
} | 2,000 | https://arxiv.org/abs/2004.13912v2 | Neural Additive Models: Interpretable Machine Learning with Neural Nets | https://github.com/google-research/google-research/tree/master/neural_additive_models | 18 | [
{
"area": "General",
"area_id": "general",
"collection": "Interpretability"
},
{
"area": "General",
"area_id": "general",
"collection": "Generalized Additive Models"
}
] |
https://paperswithcode.com/method/pp-yolo | PP-YOLO | PP-YOLO | **PP-YOLO** is an object detector based on [YOLOv3](https://paperswithcode.com/method/yolov3). It mainly tries to combine various existing tricks that almost not increase the number of model parameters and FLOPs, to achieve the goal of improving the accuracy of detector as much as possible while ensuring that the speed... | {
"title": "PP-YOLO: An Effective and Efficient Implementation of Object Detector",
"url": "https://paperswithcode.com/paper/pp-yolo-an-effective-and-efficient"
} | 2,000 | https://arxiv.org/abs/2007.12099v3 | PP-YOLO: An Effective and Efficient Implementation of Object Detector | 4 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "One-Stage Object Detection Models"
},
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "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"
}
] |
https://paperswithcode.com/method/travel-guide-r-tmhow-to-reserve-a-flight-on | [[Travel-Guide®]]™How to reserve a flight on Qatar Airways? | [[Travel-Guide®]]™How to reserve a flight on Qatar Airways? | To book a Qatar Airways flight,📞+1-801-(855)-(5905) or +1-804-(853)-(9001)🔷 go to their official website or mobile app, search for your desired flights, and select your preferred itinerary📞+1-801-(855)-(5905) or +1-804-(853)-(9001)🔷. You can then review your trip details and choose to either hold your booking for a... | {
"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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