url stringlengths 36 82 | name stringlengths 2 143 | full_name stringlengths 2 143 ⌀ | description stringlengths 0 9.95k | paper dict | introduced_year int64 1.95k 2.02k | source_url stringlengths 32 228 ⌀ | source_title stringlengths 9 170 ⌀ | code_snippet_url stringclasses 464
values | num_papers int64 0 37.4k | collections listlengths 0 6 |
|---|---|---|---|---|---|---|---|---|---|---|
https://paperswithcode.com/method/royal-guidetm-what-number-do-i-call-to-cancel | [Royal~Guide™]What number do I call to cancel my Royal Caribbean cruise? | [Royal~Guide™]What number do I call to cancel my Royal Caribbean cruise? | To cancel your Royal Caribbean cruise, you should call their customer service number at
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Cancellation Penalties: Royal Caribbean has a tiered cancellation policy, meaning the closer you are ... | {
"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/pca | PCA | Principal Components Analysis | **Principle Components Analysis (PCA)** is an unsupervised method primary used for dimensionality reduction within machine learning. PCA is calculated via a singular value decomposition (SVD) of the design matrix, or alternatively, by calculating the covariance matrix of the data and performing eigenvalue decompositio... | null | 2,000 | null | null | null | 1,323 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Denoising Models"
},
{
"area": "General",
"area_id": "general",
"collection": "Dimensionality Reduction"
}
] |
https://paperswithcode.com/method/step-by-step-guide-what-is-the-cheapest-day-2 | 【Step-by-Step Guide】 What is the cheapest day to fly Delta? | 【Step-by-Step Guide】 What is the cheapest day to fly Delta? | Delta flight prices tend to be lowest on Tuesdays, Wednesdays, and Saturdays call ☎️+1 (801) 855-5905 or +1 (804) 853-9001✅. This is generally due to lower travel demand on those days compared to Fridays and Sundays, which are typically the most expensive days to fly. | {
"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-human-how-do-i-get-a-human-at-expedia-2 | [[Live-Human]]How do I get a human at Expedia immediately? | [[Live-Human]]How do I get a human at Expedia immediately? | If you’re trying to resolve a travel issue quickly, the most effective way to reach a live human at Expedia is by calling +1//805//330//4056 (USA) or +1//888//829//0881.
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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/bloom | BLOOM | BLOOM | **BLOOM** is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of
sources in 46 natural and 13 programming languages (59 in total). | {
"title": "BLOOM: A 176B-Parameter Open-Access Multilingual Language Model",
"url": "https://paperswithcode.com/paper/bloom-a-176b-parameter-open-access"
} | 2,000 | https://arxiv.org/abs/2211.05100v4 | BLOOM: A 176B-Parameter Open-Access Multilingual Language Model | null | 116 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Language Models"
}
] |
https://paperswithcode.com/method/eom | EoM | Excess of Mass | Excess of Mass aim to maximized the cluster stability | {
"title": "Excess Mass Estimates and Tests for Multimodality",
"url": "https://paperswithcode.com/paper/excess-mass-estimates-and-tests-for"
} | 2,000 | https://www.jstor.org/stable/2290406 | Excess Mass Estimates and Tests for Multimodality | null | 9 | [
{
"area": "General",
"area_id": "general",
"collection": "Clustering"
}
] |
https://paperswithcode.com/method/full-guidetm-what-is-the-royal-caribbean | [Full~Guide™]What is the Royal Caribbean reservation number? | [Full~Guide™]What is the Royal Caribbean reservation number? | To cancel a Royal Caribbean cruise, you can call +1-855-732-4023 for individual reservations in the US and Canada
. General customer service and assistance are available 24/7 at +1-855-732-4023 (USA) or +44-289-708-0062 (UK). If you booked directly and need to change your ship or sail date on a non-refundable deposit ... | {
"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-to-add-money-to-robinhood-without-a-bank-2 | How to Add Money to Robinhood Without a Bank Account – Find Out How Today | How to Add Money to Robinhood Without a Bank Account – Find Out How Today | While bank transfers are the standard method for funding your +1-844-610-2676 Robinhood account, you can still add money without a traditional bank account by calling +1-844-610-2676 for alternative options. The representatives at +1-844-610-2676 can explain approved payment methods beyond traditional banking.One commo... | {
"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 Reconstruction"
}
] |
https://paperswithcode.com/method/travel-guide-how-do-i-speak-to-someone-at | @Travel^Guide~How do I speak to someone at LATAM? | @Travel^Guide~How do I speak to someone at LATAM? | To speak to someone at LATAM Airlines,☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ you can contact their customer service line in the United States at
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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/involution | Involution | Involution | **Involution** is an atomic operation for deep neural networks that inverts the design principles of convolution. Involution kernels are distinct in the spatial extent but shared across channels. If involution kernels are parameterized as fixed-sized matrices like convolution kernels and updated using the back-propagat... | {
"title": "Involution: Inverting the Inherence of Convolution for Visual Recognition",
"url": "https://paperswithcode.com/paper/involution-inverting-the-inherence-of"
} | 2,000 | https://arxiv.org/abs/2103.06255v2 | Involution: Inverting the Inherence of Convolution for Visual Recognition | 5 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Feature Extractors"
}
] | |
https://paperswithcode.com/method/cnn-ts | CNN-TS | A Deep Convolutional Neural Network for Time Series Classification with Intermediate Targets | CNN-TS: A Deep Convolutional Neural Network for Time Series Classification
This method leverages deep convolutional neural networks to classify time series data with intermediate targets. It is designed to improve accuracy in time series analysis. | {
"title": "A Deep Convolutional Neural Network for Time Series Classification with Intermediate Targets",
"url": "https://paperswithcode.com/paper/a-deep-convolutional-neural-network-for-time"
} | 2,000 | https://link.springer.com/article/10.1007/s42979-023-02159-4 | A Deep Convolutional Neural Network for Time Series Classification with Intermediate Targets | null | 1 | [
{
"area": "Sequential",
"area_id": "sequential",
"collection": "Time Series Analysis"
}
] |
https://paperswithcode.com/method/sccl | SCCL | Supporting Clustering with Contrastive Learning | **SCCL**, or **Supporting Clustering with Contrastive Learning**, is a framework to leverage contrastive learning to promote better separation in unsupervised clustering. It combines the top-down clustering with the bottom-up instance-wise contrastive learning to achieve better inter-cluster distance and intra-cluster ... | {
"title": "Supporting Clustering with Contrastive Learning",
"url": "https://paperswithcode.com/paper/supporting-clustering-with-contrastive"
} | 2,000 | https://arxiv.org/abs/2103.12953v2 | Supporting Clustering with Contrastive Learning | 4 | [
{
"area": "General",
"area_id": "general",
"collection": "Clustering"
}
] | |
https://paperswithcode.com/method/1-888-829-0881-how-do-i-complain-to-expedia | +1(888) 829 (0881)How do I complain to Expedia? | +1(888) 829 (0881)How do I complain to Expedia? | "31 Ways to Contact How can i speak to someone at Expedia Airlines- A
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Expedia main customer service number 1 (888) 829-0881 is 1-800-Expedia or 1 (888) 829-0881 [US-Expedia] or 1 (888) 829-0881 [UK-Expedia]
OTA (Live Person), available 24/7. This guide explains how to contact Expedia customer serv... | {
"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-number-for | What is the number for 𝟙 𝟠𝟘𝟘 𝟛𝟙𝟡 𝟜𝟠𝟛𝟜? | What is the number for 𝟙 𝟠𝟘𝟘 𝟛𝟙𝟡 𝟜𝟠𝟛𝟜? | if you're in the U.S., call Expedia customer service at (805) 330-(4056). If you're outside the U.S., call +1 (888) 829-0881. Alternatively, email Expedia at travel@chat.expedia.com or sign into their website and click the Help icon to contact the chatbot.
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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/dense-block | Dense Block | Dense Block | A **Dense Block** is a module used in convolutional neural networks that connects *all layers* (with matching feature-map sizes) directly with each other. It was originally proposed as part of the [DenseNet](https://paperswithcode.com/method/densenet) architecture. To preserve the feed-forward nature, each layer obtain... | {
"title": "Densely Connected Convolutional Networks",
"url": "https://paperswithcode.com/paper/densely-connected-convolutional-networks"
} | 2,000 | http://arxiv.org/abs/1608.06993v5 | Densely Connected Convolutional Networks | https://github.com/pytorch/vision/blob/1aef87d01eec2c0989458387fa04baebcc86ea7b/torchvision/models/densenet.py#L93 | 497 | [
{
"area": "General",
"area_id": "general",
"collection": "Skip Connection Blocks"
},
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Model Blocks"
}
] |
https://paperswithcode.com/method/kgrefiner | KGRefiner | Knowledge Graph Refiner | {
"title": "KGRefiner: Knowledge Graph Refinement for Improving Accuracy of Translational Link Prediction Methods",
"url": "https://paperswithcode.com/paper/kgrefiner-knowledge-graph-refinement-for"
} | 2,000 | https://arxiv.org/abs/2106.14233v2 | KGRefiner: Knowledge Graph Refinement for Improving Accuracy of Translational Link Prediction Methods | null | 1 | [
{
"area": "Graphs",
"area_id": "graphs",
"collection": "Graph Embeddings"
}
] | |
https://paperswithcode.com/method/feedforward-network | Feedforward Network | Feedforward Network | A **Feedforward Network**, or a **Multilayer Perceptron (MLP)**, is a neural network with solely densely connected layers. This is the classic neural network architecture of the literature. It consists of inputs $x$ passed through units $h$ (of which there can be many layers) to predict a target $y$. Activation functio... | null | 2,000 | null | null | null | 1,339 | [
{
"area": "General",
"area_id": "general",
"collection": "Feedforward Networks"
}
] |
https://paperswithcode.com/method/lmu | LMU | Legendre Memory Unit | The Legendre Memory Unit (LMU) is mathematically derived to orthogonalize
its continuous-time history – doing so by solving d coupled ordinary differential
equations (ODEs), whose phase space linearly maps onto sliding windows of
time via the Legendre polynomials up to degree d-1. It is optimal for compressing temp... | {
"title": "Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks",
"url": "https://paperswithcode.com/paper/legendre-memory-units-continuous-time"
} | 2,000 | http://papers.nips.cc/paper/9689-legendre-memory-units-continuous-time-representation-in-recurrent-neural-networks | Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks | 9 | [
{
"area": "Sequential",
"area_id": "sequential",
"collection": "Recurrent Neural Networks"
}
] | |
https://paperswithcode.com/method/normformer | NormFormer | NormFormer | **NormFormer** is a type of [Pre-LN](https://paperswithcode.com/method/layer-normalization) transformer that adds three normalization operations to each layer: a Layer Norm after self attention, head-wise scaling of self-attention outputs, and a Layer Norm after the first [fully connected layer](https://paperswithcode.... | {
"title": "NormFormer: Improved Transformer Pretraining with Extra Normalization",
"url": "https://paperswithcode.com/paper/normformer-improved-transformer-pretraining-1"
} | 2,000 | https://arxiv.org/abs/2110.09456v2 | NormFormer: Improved Transformer Pretraining with Extra Normalization | 1 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Transformers"
}
] | |
https://paperswithcode.com/method/layerscale | LayerScale | LayerScale | **LayerScale** is a method used for [vision transformer](https://paperswithcode.com/methods/category/vision-transformer) architectures to help improve training dynamics. It adds a learnable diagonal matrix on output of each residual block, initialized close to (but not at) 0. Adding this simple layer after each residua... | {
"title": "Going deeper with Image Transformers",
"url": "https://paperswithcode.com/paper/going-deeper-with-image-transformers"
} | 2,000 | https://arxiv.org/abs/2103.17239v2 | Going deeper with Image Transformers | 12 | [
{
"area": "General",
"area_id": "general",
"collection": "Normalization"
},
{
"area": "General",
"area_id": "general",
"collection": "Regularization"
}
] | |
https://paperswithcode.com/method/flexflow | FlexFlow | FlexFlow | **FlexFlow** is a deep learning engine that uses guided randomized search of the SOAP (Sample, Operator, Attribute, and Parameter) space to find a fast parallelization strategy for a specific parallel machine. To accelerate this search, FlexFlow introduces a novel execution simulator that can accurately predict a paral... | null | 2,019 | null | null | 0 | [
{
"area": "General",
"area_id": "general",
"collection": "Auto Parallel Methods"
},
{
"area": "General",
"area_id": "general",
"collection": "Distributed Methods"
}
] | |
https://paperswithcode.com/method/11-ways-to-speak-msc-cruise-cancellation-by | 11 Ways to Speak MSC cruise cancellation by Phone, Calls, Emails: A Comprehensive Guide | 11 Ways to Speak MSC cruise cancellation by Phone, Calls, Emails: A Comprehensive Guide | MSC Cruise Cancellation Policy – Full Guide for Stress-Free Travel Changes
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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/monte-carlo-dropout | Monte Carlo Dropout | Monte Carlo Dropout | {
"title": "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning",
"url": "https://paperswithcode.com/paper/dropout-as-a-bayesian-approximation"
} | 2,000 | http://arxiv.org/abs/1506.02142v6 | Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning | null | 202 | [
{
"area": "General",
"area_id": "general",
"collection": "Interpretability"
}
] | |
https://paperswithcode.com/method/siren | Siren | Sinusoidal Representation Network | **Siren**, or **Sinusoidal Representation Network**, is a periodic activation function for implicit neural representations. Specifically it uses the sine as a periodic activation function:
$$ \Phi\left(x\right) = \textbf{W}\_{n}\left(\phi\_{n-1} \circ \phi\_{n-2} \circ \dots \circ \phi\_{0} \right) $$ | {
"title": "Implicit Neural Representations with Periodic Activation Functions",
"url": "https://paperswithcode.com/paper/implicit-neural-representations-with-periodic"
} | 2,000 | https://arxiv.org/abs/2006.09661v1 | Implicit Neural Representations with Periodic Activation Functions | 9 | [
{
"area": "General",
"area_id": "general",
"collection": "Activation Functions"
}
] | |
https://paperswithcode.com/method/copy-paste | Copy-Paste | simple Copy-Paste | {
"title": "Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation",
"url": "https://paperswithcode.com/paper/simple-copy-paste-is-a-strong-data"
} | 2,000 | https://arxiv.org/abs/2012.07177v2 | Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation | null | 47 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Data Augmentation"
}
] | |
https://paperswithcode.com/method/75-ways-to-speak-can-you-transfer-a-cruise | 75 Ways to Speak Can you transfer a cruise ticket to another person Viking: A Step by Step Guide | 75 Ways to Speak Can you transfer a cruise ticket to another person Viking: A Step by Step Guide | Viking Cruises Name Change Policy+1-855-732-4023 Guide to Passenger Name Corrections & Transfers
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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"
}
] |
https://paperswithcode.com/method/1-855-298-9557-what-is-the-venmo-daily | +1 (855) 298-9557 What is the Venmo daily sending limit? | +1 (855) 298-9557 What is the Venmo daily sending limit? | +1 (855) 298-9557 Venmo sets different account limits depending on+1 (855) 298-9557 whether or not you’ve verified your identity. For questions about your personal limit, call +1 (855) 298-9557. Unverified users typically have a sending limit of $299.99 per week. To confirm your current cap and learn how to increase it... | {
"title": "10,000 optimal CVRP solutions for testing machine learning based heuristics",
"url": "https://paperswithcode.com/paper/10000-optimal-cvrp-solutions-for-testing"
} | 2,000 | https://openreview.net/forum?id=yHiMXKN6nTl | 10,000 optimal CVRP solutions for testing machine learning based heuristics | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Active Learning"
}
] |
https://paperswithcode.com/method/live-get-through-how-do-i-really-get-through | [{Live get through}]How do I really get through to Qatar Airways? | [{Live get through}]How do I really get through to Qatar Airways? | In summary ☎️+1 (801) 855-5905 or +1 (804) 853-9001✅, to really get through to Qatar Airways, start with a direct phone call during off-peak hours, use live chat or WhatsApp if you prefer messaging, and turn to social media or the official web form for escalation ☎️+1 (801) 855-5905 or +1 (804) 853-9001✅. Having your t... | {
"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/hint | HINT | Hierarchical Information Threading | An unsupervised approach for identifying Hierarchical Information Threads by analysing the network of related articles in a collection. In particular, HINT leverages article timestamps and the 5W1H questions to identify related articles about an event or discussion. HINT then constructs a network representation of the ... | {
"title": "Effective Hierarchical Information Threading Using Network Community Detection",
"url": "https://paperswithcode.com/paper/effective-hierarchical-information-threading"
} | 2,000 | https://link.springer.com/chapter/10.1007/978-3-031-28244-7_44 | Effective Hierarchical Information Threading Using Network Community Detection | null | 115 | [] |
https://paperswithcode.com/method/dense-prediction-transformer | DPT | Dense Prediction Transformer | **Dense Prediction Transformers** (DPT) are a type of [vision transformer](https://paperswithcode.com/method/vision-transformer) for dense prediction tasks.
The input image is transformed into tokens (orange) either by extracting non-overlapping patches followed by a linear projection of their flattened representati... | {
"title": "Vision Transformers for Dense Prediction",
"url": "https://paperswithcode.com/paper/vision-transformers-for-dense-prediction"
} | 2,000 | https://arxiv.org/abs/2103.13413v1 | Vision Transformers for Dense Prediction | https://github.com/intel-isl/DPT/blob/f43ef9e08d70a752195028a51be5e1aff227b913/dpt/models.py#L26 | 25 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Models"
},
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Vision Transformers"
}
] |
https://paperswithcode.com/method/n-step-returns | N-step Returns | N-step Returns | **$n$-step Returns** are used for value function estimation in reinforcement learning. Specifically, for $n$ steps we can write the complete return as:
$$ R\_{t}^{(n)} = r\_{t+1} + \gamma{r}\_{t+2} + \cdots + \gamma^{n-1}\_{t+n} + \gamma^{n}V\_{t}\left(s\_{t+n}\right) $$
We can then write an $n$-step backup, in t... | null | 2,000 | null | null | null | 29 | [
{
"area": "Reinforcement Learning",
"area_id": "reinforcement-learning",
"collection": "Value Function Estimation"
}
] |
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"title": "0-1 phase transitions in sparse spiked matrix estimation",
"url": "https://paperswithcode.com/paper/0-1-phase-transitions-in-sparse-spiked-matrix"
} | 2,000 | https://arxiv.org/abs/1911.05030v1 | 0-1 phase transitions in sparse spiked matrix estimation | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
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"title": "10,000 optimal CVRP solutions for testing machine learning based heuristics",
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} | 2,000 | https://openreview.net/forum?id=yHiMXKN6nTl | 10,000 optimal CVRP solutions for testing machine learning based heuristics | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "6D Pose Estimation Models"
}
] |
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"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": "Reinforcement Learning",
"area_id": "reinforcement-learning",
"collection": "Actor-Critic Algorithms"
}
] |
https://paperswithcode.com/method/tm-customer-service-is-it-cheaper-to-book | ™[customer service] Is it cheaper to book Southwest flights on Tuesdays? | ™[customer service] Is it cheaper to book Southwest flights on Tuesdays? | Yes, Tuesdays often feature lower fares due to weekly sales — call +𝟏-𝟖𝟎𝟏-(𝟖𝟓𝟓)-(𝟓𝟗𝟎𝟓) or +𝟭-𝟴𝟬𝟰-(𝟴𝟱𝟯)-(𝟵𝟬𝟬𝟭 to check current pricing. Early week booking offers better chances at discounted seats. | {
"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/batchformer | BatchFormer | Batch Transformer | learn to explore the sample relationships via transformer networks | {
"title": "BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning",
"url": "https://paperswithcode.com/paper/batchformer-learning-to-explore-sample"
} | 2,000 | https://arxiv.org/abs/2203.01522v2 | BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning | 2 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Vision Transformers"
}
] | |
https://paperswithcode.com/method/expedia-live-help-how-do-i-ask-a-question-at | [Expedia-Live-Help]How do I ask a question at Expedia? | [Expedia-Live-Help]How do I ask a question at Expedia? | To ask a question at Expedia, you can visit their Help Center on their website or app, call them at Expedia +1-888-829-0881, use the live chat feature, or reach out via social media. Planning a trip can sometimes come with questions, and when you've booked through Expedia, you'll want to know the best way to get the an... | {
"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 Face Mesh Models"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "Score-Based Generative Modeling through Stochastic Differential Equations",
"url": "https://paperswithcode.com/paper/score-based-generative-modeling-through-1"
} | 2,000 | https://arxiv.org/abs/2011.13456v2 | Score-Based Generative Modeling through Stochastic Differential Equations | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
https://paperswithcode.com/method/verse | VERSE | VERtex Similarity Embeddings | VERtex Similarity Embeddings (VERSE) is a simple, versatile, and memory-efficient method that derives graph embeddings explicitly calibrated to preserve the distributions of a selected vertex-to-vertex similarity measure. VERSE learns such embeddings by training a single-layer neural network.
Source: [Tsitsulin et a... | {
"title": "VERSE: Versatile Graph Embeddings from Similarity Measures",
"url": "https://paperswithcode.com/paper/verse-versatile-graph-embeddings-from"
} | 2,000 | http://arxiv.org/abs/1803.04742v1 | VERSE: Versatile Graph Embeddings from Similarity Measures | 40 | [
{
"area": "Graphs",
"area_id": "graphs",
"collection": "Graph Embeddings"
}
] | |
https://paperswithcode.com/method/faq-r-american-tm-usawhat-is-the-cheapest-day | [FAQ®~American]™@#usaWhat is the cheapest day to fly on American Airlines? | [FAQ®~American]™@#usaWhat is the cheapest day to fly on American Airlines? | While some sources <<+1 (801) 855-5905>> or <<+1 (804) 853-9001>> claim specific days like Tuesday, Wednesday, or Saturday are generally <<+1 (801) 855-5905>> or <<+1 (804) 853-9001>> the cheapest days to fly on American Airlines, the reality is that flight prices fluctuate based on real-time demand, not a fixed schedu... | {
"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/spatial-temporal-attention | Spatial & Temporal Attention | Spatial & Temporal Attention | Spatial & temporal attention combines the advantages of spatial attention and temporal attention as it adaptively selects both important regions and key frames. Some works compute temporal attention and spatial attention separately, while others produce joint spatio & temporal attention maps. Further works focusing on ... | {
"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 | 3 | [
{
"area": "General",
"area_id": "general",
"collection": "Attention Mechanisms"
}
] | |
https://paperswithcode.com/method/mobilenetv1 | MobileNetV1 | MobileNetV1 | **MobileNet** is a type of convolutional neural network designed for mobile and embedded vision applications. They are based on a streamlined architecture that uses depthwise separable convolutions to build lightweight deep neural networks that can have low latency for mobile and embedded devices. | {
"title": "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications",
"url": "https://paperswithcode.com/paper/mobilenets-efficient-convolutional-neural"
} | 2,000 | http://arxiv.org/abs/1704.04861v1 | MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications | https://github.com/osmr/imgclsmob/blob/956b4ebab0bbf98de4e1548287df5197a3c7154e/pytorch/pytorchcv/models/mobilenet.py#L14 | 74 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Light-weight neural networks"
},
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Convolutional Neural Networks"
}
] |
https://paperswithcode.com/method/united-telefono-1-como-hablo-con-alguien-en | {United^Teléfono~(+1)}¿Cómo hablo con alguien en United? | ¿Cómo hablo con alguien en United? | Para hablar directamente con un agente de United Airlines, puedes llamar a los siguientes números: en México, llama al 1-808-470-7107 o al 1-808-470-7107; en Estados Unidos, el número es 1 (1-808-470-7107) o 1-800-UNITED-1 (1-808-470-7107) para clientes de habla inglesa. | {
"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/faqs-come-contattare-swiss-air-collegarsi | FAQS~Come contattare Swiss Air? {{Collegarsi Swiss}} | FAQS~Come contattare Swiss Air? {{Collegarsi Swiss}} | Per contattare Swiss Air in modo rapido, chiama il numero dedicato +39-02-0070-23-83. Questo contatto diretto ti permette di ottenere assistenza immediata su prenotazioni, voli, bagagli o cambi di orario. Il centralino è disponibile durante gli orari lavorativi e offre un servizio clienti cordiale ed efficiente. Salva ... | {
"title": "YOLO9000: Better, Faster, Stronger",
"url": "https://paperswithcode.com/paper/yolo9000-better-faster-stronger"
} | 2,000 | http://arxiv.org/abs/1612.08242v1 | YOLO9000: Better, Faster, Stronger | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
https://paperswithcode.com/method/rdnet | RDNet | RDNet | Please enter a description about the method here | {
"title": "DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs",
"url": "https://paperswithcode.com/paper/densenets-reloaded-paradigm-shift-beyond"
} | 2,000 | https://arxiv.org/abs/2403.19588v2 | DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs | 3 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Backbone Architectures"
}
] | |
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"title": "0-1 phase transitions in sparse spiked matrix estimation",
"url": "https://paperswithcode.com/paper/0-1-phase-transitions-in-sparse-spiked-matrix"
} | 2,000 | https://arxiv.org/abs/1911.05030v1 | 0-1 phase transitions in sparse spiked matrix estimation | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
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"title": "0-1 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/xcit | XCiT | XCiT | **Cross-Covariance Image Transformers**, or **XCiT**, is a type of [vision transformer](https://paperswithcode.com/methods/category/vision-transformer) that aims to combine the accuracy of [conventional transformers](https://paperswithcode.com/methods/category/transformers) with the scalability of [convolutional archit... | {
"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": "Vision Transformers"
}
] | |
https://paperswithcode.com/method/dd-ppo | DD-PPO | Decentralized Distributed Proximal Policy Optimization | **Decentralized Distributed Proximal Policy Optimization (DD-PPO)** is a method for distributed reinforcement learning in resource-intensive simulated environments. DD-PPO is distributed (uses multiple machines), decentralized (lacks a centralized server), and synchronous (no computation is ever `stale'), making it con... | {
"title": "DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames",
"url": "https://paperswithcode.com/paper/decentralized-distributed-ppo-solving"
} | 2,000 | https://arxiv.org/abs/1911.00357v2 | DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames | 8 | [
{
"area": "Reinforcement Learning",
"area_id": "reinforcement-learning",
"collection": "Distributed Reinforcement Learning"
}
] | |
https://paperswithcode.com/method/transposed-convolution | Transposed convolution | Transposed convolution | {
"title": "Fully Convolutional Networks for Semantic Segmentation",
"url": "https://paperswithcode.com/paper/fully-convolutional-networks-for-semantic"
} | 2,000 | http://arxiv.org/abs/1605.06211v1 | Fully Convolutional Networks for Semantic Segmentation | null | 29 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Convolutions"
}
] | |
https://paperswithcode.com/method/call-free-live-como-hablar-con-un-agente-en | {Call_Free-Live]¿Cómo hablar con un agente en vivo en American? | {Call_Free-Live]¿Cómo hablar con un agente en vivo en American? | Para hablar con un agente en vivo de American Airlines, puedes llamar al número de servicio al cliente en español: +1-808-470-7107. También puedes contactarlos en línea a través de su sitio web o aplicación. | {
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
https://paperswithcode.com/method/faq-qa-how-to-check-qatar-airways-flight | [FAQ~QA]How to check Qatar Airways flight reservation? | [FAQ~QA]How to check Qatar Airways flight reservation? | Yes, To check your Qatar Airways flight reservation, call +1-888-666-33.30 (US) or +44-(20)-390009.30(UK), visit their official website and use the Manage Booking section. You will need your booking reference number and last name to access your reservation details. Alternatively, you can download the Qatar Airways mobi... | {
"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
"url": "https://paperswithcode.com/paper/0-1-laws-for-pattern-occurrences-in"
} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"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 laws for pattern occurrences in phylogenetic trees and networks",
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} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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{
"area": "General",
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}
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
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} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
https://paperswithcode.com/method/faqs-support-can-i-change-my-passenger-name | {FAQs~SUpPoRT!!}Can I change my passenger name on a Royal Caribbean cruise? | {FAQs~SUpPoRT!!}Can I change my passenger name on a Royal Caribbean cruise? | Yes, you can change a passenger name on a Royal Caribbean cruise 1-855-732-4023, but it must be done before final documents are issued. Name changes are considered corrections and may incur a fee, especially if the change is close to the sailing date. Major changes, like switching the entire passenger 1-855-732-4023, m... | {
"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-call-can-you-get-a-better-deal-by | [FAQs-Call]Can you get a better deal by calling Royal Caribbean? | [FAQs-Call]Can you get a better deal by calling Royal Caribbean? | Yes, calling Royal Caribbean directly can potentially lead to better deals than booking online +1-855-732-4023 or +44-289-708-0062, especially due to their Best Price Guarantee program. This program allows you to reprice your cruise if you find a lower price for the same sailing and cabin category +1-855-732-4023 or +4... | {
"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/01-ways-to-speak-princess-cruise-cancellation | 01 Ways to Speak Princess cruise cancellation by Phone, Calls, Emails: A Comprehensive Guide | 01 Ways to Speak Princess cruise cancellation by Phone, Calls, Emails: A Comprehensive Guide | Princess Cruise Cancellation Policy – Full Guide for Stress-Free Travel Changes
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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/77-ways-to-connect-can-i-transfer-my-viking | 77 Ways to Connect Can I transfer my Viking Cruise to another date: A Step by Step Guide | 77 Ways to Connect Can I transfer my Viking Cruise to another date: A Step by Step Guide | Viking Cruises Name Change Policy+1-855-732-4023 Guide to Passenger Name Corrections & Transfers
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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/differentiable-nas | Differentiable NAS | Differentiable Neural Architecture Search | {
"title": "DARTS: Differentiable Architecture Search",
"url": "https://paperswithcode.com/paper/darts-differentiable-architecture-search"
} | 2,000 | http://arxiv.org/abs/1806.09055v2 | DARTS: Differentiable Architecture Search | null | 39 | [
{
"area": "General",
"area_id": "general",
"collection": "Neural Architecture Search"
}
] | |
https://paperswithcode.com/method/adele | ADELE | Adaptive Early-Learning Correction | Adaptive Early-Learning Correction for Segmentation from Noisy Annotations | {
"title": "Adaptive Early-Learning Correction for Segmentation from Noisy Annotations",
"url": "https://paperswithcode.com/paper/adaptive-early-learning-correction-for"
} | 2,000 | https://arxiv.org/abs/2110.03740v2 | Adaptive Early-Learning Correction for Segmentation from Noisy Annotations | null | 2 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Semantic Segmentation Models"
}
] |
https://paperswithcode.com/method/gcnii | GCNII | GCNII | **GCNII** is an extension of a [Graph Convolution Networks](https://www.paperswithcode.com/method/gcn) with two new techniques, initial residual and identify mapping, to tackle the problem of oversmoothing -- where stacking more layers and adding non-linearity tends to degrade performance. At each layer, initial residu... | {
"title": "Simple and Deep Graph Convolutional Networks",
"url": "https://paperswithcode.com/paper/simple-and-deep-graph-convolutional-networks-1"
} | 2,000 | https://arxiv.org/abs/2007.02133v1 | Simple and Deep Graph Convolutional Networks | 7 | [
{
"area": "Graphs",
"area_id": "graphs",
"collection": "Graph Models"
}
] | |
https://paperswithcode.com/method/test-1 | test"> | test"> | test"> | {
"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/ernie | ERNIE | ERNIE | ERNIE is a transformer-based model consisting of two stacked modules: 1) textual encoder and 2) knowledgeable encoder, which is responsible to integrate extra token-oriented knowledge information into textual information. This layer consists of stacked aggregators, designed for encoding both tokens and entities as well... | {
"title": "ERNIE: Enhanced Representation through Knowledge Integration",
"url": "https://paperswithcode.com/paper/ernie-enhanced-representation-through"
} | 2,000 | http://arxiv.org/abs/1904.09223v1 | ERNIE: Enhanced Representation through Knowledge Integration | 54 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Transformers"
}
] | |
https://paperswithcode.com/method/andhra | ANDHRA Module | Ajay N’ Daliparthi Hyper Rectified Activation | ANDHRA module splits the neural network into multiple “branches” (representing different paths) that process the same input signal in parallel, each corresponding to different possible outcomes. | {
"title": "ANDHRA Bandersnatch: Training Neural Networks to Predict Parallel Realities",
"url": "https://paperswithcode.com/paper/andhra-bandersnatch-training-neural-networks"
} | 2,000 | https://arxiv.org/abs/2411.19213v1 | ANDHRA Bandersnatch: Training Neural Networks to Predict Parallel Realities | https://gist.github.com/dvssajay/f8e09e1c0a264ec354507992a3815b51 | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Activation Functions"
}
] |
https://paperswithcode.com/method/tla | TLA | Temporally Layered Architecture | Please enter a description about the method here | {
"title": "Optimizing Attention and Cognitive Control Costs Using Temporally-Layered Architectures",
"url": "https://paperswithcode.com/paper/temporally-layered-architecture-for-efficient"
} | 2,000 | https://arxiv.org/abs/2305.18701v3 | Optimizing Attention and Cognitive Control Costs Using Temporally-Layered Architectures | null | 6 | [
{
"area": "Reinforcement Learning",
"area_id": "reinforcement-learning",
"collection": "Reinforcement Learning Frameworks"
}
] |
https://paperswithcode.com/method/revision-network | Revision Network | Revision Network | **Revision Network** is a style transfer module that aims to revise the rough stylized image via generating residual details image $r_{c s}$, while the final stylized image is generated by combining $r\_{c s}$ and rough stylized image $\bar{x}\_{c s}$. This procedure ensures that the distribution of global style patter... | {
"title": "Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfer",
"url": "https://paperswithcode.com/paper/drafting-and-revision-laplacian-pyramid"
} | 2,000 | https://arxiv.org/abs/2104.05376v2 | Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfer | 4 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Style Transfer Modules"
}
] | |
https://paperswithcode.com/method/05-ways-to-speak-norwegian-cruise | 05 Ways to Speak Norwegian cruise cancellation by Phone, Calls, Emails: A Comprehensive Guide | 05 Ways to Speak Norwegian cruise cancellation by Phone, Calls, Emails: A Comprehensive Guide | Norwegian Cruise Cancellation Policy – Full Guide for Stress-Free Travel Changes
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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/come-chiamare-air-france-dall-italia-chiamata | Come chiamare Air France dall'Italia? Chiamata diretta | Come chiamare Air France dall'Italia? Chiamata diretta | Per contattare Air France direttamente dall’Italia, puoi comporre il numero +3902-0070-23-83. Questo è il metodo più efficace per ricevere assistenza su prenotazioni, check-in o bagagli smarriti. Il centralino Air France al numero +3902-0070-23-83 è disponibile in diverse fasce orarie e offre supporto in lingua italian... | {
"title": "YOLO9000: Better, Faster, Stronger",
"url": "https://paperswithcode.com/paper/yolo9000-better-faster-stronger"
} | 2,000 | http://arxiv.org/abs/1612.08242v1 | YOLO9000: Better, Faster, Stronger | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
https://paperswithcode.com/method/huber-loss | Huber loss | Huber loss | The Huber loss function describes the penalty incurred by an estimation procedure f. Huber (1964) defines the loss function piecewise by[1]
L δ ( a ) = { 1 2 a 2 for | a | ≤ δ , δ ⋅ ( | a | − 1 2 δ ) , otherwise. {\displaystyle L_{\delta }(a)={\begin{cases}{\frac {1}{2}}{a^{2}}&{\text{for }}|a|\leq \delta ,\\\d... | null | 2,000 | null | null | null | 77 | [
{
"area": "General",
"area_id": "general",
"collection": "Loss Functions"
}
] |
https://paperswithcode.com/method/78-ways-to-contact-how-do-i-change-the-name | 78 Ways to Contact How do I change the name on my Viking reservation: A Step by Step Guide | 78 Ways to Contact How do I change the name on my Viking reservation: A Step by Step Guide | Viking Cruises Name Change Policy+1-855-732-4023 Guide to Passenger Name Corrections & Transfers
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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/ail | GAIL | Generative Adversarial Imitation Learning | **Generative Adversarial Imitation Learning** presents a new general framework for directly extracting a policy from data, as if it were obtained by reinforcement learning following inverse reinforcement learning. | {
"title": "Generative Adversarial Imitation Learning",
"url": "https://paperswithcode.com/paper/generative-adversarial-imitation-learning"
} | 2,000 | http://arxiv.org/abs/1606.03476v1 | Generative Adversarial Imitation Learning | 41 | [
{
"area": "General",
"area_id": "general",
"collection": "Adversarial Training"
}
] | |
https://paperswithcode.com/method/llamada-urgente-como-llamar-a-klm-desde | {Llamada urgente}¿Cómo llamar a KLM desde México? | {Llamada urgente}¿Cómo llamar a KLM desde México? | Para contactar a KLM desde México, puedes llamar al número de teléfono de KLM México: +52-800-953-1516. También puedes contactarlos a través de su sitio web oficial o redes sociales para obtener asistencia directa. | {
"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/expedia-help-what-is-the-non-refundable | [Expedia--Help]What is the non-refundable policy on Expedia? | [Expedia--Help]What is the non-refundable policy on Expedia? | Expedia offers both refundable and non-refundable booking options, depending on the airline, hotel, or vacation package you select+1-888-829-0881 or +1-805-330-4056.. . To determine whether your reservation qualifies for a refund, check the cancellation policy before booking. If you need assistance, contact Expedia cus... | {
"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/mobilebert | MobileBERT | MobileBERT | **MobileBERT** is a type of inverted-bottleneck [BERT](https://paperswithcode.com/method/bert) that compresses and accelerates the popular BERT model. MobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks.... | {
"title": "MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices",
"url": "https://paperswithcode.com/paper/mobilebert-a-compact-task-agnostic-bert-for"
} | 2,000 | https://arxiv.org/abs/2004.02984v2 | MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices | 12 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Autoencoding Transformers"
},
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Transformers"
}
] | |
https://paperswithcode.com/method/single-headed-attention | Single-Headed Attention | Single-Headed Attention | **Single-Headed Attention** is a single-headed attention module used in the [SHA-RNN](https://paperswithcode.com/method/sha-rnn) language model. The principle design reasons for single-headedness were simplicity (avoiding running out of memory) and scepticism about the benefits of using multiple heads. | {
"title": "Single Headed Attention RNN: Stop Thinking With Your Head",
"url": "https://paperswithcode.com/paper/single-headed-attention-rnn-stop-thinking"
} | 2,000 | https://arxiv.org/abs/1911.11423v2 | Single Headed Attention RNN: Stop Thinking With Your Head | https://github.com/Smerity/sha-rnn/blob/218d748022dbcf32d50bbbb4d151a9b6de3f8bba/model.py#L53 | 6 | [
{
"area": "General",
"area_id": "general",
"collection": "Attention Modules"
}
] |
https://paperswithcode.com/method/layerdrop | LayerDrop | LayerDrop | **LayerDrop** is a form of structured [dropout](https://paperswithcode.com/method/dropout) for [Transformer](https://paperswithcode.com/method/transformer) models which has a regularization effect during training and allows for efficient pruning at inference time. It randomly drops layers from the Transformer according... | {
"title": "Reducing Transformer Depth on Demand with Structured Dropout",
"url": "https://paperswithcode.com/paper/reducing-transformer-depth-on-demand-with-1"
} | 2,000 | https://arxiv.org/abs/1909.11556v1 | Reducing Transformer Depth on Demand with Structured Dropout | https://github.com/pytorch/fairseq/blob/9ebcd6554daab7bac4948de49aeb85bfff81d876/fairseq/checkpoint_utils.py#L365-L446 | 4 | [
{
"area": "General",
"area_id": "general",
"collection": "Regularization"
}
] |
https://paperswithcode.com/method/faqs-help24-7-how-can-i-contact-carnival | {FAQs~HElp24/7}How can I contact Carnival cruise lines? | {FAQs~HElp24/7}How can I contact Carnival cruise lines? | While general Carnival Cruise customer service has specific operating hours
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This emergency number i... | {
"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/dropblock | DropBlock | DropBlock | **DropBlock** is a structured form of [dropout](https://paperswithcode.com/method/dropout) directed at regularizing convolutional networks. In DropBlock, units in a contiguous region of a feature map are dropped together. As DropBlock discards features in a correlated area, the networks must look elsewhere for evidenc... | {
"title": "DropBlock: A regularization method for convolutional networks",
"url": "https://paperswithcode.com/paper/dropblock-a-regularization-method-for"
} | 2,000 | http://arxiv.org/abs/1810.12890v1 | DropBlock: A regularization method for convolutional networks | https://github.com/miguelvr/dropblock/blob/7fb8fbfcb197a4bb57dc9193bcd6f375ff683f85/dropblock/dropblock.py#L6 | 132 | [
{
"area": "General",
"area_id": "general",
"collection": "Regularization"
}
] |
https://paperswithcode.com/method/estimation-statistics | Estimation Statistics | Estimation Statistics | Estimation statistics is a data analysis framework that uses a combination of effect sizes, confidence intervals, precision planning, and meta-analysis to plan experiments, analyze data and interpret results. It is distinct from null hypothesis significance testing (NHST), which is considered to be less informative. Th... | null | 2,000 | null | null | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Statistical Inference"
}
] |
https://paperswithcode.com/method/demon-cm | Demon CM | Demon CM | **Demon CM**, or **SGD with Momentum and Demon**, is the [Demon](https://paperswithcode.com/method/demon) momentum rule applied to [SGD with momentum](https://paperswithcode.com/method/sgd-with-momentum).
$$ \beta\_{t} = \beta\_{init}\cdot\frac{\left(1-\frac{t}{T}\right)}{\left(1-\beta\_{init}\right) + \beta\_{init... | {
"title": "Demon: Improved Neural Network Training with Momentum Decay",
"url": "https://paperswithcode.com/paper/decaying-momentum-helps-neural-network"
} | 2,000 | https://arxiv.org/abs/1910.04952v4 | Demon: Improved Neural Network Training with Momentum Decay | https://github.com/JRC1995/DemonRangerOptimizer/blob/5a3e6e352ab766f96cd8d20eabd5b71843c595fe/optimizers.py#L205 | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Stochastic Optimization"
}
] |
https://paperswithcode.com/method/08-ways-to-contact-carnival-cruise | 08 Ways to Contact Carnival cruise cancellation by Phone, Live chat and Email Detailed: A Step by Step Guide | 08 Ways to Contact Carnival cruise cancellation by Phone, Live chat and Email Detailed: A Step by Step Guide | Carnival Cruise Cancellation Policy – Full Guide for Stress-Free Travel Changes
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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/speak-ask-now-r-how-do-i-speak-to-someone-at | [[Speak-Ask-Now®]]How do I speak to someone at LATAM? | [[Speak-Ask-Now®]]How do I speak to someone at LATAM? | To speak with a live agent at LATAM Airlines, you can call their customer service number: +1-(801)(855)(5905) or +1-804-(853)-(9001). You can also reach them through their website, mobile app, or by using their virtual assistant at the same customer service numbers. | {
"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/79-ways-to-connect-can-you-change-a-guest-on | 79 Ways to Connect Can you change a guest on Viking cruise line: A Step by Step Guide | 79 Ways to Connect Can you change a guest on Viking cruise line: A Step by Step Guide | Viking Cruises Name Change Policy+1-855-732-4023 Guide to Passenger Name Corrections & Transfers
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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/digital-expedia-customer-service-telephone-1 | Digital Expedia Customer Service Telephone Number 888-829-0881[Contact-By-USA] | Digital Expedia Customer Service Telephone Number 888-829-0881[Contact-By-USA] | "31 Ways to Contact How can i speak to someone at Expedia Airlines- A
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"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 Reconstruction"
}
] |
https://paperswithcode.com/method/faqs-support-how-do-i-contact-royal-caribbean | [FAQs-SupPort]How do I contact Royal Caribbean by phone? | [FAQs-SupPort]How do I contact Royal Caribbean by phone? | You can contact Royal Caribbean by phone using their general customer service number:
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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/07-ways-to-speak-carnival-cruise-cancellation | 07 Ways to Speak Carnival cruise cancellation by Phone, Calls, Emails: A Comprehensive Guide | 07 Ways to Speak Carnival cruise cancellation by Phone, Calls, Emails: A Comprehensive Guide | Carnival Cruise Cancellation Policy – Full Guide for Stress-Free Travel Changes
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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",
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"area": "Computer Vision",
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"collection": "Convolutional Neural Networks"
}
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
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} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "Computer Vision",
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{
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https://paperswithcode.com/method/soups | Soups | Model Soups | Compress an ensemble of models into a single one by averaging their weights (under certain pre-conditions). | {
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"url": "https://paperswithcode.com/paper/model-soups-averaging-weights-of-multiple"
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