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/vime | VIME | Value Imputation and Mask Estimation | **VIME **, or **Value Imputation and Mask Estimation**, is a self- and semi-supervised learning framework for tabular data. It consists of a pretext task of estimating mask vectors from corrupted tabular data in addition to the reconstruction pretext task for self-supervised learning. | {
"title": "VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain",
"url": "https://paperswithcode.com/paper/vime-extending-the-success-of-self-and-semi"
} | 2,000 | http://proceedings.neurips.cc/paper/2020/hash/7d97667a3e056acab9aaf653807b4a03-Abstract.html | VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Deep Tabular Learning"
}
] | |
https://paperswithcode.com/method/elmo | ELMo | ELMo | **Embeddings from Language Models**, or **ELMo**, is a type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Word vectors are learned functions of the intern... | {
"title": "Deep contextualized word representations",
"url": "https://paperswithcode.com/paper/deep-contextualized-word-representations"
} | 2,000 | http://arxiv.org/abs/1802.05365v2 | Deep contextualized word representations | null | 234 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Language Models"
},
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Contextualized Word Embeddings"
},
{
"area": "Natural Language P... |
https://paperswithcode.com/method/sagan | SAGAN | Self-Attention GAN | The **Self-Attention Generative Adversarial Network**, or **SAGAN**, allows for attention-driven, long-range dependency modeling for image generation tasks. Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. In SAGAN, details ca... | {
"title": "Self-Attention Generative Adversarial Networks",
"url": "https://paperswithcode.com/paper/self-attention-generative-adversarial"
} | 2,000 | https://arxiv.org/abs/1805.08318v2 | Self-Attention Generative Adversarial Networks | https://github.com/heykeetae/Self-Attention-GAN/blob/master/sagan_models.py | 138 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Generative Adversarial Networks"
}
] |
https://paperswithcode.com/method/g3d | G3D | G3D | **G3D** is a unified spatial-temporal graph convolutional operator that directly models cross-spacetime joint dependencies. It leverages dense cross-spacetime edges as skip connections for direct information propagation across the 3D spatial-temporal graph. | {
"title": "Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition",
"url": "https://paperswithcode.com/paper/disentangling-and-unifying-graph-convolutions"
} | 2,000 | https://arxiv.org/abs/2003.14111v2 | Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition | null | 2 | [
{
"area": "General",
"area_id": "general",
"collection": "Action Recognition Blocks"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "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 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"
}
] |
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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"
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{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
"url": "https://paperswithcode.com/paper/0-1-laws-for-pattern-occurrences-in"
} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "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 phase transitions in sparse spiked matrix estimation",
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} | 2,000 | https://arxiv.org/abs/1911.05030v1 | 0-1 phase transitions in sparse spiked matrix estimation | null | 1 | [
{
"area": "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"
}
] |
https://paperswithcode.com/method/cbhg | CBHG | CBHG | **CBHG** is a building block used in the [Tacotron](https://paperswithcode.com/method/tacotron) text-to-speech model. It consists of a bank of 1-D convolutional filters, followed by highway networks and a bidirectional gated recurrent unit ([BiGRU](https://paperswithcode.com/method/bigru)).
The module is used to ex... | {
"title": "Tacotron: Towards End-to-End Speech Synthesis",
"url": "https://paperswithcode.com/paper/tacotron-towards-end-to-end-speech-synthesis"
} | 2,000 | http://arxiv.org/abs/1703.10135v2 | Tacotron: Towards End-to-End Speech Synthesis | https://github.com/mozilla/TTS/blob/3cbf9052f78ac40f025cdf598437eabc9bda9298/layers/tacotron.py#L93 | 65 | [
{
"area": "General",
"area_id": "general",
"collection": "Skip Connection Blocks"
},
{
"area": "Sequential",
"area_id": "sequential",
"collection": "Sequential Blocks"
},
{
"area": "Audio",
"area_id": "audio",
"collection": "Speech Synthesis Blocks"
}
] |
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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"
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{
"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 laws for pattern occurrences in phylogenetic trees and networks",
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{
"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"
}
] |
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"title": "Accelerating Simulation-based Inference with Emerging AI Hardware",
"url": "https://paperswithcode.com/paper/accelerating-simulation-based-inference-with"
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{
"area": "General",
"area_id": "general",
"collection": "Approximate Inference"
}
] | |
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"title": "0-1 phase transitions in sparse spiked matrix estimation",
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{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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{
"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"
}
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"title": "0-1 phase transitions in sparse spiked matrix estimation",
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} | 2,000 | https://arxiv.org/abs/1911.05030v1 | 0-1 phase transitions in sparse spiked matrix estimation | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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{
"area": "Computer Vision",
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"title": "CSPNet: A New Backbone that can Enhance Learning Capability of CNN",
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{
"area": "Computer Vision",
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"collection": "Degridding"
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https://paperswithcode.com/method/finch-clustering | FINCH Clustering | First Integer Neighbor Clustering Hierarchy (FINCH)) | FINCH is a parameter-free fast and scalable clustering algorithm. it stands out for its speed and clustering quality. | {
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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": "General",
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
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{
"area": "Computer Vision",
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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{
"area": "Computer Vision",
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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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{
"area": "Computer Vision",
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}
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/sparse-convolutions | Sparse Convolutions | Sparse Convolutions | {
"title": "Spatially-sparse convolutional neural networks",
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} | 2,000 | http://arxiv.org/abs/1409.6070v1 | Spatially-sparse convolutional neural networks | null | 37 | [
{
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}
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"title": "0-dimensional Homology Preserving Dimensionality Reduction with TopoMap",
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{
"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",
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}
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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{
"area": "General",
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}
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https://paperswithcode.com/method/deformable-convnets | Deformable ConvNets | Deformable Convolutional Networks | Deformable ConvNets do not learn an affine transformation. They divide convolution into two steps, firstly sampling features on a regular grid $ \mathcal{R} $ from the input feature map, then aggregating sampled features by weighted summation using a convolution kernel. The process can be written as:
\begin{align}
... | {
"title": "Deformable Convolutional Networks",
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} | 2,000 | http://arxiv.org/abs/1703.06211v3 | Deformable Convolutional Networks | 2 | [
{
"area": "General",
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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{
"area": "Computer Vision",
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{
"area": "Computer Vision",
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"title": "0-MMS: Zero-Shot Multi-Motion Segmentation With A Monocular Event Camera",
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} | 2,000 | https://arxiv.org/abs/2006.06158v2 | 0-MMS: Zero-Shot Multi-Motion Segmentation With A Monocular Event Camera | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
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