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 |
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 0 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 0 | [
{
"area": "Computer Vision",
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}
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{
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}
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"title": "Improving Transformer Optimization Through Better Initialization",
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{
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{
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}
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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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https://paperswithcode.com/method/gpsa | GPSA | Gated Positional Self-Attention | **Gated Positional Self-Attention (GPSA)** is a self-attention module for vision transformers, used in the [ConViT](https://paperswithcode.com/method/convit) architecture, that can be initialized as a convolutional layer -- helping a ViT learn inductive biases about locality. | {
"title": "ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases",
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{
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https://paperswithcode.com/method/regada | ReGaDa | Residual gating mechanism to compose adverb-action representations | {
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{
"area": "Computer Vision",
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{
"area": "General",
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{
"area": "Computer Vision",
"area_id": "computer-vision",
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}
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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 | 0 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
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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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{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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{
"area": "General",
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}
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{
"area": "Computer Vision",
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"collection": "Degridding"
},
{
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
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{
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}
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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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{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Representations"
}
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https://paperswithcode.com/method/swiglu | SwiGLU | SwiGLU | **SwiGLU** is an activation function which is a variant of [GLU](https://paperswithcode.com/method/glu). The definition is as follows:
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{
"area": "General",
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{
"area": "General",
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}
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{
"area": "General",
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}
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{
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}
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https://paperswithcode.com/method/ke-mlm | KE-MLM | Knowledge Enhanced Masked Language Model | {
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{
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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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