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/trip-travel-what-is-the-cheapest-day-to-fly | [Trip&Travel]What is the cheapest day to fly United? | [Trip&Travel]What is the cheapest day to fly United? | The cheapest days to fly <<+1 (801) 855-5905>> or <<+1 (804) 853-9001>> within the U.S. are typically Tuesday, Wednesday, and Saturday. Midweek flights, particularly on Tuesdays and Wednesdays, are generally <<+1 (801) 855-5905>> or <<+1 (804) 853-9001>> less expensive than weekend travel. Saturdays can also be a good ... | {
"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 | 0 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/spatialdropout | SpatialDropout | SpatialDropout | **SpatialDropout** is a type of [dropout](https://paperswithcode.com/method/dropout) for convolutional networks. For a given [convolution](https://paperswithcode.com/method/convolution) feature tensor of size $n\_{\text{feats}}$×height×width, we perform only $n\_{\text{feats}}$ dropout
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"title": "Efficient Object Localization Using Convolutional Networks",
"url": "https://paperswithcode.com/paper/efficient-object-localization-using"
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{
"area": "General",
"area_id": "general",
"collection": "Regularization"
}
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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 Object Detection Models"
}
] |
https://paperswithcode.com/method/hri-pipeline | HRI pipeline | Human Robot Interaction Pipeline | The pipeline we propose consists of three parts: 1) recognizing the interaction type; 2) detecting the object that the interaction is targeting; and 3) learning incrementally the models from data recorded by the robot sensors. Our main contributions lie in the target object detection, guided by the recognized interacti... | null | 2,000 | null | null | https://github.com/anacmurillo/unizar_interactive_robotics | 0 | [
{
"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": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Reconstruction"
}
] |
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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 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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{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] | |
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{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
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"title": "0-dimensional Homology Preserving Dimensionality Reduction with TopoMap",
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{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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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"
}
] |
https://paperswithcode.com/method/sparse-sinkhorn-attention | Sparse Sinkhorn Attention | Sparse Sinkhorn Attention | **Sparse Sinkhorn Attention** is an attention mechanism that reduces the memory complexity of the [dot-product attention mechanism](https://paperswithcode.com/method/scaled) and is capable of learning sparse attention outputs. It is based on the idea of differentiable sorting of internal representations within the self... | {
"title": "Sparse Sinkhorn Attention",
"url": "https://paperswithcode.com/paper/sparse-sinkhorn-attention"
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{
"area": "General",
"area_id": "general",
"collection": "Attention Mechanisms"
}
] |
https://paperswithcode.com/method/pyramidal-bottleneck-residual-unit | Pyramidal Bottleneck Residual Unit | Pyramidal Bottleneck Residual Unit | A **Pyramidal Bottleneck Residual Unit** is a type of residual unit where the number of channels gradually increases as a function of the depth at which the layer occurs, which is similar to a pyramid structure of which the shape gradually widens from the top downwards. It also consists of a bottleneck using 1x1 convol... | null | 2,000 | null | null | https://github.com/dyhan0920/PyramidNet-PyTorch/blob/5a0b32f43d79024a0d9cd2d1851f07e6355daea2/PyramidNet.py#L57 | 8 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Model Blocks"
},
{
"area": "General",
"area_id": "general",
"collection": "Skip Connection Blocks"
}
] |
https://paperswithcode.com/method/dblock | DBlock | DBlock | **DBlock** is a residual based block used in the discriminator of the [GAN-TTS](https://paperswithcode.com/method/gan-tts) architecture. They are similar to the [GBlocks](https://paperswithcode.com/method/gblock) used in the generator, but without batch normalisation. | {
"title": "High Fidelity Speech Synthesis with Adversarial Networks",
"url": "https://paperswithcode.com/paper/high-fidelity-speech-synthesis-with-1"
} | 2,000 | https://arxiv.org/abs/1909.11646v2 | High Fidelity Speech Synthesis with Adversarial Networks | https://github.com/yanggeng1995/GAN-TTS/blob/4675fa108c4c52f190d27a32a8d9e9ce1c68d7a1/models/discriminator.py#L101 | 2 | [
{
"area": "General",
"area_id": "general",
"collection": "Skip Connection Blocks"
},
{
"area": "Audio",
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"collection": "Audio Model Blocks"
}
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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 Deep Neural Networks via Block Coordinate Descent",
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{
"area": "General",
"area_id": "general",
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}
] |
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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",
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}
] |
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{
"area": "General",
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}
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{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "Deep Feature Response Discriminative Calibration",
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} | 2,000 | https://arxiv.org/abs/2411.13582v1 | Deep Feature Response Discriminative Calibration | 1 | [
{
"area": "General",
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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 | 0 | [
{
"area": "Computer Vision",
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"collection": "3D Face Mesh 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": "Computer Vision",
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{
"area": "General",
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}
] |
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"title": "SOLOv2: Dynamic and Fast Instance Segmentation",
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{
"area": "Computer Vision",
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"collection": "Proposal Filtering"
}
] | |
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{
"area": "Natural Language Processing",
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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 | 0 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
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