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
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{
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{
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{
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{
"area": "Computer Vision",
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{
"area": "Reinforcement Learning",
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},
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{
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}
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"title": "RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification",
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"area": "Graphs",
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}
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"title": "RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation",
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{
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{
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{
"area": "General",
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"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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{
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{
"area": "General",
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{
"area": "General",
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{
"area": "General",
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{
"area": "General",
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},
{
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$$
\begin{aligned}
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"title": "YOLOv4: Optimal Speed and Accuracy of Object Detection",
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{
"area": "Computer Vision",
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{
"area": "Computer Vision",
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}
] |
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{
"area": "General",
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"collection": "Stochastic Optimization"
},
{
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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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"title": "DeepWalk: Online Learning of Social Representations",
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{
"area": "Graphs",
"area_id": "graphs",
"collection": "Graph Embeddings"
}
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"title": "YOLOP: You Only Look Once for Panoptic Driving Perception",
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{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Lane Detection Models"
},
{
"area": "Computer Vision",
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"collection": "Semantic Segmentation Models"
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{
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"colle... | |
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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": "AccoMontage: Accompaniment Arrangement via Phrase Selection and Style Transfer",
"url": "https://paperswithcode.com/paper/accomontage-accompaniment-arrangement-via"
} | 2,000 | https://arxiv.org/abs/2108.11213v1 | AccoMontage: Accompaniment Arrangement via Phrase Selection and Style Transfer | 2 | [
{
"area": "Audio",
"area_id": "audio",
"collection": "Generative Audio 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": "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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"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": "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 | 0 | [
{
"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": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning",
"url": "https://paperswithcode.com/paper/inception-v4-inception-resnet-and-the-impact"
} | 2,000 | http://arxiv.org/abs/1602.07261v2 | Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning | https://github.com/kentsommer/keras-inceptionV4/blob/ef1db6f09b6511779c05fab47d374741bc89b5ee/inception_v4.py#L242 | 13 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Convolutional Neural Networks"
}
] |
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"title": "Log-time and Log-space Extreme Classification",
"url": "https://paperswithcode.com/paper/log-time-and-log-space-extreme-classification"
} | 2,000 | http://arxiv.org/abs/1611.01964v1 | Log-time and Log-space Extreme Classification | https://github.com/kjasinska/ltls/blob/bf542bf2791352bc9e75a70f50b7156f94bf6ecc/ltls.py#L15 | 2 | [
{
"area": "General",
"area_id": "general",
"collection": "Structured Prediction"
}
] |
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"title": "Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits",
"url": "https://paperswithcode.com/paper/multi-source-domain-adaptation-for-text"
} | 2,000 | https://arxiv.org/abs/2001.04362v3 | Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Domain Adaptation"
}
] | |
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\begin{equation}
h(X ; \{ \alpha, \theta\}) = \alpha_{0} + \sum_{k=1}^{K} \alpha_{k} b(X ; \theta_{k}),
\end{equation}
where the boosting mechan... | {
"title": "Boosting on the shoulders of giants in quantum device calibration",
"url": "https://paperswithcode.com/paper/boosting-on-the-shoulders-of-giants-in"
} | 2,000 | https://arxiv.org/abs/2005.06194v1 | Boosting on the shoulders of giants in quantum device calibration | https://github.com/a-wozniakowski/scikit-physlearn/blob/03f944c4fecf512ee763ec4552f30c4a3565612d/physlearn/supervised/regression.py#L581 | 2 | [
{
"area": "General",
"area_id": "general",
"collection": "Generalized Additive 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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} | 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"
}
] |
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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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"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",
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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",
"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"
}
] |
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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"
}
] |
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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"
}
] |
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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": "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 | 2 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] | |
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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": "6D Pose Estimation Models"
}
] |
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"title": "Learned in Translation: Contextualized Word Vectors",
"url": "https://paperswithcode.com/paper/learned-in-translation-contextualized-word"
} | 2,000 | http://arxiv.org/abs/1708.00107v2 | Learned in Translation: Contextualized Word Vectors | null | 13 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Contextualized Word Embeddings"
},
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Word Embeddings"
}
] |
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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 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",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
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"title": "GLU Variants Improve Transformer",
"url": "https://paperswithcode.com/paper/glu-variants-improve-transformer"
} | 2,000 | https://arxiv.org/abs/2002.05202v1 | GLU Variants Improve Transformer | 2 | [
{
"area": "General",
"area_id": "general",
"collection": "Activation Functions"
}
] | |
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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/how-can-i-speak-to-a-disney-cruise-line-4 | How can I speak to a Disney Cruise Line representative fast? Fast Support Guide | How can I speak to a Disney Cruise Line representative fast? Fast Support Guide | To speak with a Disney Cruise Line representative quickly, call 1-855-732-4023, although it may not be the official number. For the fastest service, it’s best to contact Disney Cruise Line directly at 1-855-732-4023, available daily from 8 AM to 10 PM ET. Call early—right at 8 AM—for the shortest wait times. When promp... | {
"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/dial-now-does-qatar-have-a-free-cancellation | [Dial~NoW]Does Qatar have a free cancellation policy? | [Dial~NoW]Does Qatar have a free cancellation policy? | Yes, Qatar Airways generally ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ offers a 24-hour free cancellation policy for flights booked directly through their website, mobile app, or call center ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅. To be eligible, the booking must be made at least seven days before the depart... | {
"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/activation-normalization | Activation Normalization | Activation Normalization | **Activation Normalization** is a type of normalization used for flow-based generative models; specifically it was introduced in the [GLOW](https://paperswithcode.com/method/glow) architecture. An ActNorm layer performs an affine transformation of the activations using a scale and bias parameter per channel, similar to... | {
"title": "Glow: Generative Flow with Invertible 1x1 Convolutions",
"url": "https://paperswithcode.com/paper/glow-generative-flow-with-invertible-1x1"
} | 2,000 | http://arxiv.org/abs/1807.03039v2 | Glow: Generative Flow with Invertible 1x1 Convolutions | https://github.com/openai/glow/blob/1f1352977cb1b21c7c0aa83b08efb24dfc216663/tfops.py#L70 | 61 | [
{
"area": "General",
"area_id": "general",
"collection": "Normalization"
}
] |
https://paperswithcode.com/method/faq-support-r-does-copa-have-free | [FAQ-suPport®]Does Copa have free cancellation? | [FAQ-suPport®]Does Copa have free cancellation? | Copa Airlines generally <<+1 (801) 855-5905>> or <<+1 (804) 853-9001>> offers a 24-hour risk-free cancellation policy for tickets purchased at least 7 days before the flight date <<+1 (801) 855-5905>> or <<+1 (804) 853-9001>>. This means you can cancel your booking within 24 hours of purchase for a full refund <<+1 (80... | {
"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/can-i-book-a-lufthansa-airlines-flight-over | [Ex~live]Is Expedia actually fully Refundable? | [Ex~live]Is Expedia actually fully Refundable? | Expedia ' cancellation policy allows you to cancel a flight within 24 hours of Booking for a full refund, as long as the departure is at least 7 days away. Cancellations after 24 hours may incur fees. For more details, contact customer care at ☎️+1-(805)-330-(4056) ⌛ or ☎️+1-(805)-330-(4056) ⌛
Can I change my fligh... | {
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] | |
https://paperswithcode.com/method/halonet | HaloNet | HaloNet | A **HaloNet** is a self-attention based model for efficient image classification. It relies on a local self-attention architecture that efficiently maps to existing hardware with haloing. The formulation breaks translational equivariance, but the authors observe that it improves throughput and accuracies over the cent... | {
"title": "Scaling Local Self-Attention for Parameter Efficient Visual Backbones",
"url": "https://paperswithcode.com/paper/scaling-local-self-attention-for-parameter"
} | 2,000 | https://arxiv.org/abs/2103.12731v3 | Scaling Local Self-Attention for Parameter Efficient Visual Backbones | null | 2 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Models"
}
] |
https://paperswithcode.com/method/reduction-a | Reduction-A | Reduction-A | **Reduction-A** is an image model block used in the [Inception-v4](https://paperswithcode.com/method/inception-v4) architecture. | {
"title": "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning",
"url": "https://paperswithcode.com/paper/inception-v4-inception-resnet-and-the-impact"
} | 2,000 | http://arxiv.org/abs/1602.07261v2 | Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning | https://github.com/Cadene/pretrained-models.pytorch/blob/8aae3d8f1135b6b13fed79c1d431e3449fdbf6e0/pretrainedmodels/models/inceptionresnetv2.py#L120 | 28 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Model Blocks"
}
] |
https://paperswithcode.com/method/expedia-dispute-resolve-how-to-make-a-claim | ((Expedia::Dispute::Resolve))How to make a claim against Expedia? | ((Expedia::Dispute::Resolve))How to make a claim against Expedia? | "
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To talk directly on Expedia (with a live person) by official Expedia number (e.g., *1➣ 805➣3... | {
"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/asked-questions-how-do-you-talk-to-someone-in | {{Asked~Questions}}How do you talk to someone in LATAM? | {{Asked~Questions}}How do you talk to someone in LATAM? | To talk to someone at LATAM Airlines,☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ you can call their customer service hotline, use their online chat feature, ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ reach out via social media ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ their help center. You can also visit thei... | {
"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/graph-neural-network | Graph Neural Network | Graph Neural Network | {
"title": "Graph Neural Networks: A Review of Methods and Applications",
"url": "https://paperswithcode.com/paper/graph-neural-networks-a-review-of-methods-and"
} | 2,000 | https://arxiv.org/abs/1812.08434v6 | Graph Neural Networks: A Review of Methods and Applications | null | 2,694 | [
{
"area": "Graphs",
"area_id": "graphs",
"collection": "Graph Representation Learning"
}
] | |
https://paperswithcode.com/method/call-the-direct-number-help-how-to-get | call the direct number.-Help]]How to get through to American Airlines fast? | call the direct number.-Help]]How to get through to American Airlines fast? | To reach American Airlines customer service quickly, you can call their customer service hotline at + 1-(801)-855-(5905). Direct Number: For speedier access, call the direct number. Have your confirmation number or booking information ready .To really get through to 𝙰𝚖𝚎𝚛𝚒𝚌𝚊𝚗 𝙰𝚒𝚛𝚕𝚒𝚗𝚎s, call their customer... | {
"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/como-solicitar-asistencia-en-united-airlines-2 | ¿Cómo solicitar asistencia en United Airlines?(Guide~@) | ¿Cómo solicitar asistencia en United Airlines? | ¿Cómo hablo con una persona en United Airlines? Para hablar con una persona en vivo en United Airlines, llama directamente al ✈+(+1-808- 470-7107) (EE. UU.) o al +(1)~808~470~(7107) (México). Estas líneas te conectarán con un representante que podrá asistirte con tus preguntas y necesidades. | {
"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/how-do-i-contact-philo-tv-customer-service | How do I contact Philo TV customer service? (( Quickly Steps )) | How do I contact Philo TV customer service? (( Quickly Steps )) | For help with Philo TV after their latest update, call 1ー855ー771ー9424 to speak with a support agent. They can assist with new features, streaming issues 1ー855ー771ー9424, or account problems. Philo's recent update improved performance 1ー855ー771ー9424, so if you face any trouble, a quick call can help you get back on track... | {
"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/gather-excite-networks | Gather-Excite Networks | Gather-Excite Networks | GENet combines part gathering and excitation operations. In the first step, it aggregates input features over large neighborhoods and models the relationship between different spatial locations. In the second step, it first generates an attention map of the same size as the input feature map, using interpolation. Then ... | {
"title": "Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks",
"url": "https://paperswithcode.com/paper/gather-excite-exploiting-feature-context-in"
} | 2,000 | http://arxiv.org/abs/1810.12348v3 | Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Attention Mechanisms"
}
] |
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