url stringlengths 36 82 | name stringlengths 2 143 | full_name stringlengths 2 143 โ | description stringlengths 0 9.95k | paper dict | introduced_year int64 1.95k 2.02k | source_url stringlengths 32 228 โ | source_title stringlengths 9 170 โ | code_snippet_url stringclasses 464
values | num_papers int64 0 37.4k | collections listlengths 0 6 |
|---|---|---|---|---|---|---|---|---|---|---|
https://paperswithcode.com/method/generalized-focal-loss | Generalized Focal Loss | Generalized Focal Loss | **Generalized Focal Loss (GFL)** is a loss function for object detection that combines Quality [Focal Loss](https://paperswithcode.com/method/focal-loss) and Distribution Focal Loss into a general form. | {
"title": "Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection",
"url": "https://paperswithcode.com/paper/generalized-focal-loss-learning-qualified-and"
} | 2,000 | https://arxiv.org/abs/2006.04388v1 | Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection | 4 | [
{
"area": "General",
"area_id": "general",
"collection": "Loss Functions"
}
] | |
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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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} | 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"
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{
"area": "Computer Vision",
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"collection": "3D Object Detection 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": "3D Object Detection Models"
}
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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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"collection": "2D Parallel Distributed Methods"
}
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{
"area": "Computer Vision",
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"collection": "3D Face Mesh Models"
}
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{
"area": "General",
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{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
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] |
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{
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{
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{
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}
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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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} | 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",
"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"
}
] |
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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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} | 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": "0.8% Nyquist computational ghost imaging via non-experimental deep learning",
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{
"area": "Computer Vision",
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"collection": "3D Representations"
}
] |
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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",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "10,000 km Straight-line Transmission using a Real-time Software-defined GPU-Based Receiver",
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{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Action Recognition 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"
}
] |
https://paperswithcode.com/method/how-do-i-ask-a-question-at-expedia-customer | How do I ask a question at Expedia?#Customer-Service | How do I ask a question at Expedia?#Customer-Service | 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": "0-1 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/does-viking-cruises-offer-senior-discounts-1 | Does Viking Cruises offer senior discounts? [[ Including Seniors]] | Does Viking Cruises offer senior discounts? [[ Including Seniors]] | Viking Cruises does not offer specific, age-based senior discounts 1-855-732-4023. Instead, they focus on providing "Inclusive Value Pricing" which includes essential elements of the cruise experience without unnecessary extras. They also have promotions like their current Spring Sale, which includes free airfare and s... | {
"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",
"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",
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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/l-gcn | L-GCN | Learnable adjacency matrix GCN | Graph structure is learnable | {
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{
"area": "Graphs",
"area_id": "graphs",
"collection": "Graph Representation Learning"
}
] |
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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",
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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",
"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 | 2 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
},
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection 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": "0.8% 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 Object Detection Models"
}
] |
https://paperswithcode.com/method/cross-resolution-features | Cross-resolution features | Cross-resolution features | {
"title": "EfficientPose: Scalable single-person pose estimation",
"url": "https://paperswithcode.com/paper/efficientpose-scalable-single-person-pose"
} | 2,000 | https://arxiv.org/abs/2004.12186v2 | EfficientPose: Scalable single-person pose estimation | null | 2 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Feature Extractors"
}
] | |
https://paperswithcode.com/method/laem | LAEM | Localization-Aware Expectation-Maximization | Method that adapts expectation-maximization (EM) to aggregate annotations that work on tasks with combined localization and classification like object detection and instance segmentation. | {
"title": "Drawing the Same Bounding Box Twice? Coping Noisy Annotations in Object Detection with Repeated Labels",
"url": "https://paperswithcode.com/paper/drawing-the-same-bounding-box-twice-coping"
} | 2,000 | https://arxiv.org/abs/2309.09742v1 | Drawing the Same Bounding Box Twice? Coping Noisy Annotations in Object Detection with Repeated Labels | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Label Correction"
}
] | |
https://paperswithcode.com/method/adaptive-feature-pooling | Adaptive Feature Pooling | Adaptive Feature Pooling | **Adaptive Feature Pooling** pools features from all levels for each proposal in object detection and fuses them for the following prediction. For each proposal, we map them to different feature levels. Following the idea of [Mask R-CNN](https://paperswithcode.com/method/adaptive-feature-pooling), [RoIAlign](https://pa... | {
"title": "Path Aggregation Network for Instance Segmentation",
"url": "https://paperswithcode.com/paper/path-aggregation-network-for-instance"
} | 2,000 | http://arxiv.org/abs/1803.01534v4 | Path Aggregation Network for Instance Segmentation | https://github.com/ShuLiu1993/PANet/blob/2644d5ad6ae98c2bf58df45c8792c019b1d7b2b9/lib/model/roi_pooling/functions/roi_pool.py#L6 | 20 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Pooling Operations"
}
] |
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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/pcnn-ranking | PCNN Ranking | Probable-Class Nearest-Neighbor Ranking | We present a new class of nearest-neighbor explanations (called Probable-Class Nearest Neighbors or PCNN) and show a novel utility of the XAI method: To improve predictions of a frozen, pretrained classifier **C.** Our method consistently improves fine-grained image classification accuracy on CUB-200, Cars-196, and Dog... | {
"title": "PCNN: Probable-Class Nearest-Neighbor Explanations Improve Fine-Grained Image Classification Accuracy for AIs and Humans",
"url": "https://paperswithcode.com/paper/advisingnets-learning-to-distinguish-correct"
} | 2,000 | https://arxiv.org/abs/2308.13651v5 | PCNN: Probable-Class Nearest-Neighbor Explanations Improve Fine-Grained Image Classification Accuracy for AIs and Humans | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Models"
}
] |
https://paperswithcode.com/method/cm | Consistency Models | Consistency Models | {
"title": "Consistency Models",
"url": "https://paperswithcode.com/paper/consistency-models"
} | 2,000 | https://arxiv.org/abs/2303.01469v2 | Consistency Models | 59 | [
{
"area": "General",
"area_id": "general",
"collection": "Diffusion Models"
}
] | ||
https://paperswithcode.com/method/gic | GIC | Graph InfoClust | {
"title": "Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning",
"url": "https://paperswithcode.com/paper/graph-infoclust-leveraging-cluster-level-node"
} | 2,000 | https://arxiv.org/abs/2009.06946v1 | Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning | null | 14 | [
{
"area": "Graphs",
"area_id": "graphs",
"collection": "Graph Embeddings"
}
] | |
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"title": "CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement",
"url": "https://paperswithcode.com/paper/cascadepsp-toward-class-agnostic-and-very"
} | 2,000 | https://arxiv.org/abs/2005.02551v1 | CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement | 2 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Semantic Segmentation 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": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
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{
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}
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"area": "General",
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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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} | 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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{
"area": "General",
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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",
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"collection": "2D Parallel Distributed Methods"
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https://paperswithcode.com/method/depthwise-convolution | Depthwise Convolution | Depthwise Convolution | **Depthwise Convolution** is a type of convolution where we apply a single convolutional filter for each input channel. In the regular 2D [convolution](https://paperswithcode.com/method/convolution) performed over multiple input channels, the filter is as deep as the input and lets us freely mix channels to generate ea... | null | 2,016 | null | null | 1,321 | [
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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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{
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{
"area": "Computer Vision",
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"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": "Computer Vision",
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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",
"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"
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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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} | 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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"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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"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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{
"area": "General",
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{
"area": "General",
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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