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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"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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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 0 | [
{
"area": "General",
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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 | 0 | [
{
"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 | 0 | [
{
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}
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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",
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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{
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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 | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] | |
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"title": "Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking",
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{
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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 | 0 | [
{
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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",
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}
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"title": "YOLO9000: Better, Faster, Stronger",
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} | 2,000 | http://arxiv.org/abs/1612.08242v1 | YOLO9000: Better, Faster, Stronger | 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",
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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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{
"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 | 0 | [
{
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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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{
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{
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{
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}
] |
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{
"area": "Computer Vision",
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}
] |
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{
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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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{
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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{
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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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}
] |
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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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{
"area": "General",
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}
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{
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}
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{
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}
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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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} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 0 | [
{
"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": "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 | 0 | [
{
"area": "General",
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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{
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"area_id": "general",
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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/linear-warmup | Linear Warmup | Linear Warmup | **Linear Warmup** is a learning rate schedule where we linearly increase the learning rate from a low rate to a constant rate thereafter. This reduces volatility in the early stages of training.
Image Credit: [Chengwei Zhang](https://www.dlology.com/about-me/) | null | 2,000 | null | null | null | 38 | [
{
"area": "General",
"area_id": "general",
"collection": "Learning Rate Schedules"
}
] |
https://paperswithcode.com/method/pafnet | PAFNet | Paddle Anchor Free Network | **PAFNet** is an anchor-free detector for object detection that removes pre-defined anchors and regresses the locations directly, which can achieve higher efficiency. The overall network is composed of a backbone, an up-sampling module, an AGS module, a localization branch and a regression branch. Specifically, ResNet... | {
"title": "PAFNet: An Efficient Anchor-Free Object Detector Guidance",
"url": "https://paperswithcode.com/paper/pafnet-an-efficient-anchor-free-object"
} | 2,000 | https://arxiv.org/abs/2104.13534v1 | PAFNet: An Efficient Anchor-Free Object Detector Guidance | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "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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Conta... | {
"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"
} | 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"
}
] |
https://paperswithcode.com/method/help-support-how-to-get-a-response-from-2 | Help & Support - How to Get a Response from American Airlines@call again at the same number. | Help & Support - How to Get a Response from American Airlines@call again at the same number. | To receive a prompt response from American Airlines, the most effective method is to call their customer service hotline at +1 (804) 853-9001. Whether your inquiry pertains to flights, delays, or refunds, this number is your best point of contact. If you are following up on emails or online claims, you should also call... | {
"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/why-does-my-cash-app-keep-saying-failed-fix | Why does my Cash App keep saying failed? Fix Cash App Fail | Why does my Cash App keep saying failed? Fix Cash App Fail | Cash App may keep saying "failed" due to reasons like insufficient funds 1-866-347-9201, incorrect payment details, poor internet connection, outdated app version, or security flags 1-866-347-9201. Verifying your info, updating the app, and checking your balance can often fix the issue. 1-866-347-9201 If problems conti... | {
"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 | 0 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
https://paperswithcode.com/method/social-media-how-do-i-ask-a-question-at | [[&&Social Media$$]]How do I ask a question at Alaska Airlines? | [[&&Social Media$$]]How do I ask a question at Alaska Airlines? | To ask a question at Alaska Airlines <<+1 (801) 855-5905>> or <<+1 (804) 853-9001>>, you have several options: use their website's "Help Center" and "Ask Alaska" AI-powered chat assistant, call their customer service line <<+1 (801) 855-5905>> or <<+1 (804) 853-9001>>, or text for flight information. | {
"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/bifpn | BiFPN | BiFPN | A **BiFPN**, or **Weighted Bi-directional Feature Pyramid Network**, is a type of feature pyramid network which allows easy and fast multi-scale feature fusion. It incorporates the multi-level feature fusion idea from [FPN](https://paperswithcode.com/method/fpn), [PANet](https://paperswithcode.com/method/panet) and [NA... | {
"title": "EfficientDet: Scalable and Efficient Object Detection",
"url": "https://paperswithcode.com/paper/efficientdet-scalable-and-efficient-object"
} | 2,000 | https://arxiv.org/abs/1911.09070v7 | EfficientDet: Scalable and Efficient Object Detection | https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch/blob/c533bc2de65135a6fe1d25ca437765c630943afb/efficientdet/model.py#L55 | 48 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Feature Pyramid Blocks"
},
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Feature Extractors"
}
] |
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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/asked-questions-does-copa-give-refunds | {{Asked~Questions}} Does copa give refunds? | {{Asked~Questions}} Does copa give refunds? | Copa does provide refunds for eligible bookings ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅. The refund policy depends on your ticket type and route. To check if your Copa ticket is refundable, call ☎️+1-801-(855)-(5905) (US) or +1-804-(853)-(9001)✅ (UK) for quick help. | {
"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 | 0 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Representations"
}
] |
https://paperswithcode.com/method/instant-guide-what-is-the-royal-caribbean-60 | [instant!!Guide]What is the Royal Caribbean 60% off promo code? | [instant!!Guide]What is the Royal Caribbean 60% off promo code? | To make a Royal Caribbean group booking of 8 or more staterooms, call their customer service line at (800) 465-3595. Alternatively, you can work with a local travel advisor. A group number will be assigned during the initial call, according to Royal Caribbean Cruises.
Yes 855-732-4023, Royal Caribbean offers several... | {
"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/local-importance-based-pooling | Local Importance-based Pooling | Local Importance-based Pooling | **Local Importance-based Pooling (LIP)** is a pooling layer that can enhance discriminative features during the downsampling procedure by learning adaptive importance weights based on inputs. By using a learnable network $G$ in $F$, the importance function now is not limited in hand-crafted forms and able to learn the ... | {
"title": "LIP: Local Importance-based Pooling",
"url": "https://paperswithcode.com/paper/lip-local-importance-based-pooling"
} | 2,000 | https://arxiv.org/abs/1908.04156v3 | LIP: Local Importance-based Pooling | https://github.com/sebgao/LIP/blob/5e85b9e55b9212fdf6abccb1fa8783d23620f53a/imagenet/lip_resnet.py#L19 | 1 | [
{
"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"
}
] |
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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 | 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 | 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 | 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 | 0 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
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