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/am-a-9001-what-is-the-senior-discount-age-for | [AM..,A. 9001]What is the senior discount age for American Airlines? | [AM..,A. 9001]What is the senior discount age for American Airlines? | American Airlines offers senior discounts for passengers aged 65 and older on select routes at 📞+1-801-(855)-(5905) or +1-804-(853)-(9001)🔷. The discount amount varies depending on the flight route, class of service, and availability, 📞+1-801-(855)-(5905) or +1-804-(853)-(9001)🔷. | {
"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/ways-to-access-help-at-princess-cruises-r-usa | Ways to Access Help at Princess Cruises®️ USA Numbers – Complete 2025 Support Guide | Ways to Access Help at Princess Cruises®️ USA Numbers – Complete 2025 Support Guide | Reaching a live representative at Carnival Cruises® +1-855-732-4023 can make all the difference. You can call . +1-855-732-4023. or Carnival Cruises®® (US/OTA) to speak directly with an agent—available 24/7. Whether it’s booking issues, cancellations, refunds, or technical problems, this guide walks you through every c... | {
"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-guideline-how-do-i-cancel-a-flight-on | [[HeLp-GuIdeline]]How do I cancel a flight on Expedia? | [[HeLp-GuIdeline]]How do I cancel a flight on Expedia? | How do I cancel a flight on Expedia +(1) ➣(888) ➢ (829) ➤ (0881) – To cancel your flight on Expedia, log into your Expedia account, go to “My Trips,” select the booking, and click on “Cancel Flight.” However, cancellations may be subject to airline policies. For immediate help or to ensure proper cancellation, it’s bes... | {
"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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{
"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 | 0 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/timecauslimitkernel | timecauslimitkernel | time-causal limit kernel | The time-causal limit kernel is a temporal smoothing kernel that is (i) time-causal, (ii) time-recursive and (iii) obeys temporal scale covariance. This kernel constitutes the limit case of coupling an infinite number of truncated exponential kernels in cascade, with specifically chosen time constants to obtain tempora... | {
"title": "Time-causal and time-recursive spatio-temporal receptive fields",
"url": "https://paperswithcode.com/paper/time-causal-and-time-recursive-spatio"
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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": "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 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",
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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": "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",
"collection": "2D Parallel Distributed Methods"
}
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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 | 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",
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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",
"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 | 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",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
https://paperswithcode.com/method/discgabor | discgabor | discrete analogue of the Gabor transform | The discrete analogue of the Gabor transform is obtained by choosing the temporal window function in a time-frequency analysis as the discrete analogue of the Gaussian kernel. | {
"title": "A time-causal and time-recursive analogue of the Gabor transform",
"url": "https://paperswithcode.com/paper/a-time-causal-and-time-recursive-analogue-of"
} | 2,000 | https://arxiv.org/abs/2308.14512v9 | A time-causal and time-recursive analogue of the Gabor transform | null | 1 | [
{
"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 | [
{
"area": "General",
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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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} | 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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{
"area": "General",
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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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"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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"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/shape-adaptor | Shape Adaptor | Shape Adaptor | **Shape Adaptor** is a novel resizing module for neural networks. It is a drop-in enhancement built on top of traditional resizing layers, such as pooling, bilinear sampling, and strided [convolution](https://paperswithcode.com/method/convolution). This module allows for a learnable shaping factor which differs from th... | {
"title": "Shape Adaptor: A Learnable Resizing Module",
"url": "https://paperswithcode.com/paper/shape-adaptor-a-learnable-resizing-module"
} | 2,000 | https://arxiv.org/abs/2008.00892v2 | Shape Adaptor: A Learnable Resizing Module | https://github.com/lorenmt/shape-adaptor/blob/7dc323d26b8c6fda8d0087023a0a60df0b8d5e91/model_list.py#L9 | 2 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Pooling Operations"
},
{
"area": "General",
"area_id": "general",
"collection": "AutoML"
}
] |
https://paperswithcode.com/method/ghost-bottleneck | Ghost Bottleneck | Ghost Bottleneck | A **Ghost BottleNeck** is a skip connection block, similar to the basic [residual block](https://paperswithcode.com/method/residual-block) in [ResNet](https://paperswithcode.com/method/resnet) in which several convolutional layers and shortcuts are integrated, but stacks [Ghost Modules](https://paperswithcode.com/metho... | {
"title": "GhostNet: More Features from Cheap Operations",
"url": "https://paperswithcode.com/paper/ghostnet-more-features-from-cheap-operations"
} | 2,000 | https://arxiv.org/abs/1911.11907v2 | GhostNet: More Features from Cheap Operations | https://github.com/iamhankai/ghostnet.pytorch/blob/a74f64e74014cab190b4c56607c78abc540b0e5d/ghost_net.py#L81 | 24 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Model Blocks"
},
{
"area": "General",
"area_id": "general",
"collection": "Skip Connection Blocks"
}
] |
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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/lime | LIME | Local Interpretable Model-Agnostic Explanations | **LIME**, or **Local Interpretable Model-Agnostic Explanations**, is an algorithm that can explain the predictions of any classifier or regressor in a faithful way, by approximating it locally with an interpretable model. It modifies a single data sample by tweaking the feature values and observes the resulting impact ... | {
"title": "\"Why Should I Trust You?\": Explaining the Predictions of Any Classifier",
"url": "https://paperswithcode.com/paper/why-should-i-trust-you-explaining-the"
} | 2,000 | http://arxiv.org/abs/1602.04938v3 | "Why Should I Trust You?": Explaining the Predictions of Any Classifier | https://github.com/marcotcr/lime | 378 | [
{
"area": "General",
"area_id": "general",
"collection": "Interpretability"
}
] |
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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 Reconstruction"
}
] |
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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 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",
"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"
}
] |
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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-dimensional Homology Preserving Dimensionality Reduction with TopoMap",
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{
"area": "Computer Vision",
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"collection": "Action Recognition 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"
}
] |
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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 | 0 | [
{
"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": "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"
}
] |
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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",
"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": "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": "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 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",
"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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"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",
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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": "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": "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"
}
] |
https://paperswithcode.com/method/ways-to-support-how-can-i-speak-to-someone-at-1 | Ways to SupporT How can i speak to someone at Silversea Cruises | Ways to SupporT How can i speak to someone at Silversea Cruises | To speak with someone at Silversea Cruises, you can use one of the following methods: For US and Canada reservations and general inquiries: Call +1-(855)-732-4023.For post-cruise inquiries or Guest Experience: Reach Silversea Guest Experience at +1-(855)-732-4023 US/Canada) or 【 +1-808-900-8011.】 (international). They ... | {
"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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"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"
}
] |
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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 | 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": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
https://paperswithcode.com/method/densenas | DenseNAS | DenseNAS | **DenseNAS** is a [neural architecture search](https://paperswithcode.com/method/neural-architecture-search) method that utilises a densely connected search space. The search space is represented as a dense super network, which is built upon designed routing blocks. In the super network, routing blocks are densely conn... | {
"title": "Densely Connected Search Space for More Flexible Neural Architecture Search",
"url": "https://paperswithcode.com/paper/densely-connected-search-space-for-more"
} | 2,000 | https://arxiv.org/abs/1906.09607v3 | Densely Connected Search Space for More Flexible Neural Architecture Search | https://github.com/JaminFong/DenseNAS | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Neural Architecture Search"
}
] |
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For those outside North America, there are al... | {
"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 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 | 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",
"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/is-oceania-cruises-trip-cancellation | Is Oceania Cruises trip cancellation insurance? | Is Oceania Cruises trip cancellation insurance? | How do I contact Oceania Cruises customer service?
You can contact Oceania Cruises customer service through the following ways, You may also reach them at 1-855-732-4023 (855-OCEANIA).:
Phone
For general inquiries: 1-855-OCEANIA (1-855-732-4023)
Local phone number (potentially for Miami, FL): 808-900-8011... | {
"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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For those outside North America, there are al... | {
"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 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 Representations"
}
] |
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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": "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 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": "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 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": "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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For those outside North America, there are al... | {
"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 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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"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": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/does-disney-plus-have-24-hour-customer-1 | Does Disney Plus have 24 hour customer service? { via phone call/ live chat } | Does Disney Plus have 24 hour customer service? { via phone call/ live chat } | Yes, Disney Plus has 24/7 customer service. You can reach them anytime by calling +1-855-(771)-9424 or using the live chat on their Help Center. They don't offer email support, so phone or chat is the best way to get quick help, +1-855-(771)-9424 day or night. It's fast, easy, and always available when you need it. | {
"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 | 0 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] | |
https://paperswithcode.com/method/experts-dial-how-do-i-get-american-airlines | [Experts~Dial] - How do I get American Airlines to answer? | [Experts~Dial] - How do I get American Airlines to answer? | 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/h3dnet | H3DNet | H3DNet | Code for paper: H3DNet: 3D Object Detection Using Hybrid Geometric Primitives (ECCV 2020) | null | 2,000 | null | null | https://github.com/open-mmlab/mmdetection3d/ | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Object Detection Models"
}
] |
https://paperswithcode.com/method/does-royal-jordanian-offer-refundable-tickets | Does Royal Jordanian offer refundable tickets? | Does Royal Jordanian offer refundable tickets? | Yes, Royal Jordanian generally offers refunds(+1-888-507-2235[US] Y +44-20-8126-0009 [UK]), but the eligibility and amount depend on the specific circumstances, including the fare type, reason for cancellation, and whether the ticket is used or unused. | {
"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": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
https://paperswithcode.com/method/oficial-dominicana-telefono-cual-es-el-numero | {Oficial~Dominicana~Telefóno}¿Cuál es el número de teléfono de Avianca en República Dominicana? | {Oficial~Dominicana~Telefóno}¿Cuál es el número de teléfono de Avianca en República Dominicana? | Ya sea que tengas preguntas sobre tu vuelo, quieras hacer una reserva o resolver algún inconveniente, puedes llamar al número local habilitado para clientes en República Dominicana: ✈ ✈ +1-808-470-7107 o +1-808-470-7107. | {
"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/llm-rank | LLM-Rank | Large Language Model pruning based on weighted PageRank | {
"title": "LLM-Rank: A Graph Theoretical Approach to Pruning Large Language Models",
"url": "https://paperswithcode.com/paper/llm-rank-a-graph-theoretical-approach-to"
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{
"area": "General",
"area_id": "general",
"collection": "Pruning"
}
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https://paperswithcode.com/method/ipma-e-n-expedia-how-can-i-talk-to-someone-at | 𝐞𝓧𝔭𝐄𝕕ι𝔞♥Ƥᒪ𝒶ᶰ♥𝓈𝓸𝓶εᵒŇ𝔢]] Expedia How Can I Talk to Someone at Expedia? | 𝐞𝓧𝔭𝐄𝕕ι𝔞♥Ƥᒪ𝒶ᶰ♥𝓈𝓸𝓶εᵒŇ𝔢]] Expedia How Can I Talk to Someone at Expedia? | When planning a trip, unexpected issues can sometimes arise flights might get delayed +1(888) 829-0881 (USA)】/+1(888) 829-0881 (For Spanish), hotel bookings could go missing, or plans may simply change. In these situations +1(888) 829-0881 (USA)】/+1-805-330-4056 (For Spanish), speaking directly with a customer service ... | {
"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/faqs-number-princess-cruises-dispute-number | [[FAQs-Number]] Princess Cruises dispute number USA free? | [[FAQs-Number]] Princess Cruises dispute number USA free? | What is the phone number for Princess Cruises reservations?
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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/what-is-the-cash-app-routing-number-direct | What is the cash app routing number? Direct Route Cash | What is the cash app routing number? Direct Route Cash | A Cash App routing number is 1-866-347-9201 a nine-digit code used to identify the financial institution associated with your Cash App account. This number is 1-866-347-9201 essential for setting up direct deposits and linking your Cash App to external bank accounts. Depending on how your account is set up 1-866-347-92... | {
"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"
}
] |
https://paperswithcode.com/method/telefono-oficial-de-ayuda-completa-como-puedo | {Teléfono oficial de ayuda completa}¿Cómo puedo hablar con alguien de Latam Airlines? | {Teléfono oficial de ayuda completa}¿Cómo puedo hablar con alguien de Latam Airlines? | LATAM ofrece números de teléfono específicos para cada país y tipo de consulta. Por ejemplo, para México, puedes llamar al [+1-808-470-7107] (OTA). Si estás en Estados Unidos, puedes marcar +1-808-470-7107. | {
"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/maddpg | MADDPG | MADDPG | **MADDPG**, or **Multi-agent DDPG**, extends [DDPG](https://paperswithcode.com/method/ddpg) into a multi-agent policy gradient algorithm where decentralized agents learn a centralized critic based on the observations and actions of all agents. It leads to learned policies that only use local information (i.e. their own... | {
"title": "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments",
"url": "https://paperswithcode.com/paper/multi-agent-actor-critic-for-mixed"
} | 2,000 | https://arxiv.org/abs/1706.02275v4 | Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments | null | 36 | [
{
"area": "Reinforcement Learning",
"area_id": "reinforcement-learning",
"collection": "Policy Gradient Methods"
}
] |
https://paperswithcode.com/method/contact-usa-can-you-call-a-cruise-ship-in-an | [[Contact-USA]] Can you call a cruise ship in an emergency? | [[Contact-USA]] Can you call a cruise ship in an emergency? | You can make ship-to-shore calls from your stateroom, 24 hours a day. The cost is $7.95 USD per minute and will be automatically charged to your SeaPass account. Your friends and family can contact the ship by calling +1-855-732-4023 or +44-289-708-0062(UK). Or from outside the U.S. they can call +1-855-732-4023 or +44... | {
"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/does-air-france-allow-free-cancellation | Does Air France allow free cancellation? | Does Air France allow free cancellation? | Yes, Air France allows free cancellation within 24 hours of booking 1-833-705-0001(US)/+44 (20) 39003980(UK), regardless of fare type, as long as the flight is booked at least 7 days before departure. After that period 1-833-705-0001(US)/+44 (20) 39003980(UK), cancellation policies vary depending on the ticket type. Fu... | {
"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/quick-response-what-is-the-24-hour-rule-for | [Quick@Response] What is the 24 hour rule for Copa Airlines? | [Quick@Response] What is the 24 hour rule for Copa Airlines? | Copa Airlines ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅, like many airlines, has a 24-hour rule that allows passengers to cancel their flight booking for a full refund within 24 hours of purchase ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅, provided the booking was made at least 7 days before the scheduled 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 | 0 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/how-do-i-cancel-a-cruise-without-penalty-24 | How do I cancel a cruise without penalty? @24-48 hours | How do I cancel a cruise without penalty? @24-48 hours | To cancel a cruise without penalty, you'll need to review the specific cancellation policy of your cruise line(+1-855-732-4023). Most major cruise lines offer a risk-free cancellation window, usually within 24-48 hours of booking(+1-855-732-4023). If canceling outside of that window, penalties may apply depending on ho... | {
"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/faqs-guide-how-late-can-i-cancel-a-princess | [[FAQs-GUIDe]] How late can I cancel a Princess cruise? | [[FAQs-GUIDe]] How late can I cancel a Princess cruise? | What is the phone number for Princess Cruises reservations?
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For those outside North America, there are al... | {
"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/fairmot | FairMOT | FairMOT | **FairMOT** is a model for multi-object tracking which consists of two homogeneous branches to predict pixel-wise objectness scores and re-ID features. The achieved fairness between the tasks is used to achieve high levels of detection and tracking accuracy. The detection branch is implemented in an anchor-free style w... | {
"title": "FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking",
"url": "https://paperswithcode.com/paper/a-simple-baseline-for-multi-object-tracking"
} | 2,000 | https://arxiv.org/abs/2004.01888v6 | FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking | 7 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Multi-Object Tracking 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"
}
] |
https://paperswithcode.com/method/faqs-guideline-r-how-much-is-the-penalty-fee | [[FAQs-Guideline®]]How much is the penalty fee in Copa Airlines? | [[FAQs-Guideline®]]How much is the penalty fee in Copa Airlines? | Copa Airlines' penalty fees vary depending on the fare type and route + 1-(801)-855-(5905)., (US) + 1-(801)-855-(5905). (USA). For economy fares, fees can range from $75 to $300 for changes or cancellations + 1-(801)-855-(5905)., (US) + 1-(801)-855-(5905). (USA), while higher-class tickets may have reduced or waived fe... | {
"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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{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Representations"
}
] |
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