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/filter-response-normalization | Filter Response Normalization | Filter Response Normalization | **Filter Response Normalization (FRN)** is a type of normalization that combines normalization and an activation function, which can be used as a replacement for other normalizations and activations. It operates on each activation channel of each batch element independently, eliminating the dependency on other batch el... | {
"title": "Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks",
"url": "https://paperswithcode.com/paper/filter-response-normalization-layer"
} | 2,000 | https://arxiv.org/abs/1911.09737v2 | Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks | https://github.com/gupta-abhay/pytorch-frn/blob/cc86a984fcbae61431ed41f37695e78f5e4b196e/frn.py#L9 | 2 | [
{
"area": "General",
"area_id": "general",
"collection": "Normalization"
}
] |
https://paperswithcode.com/method/ga | GA | Genetic Algorithms | Genetic Algorithms are search algorithms that mimic Darwinian biological evolution in order to select and propagate better solutions. | {
"title": "Genetic Algorithms and the Traveling Salesman Problem a historical Review",
"url": "https://paperswithcode.com/paper/genetic-algorithms-and-the-traveling-salesman"
} | 2,000 | http://arxiv.org/abs/1901.05737v1 | Genetic Algorithms and the Traveling Salesman Problem a historical Review | 259 | [
{
"area": "Reinforcement Learning",
"area_id": "reinforcement-learning",
"collection": "Heuristic Search Algorithms"
}
] | |
https://paperswithcode.com/method/inception-resnet-v2-c | Inception-ResNet-v2-C | Inception-ResNet-v2-C | **Inception-ResNet-v2-C** is an image model block for an 8 x 8 grid used in the [Inception-ResNet-v2](https://paperswithcode.com/method/inception-resnet-v2) architecture. It largely follows the idea of Inception modules - and grouped convolutions - but also includes residual connections. | {
"title": "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning",
"url": "https://paperswithcode.com/paper/inception-v4-inception-resnet-and-the-impact"
} | 2,000 | http://arxiv.org/abs/1602.07261v2 | Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning | https://github.com/tensorflow/models/blob/e54fcee236a1258302342bd703ee27cbba0c12e3/research/slim/nets/inception_resnet_v2.py#L84 | 18 | [
{
"area": "General",
"area_id": "general",
"collection": "Skip Connection Blocks"
},
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Model Blocks"
}
] |
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Group ... | {
"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/tabtransformer | TabTransformer | TabTransformer | **TabTransformer** is a deep tabular data modeling architecture for supervised and semi-supervised learning. The TabTransformer is built upon self-attention based Transformers. The Transformer layers transform the embeddings of categorical features into robust contextual embeddings to achieve higher prediction accuracy... | {
"title": "TabTransformer: Tabular Data Modeling Using Contextual Embeddings",
"url": "https://paperswithcode.com/paper/tabtransformer-tabular-data-modeling-using"
} | 2,000 | https://arxiv.org/abs/2012.06678v1 | TabTransformer: Tabular Data Modeling Using Contextual Embeddings | 9 | [
{
"area": "General",
"area_id": "general",
"collection": "Deep Tabular Learning"
}
] | |
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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 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 Object Detection Models"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 0 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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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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"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": "Dual Attention Network for Scene Segmentation",
"url": "https://paperswithcode.com/paper/dual-attention-network-for-scene-segmentation"
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{
"area": "General",
"area_id": "general",
"collection": "Attention Mechanisms"
}
] | |
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
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{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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{
"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"
}
] |
https://paperswithcode.com/method/codeslam | CodeSLAM | CodeSLAM | CodeSLAM represents the 3D geometry of a scene using the latent space of a variational autoencoder. The depth thus becomes a function of the RGB image and the unknown code, $D = G_\theta(I,c)$. During training time, the weights of the network $G_\theta$ are learnt by training the generator and encoder using a standard ... | {
"title": "CodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAM",
"url": "https://paperswithcode.com/paper/codeslam-learning-a-compact-optimisable"
} | 2,000 | http://arxiv.org/abs/1804.00874v2 | CodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAM | 2 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Reconstruction"
}
] | |
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{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
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{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/mixnet | MixNet | MixNet | **MixNet** is a type of convolutional neural network discovered via AutoML that utilises MixConvs instead of regular depthwise convolutions. | {
"title": "MixConv: Mixed Depthwise Convolutional Kernels",
"url": "https://paperswithcode.com/paper/mixnet-mixed-depthwise-convolutional-kernels"
} | 2,000 | https://arxiv.org/abs/1907.09595v3 | MixConv: Mixed Depthwise Convolutional Kernels | https://github.com/osmr/imgclsmob/blob/c03fa67de3c9e454e9b6d35fe9cbb6b15c28fda7/pytorch/pytorchcv/models/mixnet.py#L344 | 12 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Light-weight neural networks"
},
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Convolutional Neural Networks"
}
] |
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"title": "Contour Proposal Networks for Biomedical Instance Segmentation",
"url": "https://paperswithcode.com/paper/contour-proposal-networks-for-biomedical"
} | 2,000 | https://arxiv.org/abs/2104.03393v1 | Contour Proposal Networks for Biomedical Instance Segmentation | https://github.com/FZJ-INM1-BDA/celldetection | 14 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "One-Stage Object Detection Models"
},
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Instance Segmentation Models"
},
{
"area": "Computer Vision",
"area_id": "computer-vision"... |
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{
"area": "Sequential",
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"collection": "Recurrent Neural Networks"
}
] |
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{
"area": "Reinforcement Learning",
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}
] |
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{
"area": "General",
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"collection": "2D Parallel Distributed Methods"
}
] |
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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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"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-copa-does-copa-give-senior-discounts | [faqs#Copa]]Does Copa give senior discounts? | [faqs#Copa]]Does Copa give senior discounts? | Copa Airlines does not have a widespread,📞+1-801-(855)-(5905) or +1-804-(853)-(9001)🔷 publicly advertised senior discount program. However,📞+1-801-(855)-(5905) or +1-804-(853)-(9001)🔷 they may offer discounts to senior citizens in specific countries or regions based on local regulations.📞+1-801-(855)-(5905) or +1-... | {
"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/contact-by-phone-can-you-wear-jeans-on | [[Contact By Phone]] Can you wear jeans on Azamara? | [[Contact By Phone]] Can you wear jeans on Azamara? | What is the dress code? In all of our dining rooms we encourage guests to follow a “resort casual” dress code, following good taste and common sense +1-855-732-4023 (USA) or 44-289-708-0062 (UK). Bare feet, tank tops, baseball caps, bathing suits, shorts, and jeans are not allowed in the main dining room or two special... | {
"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/cpvt | CPVT | Conditional Position Encoding Vision Transformer | **CPVT**, or **Conditional Position Encoding Vision Transformer**, is a type of [vision transformer](https://paperswithcode.com/methods/category/vision-transformer) which utilizes [conditional positional encoding](https://paperswithcode.com/method/conditional-positional-encoding). Other than the new encodings, it follo... | {
"title": "Conditional Positional Encodings for Vision Transformers",
"url": "https://paperswithcode.com/paper/do-we-really-need-explicit-position-encodings"
} | 2,000 | https://arxiv.org/abs/2102.10882v3 | Conditional Positional Encodings for Vision Transformers | 2 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Vision Transformers"
}
] | |
https://paperswithcode.com/method/large-scale-spectral-clustering | Large-scale spectral clustering | Large-scale spectral clustering | # [Spectral Clustering](https://paperswithcode.com/method/spectral-clustering)
Spectral clustering aims to partition the data points into $k$ clusters using the spectrum of the graph Laplacians
Given a dataset $X$ with $N$ data points, spectral clustering algorithm first constructs similarity matrix ${W}$, where $... | {
"title": "Divide-and-conquer based Large-Scale Spectral Clustering",
"url": "https://paperswithcode.com/paper/divide-and-conquer-based-large-scale-spectral"
} | 2,000 | https://www.researchgate.net/publication/351270623_Divide-and-conquer_based_Large-Scale_Spectral_Clustering | Divide-and-conquer based Large-Scale Spectral Clustering | 5 | [
{
"area": "General",
"area_id": "general",
"collection": "Clustering"
}
] | |
https://paperswithcode.com/method/projection-discriminator | Projection Discriminator | Projection Discriminator | A **Projection Discriminator** is a type of discriminator for generative adversarial networks. It is motivated by a probabilistic model in which the distribution of the conditional variable $\textbf{y}$ given $\textbf{x}$ is discrete or uni-modal continuous distributions.
If we look at the original solution for the ... | {
"title": "cGANs with Projection Discriminator",
"url": "https://paperswithcode.com/paper/cgans-with-projection-discriminator"
} | 2,000 | http://arxiv.org/abs/1802.05637v2 | cGANs with Projection Discriminator | https://github.com/pfnet-research/sngan_projection/blob/3ff93ef5f294c3b48c6b2cbc9b0033ee7a103621/dis_models/snresnet_32.py#L8 | 135 | [
{
"area": "General",
"area_id": "general",
"collection": "Discriminators"
}
] |
https://paperswithcode.com/method/can-i-bring-wine-on-a-celebrity-cruise | Can I bring wine on a celebrity cruise? | Can I bring wine on a celebrity cruise? | Yes 1-855-732-4023 or 1-808-900-8011, Celebrity Cruises is generally considered a premium or upscale cruise line. While not a true luxury line 1-855-732-4023 or 1-808-900-8011, Celebrity offers a step up from mass-market cruise lines with a focus on stylish design, elevated dining experiences, and enhanced 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/experts-guide-can-we-use-whatsapp-in-qatar | [Experts~Guide]Can we use WhatsApp in Qatar airport? | [Experts~Guide]Can we use WhatsApp in Qatar airport? | Yes, you can use WhatsApp at Qatar Airport at 📞 ☎️+1-801-(855)-(5905)or +1-804-853-9001✅🎟️✔️. long as you have access to Wi-Fi or mobile data. Qatar’s Hamad International Airport provides free Wi-Fi, allowing you to send messages, make calls, and use WhatsApp features. If you need further assistance, contact customer... | {
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 0 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/copa-airlines-como-puedo-cambiar-mi-tiquete | [[Copa Airlines]]¿Cómo puedo cambiar mi tiquete de Copa Airlines? | [[Copa Airlines]]¿Cómo puedo cambiar mi tiquete de Copa Airlines? | Para hablar con una persona viva en Copa Airlines, puedes llamar a su servicio de atención al cliente. En México, puedes marcar el +𝟙-𝟠𝟘𝟠-𝟜𝟟𝟘-𝟟𝟙𝟘𝟟 o +𝟙-𝟠𝟘𝟠-𝟜𝟟𝟘-𝟟𝟙𝟘𝟟. También puedes contactarlos a través de su sitio web, redes sociales, o en persona en sus oficinas de atención al cliente en el aero... | {
"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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Azamara was sold to Sycamore Partners, a private equity group that q... | {
"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/vdo-slam | VDO-SLAM | VDO-SLAM | **VDO-SLAM** is a feature-based stereo/RGB-D dynamic SLAM system that leverages image-based semantic information to simultaneously localise the robot, map the static and dynamic structure, and track motions of rigid objects in the scene. Input images are first pre-processed to generate instance-level object segmentatio... | {
"title": "VDO-SLAM: A Visual Dynamic Object-aware SLAM System",
"url": "https://paperswithcode.com/paper/vdo-slam-a-visual-dynamic-object-aware-slam"
} | 2,000 | https://arxiv.org/abs/2005.11052v3 | VDO-SLAM: A Visual Dynamic Object-aware SLAM System | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "SLAM Methods"
}
] |
https://paperswithcode.com/method/mei | MEI | Multi-partition Embedding Interaction | **MEI** introduces the *multi-partition embedding interaction* technique with block term tensor format to systematically address the efficiency--expressiveness trade-off in knowledge graph embedding. It divides the embedding vector into multiple partitions and learns the local interaction patterns from data instead of ... | {
"title": "Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion",
"url": "https://paperswithcode.com/paper/multi-partition-embedding-interaction-with"
} | 2,000 | https://arxiv.org/abs/2006.16365v2 | Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion | https://github.com/tranhungnghiep/MEI-KGE | 14 | [
{
"area": "Graphs",
"area_id": "graphs",
"collection": "Graph Representation Learning"
},
{
"area": "Graphs",
"area_id": "graphs",
"collection": "Graph Embeddings"
}
] |
https://paperswithcode.com/method/soft-nms | Soft-NMS | Soft-NMS | Non-maximum suppression is an integral part of the object detection pipeline. First, it sorts all detection boxes on the basis of their scores. The detection box $M$ with the maximum score is selected and all other detection boxes with a significant overlap (using a pre-defined threshold)
with $M$ are suppressed. This... | {
"title": "Soft-NMS -- Improving Object Detection With One Line of Code",
"url": "https://paperswithcode.com/paper/soft-nms-improving-object-detection-with-one"
} | 2,000 | http://arxiv.org/abs/1704.04503v2 | Soft-NMS -- Improving Object Detection With One Line of Code | null | 22 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Proposal Filtering"
}
] |
https://paperswithcode.com/method/aa-how-do-i-get-american-airlines-to-respond | AA-How do I get American Airlines to respond? | AA-How do I get American Airlines to respond? | To get a response from American Airlines, you can call their customer service line at + 1-(801)-855-(5905)..(US)., contact them via their customer relations form, send an email, or use social media platforms like Twitter. For faster assistance, calling or using the online form is generally recommended. | {
"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-proper-how-do-i-get-to-an-agent-on | [FAqs-Proper]How do I get to an agent on Expedia? | [FAqs-Proper]How do I get to an agent on Expedia? | To speak with a human representative at Expedia, you can call our customer service directly. Dial "+1-888-829-0881 or +1-805-330-4056 (Live Human)" (Quick connect) or 1-805-Expedia-LINE, follow the IVR instructions, or hold the line to be connected with a live agent. How do I file a claim against Expedia? To file a cla... | {
"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"
}
] |
https://paperswithcode.com/method/26-azamara-cruise-r-usa-contact-numbers | 26 Azamara Cruise®️ USA Contact Numbers: Verified Support Numbers 2025 | 26 Azamara Cruise®️ USA Contact Numbers: Verified Support Numbers 2025 | Azamara Cruise Cancellation Policy: Best Guide to Cancel & Get Refund – Call +1-855-732-4023 Now
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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/official-call-como-llamar-a-united-airlines | {Official Call}¿Cómo llamar a United Airlines desde Chile? | {Official Call}¿Cómo llamar a United Airlines desde Chile? | Si llamas desde fuera de Chile, utiliza el +1-808-470-7107 o al +1-808-470-7107. El equipo de United está listo para ayudarte con cualquier consulta. Para más información y horarios actualizados, visita la página oficial de United Airlines o llama al +1-808-470-7107 o al +1-808-470-7107 hoy mismo. | {
"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-public-how-do-i-complain-to-expedia | [[FAQs-PUBLic]]How do I complain to Expedia? | [[FAQs-PUBLic]]How do I complain to Expedia? | How can I complain to Expedia?
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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/faqs-cancellation-how-do-i-cancel-my-azamara | [[FAQS-Cancellation]] How do I cancel my Azamara cruise? | [[FAQS-Cancellation]] How do I cancel my Azamara cruise? | 74–61 days before sailing: 25% of your total fare is forfeited 1-855-732-4023 (USA) or 44-289-708-0062 (UK) based on standard policy. 60–31 days prior: 50% of the cruise fare is charged as a penalty 1-855-732-4023 (USA) or 44-289-708-0062 (UK) so the refund is half. 30–15 days before departure: You lose 75% of the paid... | {
"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/gsop-net | GSoP-Net | Global second-order pooling convolutional networks | A Gsop block has a squeeze module and an excitation module, and uses a second-order pooling to model high-order statistics while gathering global information.
In the squeeze module, a GSoP block firstly reduces the number of channels from $c$ to $c'$ ($c' < c$) using a $1 \times 1$ convolution, then computes a $c' \... | {
"title": "Global Second-order Pooling Convolutional Networks",
"url": "https://paperswithcode.com/paper/global-second-order-pooling-convolutional"
} | 2,000 | http://arxiv.org/abs/1811.12006v2 | Global Second-order Pooling Convolutional Networks | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Attention Mechanisms"
}
] | |
https://paperswithcode.com/method/discrete-cosine-transform | Discrete Cosine Transform | Discrete Cosine Transform | **Discrete Cosine Transform (DCT)** is an orthogonal transformation method that decomposes an
image to its spatial frequency spectrum. It expresses a finite sequence of data points in terms of a sum of cosine functions oscillating at different frequencies. It is used a lot in compression tasks, e..g image compression ... | null | 2,000 | null | null | null | 212 | [
{
"area": "General",
"area_id": "general",
"collection": "Fourier-related Transforms"
}
] |
https://paperswithcode.com/method/rotnet | RotNet | RotNet | **RotNet** is a self-supervision approach that relies on predicting image rotations as the pretext task
in order to learn image representations. | {
"title": "RotNet: Fast and Scalable Estimation of Stellar Rotation Periods Using Convolutional Neural Networks",
"url": "https://paperswithcode.com/paper/rotnet-fast-and-scalable-estimation-of"
} | 2,000 | https://arxiv.org/abs/2012.01985v2 | RotNet: Fast and Scalable Estimation of Stellar Rotation Periods Using Convolutional Neural Networks | 4 | [
{
"area": "General",
"area_id": "general",
"collection": "Self-Supervised Learning"
}
] | |
https://paperswithcode.com/method/44-ways-to-call-can-i-speak-to-someone-about | 44 Ways to Call Can i speak to someone about Oceania Cruises drink Packages: A Full Support Guide | 44 Ways to Call Can i speak to someone about Oceania Cruises drink Packages: A Full Support Guide | Oceania's customer service hours vary depending on the type of assistance you need and who you booked your cruise with +1-855-732-4023 USA or +44-289-708-0062 UK. Here's a breakdown:
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Regarding a recent cruise experi... | {
"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"
}
] |
https://paperswithcode.com/method/how-do-i-get-to-talk-to-a-celebrity | How do I get to talk to a celebrity? | How do I get to talk to a celebrity? | Yes 1-855-732-4023 or 1-808-900-8011, Celebrity Cruises is generally considered a premium or upscale cruise line. While not a true luxury line 1-855-732-4023 or 1-808-900-8011, Celebrity offers a step up from mass-market cruise lines with a focus on stylish design, elevated dining experiences, and enhanced 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/quick-guide-how-early-can-i-book-qatar | {Quick~guide} How early can I book Qatar Airways tickets? | {Quick~guide} How early can I book Qatar Airways tickets? | Qatar Airways generally allows passengers to book flights up to 11 months in advance ☎️ +44(203)+900-0080(UK) and +1(888)+800-9117(US) (OTA). Booking 9–11 months ☎️ +44(203)+900-0080(UK) and +1(888)+800-9117(US) (OTA) in advance with Qatar Airways helps secure your spot before seats start filling fast. ☎️ +44(203)+900-... | null | 2,000 | null | null | null | 0 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/faqs-public-what-is-the-refundable-option-on-1 | [[FAQs-PUBLIc]]What is the refundable option on Expedia? | [[FAQs-PUBLIc]]What is the refundable option on Expedia? | The refundable option on Expedia allows travelers to cancel their bookings without penalty, typically providing a full refund if canceled within the specified timeframe ＋+1-805-(330)-4056. This feature offers flexibility and peace of mind, ensuring you don't lose money if your plans change +1-805-(330)-4056 (US) (OTA).... | {
"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/dropout | Dropout | Dropout | **Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).
The idea is to prevent co-adaptation, wh... | {
"title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"url": "https://paperswithcode.com/paper/dropout-a-simple-way-to-prevent-neural"
} | 2,000 | http://jmlr.org/papers/v15/srivastava14a.html | Dropout: A Simple Way to Prevent Neural Networks from Overfitting | https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275 | 27,477 | [
{
"area": "General",
"area_id": "general",
"collection": "Regularization"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 0 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/how-long-does-it-take-to-transfer-crypto-from | How long does it take to transfer crypto from Robinhood? | How long does it take to transfer crypto from Robinhood? | call +1.844.610.2676 To speak directly with Robinhood customer service, the first step is to request a callback in the app or call +1.844.610.2676 for assistance. This number, +1.844.610.2676, ensures that your issue is classified correctly and submitted without delay. After you complete the form, you’ll receive a conf... | {
"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 Representations"
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