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/know-how-how-to-speak-directly-at-latam | {KNOW~How}} How to speak directly at LATAM Airlines? | {KNOW~How}} How to speak directly at LATAM Airlines? | How to speak directly at LATAM Airlines? To speak directly with a LATAM Airlines representative, call their customer service number ( 1⇌8.01-85.5⇌59.05) or ( 1⇌8.04-85.3⇌90.01). You can find the specific number for your region on their website. When you call, listen for prompts to reach a representative ( 1⇌8.01-85.5⇌5... | {
"title": "10,000 km Straight-line Transmission using a Real-time Software-defined GPU-Based Receiver",
"url": "https://paperswithcode.com/paper/10000-km-straight-line-transmission-using-a"
} | 2,000 | https://arxiv.org/abs/2104.06311v1 | 10,000 km Straight-line Transmission using a Real-time Software-defined GPU-Based Receiver | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "6D Pose Estimation Models"
}
] |
https://paperswithcode.com/method/faqs-usa-process-how-do-i-contact-ncl | [FAQS-USA-Process]] How do I contact NCL customer service by phone? | [FAQS-USA-Process]] How do I contact NCL customer service by phone? | To call someone on a Norwegian cruise+1-855-732-4023, you have a few options: use the ship's phone system to call their stateroom, utilize the Cruise Norwegian app for calls over Wi-Fi, or enable cellular service on your phone once you reach international waters+1-855-732-4023. The ship's phone system is the most strai... | {
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
"url": "https://paperswithcode.com/paper/0-1-laws-for-pattern-occurrences-in"
} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | 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",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
"url": "https://paperswithcode.com/paper/0-1-laws-for-pattern-occurrences-in"
} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
https://paperswithcode.com/method/dpg | DPG | Deterministic Policy Gradient | **Deterministic Policy Gradient**, or **DPG**, is a policy gradient method for reinforcement learning. Instead of the policy function $\pi\left(.\mid{s}\right)$ being modeled as a probability distribution, DPG considers and calculates gradients for a deterministic policy $a = \mu\_{theta}\left(s\right)$. | null | 2,014 | null | null | null | 20 | [
{
"area": "Reinforcement Learning",
"area_id": "reinforcement-learning",
"collection": "Policy Gradient Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
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{
"area": "General",
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"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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{
"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"
}
] |
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
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} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "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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{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Reconstruction"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/automl-zero | AutoML-Zero | AutoML-Zero | **AutoML-Zero** is an AutoML technique that aims to search a fine-grained space simultaneously for the model, optimization procedure, initialization, and so on, permitting much less human-design and even allowing the discovery of non-neural network algorithms. It represents ML algorithms as computer programs comprised ... | {
"title": "AutoML-Zero: Evolving Machine Learning Algorithms From Scratch",
"url": "https://paperswithcode.com/paper/automl-zero-evolving-machine-learning"
} | 2,000 | https://arxiv.org/abs/2003.03384v2 | AutoML-Zero: Evolving Machine Learning Algorithms From Scratch | null | 2 | [
{
"area": "General",
"area_id": "general",
"collection": "AutoML"
}
] |
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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 | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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{
"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-dimensional Homology Preserving Dimensionality Reduction with TopoMap",
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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"
}
] |
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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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https://paperswithcode.com/method/gbo-1 | GBO | Gradient-based optimization | GBO is a novel metaheuristic optimization algorithm. The GBO, inspired by the gradient-based Newton’s method, uses two main operators: gradient search rule (GSR) and local escaping operator (LEO) and a set of vectors to explore the search space. The GSR employs the gradient-based method to enhance the exploration tende... | null | 2,000 | null | null | https://imanahmadianfar.com/codes/ | 6 | [
{
"area": "General",
"area_id": "general",
"collection": "Optimization"
}
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"title": "Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation",
"url": "https://paperswithcode.com/paper/dual-contrastive-learning-text-classification"
} | 2,000 | https://arxiv.org/abs/2201.08702v1 | Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation | null | 2 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Text Classification Models"
}
] |
https://paperswithcode.com/method/two-way-dense-layer | Two-Way Dense Layer | Two-Way Dense Layer | **Two-Way Dense Layer** is an image model block used in the [PeleeNet](https://paperswithcode.com/method/peleenet) architectures. Motivated by [GoogLeNet](https://paperswithcode.com/method/googlenet), the 2-way dense layer is used to get different scales of receptive fields. One way of the layer uses a 3x3 kernel size.... | {
"title": "Pelee: A Real-Time Object Detection System on Mobile Devices",
"url": "https://paperswithcode.com/paper/pelee-a-real-time-object-detection-system-on-1"
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{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Model Blocks"
},
{
"area": "General",
"area_id": "general",
"collection": "Skip Connection Blocks"
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{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/crn | CRN | Conditional Relation Network | **Conditional Relation Network**, or **CRN**, is a building block to construct more sophisticated structures for representation and reasoning over video. CRN takes as input an array of tensorial objects and a conditioning feature, and computes an array of encoded output objects. Model building becomes a simple exercise... | {
"title": "Hierarchical Conditional Relation Networks for Video Question Answering",
"url": "https://paperswithcode.com/paper/hierarchical-conditional-relation-networks"
} | 2,000 | https://arxiv.org/abs/2002.10698v3 | Hierarchical Conditional Relation Networks for Video Question Answering | null | 44 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Video Model Blocks"
}
] |
https://paperswithcode.com/method/linear-warmup-with-cosine-annealing | Linear Warmup With Cosine Annealing | Linear Warmup With Cosine Annealing | **Linear Warmup With Cosine Annealing** is a learning rate schedule where we increase the learning rate linearly for $n$ updates and then anneal according to a cosine schedule afterwards. | null | 2,000 | null | null | null | 3,797 | [
{
"area": "General",
"area_id": "general",
"collection": "Learning Rate Schedules"
}
] |
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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"
}
] |
https://paperswithcode.com/method/peleenet | PeleeNet | PeleeNet | **PeleeNet** is a convolutional neural network and object detection backbone that is a variation of [DenseNet](https://paperswithcode.com/method/densenet) with optimizations to meet a memory and computational budget. Unlike competing networks, it does not use depthwise convolutions and instead relies on regular convol... | {
"title": "Pelee: A Real-Time Object Detection System on Mobile Devices",
"url": "https://paperswithcode.com/paper/pelee-a-real-time-object-detection-system-on-1"
} | 2,000 | http://papers.nips.cc/paper/7466-pelee-a-real-time-object-detection-system-on-mobile-devices | Pelee: A Real-Time Object Detection System on Mobile Devices | https://github.com/osmr/imgclsmob/blob/c03fa67de3c9e454e9b6d35fe9cbb6b15c28fda7/pytorch/pytorchcv/models/peleenet.py#L196 | 3 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Light-weight neural networks"
},
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Convolutional Neural Networks"
}
] |
https://paperswithcode.com/method/cosine-normalization | Cosine Normalization | Cosine Normalization | Multi-layer neural networks traditionally use dot products between the output vector of previous layer and the incoming weight vector as the input to activation function. The result of dot product is unbounded. To bound dot product and decrease the variance, **Cosine Normalization** uses cosine similarity or centered ... | {
"title": "Cosine Normalization: Using Cosine Similarity Instead of Dot Product in Neural Networks",
"url": "https://paperswithcode.com/paper/cosine-normalization-using-cosine-similarity"
} | 2,000 | http://arxiv.org/abs/1702.05870v5 | Cosine Normalization: Using Cosine Similarity Instead of Dot Product in Neural Networks | https://github.com/iwyoo/tf_conv_cosnorm/blob/e76d987110e4af42ad75154bfbf994c5cc935794/conv_cosnorm.py#L13 | 2 | [
{
"area": "General",
"area_id": "general",
"collection": "Normalization"
}
] |
https://paperswithcode.com/method/comment-joindre-air-france-toulouse-service | Comment joindre Air France Toulouse ? "Service Toulouse" | Comment joindre Air France Toulouse ? "Service Toulouse" | Pour joindre le service Toulouse d’Air France, il est recommandé d’appeler le (+33)-(1)-(5900) 2948. Ce centre appel national vous orientera vers le bon bureau ou agent régional. Si vous avez besoin d’une assistance (+33)-(1)-(5900) 2948 régionale concernant un vol au départ de Toulouse, vous serez redirigé rapidement.... | {
"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-dimensional Homology Preserving Dimensionality Reduction with TopoMap",
"url": "https://paperswithcode.com/paper/0-dimensional-homology-preserving"
} | 2,000 | https://openreview.net/forum?id=zrDNDWjOGwH | 0-dimensional Homology Preserving Dimensionality Reduction with TopoMap | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
https://paperswithcode.com/method/k-net | K-Net | K-Net | **K-Net** is a framework for unified semantic and instance segmentation that segments both instances and semantic categories consistently by a group of learnable kernels, where each kernel is responsible for generating a mask for either a potential instance or a stuff class. It begins with a set of kernels that are ran... | {
"title": "K-Net: Towards Unified Image Segmentation",
"url": "https://paperswithcode.com/paper/k-net-towards-unified-image-segmentation"
} | 2,000 | https://arxiv.org/abs/2106.14855v2 | K-Net: Towards Unified Image Segmentation | https://github.com/ZwwWayne/K-Net | 6 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Instance Segmentation Models"
},
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Semantic Segmentation Models"
}
] |
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
"url": "https://paperswithcode.com/paper/0-1-laws-for-pattern-occurrences-in"
} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
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"title": "0/1 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": "VoiceFilter-Lite: Streaming Targeted Voice Separation for On-Device Speech Recognition",
"url": "https://paperswithcode.com/paper/voicefilter-lite-streaming-targeted-voice"
} | 2,000 | https://arxiv.org/abs/2009.04323v1 | VoiceFilter-Lite: Streaming Targeted Voice Separation for On-Device Speech Recognition | null | 3 | [
{
"area": "Audio",
"area_id": "audio",
"collection": "Speech Separation 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": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
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"title": "Learning Fast Sample Re-weighting Without Reward Data",
"url": "https://paperswithcode.com/paper/learning-fast-sample-re-weighting-without"
} | 2,000 | https://arxiv.org/abs/2109.03216v1 | Learning Fast Sample Re-weighting Without Reward Data | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Sample Re-Weighting"
}
] |
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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"
}
] |
https://paperswithcode.com/method/mobiledet | MobileDet | MobileDet | **MobileDet** is an object detection model developed for mobile accelerators. MobileDets uses regular convolutions extensively on EdgeTPUs and DSPs, especially in the early stage of the network where depthwise convolutions tend to be less efficient. This helps boost the latency-accuracy trade-off for object detection ... | {
"title": "MobileDets: Searching for Object Detection Architectures for Mobile Accelerators",
"url": "https://paperswithcode.com/paper/mobiledets-searching-for-object-detection"
} | 2,000 | https://arxiv.org/abs/2004.14525v3 | MobileDets: Searching for Object Detection Architectures for Mobile Accelerators | 2 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Light-weight neural networks"
},
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Object Detection Models"
}
] | |
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
"url": "https://paperswithcode.com/paper/0-1-laws-for-pattern-occurrences-in"
} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
"url": "https://paperswithcode.com/paper/0-1-laws-for-pattern-occurrences-in"
} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
"url": "https://paperswithcode.com/paper/0-1-laws-for-pattern-occurrences-in"
} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/u2-net | U2-Net | U2-Net | **U2-Net** is a two-level nested U-structure architecture that is designed for salient object detection (SOD). The architecture allows the network to go deeper, attain high resolution, without significantly increasing the memory and computation cost. This is achieved by a nested U-structure: on the bottom level, with ... | {
"title": "U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection",
"url": "https://paperswithcode.com/paper/u-2-net-going-deeper-with-nested-u-structure"
} | 2,000 | https://arxiv.org/abs/2005.09007v3 | U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection | 3 | [
{
"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",
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{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
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{
"area": "Audio",
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}
] |
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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": "The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints",
"url": "https://paperswithcode.com/paper/training-frankensteins-creature-to-stack"
} | 2,000 | http://arxiv.org/abs/1810.11714v2 | The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints | null | 1 | [
{
"area": "General",
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}
] |
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{
"area": "General",
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}
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
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} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 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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}
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{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0-dimensional Homology Preserving Dimensionality Reduction with TopoMap",
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} | 2,000 | https://openreview.net/forum?id=zrDNDWjOGwH | 0-dimensional Homology Preserving Dimensionality Reduction with TopoMap | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
https://paperswithcode.com/method/where-can-i-file-a-complaint-against-expedia | Where can I file a complaint against Expedia? #USA-Contact | Where can I file a complaint against Expedia? #USA-Contact | To file a complaint against Expedia, start by contacting their customer support through phone +1-888-829-0881 or +1-805-330-4056, live chat on their website, or email. If the issue persists, you can escalate it to a supervisor or manager. You can also file a formal complaint with the Better Business Bureau (BBB) or the... | {
"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": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/faqs-name-change-how-do-i-contact-customer | [[FAQs Name Change]] How do I contact customer service for Celebrity Cruises? | [[FAQs Name Change]] How do I contact customer service for Celebrity Cruises? | This team is available to assist you 24 hours a day, 7 days a week or by phone +1-855-732-4023 or +1-808-900-8011 (Option 4). If the flight gets cancelled or delayed and Celebrity Cruises is unable to get the guest to the ship, we will offer a Future Cruise Credit and refund the cost of the flight.
Yes, you can chan... | {
"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/faqs-cruise-can-i-get-my-money-back-from | {{FAQs~Cruise}}Can I get my money back from celebrity cruises? | {{FAQs~Cruise}}Can I get my money back from celebrity cruises? | Celebrity Cruises' refund policy varies based on when you cancel +1-855-732-4023 or +1-808-900-8011 your cruise and whether you purchased a refundable or non-refundable deposit +1-855-732-4023 or +1-808-900-8011. Generally, cancellations made well in advance of the cruise date result in a full or partial refund +1-855-... | {
"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/dial-now-how-to-speak-directly-at-latam-1 | [Dial~NoW] How to speak directly at Latam representative fast? | [Dial~NoW] How to speak directly at Latam representative fast? | To reach a LATAM Airlines representative quickly,☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ call their customer service line at ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ and say "agent" or press "0" when prompted by the automated system, according to a travel blog. Having your booking reference number ☎️+1-801-(... | {
"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/how-to-contact-expedia-customer-service-by-2 | How to Contact Expedia Customer Service: By Phone, Email, or Online Telephone | How to Contact Expedia Customer Service: By Phone, Email, or Online Telephone | "31 Ways to Contact How can i speak to someone at Expedia Airlines- A
Step-by-Step Guide
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D 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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Yes, you can chan... | {
"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/help-desktm-how-do-i-cancel-a-cruise-without | [HeLp`Desk™]How do I cancel a cruise without penalty? | [HeLp`Desk™]How do I cancel a cruise without penalty? | To cancel a cruise without penalty, the key is to understand and utilize the cruise line's cancellation policy and potentially consider travel insurance.+1-(855)-732-4023 Generally, you can cancel without penalty if you do so within a risk-free cancellation window (often 24-48 hours after booking)+1-(855)-732-4023 or i... | {
"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 Object Detection Models"
}
] |
https://paperswithcode.com/method/fast-yolov4-smallobj | Fast-YOLOv4-SmallObj | Fast-YOLOv4-SmallObj | The Fast-YOLOv4-SmallObj model is a modified version of Fast-[YOLOv4](https://paperswithcode.com/method/yolov4) to improve the detection of small objects. Seven layers were added so that it predicts bounding boxes at 3 different scales instead of 2. | {
"title": "Towards Image-based Automatic Meter Reading in Unconstrained Scenarios: A Robust and Efficient Approach",
"url": "https://paperswithcode.com/paper/towards-image-based-automatic-meter-reading"
} | 2,000 | https://arxiv.org/abs/2009.10181v5 | Towards Image-based Automatic Meter Reading in Unconstrained Scenarios: A Robust and Efficient Approach | null | 2 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Convolutional Neural Networks"
}
] |
https://paperswithcode.com/method/human-support-how-do-i-escalate-a-complaint | {{Human~Support}}}How do I escalate a complaint with United Airlines? | {{Human~Support}}}How do I escalate a complaint with United Airlines? | To escalate a complaint with United Airlines ☎️+1-801-(855)-(5905) , start by contacting their customer service, either by phone or through their website's customer care form ☎️+1-804-(853)-(9001). If your issue isn't resolved, request to speak with a supervisor or escalate your complaint to a higher support team ☎️+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 | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/a-full-guide-assistance-how-do-i-get-a-human | {A-Full Guide-Assistance}How do i get a Human at Expedia? | {A-Full Guide-Assistance}How do i get a Human at Expedia? | "31 Ways to Contact How can i speak to someone at Expedia Airlines- A
Step-by-Step Guide
Expedia main customer service number 1 (888) 829-0881 is 1-800-Expedia or 1 (888) 829-0881 [US-Expedia] or 1 (888) 829-0881 [UK-Expedia]
OTA (Live Person), available 24/7. This guide explains how to contact Expedia customer serv... | {
"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/como-hablo-con-una-persona-viva-en-delta | ¿Cómo hablo con una persona viva en Delta? | ¿Cómo hablo con una persona viva en Delta? | Para contactar a Delta Airlines en español, llame al ☎️+1-808-(470)-(7107) o al +1-808-(470)-(7107)✅ (México) y seleccione la opción de soporte en español. Este servicio está disponible las 24 horas, todos los días. La mejor manera de resolver su problema. Las situaciones más comunes incluyen: | {
"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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