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/quanttree | QuantTree | QuantTree histograms | Given a training set drawn from an unknown $d$-variate probability distribution, QuantTree constructs a histogram by recursively splitting $\mathbb{R}^d$. The splits are defined by a stochastic process so that each bin contains a certain proportion of the training set. These histograms can be used to define test statis... | {
"title": "QuantTree: Histograms for Change Detection in Multivariate Data Streams",
"url": "https://paperswithcode.com/paper/quanttree-histograms-for-change-detection-in"
} | 2,000 | https://icml.cc/Conferences/2018/Schedule?showEvent=2268 | QuantTree: Histograms for Change Detection in Multivariate Data Streams | null | 3 | [
{
"area": "General",
"area_id": "general",
"collection": "Distribution Approximation"
}
] |
https://paperswithcode.com/method/squeeze-and-excitation-block | Squeeze-and-Excitation Block | Squeeze-and-Excitation Block | The **Squeeze-and-Excitation Block** is an architectural unit designed to improve the representational power of a network by enabling it to perform dynamic channel-wise feature recalibration. The process is:
- The block has a convolutional block as an input.
- Each channel is "squeezed" into a single numeric value ... | {
"title": "Squeeze-and-Excitation Networks",
"url": "https://paperswithcode.com/paper/squeeze-and-excitation-networks"
} | 2,000 | https://arxiv.org/abs/1709.01507v4 | Squeeze-and-Excitation Networks | https://github.com/osmr/imgclsmob/blob/68335927ba27f2356093b985bada0bc3989836b1/pytorch/pytorchcv/models/common.py#L731 | 543 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Model Blocks"
}
] |
https://paperswithcode.com/method/pixelcnn | PixelCNN | PixelCNN | A **PixelCNN** is a generative model that uses autoregressive connections to model images pixel by pixel, decomposing the joint image distribution as a product of conditionals. PixelCNNs are much faster to train than [PixelRNNs](https://paperswithcode.com/method/pixelrnn) because convolutions are inherently easier to p... | {
"title": "Pixel Recurrent Neural Networks",
"url": "https://paperswithcode.com/paper/pixel-recurrent-neural-networks"
} | 2,000 | http://arxiv.org/abs/1601.06759v3 | Pixel Recurrent Neural Networks | https://github.com/openai/pixel-cnn | 45 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Likelihood-Based Generative Models"
},
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Generative Models"
}
] |
https://paperswithcode.com/method/alq-and-amq | ALQ and AMQ | Gradient Quantization with Adaptive Levels/Multiplier | Many communication-efficient variants of [SGD](https://paperswithcode.com/method/sgd) use gradient quantization schemes. These schemes are often heuristic and fixed over the course of training. We empirically observe that the statistics of gradients of deep models change during the training. Motivated by this observati... | {
"title": "Adaptive Gradient Quantization for Data-Parallel SGD",
"url": "https://paperswithcode.com/paper/adaptive-gradient-quantization-for-data"
} | 2,000 | https://arxiv.org/abs/2010.12460v1 | Adaptive Gradient Quantization for Data-Parallel SGD | https://github.com/tabrizian/learning-to-quantize | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Data Parallel Methods"
}
] |
https://paperswithcode.com/method/faqstm24-free-solution-how-to-speak-directly | [Faqs™24 free~Solution]How to speak directly at Delta Airlines? | [Faqs™24 free~Solution]How to speak directly at Delta Airlines? | To talk to a directly At Delta Airlines ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ live 𝗿𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝘃𝗲 in a quick time, you can initiate a request in several ways. You can call their dedicated ℂ𝕦𝕤𝕥𝕠𝕞𝕖𝕣 service line at ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ (No Wait Time),” start a chat, u... | {
"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-cancel-a-royal-caribbean-cruise-1 | How to cancel a Royal Caribbean cruise without penalty?{[FAQs~!GuiDE]} | How to cancel a Royal Caribbean cruise without penalty?{[FAQs~!GuiDE]} | Days Prior to Cruise Departure and cancellation Charges 1-855-732-4023 USA or +44-289-708-0062: 151 or more days: deposit is refundable except in the case of non- refundable deposit promotions and airfares 1-855-732-4023 USA or +44-289-708-0062. 150-71 days: loss of deposit. 70-46 days: 25% of fare*
To cancel a crui... | {
"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-speak-directly-at-latam-representative | How to speak directly at LATAM representative fast? | How to speak directly at LATAM representative fast? | How to speak directly at LATAM representative fast?
To quickly connect with a live representative at Latam Airlines,+1-833-667-0020 (US) or +44-203-970-0065 (UK) call customer service at +1-833-667-0020 (US) or +44-203-970-0065 (UK) (OTA) through an Online Travel Agency (OTA) at +44-203-970-0065. When calling, cho... | {
"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/metanext | MetaNeXt | MetaNeXt | MetaNeXt is a meta-architecture abstracted from ConvNeXt, which can be regarded as a simpler version obtained from MetaFormer by merging token mixer and MLP into one. | {
"title": "InceptionNeXt: When Inception Meets ConvNeXt",
"url": "https://paperswithcode.com/paper/inceptionnext-when-inception-meets-convnext"
} | 2,000 | https://arxiv.org/abs/2303.16900v2 | InceptionNeXt: When Inception Meets ConvNeXt | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Models"
}
] | |
https://paperswithcode.com/method/panet | PANet | PANet | **Path Aggregation Network**, or **PANet**, aims to boost information flow in a proposal-based instance segmentation framework. Specifically, the feature hierarchy is enhanced with accurate localization signals in lower layers by [bottom-up path augmentation](https://paperswithcode.com/method/bottom-up-path-augmentatio... | {
"title": "Path Aggregation Network for Instance Segmentation",
"url": "https://paperswithcode.com/paper/path-aggregation-network-for-instance"
} | 2,000 | http://arxiv.org/abs/1803.01534v4 | Path Aggregation Network for Instance Segmentation | https://github.com/ShuLiu1993/PANet/blob/2644d5ad6ae98c2bf58df45c8792c019b1d7b2b9/lib/modeling/FPN.py#L135 | 18 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Object Detection Models"
},
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Instance Segmentation Models"
}
] |
https://paperswithcode.com/method/gin | GIN | Graph Isomorphism Network | Per the authors, Graph Isomorphism Network (GIN) generalizes the WL test and hence achieves maximum discriminative power among GNNs. | {
"title": "How Powerful are Graph Neural Networks?",
"url": "https://paperswithcode.com/paper/how-powerful-are-graph-neural-networks"
} | 2,000 | http://arxiv.org/abs/1810.00826v3 | How Powerful are Graph Neural Networks? | 43 | [
{
"area": "Graphs",
"area_id": "graphs",
"collection": "Graph Models"
},
{
"area": "Graphs",
"area_id": "graphs",
"collection": "Graph Embeddings"
}
] | |
https://paperswithcode.com/method/wgan-gp | WGAN GP | Wasserstein GAN (Gradient Penalty) | **Wasserstein GAN + Gradient Penalty**, or **WGAN-GP**, is a generative adversarial network that uses the Wasserstein loss formulation plus a gradient norm penalty to achieve Lipschitz continuity.
The original [WGAN](https://paperswithcode.com/method/wgan) uses weight clipping to achieve 1-Lipschitz functions, but t... | {
"title": "Improved Training of Wasserstein GANs",
"url": "https://paperswithcode.com/paper/improved-training-of-wasserstein-gans"
} | 2,000 | http://arxiv.org/abs/1704.00028v3 | Improved Training of Wasserstein GANs | https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/wgan_gp/wgan_gp.py | 11 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Generative Adversarial Networks"
}
] |
https://paperswithcode.com/method/other-cancellations-is-latam-airlines-fully | [@@Other Cancellations@@]Is LATAM Airlines fully refundable? | [@@Other Cancellations@@]Is LATAM Airlines fully refundable? | LATAM Airlines <<+1 (801) 855-5905>> or <<+1 (804) 853-9001>> does not offer fully refundable tickets in all cases. While they have a 24-hour cancellation policy <<+1 (801) 855-5905>> or <<+1 (804) 853-9001>> for purchases made at least seven days before departure, allowing for a full refund, other cancellations depend... | {
"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/soft-split-and-soft-composition | Soft Split and Soft Composition | Soft Split and Soft Composition | **Soft Split and Soft Composition** are video frame based operations used in the [FuseFormer](https://paperswithcode.com/method/fuseformer) architecture, specifically the [FuseFormer blocks](https://paperswithcode.com/method/fuseformer-block). We softly split each frame into overlapped patches and then softly composite... | {
"title": "FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting",
"url": "https://paperswithcode.com/paper/fuseformer-fusing-fine-grained-information-in"
} | 2,000 | https://arxiv.org/abs/2109.02974v1 | FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Video Model Blocks"
}
] | |
https://paperswithcode.com/method/faqs-solutiontm-can-i-get-a-full-refund-if-i | [Faqs`Solution™]Can I get a full refund if I cancel my cruise? | [Faqs`Solution™]Can I get a full refund if I cancel my cruise? | Whether or not you can get a full refund for canceling a cruise depends on the cruise line's cancellation policy and how far in advance you cancel. Generally,+1-(855)-732-4023 most cruise lines offer full refunds if you cancel within a specific timeframe, often 90-120 days before the sail date. However,+1-808-900-8011 ... | {
"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 | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/partial-hybrid-transfer-learning | Partial Hybrid Transfer Learning | Partial Hybrid Transfer Learning | While the typical approach to leverage transfer learning in image segmentation models involves replacing the entire encoder, this can restrict the customization and unique strengths of the main network. To address this limitation, we propose a hybrid transfer learning strategy that incorporates pre-trained convolutiona... | {
"title": "Hi-gMISnet: generalized medical image segmentation using DWT based multilayer fusion and dual mode attention into high resolution pGAN",
"url": "https://paperswithcode.com/paper/hi-gmisnet-generalized-medical-image"
} | 2,000 | https://iopscience.iop.org/article/10.1088/1361-6560/ad3cb3 | Hi-gMISnet: generalized medical image segmentation using DWT based multilayer fusion and dual mode attention into high resolution pGAN | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Convolutional Neural Networks"
}
] |
https://paperswithcode.com/method/nvae-encoder-residual-cell | NVAE Encoder Residual Cell | NVAE Encoder Residual Cell | The **NVAE Encoder Residual Cell** is a [residual connection](https://paperswithcode.com/method/residual-connection) block used in the [NVAE](https://paperswithcode.com/method/nvae) architecture for the encoder. It applies two series of BN-[Swish](https://paperswithcode.com/method/swish)-Conv layers without changing th... | {
"title": "NVAE: A Deep Hierarchical Variational Autoencoder",
"url": "https://paperswithcode.com/paper/nvae-a-deep-hierarchical-variational"
} | 2,000 | https://arxiv.org/abs/2007.03898v3 | NVAE: A Deep Hierarchical Variational Autoencoder | null | 5 | [
{
"area": "General",
"area_id": "general",
"collection": "Skip Connection Blocks"
},
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Model Blocks"
}
] |
https://paperswithcode.com/method/cutblur | CutBlur | CutBlur | **CutBlur** is a data augmentation method that is specifically designed for the low-level vision tasks. It cuts a low-resolution patch and pastes it to the corresponding high-resolution image region and vice versa. The key intuition of Cutblur is to enable a model to learn not only "how" but also "where" to super-resol... | {
"title": "Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy",
"url": "https://paperswithcode.com/paper/rethinking-data-augmentation-for-image-super"
} | 2,000 | https://arxiv.org/abs/2004.00448v2 | Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Data Augmentation"
}
] |
https://paperswithcode.com/method/context2vec | context2vec | context2vec | **context2vec** is an unsupervised model for learning generic context embedding of wide sentential contexts, using a bidirectional [LSTM](https://paperswithcode.com/method/lstm). A large plain text corpora is trained on to learn a neural model that embeds entire sentential contexts and target words in the same low-dime... | {
"title": "context2vec: Learning Generic Context Embedding with Bidirectional LSTM",
"url": "https://paperswithcode.com/paper/context2vec-learning-generic-context"
} | 2,000 | https://aclanthology.org/K16-1006 | context2vec: Learning Generic Context Embedding with Bidirectional LSTM | null | 6 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Contextualized Word Embeddings"
},
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Word Embeddings"
}
] |
https://paperswithcode.com/method/vistr | VisTR | VisTR | **VisTR** is a [Transformer](https://paperswithcode.com/method/transformer) based video instance segmentation model. It views video instance segmentation as a direct end-to-end parallel sequence decoding/prediction problem. Given a video clip consisting of multiple image frames as input, VisTR outputs the sequence of m... | {
"title": "End-to-End Video Instance Segmentation with Transformers",
"url": "https://paperswithcode.com/paper/end-to-end-video-instance-segmentation-with"
} | 2,000 | https://arxiv.org/abs/2011.14503v5 | End-to-End Video Instance Segmentation with Transformers | null | 3 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Video Instance Segmentation Models"
},
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Instance Segmentation Models"
}
] |
https://paperswithcode.com/method/batch-nuclear-norm-maximization | Batch Nuclear-norm Maximization | Batch Nuclear-norm Maximization | **Batch Nuclear-norm Maximization** is an approach for aiding classification in label insufficient situations. It involves maximizing the nuclear-norm of the batch output matrix. The nuclear-norm of a matrix is an upper bound of the Frobenius-norm of the matrix. Maximizing nuclear-norm ensures large Frobenius-norm of t... | {
"title": "Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situations",
"url": "https://paperswithcode.com/paper/towards-discriminability-and-diversity-batch"
} | 2,000 | https://arxiv.org/abs/2003.12237v1 | Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situations | 3 | [
{
"area": "General",
"area_id": "general",
"collection": "Regularization"
}
] | |
https://paperswithcode.com/method/lufthansa-l-what-is-the-cancellation-policy | [@LUFTHANSA~L@] (𝑳𝒊𝒗𝒆 𝑯𝒖𝒎𝒂𝒏)What is the cancellation policy with Lufthansa? | [@LUFTHANSA~L@] (𝑳𝒊𝒗𝒆 𝑯𝒖𝒎𝒂𝒏)What is the cancellation policy with Lufthansa? | Lufthansa generally ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ offers a 24-hour cancellation policy for most bookings, allowing for a full refund if canceled within 24 hours of booking ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅, provided the flight is at least seven days away. After this period, refunds and fees ... | {
"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/visformer | Visformer | Visformer | **Visformer**, or **Vision-friendly Transformer**, is an architecture that combines [Transformer](https://paperswithcode.com/methods/category/transformers)-based architectural features with those from [convolutional neural network](https://paperswithcode.com/methods/category/convolutional-neural-networks) architectures... | {
"title": "Visformer: The Vision-friendly Transformer",
"url": "https://paperswithcode.com/paper/visformer-the-vision-friendly-transformer"
} | 2,000 | https://arxiv.org/abs/2104.12533v4 | Visformer: The Vision-friendly Transformer | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Vision Transformers"
}
] | |
https://paperswithcode.com/method/levenshtein-transformer | Levenshtein Transformer | Levenshtein Transformer | The **Levenshtein Transformer** (LevT) is a type of [transformer](https://paperswithcode.com/method/transformer) that aims to address the lack of flexibility of previous decoding models. Notably, in previous frameworks, the length of generated sequences is either fixed or monotonically increased as the decoding proceed... | {
"title": "Levenshtein Transformer",
"url": "https://paperswithcode.com/paper/levenshtein-transformer"
} | 2,000 | https://arxiv.org/abs/1905.11006v2 | Levenshtein Transformer | https://github.com/pytorch/fairseq/blob/28876638114948711fd4bd4e350fdd6809013f1e/fairseq/models/nat/levenshtein_transformer.py#L34 | 12 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Autoregressive Transformers"
},
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Transformers"
}
] |
https://paperswithcode.com/method/hierarchical-mtl | Hierarchical MTL | Hierarchical Multi-Task Learning | Multi-task learning (MTL) introduces an inductive bias, based on a-priori relations between tasks: the trainable model is compelled to model more general dependencies by using the abovementioned relation as an important data feature. Hierarchical MTL, in which different tasks use different levels of the deep neural net... | {
"title": "Deep multi-task learning with low level tasks supervised at lower layers",
"url": "https://paperswithcode.com/paper/deep-multi-task-learning-with-low-level-tasks"
} | 2,000 | https://aclanthology.org/P16-2038 | Deep multi-task learning with low level tasks supervised at lower layers | null | 5 | [
{
"area": "General",
"area_id": "general",
"collection": "Deep Tabular Learning"
}
] |
https://paperswithcode.com/method/scan | SCAN-clustering | Semantic Clustering by Adopting Nearest Neighbours | SCAN automatically groups images into semantically meaningful clusters when ground-truth annotations are absent. SCAN is a two-step approach where feature learning and clustering are decoupled. First, a self-supervised task is employed to obtain semantically meaningful features. Second, the obtained features are used a... | {
"title": "SCAN: Learning to Classify Images without Labels",
"url": "https://paperswithcode.com/paper/learning-to-classify-images-without-labels"
} | 2,000 | https://arxiv.org/abs/2005.12320v2 | SCAN: Learning to Classify Images without Labels | 3 | [
{
"area": "General",
"area_id": "general",
"collection": "Clustering"
}
] | |
https://paperswithcode.com/method/non-maximum-suppression | Non Maximum Suppression | Non Maximum Suppression | **Non Maximum Suppression** is a computer vision method that selects a single entity out of many overlapping entities (for example bounding boxes in object detection). The criteria is usually discarding entities that are below a given probability bound. With remaining entities we repeatedly pick the entity with the hig... | null | 2,000 | null | null | null | 389 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Proposal Filtering"
}
] |
https://paperswithcode.com/method/precise-roi-pooling | Precise RoI Pooling | Precise RoI Pooling | **Precise RoI Pooling**, or **PrRoI Pooling**, is a region of interest feature extractor that avoids any quantization of coordinates and has a continuous gradient on bounding box coordinates. Given the feature map $\mathcal{F}$ before RoI/PrRoI Pooling (eg from Conv4 in [ResNet](https://paperswithcode.com/method/resnet... | {
"title": "Acquisition of Localization Confidence for Accurate Object Detection",
"url": "https://paperswithcode.com/paper/acquisition-of-localization-confidence-for"
} | 2,000 | http://arxiv.org/abs/1807.11590v1 | Acquisition of Localization Confidence for Accurate Object Detection | https://github.com/vacancy/PreciseRoIPooling/blob/070a7950db6a945e30e8e3296204a1e975f131e8/pytorch/prroi_pool/prroi_pool.py#L19 | 4 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "RoI Feature Extractors"
}
] |
https://paperswithcode.com/method/dfa-random-walk | DFA (Random Walk) | Detrended fluctuation analysis | In stochastic processes, chaos theory and time series analysis, detrended fluctuation analysis (DFA) is a method for determining the statistical self-affinity of a signal. It is useful for analysing time series that appear to be long-memory processes (diverging correlation time, e.g. power-law decaying autocorrelation ... | {
"title": "Mosaic organization of DNA nucleotides",
"url": "https://paperswithcode.com/paper/mosaic-organization-of-dna-nucleotides"
} | 2,000 | https://journals.aps.org/pre/abstract/10.1103/PhysRevE.49.1685 | Mosaic organization of DNA nucleotides | 7 | [
{
"area": "Sequential",
"area_id": "sequential",
"collection": "Time Series Analysis"
}
] | |
https://paperswithcode.com/method/faq-s-policy-como-puedo-hablar-con-un-asesor | [[FAQ's@PoLiCy!!]]¿Cómo puedo hablar con un asesor de American? | ¿Cómo puedo hablar con un asesor de American? | Para hablar con un asesor de American Airlines en español, llama al+1-808-(470)-(7107) (MX) o al +1-808-(470)-(7107) (EE. UU.). Estos números están disponibles 24/7 y te conectan directamente con un representante que puede ayudarte con reservas, cambios o dudas generales+1-808-(470)-(7107). ¿Necesitas hablar con un ase... | {
"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/harm-net | Harm-Net | Harm-Net | A **Harmonic Network**, or **Harm-Net**, is a type of convolutional neural network that replaces convolutional layers with "harmonic blocks" that use [Discrete Cosine Transform](https://paperswithcode.com/method/discrete-cosine-transform) (DCT) filters. These blocks can be useful in truncating high-frequency informati... | {
"title": "Harmonic Convolutional Networks based on Discrete Cosine Transform",
"url": "https://paperswithcode.com/paper/harmonic-convolutional-networks-based-on"
} | 2,000 | https://arxiv.org/abs/2001.06570v3 | Harmonic Convolutional Networks based on Discrete Cosine Transform | https://github.com/matej-ulicny/harmonic-networks/blob/0fccf674806a0b876e641ef5271aad520ff90739/imagenet/models/resnet.py#L113 | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Convolutional Neural Networks"
}
] |
https://paperswithcode.com/method/pattern-exploiting-training | Pattern-Exploiting Training | Pattern-Exploiting Training | **Pattern-Exploiting Training** is a semi-supervised training procedure that reformulates input examples as cloze-style phrases to help language models understand a given task. These phrases are then used to assign soft labels to a large set of unlabeled examples. Finally, standard supervised training is performed on t... | {
"title": "Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference",
"url": "https://paperswithcode.com/paper/exploiting-cloze-questions-for-few-shot-text"
} | 2,000 | https://arxiv.org/abs/2001.07676v3 | Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference | null | 6 | [
{
"area": "General",
"area_id": "general",
"collection": "Semi-Supervised Learning Methods"
}
] |
https://paperswithcode.com/method/lapeigen | LapEigen | Laplacian EigenMap | {
"title": "Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering",
"url": "https://paperswithcode.com/paper/laplacian-eigenmaps-and-spectral-techniques"
} | 2,000 | https://ieeexplore.ieee.org/abstract/document/6789755 | Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering | null | 297 | [
{
"area": "Graphs",
"area_id": "graphs",
"collection": "Graph Embeddings"
}
] | |
https://paperswithcode.com/method/sbert | SBERT | Sentence-BERT | {
"title": "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
"url": "https://paperswithcode.com/paper/sentence-bert-sentence-embeddings-using"
} | 2,000 | https://arxiv.org/abs/1908.10084v1 | Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks | null | 64 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Sentence Embeddings"
}
] | |
https://paperswithcode.com/method/gpt-4 | GPT-4 | GPT-4 | **GPT-4** is a transformer based model pre-trained to predict the next token in a document. | {
"title": "GPT-4 Technical Report",
"url": "https://paperswithcode.com/paper/gpt-4-technical-report-1"
} | 2,000 | https://arxiv.org/abs/2303.08774v5 | GPT-4 Technical Report | 2,871 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Language Models"
}
] | |
https://paperswithcode.com/method/cida | CIDA | Continuously Indexed Domain Adaptation | **Continuously Indexed Domain Adaptation** combines traditional adversarial adaptation with a novel discriminator that models the encoding-conditioned domain index distribution.
Image Source: [Wang et al.](https://arxiv.org/pdf/2007.01807v2.pdf) | {
"title": "Continuously Indexed Domain Adaptation",
"url": "https://paperswithcode.com/paper/continuously-indexed-domain-adaptation"
} | 2,000 | https://arxiv.org/abs/2007.01807v2 | Continuously Indexed Domain Adaptation | 3 | [
{
"area": "General",
"area_id": "general",
"collection": "Adversarial Training"
}
] | |
https://paperswithcode.com/method/mdtvsfa | MDTVSFA | MDTVSFA | {
"title": "Unified Quality Assessment of In-the-Wild Videos with Mixed Datasets Training",
"url": "https://paperswithcode.com/paper/unified-quality-assessment-of-in-the-wild"
} | 2,000 | https://arxiv.org/abs/2011.04263v2 | Unified Quality Assessment of In-the-Wild Videos with Mixed Datasets Training | null | 3 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Video Quality Models"
}
] | |
https://paperswithcode.com/method/noisynet-dueling | NoisyNet-Dueling | NoisyNet-Dueling | **NoisyNet-Dueling** is a modification of a [Dueling Network](https://paperswithcode.com/method/dueling-network) that utilises noisy linear layers for exploration instead of $\epsilon$-greedy exploration as in the original Dueling formulation. | {
"title": "Noisy Networks for Exploration",
"url": "https://paperswithcode.com/paper/noisy-networks-for-exploration"
} | 2,000 | https://arxiv.org/abs/1706.10295v3 | Noisy Networks for Exploration | null | 1 | [
{
"area": "Reinforcement Learning",
"area_id": "reinforcement-learning",
"collection": "Q-Learning Networks"
}
] |
https://paperswithcode.com/method/rfb-net | RFB Net | RFB Net | **RFB Net** is a one-stage object detector that utilises a receptive field block module. It utilises a VGG16 backbone, and is otherwise quite similar to the [SSD](https://paperswithcode.com/method/ssd) architecture. | {
"title": "Receptive Field Block Net for Accurate and Fast Object Detection",
"url": "https://paperswithcode.com/paper/receptive-field-block-net-for-accurate-and"
} | 2,000 | http://arxiv.org/abs/1711.07767v3 | Receptive Field Block Net for Accurate and Fast Object Detection | https://github.com/ruinmessi/RFBNet | 2 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "One-Stage Object Detection Models"
},
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Object Detection Models"
}
] |
https://paperswithcode.com/method/lamb | LAMB | LAMB | **LAMB** is a a layerwise adaptive large batch optimization technique. It provides a strategy for adapting the learning rate in large batch settings. LAMB uses [Adam](https://paperswithcode.com/method/adam) as the base algorithm and then forms an update as:
$$r\_{t} = \frac{m\_{t}}{\sqrt{v\_{t}} + \epsilon}$$
$$x\_... | {
"title": "Large Batch Optimization for Deep Learning: Training BERT in 76 minutes",
"url": "https://paperswithcode.com/paper/reducing-bert-pre-training-time-from-3-days"
} | 2,000 | https://arxiv.org/abs/1904.00962v5 | Large Batch Optimization for Deep Learning: Training BERT in 76 minutes | https://github.com/cybertronai/pytorch-lamb/blob/5ef3ebd5e32f7a7bdcddbb2ce55879bfa88f6a5f/pytorch_lamb/lamb.py#L24 | 199 | [
{
"area": "General",
"area_id": "general",
"collection": "Large Batch Optimization"
}
] |
https://paperswithcode.com/method/cs-gan | CS-GAN | CS-GAN | **CS-GAN** is a type of generative adversarial network that uses a form of deep compressed sensing, and [latent optimisation](https://paperswithcode.com/method/latent-optimisation), to improve the quality of generated samples. | {
"title": "Deep Compressed Sensing",
"url": "https://paperswithcode.com/paper/deep-compressed-sensing"
} | 2,000 | https://arxiv.org/abs/1905.06723v2 | Deep Compressed Sensing | https://github.com/deepmind/deepmind-research/tree/master/cs_gan | 3 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Generative Adversarial Networks"
},
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Generative Models"
}
] |
https://paperswithcode.com/method/melgan | MelGAN | MelGAN | **MelGAN** is a non-autoregressive feed-forward convolutional architecture to perform audio waveform generation in a [GAN](https://paperswithcode.com/method/gan) setup. The architecture is a fully convolutional feed-forward network with mel-spectrogram $s$ as input and raw waveform $x$ as output. Since the mel-spectrog... | {
"title": "MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis",
"url": "https://paperswithcode.com/paper/melgan-generative-adversarial-networks-for"
} | 2,000 | https://arxiv.org/abs/1910.06711v3 | MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis | 14 | [
{
"area": "Audio",
"area_id": "audio",
"collection": "Generative Audio Models"
}
] | |
https://paperswithcode.com/method/1-888-829-0881-does-expedia-actually-refund-1 | (1) (888) 829 (0881)Does Expedia actually refund your money? | (1) (888) 829 (0881)Does Expedia actually refund your money? | How Do I File a Dispute with Expedia If you need to dispute a charge with Expedia, call their customer service at +1-888-829-0881 or +1-805-330-4056. For a quicker resolution, be prepared with your booking details, payment receipts, and any supporting documents when speaking with a representative.
To resolve a dispu... | {
"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 Reconstruction"
}
] |
https://paperswithcode.com/method/3d-cnn | 3D CNN | 3 Dimensional Convolutional Neural Network | {
"title": "Uniformizing Techniques to Process CT scans with 3D CNNs for Tuberculosis Prediction",
"url": "https://paperswithcode.com/paper/uniformizing-techniques-to-process-ct-scans"
} | 2,000 | https://arxiv.org/abs/2007.13224v1 | Uniformizing Techniques to Process CT scans with 3D CNNs for Tuberculosis Prediction | null | 178 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Convolutional Neural Networks"
}
] | |
https://paperswithcode.com/method/mlfpn | MLFPN | MLFPN | **Multi-Level Feature Pyramid Network**, or **MLFPN**, is a feature pyramid block used in object detection models, notably [M2Det](https://paperswithcode.com/method/m2det). We first fuse multi-level features (i.e. multiple layers) extracted by a backbone as a base feature, and then feed it into a block of alternating j... | {
"title": "M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network",
"url": "https://paperswithcode.com/paper/m2det-a-single-shot-object-detector-based-on"
} | 2,000 | http://arxiv.org/abs/1811.04533v3 | M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network | https://github.com/qijiezhao/M2Det/blob/ade4f3d12979800c367bf1e46d2e316e73a87514/m2det.py | 2 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Feature Pyramid Blocks"
},
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Feature Extractors"
}
] |
https://paperswithcode.com/method/mhma | MHMA | Multi-Heads of Mixed Attention | The multi-head of mixed attention combines both self- and cross-attentions, encouraging high-level learning of interactions between entities captured in the various attention features. It is build with several attention heads, each of the head can implement either self or cross attention. A self attention is when the k... | {
"title": "Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos",
"url": "https://paperswithcode.com/paper/rendezvous-attention-mechanisms-for-the"
} | 2,000 | https://arxiv.org/abs/2109.03223v2 | Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos | https://github.com/CAMMA-public/rendezvous | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Rendezvous"
},
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Vision Transformers"
},
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"c... |
https://paperswithcode.com/method/xgrad-cam | XGrad-CAM | XGrad-CAM | **XGrad-CAM**, or **Axiom-based Grad-CAM**, is a class-discriminative visualization method and able to highlight the regions belonging to the objects of interest. Two axiomatic properties are introduced in the derivation of XGrad-CAM: Sensitivity and Conservation. In particular, the proposed XGrad-CAM is still a linear... | {
"title": "Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs",
"url": "https://paperswithcode.com/paper/axiom-based-grad-cam-towards-accurate"
} | 2,000 | https://arxiv.org/abs/2008.02312v4 | Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs | 3 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Explainable CNNs"
}
] | |
https://paperswithcode.com/method/whats-the-difference-between-market-and | What’s the Difference Between Market and Instant Buy Orders on CoinSpot? – Trade Smarter Today! 📈 | What’s the Difference Between Market and Instant Buy Orders on CoinSpot? – Trade Smarter Today! 📈 | Curious call +61★3★5929★4808 about the difference between market and instant buy orders on CoinSpot? 😊 Call +61-3-5929-4808 or +61★3★5929★4808 {AU} for details. Market orders, also called instant buys, execute immediately at the current price with a 1% fee—perfect for quick purchases. Limit orders let you set a specif... | {
"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/contact-qatar-how-do-i-connect-with-someone | !!Contact~Qatar!!How do I connect with someone on Qatar Airways? | !!Contact~Qatar!!How do I connect with someone on Qatar Airways? | To connect with someone at Qatar Airways, ☎️+1-801-(855)-(5905)or +1-804-853-9001✅✈️ you can contact their customer service team via phone, live chat, email, or social media ☎️+1-801-(855)-(5905)or +1-804-853-9001✅✈️ The fastest way to speak with a live agent is by calling their customer support number ☎️+1-801-(855)-(... | {
"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": "Hybrid Parallel Methods"
}
] |
https://paperswithcode.com/method/du-gan | DU-GAN | DU-GAN | **DU-GAN** is a [generative adversarial network](https://www.paperswithcode.com/methods/category/generative-adversarial-networks) for LDCT denoising in medical imaging. The generator produces denoised LDCT images, and two independent branches with [U-Net](https://paperswithcode.com/method/u-net) based discriminators pe... | {
"title": "DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoising",
"url": "https://paperswithcode.com/paper/du-gan-generative-adversarial-networks-with"
} | 2,000 | https://arxiv.org/abs/2108.10772v2 | DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoising | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Denoising Models"
},
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Generative Adversarial Networks"
}
] |
https://paperswithcode.com/method/como-obtener-un-descuento-para-personas | ¿Cómo obtener un descuento para personas mayores en American Airlines?#manera fácil | ¿Cómo obtener un descuento para personas mayores en American Airlines?#manera fácil | 𝐀𝐦𝐞𝐫𝐢𝐜𝐚𝐧 𝐀𝐢𝐫𝐥𝐢𝐧𝐞𝐬 no ofrece descuentos específicos para +1-808-(470)-(7107) personas mayores en todos sus vuelos +1-808-(470)-(7107). Sin embargo, pueden encontrarse promociones para viajeros mayores en vuelos seleccionados +1-808-(470)-(7107), por lo que es recomendable consultar la disponibilidad y 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 | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/nas-fpn | NAS-FPN | NAS-FPN | **NAS-FPN** is a Feature Pyramid Network that is discovered via [Neural Architecture Search](https://paperswithcode.com/method/neural-architecture-search) in a novel scalable search space covering all cross-scale connections. The discovered architecture consists of a combination of top-down and bottom-up connections to... | {
"title": "NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection",
"url": "https://paperswithcode.com/paper/nas-fpn-learning-scalable-feature-pyramid"
} | 2,000 | http://arxiv.org/abs/1904.07392v1 | NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection | https://github.com/tensorflow/tpu/blob/f3bafc6a96197b770178cc6c373dc071387b8cfe/models/official/detection/modeling/architecture/nasfpn.py#L165 | 11 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Feature Pyramid Blocks"
},
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Feature Extractors"
}
] |
https://paperswithcode.com/method/wizard | Wizard | Wizard: Unsupervised goats tracking algorithm | Computer vision is an interesting tool for animal behavior monitoring, mainly because it limits animal handling and it can be used to record various traits using only one sensor. From previous studies, this technic has shown to be suitable for various species and behavior. However it remains challenging to collect indi... | null | 2,000 | null | null | null | 64 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Multi-Object Tracking Models"
}
] |
https://paperswithcode.com/method/depthwise-separable-convolution | Depthwise Separable Convolution | Depthwise Separable Convolution | While [standard convolution](https://paperswithcode.com/method/convolution) performs the channelwise and spatial-wise computation in one step, **Depthwise Separable Convolution** splits the computation into two steps: [depthwise convolution](https://paperswithcode.com/method/depthwise-convolution) applies a single con... | {
"title": "Xception: Deep Learning With Depthwise Separable Convolutions",
"url": "https://paperswithcode.com/paper/xception-deep-learning-with-depthwise-1"
} | 2,000 | http://openaccess.thecvf.com/content_cvpr_2017/html/Chollet_Xception_Deep_Learning_CVPR_2017_paper.html | Xception: Deep Learning With Depthwise Separable Convolutions | https://github.com/kwotsin/TensorFlow-Xception/blob/c42ad8cab40733f9150711be3537243278612b22/xception.py#L67 | 1,174 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Convolutions"
}
] |
https://paperswithcode.com/method/groupdnet | GroupDNet | Group Decreasing Network | **Group Decreasing Network**, or **GroupDNet**, is a type of convolutional neural network for multi-modal image synthesis. GroupDNet contains one encoder and one decoder. Inspired by the idea of [VAE](https://paperswithcode.com/method/vae) and SPADE, the encoder $E$ produces a
latent code $Z$ that is supposed to follo... | {
"title": "Semantically Multi-modal Image Synthesis",
"url": "https://paperswithcode.com/paper/semantically-mutil-modal-image-synthesis"
} | 2,000 | https://arxiv.org/abs/2003.12697v3 | Semantically Multi-modal Image Synthesis | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Generation Models"
}
] |
https://paperswithcode.com/method/step-reservation-was-what-is-the-24-hour-rule | [step reservation was]what is the 24-hour rule with lufthansa | [step reservation was]what is the 24-hour rule with lufthansa | Lufthansa follows the 24-hour rule, allowing passengers to cancel their flight within 24 hours of booking and receive a full refund + 1-(801)-855-(5905).𝙐𝙎 + 1-(801)-855-(5905).𝙐𝙆., if the reservation was made seven days or more prior to the flight's departure. | {
"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/sparse-switchable-normalization | Sparse Switchable Normalization | Sparse Switchable Normalization | **Sparse Switchable Normalization (SSN)** is a variant on [Switchable Normalization](https://paperswithcode.com/method/switchable-normalization) where the importance ratios are constrained to be sparse. Unlike $\ell_1$ and $\ell_0$ constraints that impose difficulties in optimization, the constrained optimization probl... | {
"title": "SSN: Learning Sparse Switchable Normalization via SparsestMax",
"url": "https://paperswithcode.com/paper/ssn-learning-sparse-switchable-normalization"
} | 2,000 | http://arxiv.org/abs/1903.03793v1 | SSN: Learning Sparse Switchable Normalization via SparsestMax | https://github.com/switchablenorms/Sparse_SwitchNorm/blob/875db480b5ce755cd569c2ce54655b637891ce58/utils/sparse_switchable_norm.py#L6 | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Normalization"
}
] |
https://paperswithcode.com/method/ways-to-access-msc-cruises-r-fast-support | Ways to Access msc Cruises®️ Fast Support Guide 2025 | Ways to Access msc Cruises®️ Fast Support Guide 2025 | Planning a msc Cruises is exciting +1-855-732-4023 but sometimes unforeseen events force a change in plans +1-855-732-4023 and you may need to cancel your msc Cruises booking. Whether you’re facing a +1-855-732-4023 medical emergency, schedule change, or simply rethinking your trip +1-855-732-4023 msc Cruises of... | {
"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/mad-learning | MAD Learning | Memory-Associated Differential Learning | **Memory-Associated Differential** (**MAD**) Learning was developed to inference from the memorized facts that we already know to predict what we want to know.
Image source: [Luo et al.](https://arxiv.org/pdf/2102.05246v1.pdf) | {
"title": "Memory-Associated Differential Learning",
"url": "https://paperswithcode.com/paper/memory-associated-differential-learning"
} | 2,000 | https://arxiv.org/abs/2102.05246v2 | Memory-Associated Differential Learning | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Semi-Supervised Learning Methods"
}
] | |
https://paperswithcode.com/method/seq2edits | Seq2Edits | Seq2Edits | **Seq2Edits** is an open-vocabulary approach to sequence editing for natural language processing (NLP) tasks with a high degree of overlap between input and output texts. In this approach, each sequence-to-sequence transduction is represented as a sequence of edit operations, where each operation either replaces an ent... | {
"title": "Seq2Edits: Sequence Transduction Using Span-level Edit Operations",
"url": "https://paperswithcode.com/paper/seq2edits-sequence-transduction-using-span"
} | 2,000 | https://arxiv.org/abs/2009.11136v1 | Seq2Edits: Sequence Transduction Using Span-level Edit Operations | 1 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Sequence Editing Models"
}
] | |
https://paperswithcode.com/method/cluster-gcn | Cluster-GCN | Cluster-GCN | Cluster-GCN is a novel GCN algorithm that is suitable for SGD-based training by exploiting the graph clustering structure. Cluster-GCN works as the following: at each step, it samples a block of nodes that associate with a dense subgraph identified by a graph clustering algorithm, and restricts the neighborhood search ... | {
"title": "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks",
"url": "https://paperswithcode.com/paper/cluster-gcn-an-efficient-algorithm-for"
} | 2,000 | https://arxiv.org/abs/1905.07953v2 | Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks | 3 | [
{
"area": "Graphs",
"area_id": "graphs",
"collection": "Graph Models"
}
] | |
https://paperswithcode.com/method/what-age-is-senior-discount-on-princess | What age is senior discount on Princess Cruises? | What age is senior discount on Princess Cruises? | Princess Cruises typically offers senior discounts to guests who are 55 years of age or older 1-800-950-4401. These discounts can vary based on the itinerary, sailing date, and availability. Seniors may receive reduced fares, onboard credit 1-800-950-4401, or special offers through promotions. To access these savings, ... | {
"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/what-is-the-phone-number-for-windstar-booking | What is the phone number for Windstar booking? @24/7 Support line | What is the phone number for Windstar booking? @24/7 Support line | To contact a live representative at Windstar Cruises, call their 24/7 +1-(855) 732^4023 +44-(289)-708^0062 or 1-855-Windstar Cruises(+1-855-732-4023). You can also use their website's live chat or email for assistance.
Reservations: Call +1-855-732-4023 (USA) +44-289-708-0062 (UK) to create or modify a reservation, ... | {
"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/american-does-american-airlines-actually | {@american`} Does American Airlines actually refund money? | {@american`} Does American Airlines actually refund money? | Yes, American Airlines does offer refunds, but the specifics depend on the type of ticket, when it was purchased ☎️+𝟙-𝟠𝟘𝟙-(𝟠𝟝𝟝)-(𝟝𝟡𝟘𝟝)𝕠𝕣 +𝟙-𝟠𝟘𝟜-𝟠𝟝𝟛-𝟡𝟘𝟘𝟙✅, and the circumstances of the cancellation. Generally, you can get a full refund for tickets canceled within 24 hours of purchase, as long as ... | {
"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/transformer-xl | Transformer-XL | Transformer-XL | **Transformer-XL** (meaning extra long) is a [Transformer](https://paperswithcode.com/method/transformer) architecture that introduces the notion of recurrence to the deep self-attention network. Instead of computing the hidden states from scratch for each new segment, Transformer-XL reuses the hidden states obtained i... | {
"title": "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context",
"url": "https://paperswithcode.com/paper/transformer-xl-attentive-language-models"
} | 2,000 | https://arxiv.org/abs/1901.02860v3 | Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context | null | 64 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Autoregressive Transformers"
},
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Transformers"
}
] |
https://paperswithcode.com/method/is-princess-a-high-end-cruise-line | Is Princess a high end cruise line? | Is Princess a high end cruise line? | Princess Cruises is considered a premium cruise line rather than a luxury or ultra-high-end one 1-800-950-4401. It offers upscale amenities, elegant dining, and personalized service, appealing to travelers seeking comfort and quality at a reasonable price. While not as opulent as luxury lines like Regent or Silversea 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/asaf | ASAF | Adaptive Spline Activation Function | Stefano Guarnieri, Francesco Piazza, and Aurelio Uncini
"Multilayer Feedforward Networks with Adaptive Spline Activation Function,"
IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 10, NO. 3, MAY 1999
Abstract — In this paper, a new adaptive spline activation function neural network (ASNN) is presented. Due to the AS... | {
"title": "Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization",
"url": "https://paperswithcode.com/paper/adversarial-soft-advantage-fitting-imitation"
} | 2,000 | https://arxiv.org/abs/2006.13258v6 | Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization | 0 | [
{
"area": "General",
"area_id": "general",
"collection": "Adversarial Training"
},
{
"area": "General",
"area_id": "general",
"collection": "Activation Functions"
}
] | |
https://paperswithcode.com/method/does-latam-have-free-cancellation-48-hours | Does Latam have free cancellation?"48 hours before departure. | Does Latam have free cancellation?"48 hours before departure. | Latam offers 24-hour free cancellation. Latam Airlines offers free cancellation within 24 hours by calling their Customer Support team at + 𝟭-𝟴𝟬𝟭-𝟴𝟱𝟱-𝟱𝟵𝟬𝟱.(USA) or + 𝟭-𝟴𝟬𝟭-𝟴𝟱𝟱-𝟱𝟵𝟬𝟱.(UK) for flights booked at least 48 hours before departure. | {
"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/speak-now-how-to-speak-directly-at-latam | [Speak🗣️Now]How to speak directly at LATAM Airlines? | [Speak🗣️Now]How to speak directly at LATAM Airlines? | To talk to someone at LATAM Airlines, you can call their customer service number at+1-801-855-5905 or+1-801-855-5905. This line is available for various inquiries, including booking assistance, flight changes, cancellations, and general customer service. To reach a live agent at LATAM Airlines, dial +1-801-855-5905(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 | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/how-do-i-contact-regent-seven-seas-cruises | How do I contact Regent Seven Seas Cruises? [[Customer Service Number]] | How do I contact Regent Seven Seas Cruises? [[Customer Service Number]] | The phone number for Regent Seven Seas Reservations is 1-855-4REGENT (+1-855-732-4023 (USA) or +44-289-708-0062 (UK)).
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. If you have already booked... | {
"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"
}
] |
https://paperswithcode.com/method/faqs-linetm-how-do-i-contact-silversea | [Faqs`Line™] How do I contact Silversea customer service? | [Faqs`Line™] How do I contact Silversea customer service? | Silversea Cruises provides a guest experience line for post-cruise inquiries and feedback at +𝟭855--732-4023
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Additional contact details for international callers an... | {
"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
"url": "https://paperswithcode.com/paper/0-1-laws-for-pattern-occurrences-in"
} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/faqs-guide-how-do-i-talk-to-royal-caribbean-1 | [[FAQs~~GuidE]]How do I talk to Royal Caribbean Customer Service? | [[FAQs~~GuidE]]How do I talk to Royal Caribbean Customer Service? | General Customer Service (US & Canada): +1-855-732-4023 or +1-808-900-8011 . This number can be used for general inquiries, booking changes, and pre-cruise assistance. You can also text this number for post-cruise assistance.
Individual Reservations (US & Canada): 1-855-732-4023 or +1-808-900-8011 . Available 7 days... | {
"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"
}
] | |
https://paperswithcode.com/method/faqs-cruise-how-do-i-check-in-online-for-my | [[FAQS=Cruise]]How do I check in online for my celebrity cruise? | [[FAQS=Cruise]How do I check in online for my celebrity cruise? | celebrity cruise reservations number
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For group booking... | {
"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-helptm-what-is-the-24-hour-rule-for | [FAQs~HELP™] - What is the 24-hour rule for 𝓠𝓪𝓽𝓪𝓻 𝓐𝓲𝓻𝔀𝓪𝔂𝓼? | [FAQs~HELP™] - What is the 24-hour rule for 𝓠𝓪𝓽𝓪𝓻 𝓐𝓲𝓻𝔀𝓪𝔂𝓼? | In summary ☎️+1-801-(855)-(5905)or +1-804-853-9001✅, to really get through to Qatar Airways, start with a direct phone call during off-peak hours, use live chat or WhatsApp if you prefer messaging, and turn to social media or the official web form for escalation ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. Having your tra... | {
"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/psanet | PSANet | PSANet | **PSANet** is a semantic segmentation architecture that utilizes a [Point-wise Spatial Attention](https://paperswithcode.com/method/point-wise-spatial-attention) (PSA) module to aggregate long-range contextual information in a flexible and adaptive manner. Each position in the feature map is connected with all other on... | {
"title": "PSANet: Point-wise Spatial Attention Network for Scene Parsing",
"url": "https://paperswithcode.com/paper/psanet-point-wise-spatial-attention-network"
} | 2,000 | http://openaccess.thecvf.com/content_ECCV_2018/html/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.html | PSANet: Point-wise Spatial Attention Network for Scene Parsing | https://github.com/hszhao/semseg/blob/7192f922b99468969cfd4535e3e35a838994b115/model/psanet.py#L101 | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Semantic Segmentation Models"
}
] |
https://paperswithcode.com/method/how-far-in-advance-should-i-book-singaporeair | How far in advance should I book SingaporeAir? | How far in advance should I book SingaporeAir? | If you're heading to popular places like Singapore,+1-833-667-0020 +44-(203)-970-0065(UK) Tokyo, or Sydney, 6–9 months in advance is ideal. For Europe or the U.S., 4–6 months works well, but again, the sooner the better +1-833-667-0020 .
If you're planning to visit popular destinations like +1-833-667-0020 (US) +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/booking-flights-what-is-the-best-day-to-book | {{Booking~Flights}} What is the best day to book flights on Qatar Airways? | What is the best day to book flights on Qatar Airways? | The best day to book flights on Qatar Airways is typically Tuesday or Wednesday, +1-833-666-33.30 (U.S) OR +(44-203-900-09.30) (U.K) 🎟️✔️. When airlines often release mid-week deals. Booking early in the morning can also help you find lower fares. For personalized assistance or exclusive offers, contact Qatar Airways ... | {
"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/faq-support-r-how-do-i-file-a-claim-with | [FAQ-suPport®] How do I file a claim with Breeze Airways? | [FAQ-suPport®] How do I file a claim with Breeze Airways? | To file a claim with Breeze Airways,☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ you can submit a request through their website, send a letter, or contact their Guest Empowerment Team ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅. For lost or damaged bags, report it to an Airport Team Member immediately or contact the... | {
"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/ways-to-access-american-cruises-r-usa-contact-1 | Ways to Access American Cruises®️ USA Contact Numbers – A Comprehensive Guide | Ways to Access American Cruises®️ USA Contact Numbers – A Comprehensive Guide | Planning a American Cruises is exciting +1-855-732-4023 but sometimes unforeseen events force a change in plans +1-855-732-4023 and you may need to cancel your American Cruises booking. Whether you’re facing a +1-855-732-4023 medical emergency, schedule change, or simply rethinking your trip +1-855-732-4023 Amer... | {
"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-helpline-can-you-get-a-better-deal-by-1 | [[FAQS~~HELPLINE]]Can you get a better deal by calling Royal Caribbean? | [[FAQS~~HELPLINE]]Can you get a better deal by calling Royal Caribbean? | General Customer Service (US & Canada): +1-855-732-4023 or +1-808-900-8011 . This number can be used for general inquiries, booking changes, and pre-cruise assistance. You can also text this number for post-cruise assistance.
Individual Reservations (US & Canada): 1-855-732-4023 or +1-808-900-8011 . Available 7 days... | {
"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"
}
] | |
https://paperswithcode.com/method/impact-of-additional-services-what-is-the-24 | [@@Impact of additional services@@]What is the 24 hour rule for Copa Airlines? | [@@Impact of additional services@@]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. This policy allows passengers to cancel their flight booking <<+1 (801) 855-5905>> or <<+1 (804) 853-9001>> and receive a full refund, without any cancellation fees, if the cancellation is made within 24 hours of the o... | {
"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/concatenation-affinity | Concatenation Affinity | Concatenation Affinity | **Concatenation Affinity** is a type of affinity or self-similarity function between two points $\mathbb{x\_{i}}$ and $\mathbb{x\_{j}}$ that uses a concatenation function:
$$ f\left(\mathbb{x\_{i}}, \mathbb{x\_{j}}\right) = \text{ReLU}\left(\mathbb{w}^{T}\_{f}\left[\theta\left(\mathbb{x}\_{i}\right), \phi\left(\math... | {
"title": "Non-local Neural Networks",
"url": "https://paperswithcode.com/paper/non-local-neural-networks"
} | 2,000 | http://arxiv.org/abs/1711.07971v3 | Non-local Neural Networks | https://github.com/tea1528/Non-Local-NN-Pytorch/blob/986937674eb3b85d3d3fbaaa8f384c0a26624121/models/non_local.py#L105 | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Affinity Functions"
}
] |
https://paperswithcode.com/method/arch | ARCH | Animatable Reconstruction of Clothed Humans | **Animatable Reconstruction of Clothed Humans** is an end-to-end framework for accurate reconstruction of animation-ready 3D clothed humans from a monocular image. ARCH is a learned pose-aware model that produces detailed 3D rigged full-body human avatars from a single unconstrained RGB image. A Semantic Space and a Se... | {
"title": "ARCH: Animatable Reconstruction of Clothed Humans",
"url": "https://paperswithcode.com/paper/arch-animatable-reconstruction-of-clothed"
} | 2,000 | https://arxiv.org/abs/2004.04572v2 | ARCH: Animatable Reconstruction of Clothed Humans | null | 23 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Reconstruction"
}
] |
https://paperswithcode.com/method/does-royal-caribbean-do-group-discounts-group | Does Royal Caribbean do group discounts:{Group Travel: Cruise Group Booking & Rates | Does Royal Caribbean do group discounts:{Group Travel: Cruise Group Booking & Rates | Yes, Royal Caribbean offers group discounts for travelers booking eight or more staterooms 1-800-950-4401. These discounts can include reduced fares, onboard credit, complimentary amenities 1-800-950-4401, or even free berths depending on the group size and sailing. Group bookings also come with flexible payment option... | {
"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/permuteformer | PermuteFormer | PermuteFormer | **PermuteFormer** is a [Performer](https://paperswithcode.com/method/performer)-based model with relative position encoding that scales linearly on long sequences. PermuteFormer applies position-dependent transformation on queries and keys to encode positional information into the attention module. This transformation ... | {
"title": "PermuteFormer: Efficient Relative Position Encoding for Long Sequences",
"url": "https://paperswithcode.com/paper/permuteformer-efficient-relative-position"
} | 2,000 | https://arxiv.org/abs/2109.02377v2 | PermuteFormer: Efficient Relative Position Encoding for Long Sequences | null | 1 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Transformers"
}
] |
https://paperswithcode.com/method/mdetr | MDETR | MDETR | **MDETR** is an end-to-end modulated detector that detects objects in an image conditioned on a raw text query, like a caption or a question. It utilizes a [transformer](https://paperswithcode.com/method/transformer)-based architecture to reason jointly over text and image by fusing the two modalities at an early stage... | {
"title": "MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding",
"url": "https://paperswithcode.com/paper/mdetr-modulated-detection-for-end-to-end"
} | 2,000 | https://arxiv.org/abs/2104.12763v2 | MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding | null | 13 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Object Detection Models"
}
] |
https://paperswithcode.com/method/faqs-guide-how-do-i-talk-to-royal-caribbean-2 | [[FAQs~~GUIDE]]How do i talk to royal caribbean customer service 24 7? | [[FAQs~~GUIDE]]How do i talk to royal caribbean customer service 24 7? | General Customer Service (US & Canada): +1-855-732-4023 or +1-808-900-8011 . This number can be used for general inquiries, booking changes, and pre-cruise assistance. You can also text this number for post-cruise assistance.
Individual Reservations (US & Canada): 1-855-732-4023 or +1-808-900-8011 . Available 7 days... | {
"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"
}
] | |
https://paperswithcode.com/method/full-supporttm-what-is-the-number-for-msc | [Full~SuPporT™]What is the number for MSC cruise refund? | [Full~SuPporT™]What is the number for MSC cruise refund? | The primary contact number for MSC Cruises customer service, including inquiries about refunds, is (+1-(855)-732-4023), according to cruisessupportassistance.tawk.help. This line is available 24/7 for assistance with booking changes, flight cancellations, and refund-related questions. ou can contact MSC Cruises custome... | {
"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/can-i-cancel-a-royal-caribbean-cruise-without | Can I cancel a Royal Caribbean cruise without penalty? | Can I cancel a Royal Caribbean cruise without penalty? | You can cancel a Royal Caribbean cruise without penalty +1-855-732-4023 or +1-808-900-8011, but it depends on how far in advance you cancel and the type of fare you booked. For most cruises +1-855-732-4023 or +1-808-900-8011, you can receive a full refund if you cancel at least 75 days before the sail date. For shorter... | {
"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/support-what-is-the-cheapest-day-to-buy-delta | (Support)What is the cheapest day to buy Delta tickets? | (^Support)What is the cheapest day to buy Delta tickets? | The 𝖈𝖍𝖊𝖆𝖕𝖊𝖘𝖙 days to 𝗳𝗹𝘆 on 𝗗𝗲𝗹𝘁𝗮 𝓯𝓵𝓲𝓰𝓱𝓽𝓼 are typically Tuesday, Wednesday, and Saturdays （+ 𝟭-(𝟴𝟬𝟭)-𝟴𝟱𝟱-(𝟱𝟵𝟬𝟱).. (USA). These are considered off-peak travel days when demand is lower, leading to cheaper fares （+ 𝟭-(𝟴𝟬𝟭)-𝟴𝟱𝟱-(𝟱𝟵𝟬𝟱)., Typically, the most affordable days to bo... | {
"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/altdiffusion | AltDiffusion | AltDiffusion | In this work, we present a conceptually simple and effective method to train a strong bilingual multimodal representation model. Starting from the pretrained multimodal representation model CLIP released by OpenAI, we switched its text encoder with a pretrained multilingual text encoder XLM-R, and aligned both language... | {
"title": "AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities",
"url": "https://paperswithcode.com/paper/altclip-altering-the-language-encoder-in-clip"
} | 2,000 | https://arxiv.org/abs/2211.06679v2 | AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities | 2 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Generation Models"
}
] | |
https://paperswithcode.com/method/speak-now-r-how-do-i-speak-to-someone-at | [[Speak||Now®]]How do I speak to someone at LATAM? | [[Speak||Now®]]How do I speak to someone at LATAM? | To talk to someone at LATAM Airlines, you can call their customer service number at ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅. This line is available for various inquiries, including booking assistance, flight changes, cancellations, and general 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 | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/guide-anyway-how-to-cancel-a-cruise-without | [[Guide`Anyway]]How to cancel a cruise without penalty? | [[Guide`Anyway]]How to cancel a cruise without penalty? | How to cancel a cruise without penalty?
To cancel a cruise without penalty, cancel before the cruise line’s deadline—usually 75–120 days before departure. Policies vary. For exact info, call +1-855-732-4023 (USA/Canada) or +1 (855) 732-4023 (UK/Europe).
How to cancel a cruise without penalty?
Avoid penalties by re... | {
"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/royal-guidetm-what-is-the-3-1-1-rule-for-a | {[Royal`Guide™]}What is the 3-1-1 rule for a Carnival Cruise? | {[Royal`Guide™]}What is the 3-1-1 rule for a Carnival Cruise? | The 3-1-1 rule on a Carnival Cruise refers to TSA's guideline for carrying liquids in your carry-on when boarding a flight to your cruise 1-855-732-4023. It stands for 3.4-ounce containers (100 ml),+1-808-900-8011. 1 quart-sized clear plastic bag, and 1 bag per passenger. This rule applies to toiletries like shampoo, ... | {
"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/full-supporttm-what-is-the-phone-number-for | [Full~SuPporT™]What is the phone number for Royal Caribbean cruise cancellation? | [Full~SuPporT™]What is the phone number for Royal Caribbean cruise cancellation? | Based on the search results, there appear to be several different phone numbers provided for Royal Caribbean customer service and cancellations. To ensure you reach the correct department for canceling your cruise, you might consider using the following numbers:
+1-855-732-4023 This number is listed as the primary R... | {
"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 Representations"
}
] |
https://paperswithcode.com/method/wavegrad-dblock | WaveGrad DBlock | WaveGrad DBlock | **WaveGrad DBlocks** are used to downsample the temporal dimension of noisy waveform in [WaveGrad](https://paperswithcode.com/method/wavegrad). They are similar to UBlocks except that only one [residual block](https://paperswithcode.com/method/residual-block) is included. The dilation factors are 1, 2, 4 in the main br... | {
"title": "WaveGrad: Estimating Gradients for Waveform Generation",
"url": "https://paperswithcode.com/paper/wavegrad-estimating-gradients-for-waveform"
} | 2,000 | https://arxiv.org/abs/2009.00713v2 | WaveGrad: Estimating Gradients for Waveform Generation | 7 | [
{
"area": "Audio",
"area_id": "audio",
"collection": "Audio Model Blocks"
}
] | |
https://paperswithcode.com/method/crf | CRF | Conditional Random Field | **Conditional Random Fields** or **CRFs** are a type of probabilistic graph model that take neighboring sample context into account for tasks like classification. Prediction is modeled as a graphical model, which implements dependencies between the predictions. Graph choice depends on the application, for example linea... | null | 2,000 | null | null | null | 468 | [
{
"area": "General",
"area_id": "general",
"collection": "Structured Prediction"
}
] |
https://paperswithcode.com/method/what-is-the-3-1-1-rule-for-a-carnival-cruise | What is the 3-1-1 rule for a Carnival Cruise? | What is the 3-1-1 rule for a Carnival Cruise? | The 3-1-1 rule on a Carnival Cruise refers to TSA's guideline for carrying liquids in your carry-on when boarding a flight to your cruise 1-800-950-4401. It stands for 3.4-ounce containers (100 ml), 1 quart-sized clear plastic bag, and 1 bag per passenger. This rule applies to toiletries like shampoo, lotion, or toothp... | {
"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/pangu-a | PanGu-$α$ | PanGu-$α$ | **PanGu-$α$** is an autoregressive language model (ALM) with up to 200 billion parameters pretrained on a large corpus of text, mostly in Chinese language. The architecture of PanGu-$α$ is based on Transformer, which has been extensively used as the backbone of a variety of pretrained language models such as [BERT](htt... | {
"title": "PanGu-$α$: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation",
"url": "https://paperswithcode.com/paper/pangu-a-large-scale-autoregressive-pretrained"
} | 2,000 | https://arxiv.org/abs/2104.12369v1 | PanGu-$α$: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation | 1 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Language Models"
}
] | |
https://paperswithcode.com/method/kaleido-bert | Kaleido-BERT | Kaleido-BERT | **Kaleido-BERT**(CVPR2021) is the pioneering work that focus on solving PTM in e-commerce field. It achieves SOTA performances compared with many models published in general domain. | null | 2,000 | null | null | https://github.com/mczhuge/Kaleido-BERT | 2 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Vision and Language Pre-Trained Models"
}
] |
https://paperswithcode.com/method/faqs-support-can-i-cancel-my-royal-caribbean | {FaQs~SuPporT}Can I cancel my Royal Caribbean cruise without a penalty? | {FaQs~SuPporT}Can I cancel my Royal Caribbean cruise without a penalty? | Yes, you can cancel your Royal Caribbean cruise without a penalty if you do so within 48 hours of booking 1-855-732-4023(US)/+44 (28) 97085562(UK), as part of their Refundable Deposit policy. For cancellations beyond that, penalties depend on how far in advance you cancel. Royal Caribbean’s “Cruise with Confidence” pro... | {
"title": "0-1 phase transitions in sparse spiked matrix estimation",
"url": "https://paperswithcode.com/paper/0-1-phase-transitions-in-sparse-spiked-matrix"
} | 2,000 | https://arxiv.org/abs/1911.05030v1 | 0-1 phase transitions in sparse spiked matrix estimation | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
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