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/faq-how-to-communicate-with-someone-in-qatar | FAQ-How to communicate with someone in Qatar?[FaQs!!UndeRstanDing®️]™ | FAQ-How to communicate with someone in Qatar?[FaQs!!UndeRstanDing®️]™ | 𝙲𝚊𝚕𝚕𝚒𝚗𝚐 𝚀𝚊𝚝𝚊𝚛 𝙳𝚒𝚛𝚎𝚌𝚝𝚕𝚢: 𝙳𝚒𝚊𝚕 𝚝𝚑𝚎 𝚌𝚘𝚞𝚗𝚝𝚛𝚢 𝚌𝚘𝚍𝚎 ☎️ +1--801--855--5905.OR (+1--801--855--5905.), 𝚏𝚘𝚕𝚕𝚘𝚠𝚎𝚍 𝚋𝚢 𝚝𝚑𝚎 𝚛𝚎𝚌𝚒𝚙𝚒𝚎𝚗𝚝'𝚜 𝚙𝚑𝚘𝚗𝚎 𝚗𝚞𝚖𝚋𝚎𝚛. | {
"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/what-is-the-cheapest-month-to-go-on-a-3 | (𝗖𝗵𝗮𝗻𝗴𝗲𝘀_𝗙𝗿𝗲𝗲)What is the cheapest month to go on a Carnival cruise? | (𝗖𝗵𝗮𝗻𝗴𝗲𝘀_𝗙𝗿𝗲𝗲)What is the cheapest month to go on a Carnival cruise? | The cheapest time to take a Carnival cruise is typically between Thanksgiving and Christmas, excluding the actual holiday dates, as well as in mid-January to early February+𝟭-𝟴𝟱𝟱-𝟳𝟯𝟮-𝟰𝟬𝟮𝟯 . Fall shoulder season (September and October) also often offers good deals+𝟭-𝟴𝟱𝟱-𝟳𝟯𝟮-𝟰𝟬𝟮𝟯 . Avoid April, whic... | {
"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/bs-net | BS-Net | BS-Net | **BS-Net** is an architecture for COVID-19 severity prediction based on clinical data from different modalities. The architecture comprises 1) a shared multi-task feature extraction backbone, 2) a lung segmentation branch, 3) an original registration mechanism that acts as a ”multi-resolution feature alignment” block o... | {
"title": "BS-Net: learning COVID-19 pneumonia severity on a large Chest X-Ray dataset",
"url": "https://paperswithcode.com/paper/end-to-end-learning-for-semiquantitative"
} | 2,000 | https://arxiv.org/abs/2006.04603v3 | BS-Net: learning COVID-19 pneumonia severity on a large Chest X-Ray dataset | 8434 | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Medical Image Models"
}
] |
https://paperswithcode.com/method/do-you-lose-your-deposit-if-you-cancel-a | Do you lose your deposit if you cancel a celebrity cruise?[FAQs~REfunD[ | Do you lose your deposit if you cancel a celebrity cruise?[FAQs~REfunD[ | 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*
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/snet | SNet | SNet | **SNet** is a convolutional neural network architecture and object detection backbone used for the [ThunderNet](https://paperswithcode.com/method/thundernet) two-stage object detector. SNet uses ShuffleNetV2 basic blocks but replaces all 3×3 depthwise convolutions with 5×5 depthwise convolutions. | {
"title": "ThunderNet: Towards Real-time Generic Object Detection",
"url": "https://paperswithcode.com/paper/thundernet-towards-real-time-generic-object"
} | 2,000 | https://arxiv.org/abs/1903.11752v3 | ThunderNet: Towards Real-time Generic Object Detection | https://github.com/ouyanghuiyu/Thundernet_Pytorch/blob/ab66b733a39c9d1c60b5373f84f861d9627d8c20/lib/model/faster_rcnn/Snet.py#L6 | 6 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Convolutional Neural Networks"
}
] |
https://paperswithcode.com/method/speak-up-get-help-can-you-transfer-a-ticket | [Speak~up!Get~Help]Can you transfer a ticket to another person on Delta? | [Speak~up!Get~Help]Can you transfer a ticket to another person on Delta? | Delta Air Lines does not allow you to transfer a ticket to another person 1-833-705-0001(US)/ +44 (20) 39003980(UK), as tickets are non-transferable once issued. If you made a mistake in the name, Delta may allow minor name corrections (such as spelling errors) with proper documentation 1-833-705-0001(US)/ +44 (20) 390... | {
"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-are-the-deposit-and-withdrawal-limits-on-2 | What are the deposit and withdrawal limits on CoinSpot?+61-3-5929-4808 | What are the deposit and withdrawal limits on CoinSpot?+61-3-5929-4808 | call ６１ー（３）ー５９２９ー４８０８. To know your specific CoinSpot deposit and withdrawal limits, call ６１ー（３）ー５９２９ー４８０８. for a detailed breakdown. ６１ー（３）ー５９２９ー４８０８. will confirm how much AUD you can deposit via POLi, PayID, or BPAY. For crypto withdrawals, ６１ー（３）ー５９２９ー４８０８. will explain daily and monthly limits based on your accou... | {
"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"
}
] |
https://paperswithcode.com/method/mdpo | MDPO | Mirror Descent Policy Optimization | **Mirror Descent Policy Optimization (MDPO)** is a policy gradient algorithm based on the idea of iteratively solving a trust-region problem that minimizes a sum of two terms: a linearization of the standard RL objective function and a proximity term that restricts two consecutive updates to be close to each other. It ... | {
"title": "Mirror Descent Policy Optimization",
"url": "https://paperswithcode.com/paper/mirror-descent-policy-optimization"
} | 2,000 | https://arxiv.org/abs/2005.09814v5 | Mirror Descent Policy Optimization | null | 4 | [
{
"area": "Reinforcement Learning",
"area_id": "reinforcement-learning",
"collection": "Policy Gradient Methods"
}
] |
https://paperswithcode.com/method/mim | MIM | Mutual Information Machine/Mask Image Modeling | {
"title": "MIM: Mutual Information Machine",
"url": "https://paperswithcode.com/paper/mim-mutual-information-machine"
} | 2,000 | https://arxiv.org/abs/1910.03175v5 | MIM: Mutual Information Machine | 150 | [
{
"area": "General",
"area_id": "general",
"collection": "Representation Learning"
}
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https://paperswithcode.com/method/ways-to-access-msc-cruises-r-usa-contact | Ways to Access msc Cruises®️ USA Contact Numbers – Full Support Guide | Ways to Access msc Cruises®️ USA Contact Numbers – Full Support Guide | 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/regnetx | RegNetX | RegNetX | **RegNetX** is a convolutional network design space with simple, regular models with parameters: depth $d$, initial width $w\_{0} > 0$, and slope $w\_{a} > 0$, and generates a different block width $u\_{j}$ for each block $j < d$. The key restriction for the RegNet types of model is that there is a linear parameterisat... | {
"title": "Designing Network Design Spaces",
"url": "https://paperswithcode.com/paper/designing-network-design-spaces"
} | 2,000 | https://arxiv.org/abs/2003.13678v1 | Designing Network Design Spaces | https://github.com/facebookresearch/pycls/blob/ecfb53186b426002020f1a580c3d7d7ad723e283/pycls/models/regnet.py#L50 | 2 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Convolutional Neural Networks"
}
] |
https://paperswithcode.com/method/lda | LDA | Linear Discriminant Analysis | **Linear discriminant analysis** (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics, pattern recognition, and machine learning to find a linear combination of features that characterizes or separates two or more c... | null | 2,000 | null | null | null | 459 | [
{
"area": "General",
"area_id": "general",
"collection": "Dimensionality Reduction"
}
] |
https://paperswithcode.com/method/rezero | ReZero | ReZero | **ReZero** is a [normalization](https://paperswithcode.com/methods/category/normalization) approach that dynamically facilitates well-behaved gradients and arbitrarily deep signal propagation. The idea is simple: ReZero initializes each layer to perform the identity operation. For each layer, a [residual connection](h... | {
"title": "ReZero is All You Need: Fast Convergence at Large Depth",
"url": "https://paperswithcode.com/paper/rezero-is-all-you-need-fast-convergence-at"
} | 2,000 | https://arxiv.org/abs/2003.04887v2 | ReZero is All You Need: Fast Convergence at Large Depth | 7 | [
{
"area": "General",
"area_id": "general",
"collection": "Normalization"
}
] | |
https://paperswithcode.com/method/adamod | AdaMod | AdaMod | **AdaMod** is a stochastic optimizer that restricts adaptive learning rates with adaptive and momental upper bounds. The dynamic learning rate bounds are based on the exponential moving averages of the adaptive learning rates themselves, which smooth out unexpected large learning rates and stabilize the training of dee... | {
"title": "An Adaptive and Momental Bound Method for Stochastic Learning",
"url": "https://paperswithcode.com/paper/an-adaptive-and-momental-bound-method-for"
} | 2,000 | https://arxiv.org/abs/1910.12249v1 | An Adaptive and Momental Bound Method for Stochastic Learning | https://github.com/jettify/pytorch-optimizer/blob/155246597d66dd774156599be0f07a8c6f7758aa/torch_optimizer/adamod.py#L11 | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Stochastic Optimization"
}
] |
https://paperswithcode.com/method/how-do-i-get-a-refund-from-expedia-a-fully | How do I get a refund from Expedia? A Fully Explained Guide | How do I get a refund from Expedia? A Fully Explained Guide | To get a refund from E X P E D I A, call💻⭐1.805.330.4056-1.805.330.4056for step-by-step support from a live agent. Whether you need to cancel a flight, hotel, or package, dial💻⭐1.805.330.4056-1.805.330.4056to check your eligibility and start the refund process. Have your booking ID and travel details ready before cal... | {
"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/t5 | T5 | T5 | **T5**, or **Text-to-Text Transfer Transformer**, is a [Transformer](https://paperswithcode.com/method/transformer) based architecture that uses a text-to-text approach. Every task – including translation, question answering, and classification – is cast as feeding the model text as input and training it to generate so... | {
"title": "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer",
"url": "https://paperswithcode.com/paper/exploring-the-limits-of-transfer-learning"
} | 2,000 | https://arxiv.org/abs/1910.10683v4 | Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer | 708 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Autoencoding Transformers"
},
{
"area": "Sequential",
"area_id": "sequential",
"collection": "Sequence To Sequence Models"
},
{
"area": "Natural Language Processing",
"area_id": ... | |
https://paperswithcode.com/method/ccac | CCAC | Confidence Calibration with an Auxiliary Class) | **Confidence Calibration with an Auxiliary Class**, or **CCAC**, is a post-hoc confidence calibration method for DNN classifiers on OOD datasets. The key feature of CCAC is an auxiliary class in the calibration model which separates mis-classified samples from correctly classified ones, thus effectively mitigating the ... | {
"title": "Calibrating Deep Neural Network Classifiers on Out-of-Distribution Datasets",
"url": "https://paperswithcode.com/paper/calibrating-deep-neural-network-classifiers"
} | 2,000 | https://arxiv.org/abs/2006.08914v1 | Calibrating Deep Neural Network Classifiers on Out-of-Distribution Datasets | 2 | [
{
"area": "General",
"area_id": "general",
"collection": "Confidence Calibration"
}
] | |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/how-to-add-money-to-robinhood-without-a-bank-4 | How to Add Money to Robinhood Without a Bank Account? Call +1-844-610-2676 for Instant Support! | How to Add Money to Robinhood Without a Bank Account? Call +1-844-610-2676 for Instant Support! | In today’s modern fintech +1-844-610-2676 playground, not everyone wants—or needs—a traditional bank account. So if you're +1-844-610-2676 wondering how to add money to Robinhood without a bank account, you're not alone. The great news is, there are creative, alternative routes that still let you grow your investments.... | {
"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 Reconstruction"
}
] |
https://paperswithcode.com/method/vl-bert | VL-BERT | Visual-Linguistic BERT | VL-BERT is pre-trained on a large-scale image-captions dataset together with text-only corpus. The input to the model are either words from the input sentences or regions-of-interest (RoI) from input images. It can be fine-tuned to fit most visual-linguistic downstream tasks. Its backbone is a multi-layer bidirectional... | {
"title": "VL-BERT: Pre-training of Generic Visual-Linguistic Representations",
"url": "https://paperswithcode.com/paper/vl-bert-pre-training-of-generic-visual"
} | 2,000 | https://arxiv.org/abs/1908.08530v4 | VL-BERT: Pre-training of Generic Visual-Linguistic Representations | 4 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Vision and Language Pre-Trained Models"
}
] | |
https://paperswithcode.com/method/dial-now-how-do-i-get-a-senior-discount-on | [Dial~NoW]🤙✈ How do I get a senior discount on Delta Air Lines? | [Dial~NoW]🤙✈ How do I get a senior discount on Delta Air Lines? | Delta Airlines offers senior discounts to passengers 65 and older,☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ but these are generally not available online. To inquire about and book senior fares, you'll need to contact Delta's reservations line directly ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅. The discounts are... | {
"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-24-7help-how-do-i-contact-royal | {FAQs~24/7HeLP}How do I contact Royal Caribbean customer service by phone? | {FAQs~24/7HeLP}How do I contact Royal Caribbean customer service by phone? | For changes or cancellations to reservations (especially those with a non-refundable deposit): (855) 732-4023 USA or +44-289-708-0062 UK.
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"title": "0-1 phase transitions in sparse spiked matrix estimation",
"url": "https://paperswithcode.com/paper/0-1-phase-transitions-in-sparse-spiked-matrix"
} | 2,000 | https://arxiv.org/abs/1911.05030v1 | 0-1 phase transitions in sparse spiked matrix estimation | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
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"collection": "2D Parallel Distributed Methods"
}
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
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https://paperswithcode.com/method/kip | KIP | Kernel Inducing Points | **Kernel Inducing Points**, or **KIP**, is a meta-learning algorithm for learning datasets that can mitigate the challenges which occur for naturally occurring datasets without a significant sacrifice in performance. KIP uses kernel-ridge regression to learn $\epsilon$-approximate datasets. It can be regarded as an ad... | {
"title": "Dataset Meta-Learning from Kernel Ridge-Regression",
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} | 2,000 | https://arxiv.org/abs/2011.00050v3 | Dataset Meta-Learning from Kernel Ridge-Regression | null | 3 | [
{
"area": "General",
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
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{
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
https://paperswithcode.com/method/global-context-block | Global Context Block | Global Context Block | A **Global Context Block** is an image model block for global context modeling. The aim is to have both the benefits of the simplified [non-local block](https://paperswithcode.com/method/non-local-block) with effective modeling of long-range dependencies, and the [squeeze-excitation block](https://paperswithcode.com/me... | {
"title": "GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond",
"url": "https://paperswithcode.com/paper/gcnet-non-local-networks-meet-squeeze"
} | 2,000 | http://arxiv.org/abs/1904.11492v1 | GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond | https://github.com/xvjiarui/GCNet/blob/a9fcc88c4bd3a0b89de3678b4629c9dfd190575f/mmdet/ops/gcb/context_block.py#L13 | 12 | [
{
"area": "General",
"area_id": "general",
"collection": "Skip Connection Blocks"
},
{
"area": "General",
"area_id": "general",
"collection": "Attention Modules"
},
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Model Blocks"
}
] |
https://paperswithcode.com/method/nearestadvocate | NearestAdvocate | Nearest Advocate | This package focuses on the time delay estimation between two event-based time-series that are relatively shifted by an unknown time offset. An event-based time-series is given by a set of timestamps of certain events. If you want to guarantee synchronous measurements in advance or estimate the time delay of continuous... | {
"title": "Nearest advocate: a novel event-based time delay estimation algorithm for multi-sensor time-series data synchronization",
"url": "https://paperswithcode.com/paper/nearest-advocate-a-novel-event-based-time"
} | 2,000 | https://link.springer.com/article/10.1186/s13634-024-01143-1 | Nearest advocate: a novel event-based time delay estimation algorithm for multi-sensor time-series data synchronization | null | 1 | [
{
"area": "Sequential",
"area_id": "sequential",
"collection": "Time Delay Estimation"
},
{
"area": "Sequential",
"area_id": "sequential",
"collection": "Time Series Analysis"
}
] |
https://paperswithcode.com/method/sirm | SIRM | Skim and Intensive Reading Model | **Skim and Intensive Reading Model**, or **SIRM**, is a deep neural network for figuring out implied textual meaning. It consists of two main components, namely the skim reading component and intensive reading component. N-gram features are quickly extracted from the skim reading component, which is a combination of se... | {
"title": "Read Beyond the Lines: Understanding the Implied Textual Meaning via a Skim and Intensive Reading Model",
"url": "https://paperswithcode.com/paper/read-beyond-the-lines-understanding-the"
} | 2,000 | https://arxiv.org/abs/2001.00572v2 | Read Beyond the Lines: Understanding the Implied Textual Meaning via a Skim and Intensive Reading Model | 2 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Textual Meaning"
}
] | |
https://paperswithcode.com/method/faqs-customer-service-how-do-i-contact | {{FAQs=Customer Service}}How do I contact Celebrity Cruises by phone in the USA? | {{FAQs=Customer Service}}How do I contact Celebrity Cruises by phone in the USA? | celebrity cruise reservations number
To make a reservation with Celebrity Cruises, you can use one of the following phone numbers or options:
To reserve a new Celebrity Cruise vacation or for questions about an existing reservation: Call Celebrity Cruises at 1-855-732-4023 or +1-808-900-8011.
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/booking-airline-ticket-how-to-reserve-a | [Booking airline ticket]How to reserve a flight on Qatar Airways? | [Booking airline ticket]How to reserve a flight on Qatar Airways? | Yes, To reserve a flight on Qatar Airways, +33 (1) 5900 2948 (France) or +44‑203‑900‑09.30 (UK), visit their official website or use their mobile app. You can select your destination, travel dates, and preferred cabin class. For assistance, call +33 (1) 59002948 in France or +44-203-900-0930 in the UK. A representativ... | {
"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/pga | PGA | Prompt Gradient Alignment | {
"title": "Enhancing Domain Adaptation through Prompt Gradient Alignment",
"url": "https://paperswithcode.com/paper/enhancing-domain-adaptation-through-prompt"
} | 2,000 | https://arxiv.org/abs/2406.09353v3 | Enhancing Domain Adaptation through Prompt Gradient Alignment | 5 | [
{
"area": "General",
"area_id": "general",
"collection": "Domain Adaptation"
}
] | ||
https://paperswithcode.com/method/camoe | CAMoE | CAMoE | **CAMoE** is a multi-stream Corpus Alignment network with single gate Mixture-of-Experts (MoE) for video-text retrieval. The CAMoE employs Mixture-of-Experts (MoE) to extract multi-perspective video representations, including action, entity, scene, etc., then align them with the corresponding part of the text. A [Dual ... | {
"title": "Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss",
"url": "https://paperswithcode.com/paper/improving-video-text-retrieval-by-multi"
} | 2,000 | https://arxiv.org/abs/2109.04290v3 | Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss | null | 2 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Video-Text Retrieval Models"
}
] |
https://paperswithcode.com/method/dynamic-r-cnn | Dynamic R-CNN | Dynamic R-CNN | **Dynamic R-CNN** is an object detection method that adjusts the label assignment criteria (IoU threshold) and the shape of regression loss function (parameters of Smooth L1 Loss) automatically based on the statistics of proposals during training. The motivation is that in previous two-stage object detectors, there is ... | {
"title": "Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training",
"url": "https://paperswithcode.com/paper/dynamic-r-cnn-towards-high-quality-object"
} | 2,000 | https://arxiv.org/abs/2004.06002v2 | Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training | https://github.com/hkzhang95/DynamicRCNN | 3 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Object Detection Models"
}
] |
https://paperswithcode.com/method/greedynas-b | GreedyNAS-B | GreedyNAS-B | **GreedyNAS-B** is a convolutional neural network discovered using the [GreedyNAS](https://paperswithcode.com/method/greedynas) [neural architecture search](https://paperswithcode.com/method/neural-architecture-search) method. The basic building blocks used are inverted residual blocks (from [MobileNetV2](https://paper... | {
"title": "GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet",
"url": "https://paperswithcode.com/paper/greedynas-towards-fast-one-shot-nas-with"
} | 2,000 | https://arxiv.org/abs/2003.11236v1 | GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Convolutional Neural Networks"
}
] |
https://paperswithcode.com/method/travel-guide-r-tmcan-i-cancel-my-flight-and | [[Travel-Guide®]]™Can I cancel my flight and get a refund on Frontier? | [[Travel-Guide®]]™Can I cancel my flight and get a refund on Frontier? | Generally, Frontier Airlines tickets are non-refundable. However, there are exceptions. You can get a full refund if you cancel your flight within 24 hours of booking, provided your flight is scheduled to depart at least 7 days in the future,📞+1-801-(855)-(5905) or +1-804-(853)-(9001)🔷according to Frontier Airlines. ... | {
"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/chebnet | ChebNet | ChebNet | ChebNet involves a formulation of CNNs in the context of spectral graph theory, which provides the necessary mathematical background and efficient numerical schemes to design fast localized convolutional filters on graphs.
Description from: [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filter... | {
"title": "Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering",
"url": "https://paperswithcode.com/paper/convolutional-neural-networks-on-graphs-with"
} | 2,000 | http://arxiv.org/abs/1606.09375v3 | Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering | null | 6 | [
{
"area": "Graphs",
"area_id": "graphs",
"collection": "Graph Models"
}
] |
https://paperswithcode.com/method/truncation-trick | Truncation Trick | Truncation Trick | The **Truncation Trick** is a latent sampling procedure for generative adversarial networks, where we sample $z$ from a truncated normal (where values which fall outside a range are resampled to fall inside that range).
The original implementation was in [Megapixel Size Image Creation with GAN](https://paperswithcode... | {
"title": "Megapixel Size Image Creation using Generative Adversarial Networks",
"url": "https://paperswithcode.com/paper/megapixel-size-image-creation-using"
} | 2,000 | http://arxiv.org/abs/1706.00082v1 | Megapixel Size Image Creation using Generative Adversarial Networks | https://github.com/ajbrock/BigGAN-PyTorch/blob/7b65e82d058bfe035fc4e299f322a1f83993e04c/TFHub/biggan_v1.py#L16 | 136 | [
{
"area": "General",
"area_id": "general",
"collection": "Latent Variable Sampling"
}
] |
https://paperswithcode.com/method/residual-block | Residual Block | Residual Block | **Residual Blocks** are skip-connection blocks that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. They were introduced as part of the [ResNet](https://paperswithcode.com/method/resnet) architecture.
Formally, denoting the desired underlying mapping as $\mat... | {
"title": "Deep Residual Learning for Image Recognition",
"url": "https://paperswithcode.com/paper/deep-residual-learning-for-image-recognition"
} | 2,000 | http://arxiv.org/abs/1512.03385v1 | Deep Residual Learning for Image Recognition | https://github.com/pytorch/vision/blob/1aef87d01eec2c0989458387fa04baebcc86ea7b/torchvision/models/resnet.py#L35 | 2,807 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Model Blocks"
},
{
"area": "General",
"area_id": "general",
"collection": "Skip Connection Blocks"
}
] |
https://paperswithcode.com/method/faqs-contact-how-do-i-talk-to-royal-caribbean | [[FAQs--Contact]]How do i talk to royal caribbean customer service 24 7? | [[FAQs--Contact]]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/faqs-reservation-how-do-i-look-up-my | [[FAQs=Reservation]]How do I look up my celebrity cruise reservation? | [[FAQs=Reservation]]How do I look up my celebrity cruise reservation? | celebrity cruise reservations number
To make a reservation with Celebrity Cruises, you can use one of the following phone numbers or options:
To reserve a new Celebrity Cruise vacation or for questions about an existing reservation: Call Celebrity Cruises at 1-855-732-4023 or +1-808-900-8011.
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/logan | LOGAN | LOGAN | **LOGAN** is a generative adversarial network that uses a latent optimization approach using [natural gradient descent](https://paperswithcode.com/method/natural-gradient-descent) (NGD). For the Fisher matrix in NGD, the authors use the empirical Fisher $F'$ with Tikhonov damping:
$$ F' = g \cdot g^{T} + \beta{I} $$... | {
"title": "LOGAN: Latent Optimisation for Generative Adversarial Networks",
"url": "https://paperswithcode.com/paper/logan-latent-optimisation-for-generative-1"
} | 2,000 | https://arxiv.org/abs/1912.00953v2 | LOGAN: Latent Optimisation for Generative Adversarial Networks | https://github.com/Hosein47/LOGAN | 6 | [
{
"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/re-net | RE-NET | Recurrent Event Network | Recurrent Event Network (RE-NET) is an autoregressive architecture for predicting future interactions. The occurrence of a fact (event) is modeled as a probability distribution conditioned on temporal sequences of past knowledge graphs. RE-NET employs a recurrent event encoder to encode past facts and uses a neighborho... | {
"title": "Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs",
"url": "https://paperswithcode.com/paper/recurrent-event-network-for-reasoning-over"
} | 2,000 | https://arxiv.org/abs/1904.05530v4 | Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs | null | 2 | [
{
"area": "Graphs",
"area_id": "graphs",
"collection": "Graph Models"
}
] |
https://paperswithcode.com/method/faqs-guide-how-to-find-celebrity-cruise | [[FAQs=GUidE]]How to find celebrity cruise member number? | [[FAQs=GUidE]]How to find celebrity cruise member number? | celebrity cruise reservations number
To make a reservation with Celebrity Cruises, you can use one of the following phone numbers or options:
To reserve a new Celebrity Cruise vacation or for questions about an existing reservation: Call Celebrity Cruises at 1-855-732-4023 or +1-808-900-8011.
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/american-airlines-support-how-can-i-speak-to | American Airlines. & Support - How can I speak to someone at directly At American airlines Vacations for reservations? | American Airlines. & Support - How can I speak to someone at directly At American airlines Vacations for reservations? | To speak to a representative at directly At American airlines Vacations and make reservations for your travel packages, including airfare, call the dedicated hotline at ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. (OTA) where agents are available during specified phone hours. call to American airline rep, speak directly a... | {
"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/faq-s-guide-how-do-i-speak-to-someone-at | {{FAQ's~Guide}} - How do I speak to someone at Latam 𝔸𝕚𝕣𝕝𝕚𝕟𝕖? | {{FAQ's~Guide}} - How do I speak to someone at Latam 𝔸𝕚𝕣𝕝𝕚𝕟𝕖? | Alternatively, you can start a chat conversation online for assistance at ☎️+1-801-(855)-(5905)or +1-804-853-9001✅𝙐𝙆. The most convenient and fastest way to speak to someone at Latam 𝔸𝕚𝕣𝕝𝕚𝕟𝕖 is by calling their customer service number at:☎️+1-801-(855)-(5905)or +1-804-853-9001✅𝙐𝙆. | {
"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-many-drinks-a-day-can-you-get-with-a | How many drinks a day can you get with a Carnival drink package? | How many drinks a day can you get with a Carnival drink package? | With Carnival's "CHEERS!" drink package, you are limited to 15 alcoholic drinks per day 1-855-732-4023. This limit applies to each 24-hour period (6:00 AM to 6:00 AM)(1-855-732-4023). Once you reach your 15 alcoholic drinks, you won't be able to purchase any more within that 24-hour period 1-855-732-4023. The package a... | {
"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/mae | MAE | Masked autoencoder | {
"title": "Masked Autoencoders Are Scalable Vision Learners",
"url": "https://paperswithcode.com/paper/masked-autoencoders-are-scalable-vision"
} | 2,000 | https://arxiv.org/abs/2111.06377v2 | Masked Autoencoders Are Scalable Vision Learners | null | 722 | [
{
"area": "General",
"area_id": "general",
"collection": "Self-Supervised Learning"
}
] | |
https://paperswithcode.com/method/dabmd | DABMD | Distributed Any-Batch Mirror Descent | **Distributed Any-Batch Mirror Descent** (DABMD) is based on distributed Mirror Descent but uses a fixed per-round computing time to limit the waiting by fast nodes to receive information updates from slow nodes. DABMD is characterized by varying minibatch sizes across nodes. It is applicable to a broader range of prob... | null | 2,020 | null | null | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Replicated Data Parallel"
},
{
"area": "General",
"area_id": "general",
"collection": "Data Parallel Methods"
},
{
"area": "General",
"area_id": "general",
"collection": "Optimization"
},
{
"area": "General... |
https://paperswithcode.com/method/meshgraphnet | MeshGraphNet | MeshGraphNet | **MeshGraphNet** is a framework for learning mesh-based simulations using [graph neural networks](https://paperswithcode.com/methods/category/graph-models). The model can be trained to pass messages on a mesh graph and to adapt the mesh discretization during forward simulation. The model uses an Encode-Process-Decode a... | {
"title": "Learning Mesh-Based Simulation with Graph Networks",
"url": "https://paperswithcode.com/paper/learning-mesh-based-simulation-with-graph-1"
} | 2,000 | https://arxiv.org/abs/2010.03409v4 | Learning Mesh-Based Simulation with Graph Networks | 5 | [
{
"area": "General",
"area_id": "general",
"collection": "Mesh-Based Simulation Models"
},
{
"area": "Graphs",
"area_id": "graphs",
"collection": "Graph Models"
}
] | |
https://paperswithcode.com/method/klm-whatsapp-service-does-klm-have-whatsapp | [KLM WhatsApp service]Does KLM have WhatsApp? | [KLM WhatsApp service]Does KLM have WhatsApp? | Yes, KLM does offer customer service via WhatsApp +33 (1) 5900 2948 (France) or +44‑203‑900‑09.30 (UK) for quick and convenient support. You can reach them through the WhatsApp number +33 (1) 59002948. For phone assistance, you can also contact KLM at +33 (1) 5900 2948 or +44‑203‑900‑09.30 . if you're in the UK. Thei... | {
"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-booking-how-do-i-talk-to-royal-caribbean | [[FAQs--Booking]]How do i talk to royal caribbean customer service live chat? | [[FAQs--Booking]]How do i talk to royal caribbean customer service live chat? | 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/odl | ODL | online deep learning | Deep Neural Networks (DNNs) are typically trained by backpropagation in a batch learning setting, which requires the entire training data to be made available prior to the learning task. This is not scalable for many real-world scenarios where new data arrives sequentially in a stream form. We aim to address an open ch... | {
"title": "Online Deep Learning: Learning Deep Neural Networks on the Fly",
"url": "https://paperswithcode.com/paper/online-deep-learning-learning-deep-neural"
} | 2,000 | http://arxiv.org/abs/1711.03705v1 | Online Deep Learning: Learning Deep Neural Networks on the Fly | null | 11 | [
{
"area": "General",
"area_id": "general",
"collection": "Deep Tabular Learning"
}
] |
https://paperswithcode.com/method/scn | SCN | Self-Cure Network | **Self-Cure Network**, or **SCN**, is a method for suppressing uncertainties for large-scale facial expression recognition, prventing deep networks from overfitting uncertain facial images. Specifically, SCN suppresses the uncertainty from two different aspects: 1) a self-attention mechanism over mini-batch to weight e... | {
"title": "Suppressing Uncertainties for Large-Scale Facial Expression Recognition",
"url": "https://paperswithcode.com/paper/suppressing-uncertainties-for-large-scale"
} | 2,000 | https://arxiv.org/abs/2002.10392v2 | Suppressing Uncertainties for Large-Scale Facial Expression Recognition | null | 18 | [
{
"area": "General",
"area_id": "general",
"collection": "Regularization"
}
] |
https://paperswithcode.com/method/stylegan2 | StyleGAN2 | StyleGAN2 | **StyleGAN2** is a generative adversarial network that builds on [StyleGAN](https://paperswithcode.com/method/stylegan) with several improvements. First, [adaptive instance normalization](https://paperswithcode.com/method/adaptive-instance-normalization) is redesigned and replaced with a normalization technique called ... | {
"title": "Analyzing and Improving the Image Quality of StyleGAN",
"url": "https://paperswithcode.com/paper/analyzing-and-improving-the-image-quality-of"
} | 2,000 | https://arxiv.org/abs/1912.04958v2 | Analyzing and Improving the Image Quality of StyleGAN | https://github.com/NVlabs/stylegan2 | 49 | [
{
"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/par-transformer | PAR Transformer | PAR Transformer | **PAR Transformer** is a [Transformer](https://paperswithcode.com/methods/category/transformers) model that uses 63% fewer [self-attention blocks](https://paperswithcode.com/method/scaled), replacing them with [feed-forward blocks](https://paperswithcode.com/method/position-wise-feed-forward-layer), while retaining tes... | {
"title": "Pay Attention when Required",
"url": "https://paperswithcode.com/paper/pay-attention-when-required"
} | 2,000 | https://arxiv.org/abs/2009.04534v3 | Pay Attention when Required | 1 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Transformers"
}
] | |
https://paperswithcode.com/method/faqs-available-how-do-i-talk-to-royal | [[FAQs--Available]]How do i talk to royal caribbean customer service phone number? | [[FAQs--Available]]How do i talk to royal caribbean customer service phone number? | 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/call-now-como-hablo-con-una-persona-real-en | ((!!*Call~~Now~!))¿Cómo hablo con una persona real en Etihad Airways? | ¿Cómo hablo con una persona real en Etihad Airways? | Para hablar con una persona real en Etihad Airways+1-808-(470)-(7107), puedes contactarlos a través de su servicio de atención al cliente por teléfono o WhatsApp+1-808-(470)-(7107). También puedes chatear con su asistente virtual y+1-808-(470)-(7107), si es necesario, solicitar ser atendido por un agente. | {
"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/13-ways-to-contact-how-can-i-speak-to-someone-11 | 13 Ways to Contact How Can I Speak to Someone at Disney Cruise Line . | 13 Ways to Contact How Can I Speak to Someone at Disney Cruise Line . | To reach a Disney Cruise Line representative quickly, the fastest method is typically via Live Chat on the Disney Cruise Line website.+1-(855)-732-4023 Alternatively, calling early in the morning (e.g., between 7:00 AM and 9:00 AM EST) can minimize wait times.
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"title": "0-1 phase transitions in sparse spiked matrix estimation",
"url": "https://paperswithcode.com/paper/0-1-phase-transitions-in-sparse-spiked-matrix"
} | 2,000 | https://arxiv.org/abs/1911.05030v1 | 0-1 phase transitions in sparse spiked matrix estimation | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
https://paperswithcode.com/method/full-supporttm-how-do-i-get-a-refund-from | [Full~SuPporT™]]How do I get a refund from Disney Cruise? | [Full~SuPporT™]How do I get a refund from Disney Cruise? | Way to contact disney cruise refund phone number (+1-(855)-732-4023)
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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/leverage-learning | Leverage Learning | Leverage Learning | Leverage learning suggests that it is possible to strategically use minimal task-specific data to enhance task-specific capabilities, while non-specific capabilities can be learned from more general data. | {
"title": "Token-Efficient Leverage Learning in Large Language Models",
"url": "https://paperswithcode.com/paper/token-efficient-leverage-learning-in-large"
} | 2,000 | https://arxiv.org/abs/2404.00914v1 | Token-Efficient Leverage Learning in Large Language Models | null | 3 | [
{
"area": "General",
"area_id": "general",
"collection": "Fine-Tuning"
}
] |
https://paperswithcode.com/method/panoptic-fpn | Panoptic FPN | Panoptic FPN | A **Panoptic FPN** is an extension of an [FPN](https://paperswithcode.com/method/fpn) that can generate both instance and semantic segmentations via FPN. The approach starts with an FPN backbone and adds a branch for performing semantic segmentation in parallel with the existing region-based branch for instance segment... | {
"title": "Panoptic Feature Pyramid Networks",
"url": "https://paperswithcode.com/paper/panoptic-feature-pyramid-networks"
} | 2,000 | http://arxiv.org/abs/1901.02446v2 | Panoptic Feature Pyramid Networks | https://github.com/facebookresearch/detectron2/blob/1b09e42cc87d47a6e0a3892cd86e780d86a9b122/detectron2/modeling/meta_arch/panoptic_fpn.py#L20 | 3 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Feature Extractors"
}
] |
https://paperswithcode.com/method/contact-us-how-do-i-connect-with-someone-on | !!Contact~us!!How do I connect with someone on Qatar Airways? | !!Contact~us!!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 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 | Ways to Access American Cruises®️ USA Contact Numbers – Full Support Guide | Ways to Access American Cruises®️ USA Contact Numbers – Full Support 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/hard-sigmoid | Hard Sigmoid | Hard Sigmoid | The **Hard Sigmoid** is an activation function used for neural networks of the form:
$$f\left(x\right) = \max\left(0, \min\left(1,\frac{\left(x+1\right)}{2}\right)\right)$$
Image Source: [Rinat Maksutov](https://towardsdatascience.com/deep-study-of-a-not-very-deep-neural-network-part-2-activation-functions-fd9bd8... | {
"title": "BinaryConnect: Training Deep Neural Networks with binary weights during propagations",
"url": "https://paperswithcode.com/paper/binaryconnect-training-deep-neural-networks"
} | 2,000 | http://arxiv.org/abs/1511.00363v3 | BinaryConnect: Training Deep Neural Networks with binary weights during propagations | https://github.com/tensorflow/tensorflow/blob/2b96f3662bd776e277f86997659e61046b56c315/tensorflow/python/keras/backend.py#L4716 | 4 | [
{
"area": "General",
"area_id": "general",
"collection": "Activation Functions"
}
] |
https://paperswithcode.com/method/faqs-us-what-is-the-phone-number-for-windstar | {{{FAQs-us}}} What is the phone number for Windstar travel agent? @Contact them at +1-855-732-4023! | {{{FAQs-us}}} What is the phone number for Windstar travel agent? @Contact them at +1-855-732-4023! | The main phone number for Windstar Cruises, which includes travel agent assistance, is +1-855-732-4023 (USA) OR +44-289-708-0062 (UK). You can also contact them at +1-855-732-4023 (USA) OR +44-289-708-0062 (UK) or through email at info@windstarcruises.com. For accessible sailing information, the number is +1-855-732-40... | {
"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/grouped-convolution | Grouped Convolution | Grouped Convolution | A **Grouped Convolution** uses a group of convolutions - multiple kernels per layer - resulting in multiple channel outputs per layer. This leads to wider networks helping a network learn a varied set of low level and high level features. The original motivation of using Grouped Convolutions in [AlexNet](https://papers... | {
"title": "ImageNet Classification with Deep Convolutional Neural Networks",
"url": "https://paperswithcode.com/paper/imagenet-classification-with-deep"
} | 2,000 | http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks | ImageNet Classification with Deep Convolutional Neural Networks | https://github.com/prlz77/ResNeXt.pytorch/blob/39fb8d03847f26ec02fb9b880ecaaa88db7a7d16/models/model.py#L42 | 575 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Convolutions"
}
] |
https://paperswithcode.com/method/faqs-customer-service-how-do-i-talk-to-royal | [[FAQs--Customer Service]]How do I talk to Royal Caribbean Customer Service? | [[FAQs--Customer Service]]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.
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] | |
https://paperswithcode.com/method/ways-to-access-msc-cruises-r-usa-contact-1 | Ways to Access msc Cruises®️ USA Contact Numbers – A Comprehensive Guide | Ways to Access msc Cruises®️ USA Contact Numbers – A Comprehensive Guide | 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/coop | CoOp | Context Optimization | **CoOp**, or **Context Optimization**, is an automated prompt engineering method that avoids manual prompt tuning by modeling context words with continuous vectors that are end-to-end learned from data. The context could be shared among all classes or designed to be class-specific. During training, we simply minimize t... | {
"title": "Learning to Prompt for Vision-Language Models",
"url": "https://paperswithcode.com/paper/learning-to-prompt-for-vision-language-models"
} | 2,000 | https://arxiv.org/abs/2109.01134v6 | Learning to Prompt for Vision-Language Models | 30 | [
{
"area": "General",
"area_id": "general",
"collection": "Prompt Engineering"
}
] | |
https://paperswithcode.com/method/faqs-help-what-day-is-the-cheapest-to-book-1 | [FAQs^Help]What day is the cheapest to book American Airlines? | [FAQs^Help]What day is the cheapest to book American Airlines? | The cheapest days to book American Airlines flights are typically Tuesdays,☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ Wednesdays,☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ and Saturdays. ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅These days often have lower demand, leading to more competitive pricing. While som... | {
"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/macaw | Macaw | Macaw | **Macaw** is a generative question-answering (QA) system that is built on UnifiedQA, itself built on [T5](https://paperswithcode.com/method/t5). Macaw has three interesting features. First, it often produces high-quality answers to questions far outside the domain it was trained on, sometimes surprisingly so. Second, M... | {
"title": "General-Purpose Question-Answering with Macaw",
"url": "https://paperswithcode.com/paper/general-purpose-question-answering-with-macaw"
} | 2,000 | https://arxiv.org/abs/2109.02593v1 | General-Purpose Question-Answering with Macaw | 5 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Question Answering Models"
}
] | |
https://paperswithcode.com/method/demon | Demon | Demon | **Decaying Momentum**, or **Demon**, is a stochastic optimizer motivated by decaying the total contribution of a gradient to all future updates. By decaying the momentum parameter, the total contribution of a gradient to all future updates is decayed. A particular gradient term $g\_{t}$ contributes a total of $\eta\su... | {
"title": "Demon: Improved Neural Network Training with Momentum Decay",
"url": "https://paperswithcode.com/paper/decaying-momentum-helps-neural-network"
} | 2,000 | https://arxiv.org/abs/1910.04952v4 | Demon: Improved Neural Network Training with Momentum Decay | https://github.com/JRC1995/DemonRangerOptimizer/blob/5a3e6e352ab766f96cd8d20eabd5b71843c595fe/optimizers.py#L205 | 16 | [
{
"area": "General",
"area_id": "general",
"collection": "Momentum Rules"
}
] |
https://paperswithcode.com/method/faqs-call-can-you-get-a-better-deal-by-1 | [[FAQs_--CALL]]Can you get a better deal by calling Royal Caribbean? | [[FAQs_--CALL]]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.
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] | |
https://paperswithcode.com/method/travel-como-llamar-a-united-airlines-desde-el | {TRAVEL}¿Cómo llamar a United Airlines desde El Salvador? | ¿Cómo llamar a United Airlines desde El Salvador? | Consulta sobre promociones o cambios Si tu motivo es conocer promociones o realizar cambios en tu pasaje, llama al +1-808-470-7107 o al +1-808-470-7107. Los asesores pueden ayudarte con tarifas especiales o reprogramaciones. 7. Haz un seguimiento de tu caso Si ya hiciste un reclamo o consulta, puedes hacer seguimiento ... | {
"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/hit-detector | Hit-Detector | Hit-Detector | **Hit-Detector** is a neural architectures search algorithm that simultaneously searches all components of an object detector in an end-to-end manner. It is a hierarchical approach to mine the proper subsearch space from the large volume of operation candidates. It consists of two main procedures. First, given a large ... | {
"title": "Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection",
"url": "https://paperswithcode.com/paper/hit-detector-hierarchical-trinity"
} | 2,000 | https://arxiv.org/abs/2003.11818v1 | Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection | https://github.com/ggjy/HitDet.pytorch | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Neural Architecture Search"
}
] |
https://paperswithcode.com/method/flow-alignment-module | Flow Alignment Module | Flow Alignment Module | **Flow Alignment Module**, or **FAM**, is a flow-based align module for scene parsing to learn Semantic Flow between feature maps of adjacent levels and broadcast high-level features to high resolution features effectively and efficiently. The concept of Semantic Flow is inspired from optical flow, which is widely used... | {
"title": "Semantic Flow for Fast and Accurate Scene Parsing",
"url": "https://paperswithcode.com/paper/semantic-flow-for-fast-and-accurate-scene"
} | 2,000 | https://arxiv.org/abs/2002.10120v3 | Semantic Flow for Fast and Accurate Scene Parsing | 3 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Semantic Segmentation Modules"
}
] | |
https://paperswithcode.com/method/reformer | Reformer | Reformer | **Reformer** is a [Transformer](https://paperswithcode.com/method/transformer) based architecture that seeks to make efficiency improvements. [Dot-product attention](https://paperswithcode.com/method/dot-product-attention) is replaced by one that uses locality-sensitive hashing, changing its complexity
from O($L^2$) t... | {
"title": "Reformer: The Efficient Transformer",
"url": "https://paperswithcode.com/paper/reformer-the-efficient-transformer-1"
} | 2,000 | https://arxiv.org/abs/2001.04451v2 | Reformer: The Efficient Transformer | null | 20 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Transformers"
}
] |
https://paperswithcode.com/method/vocgan | VocGAN | VocGAN | Please enter a description about the method here | {
"title": "VocGAN: A High-Fidelity Real-time Vocoder with a Hierarchically-nested Adversarial Network",
"url": "https://paperswithcode.com/paper/vocgan-a-high-fidelity-real-time-vocoder-with"
} | 2,000 | https://arxiv.org/abs/2007.15256v1 | VocGAN: A High-Fidelity Real-time Vocoder with a Hierarchically-nested Adversarial Network | null | 1 | [
{
"area": "Audio",
"area_id": "audio",
"collection": "Generative Audio Models"
}
] |
https://paperswithcode.com/method/lcc | LCC | Lipschitz Constant Constraint | Please enter a description about the method here | {
"title": "Regularisation of Neural Networks by Enforcing Lipschitz Continuity",
"url": "https://paperswithcode.com/paper/regularisation-of-neural-networks-by"
} | 2,000 | https://arxiv.org/abs/1804.04368v3 | Regularisation of Neural Networks by Enforcing Lipschitz Continuity | null | 33 | [
{
"area": "General",
"area_id": "general",
"collection": "Regularization"
}
] |
https://paperswithcode.com/method/faq-s-refund-ow-do-i-get-a-refund-from-disney | []Faq`s~RefuNd[]ow do I get a refund from Disney Cruise? | []Faq`s~RefuNd[]ow do I get a refund from Disney Cruise? | For general refund inquiries related to Disney Cruise Line, the phone number is +1-(855)-732-4023. If you are looking to cancel your cruise and are within the refund window (generally 90 days or more before the sailing date), you can also contact this number. For specific refund requests related to cancellations due to... | {
"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/k-means-clustering | k-Means Clustering | k-Means Clustering | **k-Means Clustering** is a clustering algorithm that divides a training set into $k$ different clusters of examples that are near each other. It works by initializing $k$ different centroids {$\mu\left(1\right),\ldots,\mu\left(k\right)$} to different values, then alternating between two steps until convergence:
(i)... | null | 2,000 | null | null | https://cryptoabout.info | 772 | [
{
"area": "General",
"area_id": "general",
"collection": "Clustering"
}
] |
https://paperswithcode.com/method/faqs-what-is-the-cheapest-day-to-buy-american | {(FAQs#)}What is the cheapest day to buy American Airlines tickets? | {(FAQs#)}What is the cheapest day to buy American Airlines tickets? | The cheapest days to buy American Airlines tickets are generally Tuesdays,☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ Wednesdays,☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ and Saturdays. Airlines often release new fares and sales on Monday evenings,☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ making Tuesday a goo... | {
"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/avoiding-peak-travel-times-what-is-the | [@@Avoiding Peak Travel Times@@]What is the cheapest day to buy flights on United? | [@@Avoiding Peak Travel Times@@]What is the cheapest day to buy flights on United? | The cheapest days to fly with United Airlines <<+1 (801) 855-5905>> or <<+1 (804) 853-9001>> are typically mid-week, specifically Tuesdays and Wednesdays. Demand for flights is generally <<+1 (801) 855-5905>> or <<+1 (804) 853-9001>> lower on these days compared to weekends, which can result in lower fares. Some source... | {
"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/mixer-layer | Mixer Layer | MLP-Mixer Layer | A Mixer layer is a layer used in the MLP-Mixer architecture proposed by Tolstikhin et. al (2021) for computer vision. Mixer layers consist purely of MLPs, without convolutions or attention. It takes an input of embedded image patches (tokens), with its output having the same shape as its input, similar to that of a Vis... | {
"title": "MLP-Mixer: An all-MLP Architecture for Vision",
"url": "https://paperswithcode.com/paper/mlp-mixer-an-all-mlp-architecture-for-vision"
} | 2,000 | https://arxiv.org/abs/2105.01601v4 | MLP-Mixer: An all-MLP Architecture for Vision | https://github.com/google-research/vision_transformer | 7 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Model Blocks"
}
] |
https://paperswithcode.com/method/stategame-maintain-picture-balanced-play | STATEGAME MAINTAIN PICTURE BALANCED PLAY STABLE | ATTEMPT THIS FATHINETUTE TO REPOPULATE ALREADY POPULATED SYSTEM | {
"title": "0+ and 1+ heavy-light exotic mesons at N2LO in the chiral limit",
"url": "https://paperswithcode.com/paper/0-and-1-heavy-light-exotic-mesons-at-n2lo-in"
} | 2,000 | http://arxiv.org/abs/1801.09110v1 | 0+ and 1+ heavy-light exotic mesons at N2LO in the chiral limit | null | 2 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Portrait Matting Models"
}
] | |
https://paperswithcode.com/method/schnet | SchNet | Schrödinger Network | **SchNet** is an end-to-end deep neural network architecture based on continuous-filter convolutions. It follows the deep tensor neural network framework, i.e. atom-wise representations are constructed by starting from embedding vectors that characterize the atom type before introducing the configuration of the system ... | {
"title": "SchNet: A continuous-filter convolutional neural network for modeling quantum interactions",
"url": "https://paperswithcode.com/paper/schnet-a-continuous-filter-convolutional"
} | 2,000 | http://arxiv.org/abs/1706.08566v5 | SchNet: A continuous-filter convolutional neural network for modeling quantum interactions | 20 | [
{
"area": "Graphs",
"area_id": "graphs",
"collection": "Graph Models"
}
] | |
https://paperswithcode.com/method/dispute-service-how-do-i-file-a-dispute-with | Dispute@Service--How do I file a dispute with Expedia? | Dispute@Service--How do I file a dispute with Expedia? | How do I file a dispute with Expedia? To file a dispute with Expedia, contact their customer support at +1➤805➢330➤4056 or +1-805::330::4056 Clearly explain your issue with all booking details. If unresolved, escalate to a supervisor, use online chat or email, and consider filing a complaint with the BBB or disputing t... | {
"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/coordconv | CoordConv | CoordConv | A **CoordConv** layer is a simple extension to the standard convolutional layer. It has the same functional signature as a convolutional layer, but accomplishes the mapping by first concatenating extra channels to the incoming representation. These channels contain hard-coded coordinates, the most basic version of whic... | {
"title": "An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution",
"url": "https://paperswithcode.com/paper/an-intriguing-failing-of-convolutional-neural"
} | 2,000 | http://arxiv.org/abs/1807.03247v2 | An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution | https://github.com/uber-research/CoordConv/blob/27fab8b86efac87c262c7c596a0c384b83c9d806/CoordConv.py#L87 | 16 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Convolutions"
}
] |
https://paperswithcode.com/method/holographic-reduced-representation | Holographic Reduced Representation | Holographic Reduced Representation | **Holographic Reduced Representations** are a simple mechanism to represent an associative array of key-value pairs in a fixed-size vector. Each individual key-value pair is the same size as the entire associative array; the array is represented by the sum of the pairs. Concretely, consider a complex vector key $r = (a... | null | 2,003 | null | null | null | 4 | [
{
"area": "General",
"area_id": "general",
"collection": "Miscellaneous Components"
}
] |
https://paperswithcode.com/method/distributional-generalization | Distributional Generalization | Distributional Generalization | **Distributional Generalization** is a type of generalization that roughly states that outputs of a classifier at train and test time are close as distributions, as opposed to close in just their average error. This behavior is not captured by classical generalization, which would only consider the average error and no... | {
"title": "Distributional Generalization: A New Kind of Generalization",
"url": "https://paperswithcode.com/paper/distributional-generalization-a-new-kind-of"
} | 2,000 | https://arxiv.org/abs/2009.08092v2 | Distributional Generalization: A New Kind of Generalization | null | 4 | [
{
"area": "General",
"area_id": "general",
"collection": "Generalization"
}
] |
https://paperswithcode.com/method/bam | BAM | Bottleneck Attention Module | Park et al. proposed the bottleneck attention module (BAM), aiming
to efficiently improve the representational capability of networks.
It uses dilated convolution to enlarge the receptive field of the spatial attention sub-module, and build a bottleneck structure as suggested by ResNet to save computational cost.
... | {
"title": "BAM: Bottleneck Attention Module",
"url": "https://paperswithcode.com/paper/bam-bottleneck-attention-module"
} | 2,000 | http://arxiv.org/abs/1807.06514v2 | BAM: Bottleneck Attention Module | 33 | [
{
"area": "General",
"area_id": "general",
"collection": "Attention Mechanisms"
}
] | |
https://paperswithcode.com/method/customer-service-how-do-i-really-get-through | {{customer~service}} How do I really get through to American Airlines? | {{customer~service}} How do I really get through to American Airlines? | To reach American Airlines customer service,☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ the fastest methods are typically through their mobile app's chat feature or by calling their main customer service line ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅. You can also contact them via email, social media, or through ... | {
"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/copa-c-what-is-the-24-hour-rule-for-copa | [@COPA~C@] (𝑳𝒊𝒗𝒆 𝑯𝒖𝒎𝒂𝒏)What is the 24 hour rule for Copa Airlines? | [@COPA~C@] (𝑳𝒊𝒗𝒆 𝑯𝒖𝒎𝒂𝒏)What is the 24 hour rule for Copa Airlines? | Copa Airlines ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅, like many airlines, follows a US Department of Transportation (DOT) 24-hour rule. This rule allows passengers to cancel a flight booking ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ for a full refund within 24 hours of purchase, provided the booking was made... | {
"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/k-sparse-autoencoder | k-Sparse Autoencoder | k-Sparse Autoencoder | **k-Sparse Autoencoders** are autoencoders with linear activation function, where in hidden layers only the $k$ highest activities are kept. This achieves exact sparsity in the hidden representation. Backpropagation only goes through the the top $k$ activated units. This can be achieved with a [ReLU](https://paperswith... | {
"title": "k-Sparse Autoencoders",
"url": "https://paperswithcode.com/paper/k-sparse-autoencoders"
} | 2,000 | http://arxiv.org/abs/1312.5663v2 | k-Sparse Autoencoders | https://github.com/snooky23/K-Sparse-AutoEncoder | 3 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Generative Models"
}
] |
https://paperswithcode.com/method/simcse | SimCSE | SimCSE | **SimCSE** is a contrastive learning framework for generating sentence embeddings. It utilizes an unsupervised approach, which takes an input sentence and predicts itself in contrastive objective, with only standard [dropout](https://paperswithcode.com/method/dropout) used as noise. The authors find that dropout acts a... | {
"title": "SimCSE: Simple Contrastive Learning of Sentence Embeddings",
"url": "https://paperswithcode.com/paper/simcse-simple-contrastive-learning-of"
} | 2,000 | https://arxiv.org/abs/2104.08821v4 | SimCSE: Simple Contrastive Learning of Sentence Embeddings | 52 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Sentence Embeddings"
}
] | |
https://paperswithcode.com/method/expert-team-como-hablo-con-una-persona-en | [Expert Team]¿Cómo hablo con una persona en vivo en United Airlines? | [Expert Team]¿Cómo hablo con una persona en vivo en United Airlines? | Para hablar directamente con un agente de United Airlines, puedes llamar a su línea de atención al cliente. Si te encuentras en Estados Unidos, puedes llamar al 1-800-UNITED-1 (+𝟙—𝟠𝟘𝟠—𝟜𝟟𝟘 —𝟟𝟙 𝟘:𝟟). También puedes encontrar números específicos para México y otros países en su sitio web. Tiene múltiples canale... | {
"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/discriminative-fine-tuning | Discriminative Fine-Tuning | Discriminative Fine-Tuning | **Discriminative Fine-Tuning** is a fine-tuning strategy that is used for [ULMFiT](https://paperswithcode.com/method/ulmfit) type models. Instead of using the same learning rate for all layers of the model, discriminative fine-tuning allows us to tune each layer with different learning rates. For context, the regular s... | {
"title": "Universal Language Model Fine-tuning for Text Classification",
"url": "https://paperswithcode.com/paper/universal-language-model-fine-tuning-for-text"
} | 2,000 | http://arxiv.org/abs/1801.06146v5 | Universal Language Model Fine-tuning for Text Classification | https://github.com/fastai/fastai/blob/43001e17ba469308e9688dfe99a891018bcf7ad4/courses/dl2/imdb_scripts/finetune_lm.py#L132 | 1,990 | [
{
"area": "General",
"area_id": "general",
"collection": "Fine-Tuning"
}
] |
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