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/help-support-how-do-i-call-american-airlines | Help & Support - How do I call American Airlines customer service? | Help & Support - How do I call American Airlines customer service? | To contact American Airlines in Spanish, call ☎️+1-801-(855)-(5905)or +1-804-853-9001✅ (Mexico) and select the option for support in Spanish. This service is available 24 hours a day, every day. | {
"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-much-is-a-ryanair-cancellation-fee | How much is a Ryanair cancellation fee? | How much is a Ryanair cancellation fee? | Ryanair generally does not allow cancellations for a refund +44-(20)-39003009(UK) or 1-808-751-2262(US), but if you cancel a flight, you won’t receive a refund of the fare. However, government taxes may be refunded upon request +44-(20)-39003009(UK) or 1-808-751-2262(US). If you miss your flight or choose not to travel... | {
"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/policy-similarity-metric | Policy Similarity Metric | Policy Similarity Metric | **Policy Similarity Metric**, or **PSM**, is a similarity metric for measuring behavioral similarity between states in reinforcement learning. It assigns high similarity to states for which the optimal policies in those states as well as in future states are similar. PSM is reward-agnostic, making it more robust for ge... | {
"title": "Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning",
"url": "https://paperswithcode.com/paper/contrastive-behavioral-similarity-embeddings-1"
} | 2,000 | https://arxiv.org/abs/2101.05265v2 | Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning | null | 2 | [
{
"area": "Reinforcement Learning",
"area_id": "reinforcement-learning",
"collection": "State Similarity Metrics"
}
] |
https://paperswithcode.com/method/delta-airlines-peru-tm-como-llamar-a-delta-en | {Delta Airlines Peru}™¿Cómo llamar a Delta en Perú? | {Delta Airlines Peru}™¿Cómo llamar a Delta en Perú? | ¿Cómo llamar a Delta de Perú? Para contactar a Delta en Perú, puedes llamar al número de atención al cliente: +5117006251 o al número internacional: +5117006251 También puedes comunicarte a través de su página web, Delta.
Para llamar a Delta desde Perú, puedes marcar a Delta teléfono Perú +5117006251 o +5117006251 (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/how-do-i-withdraw-cryptocurrency-from | How do I withdraw cryptocurrency from CoinSpot? | How do I withdraw cryptocurrency from CoinSpot? | call ６１ー（３）ー５９２９ー４８０８. To contact CoinSpot customer service directly, simply call ６１ー（３）ー５９２９ー４８０８. for live help with any account issue. If you’re locked out of your account, ６１ー（３）ー５９２９ー４８０８. can reset your access or verify your identity. Whether you need help with deposits, withdrawals, or crypto transfers, ６１ー（３）ー... | {
"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 Face Mesh Models"
}
] |
https://paperswithcode.com/method/source-hypothesis-transfer | Source Hypothesis Transfer | Source Hypothesis Transfer | **Source Hypothesis Transfer**, or **SHOT**, is a representation learning framework for unsupervised domain adaptation. SHOT freezes the classifier module (hypothesis) of the source model and learns the target-specific feature extraction module by exploiting both information maximization and self-supervised pseudo-labe... | {
"title": "Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation",
"url": "https://paperswithcode.com/paper/do-we-really-need-to-access-the-source-data"
} | 2,000 | https://arxiv.org/abs/2002.08546v6 | Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation | 3 | [
{
"area": "General",
"area_id": "general",
"collection": "Domain Adaptation"
}
] | |
https://paperswithcode.com/method/adagpr | AdaGPR | AdaGPR | **AdaGPR** is an adaptive, layer-wise graph [convolution](https://paperswithcode.com/method/convolution) model. AdaGPR applies adaptive generalized Pageranks at each layer of a [GCNII](https://paperswithcode.com/method/gcnii) model by learning to predict the coefficients of generalized Pageranks using sparse solvers. | {
"title": "Layer-wise Adaptive Graph Convolution Networks Using Generalized Pagerank",
"url": "https://paperswithcode.com/paper/adaptive-and-interpretable-graph-convolution"
} | 2,000 | https://arxiv.org/abs/2108.10636v3 | Layer-wise Adaptive Graph Convolution Networks Using Generalized Pagerank | null | 1 | [
{
"area": "Graphs",
"area_id": "graphs",
"collection": "Graph Models"
}
] |
https://paperswithcode.com/method/quick-guide-what-day-is-the-cheapest-to-buy | [Quick~Guide] What day is the cheapest to buy Delta flights? | [Quick~Guide] What day is the cheapest to buy Delta flights? | The cheapest day to buy Delta Airlines ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ Airline tickets is typically on Tuesdays, Wednesdays, and Saturdays ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ (OTA . Air-line often release new fares and sales on Monday evenings, making Tuesday a prime day for finding discounted 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/how-can-help-you-carnival-customer-service | How can help you carnival customer service hours? | How can help you carnival customer service hours? | If you have questions about a cruise you've already sailed on and need assistance, including potential refunds for issues related to a past cruise:
You can contact the Guest Care team via their Post-Cruise Inquiries page or by calling the general customer service numbers listed above.
For information about refund... | {
"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/srdc | SRDC | Structurally Regularized Deep Clustering | **Structurally Regularized Deep Clustering**, or **SRDC**, is a deep network based discriminative clustering method for domain adaptation that minimizes the KL divergence between predictive label distribution of the network and an introduced auxiliary one. Replacing the auxiliary distribution with that formed by ground... | {
"title": "Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering",
"url": "https://paperswithcode.com/paper/unsupervised-domain-adaptation-via-4"
} | 2,000 | https://arxiv.org/abs/2003.08607v1 | Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Domain Adaptation"
}
] |
https://paperswithcode.com/method/detnasnet | DetNASNet | DetNASNet | **DetNASNet** is a convolutional neural network designed to be an object detection backbone and discovered through [DetNAS](https://paperswithcode.com/method/detnas) architecture search. It uses [ShuffleNet V2](https://paperswithcode.com/method/shufflenet-v2) blocks as its basic building block. | {
"title": "DetNAS: Backbone Search for Object Detection",
"url": "https://paperswithcode.com/paper/detnas-neural-architecture-search-on-object"
} | 2,000 | https://arxiv.org/abs/1903.10979v4 | DetNAS: Backbone Search for Object Detection | https://github.com/megvii-model/DetNAS/blob/94623fd3c65934d1fba976c5c5d9d7f6b855ea90/Supernet-ImageNet/detnasnet.py#L5 | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Convolutional Neural Networks"
}
] |
https://paperswithcode.com/method/feedback-transformer | Feedback Transformer | Feedback Transformer | A **Feedback Transformer** is a type of sequential transformer that exposes all previous representations to all future representations, meaning the lowest representation of the current timestep is formed from the highest-level abstract representation of the past. This feedback nature allows this architecture to perform... | {
"title": "Addressing Some Limitations of Transformers with Feedback Memory",
"url": "https://paperswithcode.com/paper/accessing-higher-level-representations-in"
} | 2,000 | https://arxiv.org/abs/2002.09402v3 | Addressing Some Limitations of Transformers with Feedback Memory | 2 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Language Models"
},
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Autoregressive Transformers"
},
{
"area": "Natural Language Proc... | |
https://paperswithcode.com/method/pomo | POMO | POMO | {
"title": "POMO: Policy Optimization with Multiple Optima for Reinforcement Learning",
"url": "https://paperswithcode.com/paper/pomo-policy-optimization-with-multiple-optima"
} | 2,000 | https://arxiv.org/abs/2010.16011v3 | POMO: Policy Optimization with Multiple Optima for Reinforcement Learning | null | 6 | [
{
"area": "Reinforcement Learning",
"area_id": "reinforcement-learning",
"collection": "Reinforcement Learning Frameworks"
}
] | |
https://paperswithcode.com/method/mxmnet | MXMNet | Multiplex Molecular Graph Neural Network | The **Multiplex Molecular Graph Neural Network (MXMNet)** is an approach for the representation learning of molecules. The molecular interactions are divided into two categories: local and global. Then a two-layer multiplex graph $G = \\{ G_{l}, G_{g} \\}$ is constructed for a molecule. In $G$, the local layer $G_{l}$ ... | {
"title": "Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures",
"url": "https://paperswithcode.com/paper/molecular-mechanics-driven-graph-neural"
} | 2,000 | https://arxiv.org/abs/2011.07457v1 | Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures | 1 | [
{
"area": "Graphs",
"area_id": "graphs",
"collection": "Graph Models"
}
] | |
https://paperswithcode.com/method/represantive-call-how-to-speak-directly-at | [Represantive~Call]How to speak directly at Hawaiian Airlines? | [Represantive~Call]How to speak directly at Hawaiian Airlines? | To speak directly ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ with a Hawaiian Airlines representative, call their customer service line at ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅. After the automated prompts, you can say "agent" or press "0" to be connected to a live person. You can also find additional contact... | {
"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/stdc | STDC | Short-Term Dense Concatenate | **STDC**, or **Short-Term Dense Concatenate**, is a module for semantic segmentation to extract deep features with scalable
receptive field and multi-scale information. It aims to remove structure redundancy in the BiSeNet architecture, specifically BiSeNet adds an extra path to encode spatial information which can be... | {
"title": "Rethinking BiSeNet For Real-time Semantic Segmentation",
"url": "https://paperswithcode.com/paper/rethinking-bisenet-for-real-time-semantic"
} | 2,000 | https://arxiv.org/abs/2104.13188v1 | Rethinking BiSeNet For Real-time Semantic Segmentation | null | 4 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Semantic Segmentation Modules"
}
] |
https://paperswithcode.com/method/convbert | ConvBERT | ConvBERT | **ConvBERT** is a modification on the [BERT](https://paperswithcode.com/method/bert) architecture which uses a [span-based dynamic convolution](https://paperswithcode.com/method/span-based-dynamic-convolution) to replace self-attention heads to directly model local dependencies. Specifically a new [mixed attention modu... | {
"title": "ConvBERT: Improving BERT with Span-based Dynamic Convolution",
"url": "https://paperswithcode.com/paper/convbert-improving-bert-with-span-based"
} | 2,000 | https://arxiv.org/abs/2008.02496v3 | ConvBERT: Improving BERT with Span-based Dynamic Convolution | 5 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Autoencoding Transformers"
},
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Transformers"
}
] | |
https://paperswithcode.com/method/rga | RGA | Relation-aware Global Attention | In relation-aware global attention (RGA) stresses the importance of global structural information provided by pairwise relations, and uses it to produce attention maps.
RGA comes in two forms, spatial RGA (RGA-S) and channel RGA (RGA-C). RGA-S first reshapes the input feature map $X$ to $C\times (H\times W)$ and t... | {
"title": "Relation-Aware Global Attention for Person Re-identification",
"url": "https://paperswithcode.com/paper/relation-aware-global-attention"
} | 2,000 | https://arxiv.org/abs/1904.02998v2 | Relation-Aware Global Attention for Person Re-identification | 11 | [
{
"area": "General",
"area_id": "general",
"collection": "Attention Mechanisms"
}
] | |
https://paperswithcode.com/method/just-call-what-is-the-cheapest-day-to-book | [Just~Call] what is the cheapest day to book flights with delta | [Just~Call] what is the cheapest day to book flights with delta | The cheapest day to book flights on delta is typically Tuesday, Wednesday, and Saturdays +𝟙-𝟠𝟘𝟙-𝟠𝟝𝟝-𝟝𝟡𝟘𝟝 (USA). These are considered off-peak travel days when demand is lower, leading to cheapest fares +𝟙-𝟠𝟘𝟙-𝟠𝟝𝟝-𝟝𝟡𝟘𝟝 (USA). | {
"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/contact-us-can-i-change-my-flight-ticket-name | [contact~us]Can I change my flight ticket name to another person? | [contact~us]Can I change my flight ticket name to another person? | Can I change my flight ticket name to another person?
Typically, you cannot change the name on a plane ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ ticket to another person after booking it. But as long as it's still going to be you traveling, airlines have policies in place ☎️+1-801-(855)-(5905) or +1-804-(853)-(9... | {
"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/dashf | DAHSF | Digestion Algorithm in Hierarchical Symbolic Forests | A brand new Foundation Model framework for deep learning in the future. | {
"title": "Digestion Algorithm in Hierarchical Symbolic Forests: A Fast Text Normalization Algorithm and Semantic Parsing Framework for Specific Scenarios and Lightweight Deployment",
"url": "https://paperswithcode.com/paper/digestion-algorithm-in-hierarchical-symbolic"
} | 2,000 | https://arxiv.org/abs/2412.14054v1 | Digestion Algorithm in Hierarchical Symbolic Forests: A Fast Text Normalization Algorithm and Semantic Parsing Framework for Specific Scenarios and Lightweight Deployment | 1 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Language Models"
}
] | |
https://paperswithcode.com/method/llama | LLaMA | LLaMA | **LLaMA** is a collection of foundation language models ranging from 7B to 65B parameters. It is based on the transformer architecture with various improvements that were subsequently proposed. The main difference with the original architecture are listed below.
- RMSNorm normalizing function is used to improve the ... | {
"title": "LLaMA: Open and Efficient Foundation Language Models",
"url": "https://paperswithcode.com/paper/llama-open-and-efficient-foundation-language-1"
} | 2,000 | https://arxiv.org/abs/2302.13971v1 | LLaMA: Open and Efficient Foundation Language Models | 1,062 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Language Models"
}
] | |
https://paperswithcode.com/method/patch-merger | Patch Merger | Patch Merger Module | PatchMerger is a module for Vision Transformers that decreases the number of tokens/patches passed onto each individual transformer encoder block whilst maintaining performance and reducing compute. PatchMerger takes linearly transforms an input of shape N patches × D dimensions through a learnable weight matrix of sha... | {
"title": "Learning to Merge Tokens in Vision Transformers",
"url": "https://paperswithcode.com/paper/learning-to-merge-tokens-in-vision"
} | 2,000 | https://arxiv.org/abs/2202.12015v1 | Learning to Merge Tokens in Vision Transformers | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Model Blocks"
}
] | |
https://paperswithcode.com/method/a3c | A3C | A3C | **A3C**, **Asynchronous Advantage Actor Critic**, is a policy gradient algorithm in reinforcement learning that maintains a policy $\pi\left(a\_{t}\mid{s}\_{t}; \theta\right)$ and an estimate of the value
function $V\left(s\_{t}; \theta\_{v}\right)$. It operates in the forward view and uses a mix of $n$-step returns t... | {
"title": "Asynchronous Methods for Deep Reinforcement Learning",
"url": "https://paperswithcode.com/paper/asynchronous-methods-for-deep-reinforcement"
} | 2,000 | http://arxiv.org/abs/1602.01783v2 | Asynchronous Methods for Deep Reinforcement Learning | null | 57 | [
{
"area": "Reinforcement Learning",
"area_id": "reinforcement-learning",
"collection": "Policy Gradient Methods"
}
] |
https://paperswithcode.com/method/run | RUN | RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method | The optimization field suffers from the metaphor-based “pseudo-novel” or “fancy” optimizers. Most of these cliché methods mimic animals' searching trends and possess a small contribution to the optimization process itself. Most of these cliché methods suffer from the locally efficient performance, biased verification m... | null | 2,000 | null | null | https://aliasgharheidari.com/RUN.html | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Optimization"
}
] |
https://paperswithcode.com/method/how-can-i-talk-to-american-airlines | @!@How Can I Talk to American Airlines Representative Fast? | @!@How Can I Talk to American Airlines Representative Fast? | In summary, to speak with an American Airlines representative fast, always start by calling ✈️☎️+1-804-853-9001 . It's the most direct, efficient option for resolving any issue quickly. Save yourself time by calling ✈️☎️+𝟣-𝟪01_855-5905. today. | {
"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-ask-a-question-at | How do I ask a question at 𝗘𝘅𝗽𝗲𝗱𝗶𝗮? | [[ask~expert]]How do I ask a question at 𝗘𝘅𝗽𝗲𝗱𝗶𝗮? | How do I ask a question at 𝗘𝘅𝗽𝗲𝗱𝗶𝗮? To ask a question at 𝗘𝘅𝗽𝗲𝗱𝗶𝗮, visit their Help Center on the website or app. You can also call +1(888)-829-0881 , use the live chat feature, or reach out via social media. Check the FAQ section for quick answers. 𝗘𝘅𝗽𝗲𝗱𝗶𝗮 is one of the world's leading travel booki... | {
"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/instant-help-24-7-how-do-i-file-a-complaint | [Instant help 24/7]How do I file a complaint with the airlines? | [Instant help 24/7]How do I file a complaint with the airlines? | To file a complaint with the airline, visit their official website and look for the “Customer Service” or “Contact Us” section ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅. There you’ll often find a complaint form to fill out. Be sure to include your booking details and the phone number used during your reservation. I... | {
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/way-to-response-how-to-get-a-response-from | 【Way_To_Response】 How to Get a Response from American Airlines | 【Way_To_Response】 How to Get a Response from American Airlines | To receive a prompt response from American Airlines, the most effective method is to call their customer service hotline at +1 (804) 853-9001. Whether your inquiry pertains to flights, delays, or refunds, this number is your best point of contact. If you are following up on emails or online claims, you should also call... | {
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/double-q-learning | Double Q-learning | Double Q-learning | **Double Q-learning** is an off-policy reinforcement learning algorithm that utilises double estimation to counteract overestimation problems with traditional Q-learning.
The max operator in standard [Q-learning](https://paperswithcode.com/method/q-learning) and [DQN](https://paperswithcode.com/method/dqn) uses the... | {
"title": "Double Q-learning",
"url": "https://paperswithcode.com/paper/double-q-learning"
} | 2,000 | http://papers.nips.cc/paper/3964-double-q-learning | Double Q-learning | 112 | [
{
"area": "Reinforcement Learning",
"area_id": "reinforcement-learning",
"collection": "Off-Policy TD Control"
}
] | |
https://paperswithcode.com/method/how-do-i-find-airline-reservations | How do I find airline reservations? | How do I find airline reservations? | To find your airline reservations, check your confirmation email for a booking reference or ticket number 1-833-705-0001(US)/+44 (20) 39003980(UK). You can also log in to the airline’s website or app using your last name and booking code under the “Manage Booking” or “My Trips” section 1-833-705-0001(US)/+44 (20) 39003... | {
"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/avg-age-what-is-the-average-age-for-regent | [[AVG-Age]] What is the average age for Regent Seven Seas Cruises? | [[AVG-Age]] What is the average age for Regent Seven Seas Cruises? | The average age of passengers on Regent Seven Seas Cruises is generally in the mid-60s +1-855-732-4023, though it can vary depending on the cruise length and destination. Longer cruises and those in exotic locations tend to attract older passengers +1-855-732-4023, while shorter cruises and those in the Caribbean may h... | {
"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
"url": "https://paperswithcode.com/paper/0-1-laws-for-pattern-occurrences-in"
} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
https://paperswithcode.com/method/how-long-do-coinspot-withdrawals-take-learn | How long do CoinSpot withdrawals take – Learn about timelines. | How long do CoinSpot withdrawals take – Learn about timelines. | The time it takes for CoinSpot [+61-3-5929-4808] withdrawals to process can vary based on several factors, including the type of withdrawal and the payment method used. [+61-3-5929-4808] For AUD withdrawals, CoinSpot [+61-3-5929-4808] typically processes transactions within 1-2 business days. [+61-3-5929-4808] Th... | {
"title": "0.8% Nyquist computational ghost imaging via non-experimental deep learning",
"url": "https://paperswithcode.com/paper/0-8-nyquist-computational-ghost-imaging-via"
} | 2,000 | https://arxiv.org/abs/2108.07673v1 | 0.8% Nyquist computational ghost imaging via non-experimental deep learning | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Representations"
}
] |
https://paperswithcode.com/method/faqs-help-tm-how-do-i-complain-to-seabourn | [Faqs~HeLp`™]How do I complain to Seabourn? | [Faqs~HeLp`™]How do I complain to Seabourn? | While specific phone numbers designated solely for “disputes” or a “disputes department” aren’t explicitly mentioned in the provided information, you can, +1-(855)-732-4023 use Seabourn Cruise Line’s main customer service numbers to initiate discussions regarding any dispute or complaint. Here are the main customer ser... | {
"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/ayuda-rapida-como-llamar-a-american-airlines | Guide-¿Cómo llamar a American Airlines en español? | Ayuda rápida-¿Cómo llamar a American Airlines en español? | Para llamar a American Airlines en español, marca el número de servicio al cliente +1-808-(470)-(7107) (EE. UU.) o al +1-808-(470)-(7107) (México) y selecciona la opción para atención en español. Este servicio está disponible las 24 horas del día, los 7 días de la semana. Puedes realizar reservas, consultar vuelos, cam... | {
"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/expert-guide-como-llamar-a-american-airlines | {Expert-Guide}¿Cómo llamar a American Airlines en español? | {Expert-Guide}¿Cómo llamar a American Airlines en español? | Para llamar a 𝐀𝐦𝐞𝐫𝐢𝐜𝐚𝐧 𝐀𝐢𝐫𝐥𝐢𝐧𝐞𝐬 en español, marca el número de servicio al cliente +1-808-(470)-(7107) (EE. UU.) o al +1-808-(470)-(7107) (México) y selecciona la opción para atención en español. Este servicio está disponible las 24 horas del día, los 7 días de la semana. Puedes realizar reservas, consu... | {
"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/fcpose | FCPose | FCPose | **FCPose** is a fully convolutional multi-person [pose estimation framework](https://paperswithcode.com/methods/category/pose-estimation-models) using dynamic instance-aware convolutions. Different from existing methods, which often require ROI (Region of Interest) operations and/or grouping post-processing, FCPose eli... | {
"title": "FCPose: Fully Convolutional Multi-Person Pose Estimation with Dynamic Instance-Aware Convolutions",
"url": "https://paperswithcode.com/paper/fcpose-fully-convolutional-multi-person-pose"
} | 2,000 | https://arxiv.org/abs/2105.14185v1 | FCPose: Fully Convolutional Multi-Person Pose Estimation with Dynamic Instance-Aware Convolutions | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Pose Estimation Models"
}
] | |
https://paperswithcode.com/method/differential-attention-for-visual-question | Differential attention for visual question answering | Differential attention for visual question answering | In this paper we aim to answer questions based on images when provided with a dataset of question-answer pairs for a number of images during training. A number of methods have focused on solving this problem by using image based attention. This is done by focusing on a specific part of the image while answering the que... | null | 2,000 | null | null | 0 | [
{
"area": "General",
"area_id": "general",
"collection": "Attention Mechanisms"
}
] | |
https://paperswithcode.com/method/multi-loss-bce-loss-focal-loss-dice-loss | Multi Loss ( BCE Loss + Focal Loss ) + Dice Loss | Multi Loss ( BCE Loss + Focal Loss ) + Dice Loss | Our proposed loss function is a combination of BCE Loss, Focal Loss, and Dice loss. Each one of them contributes individually to improve performance further details of loss functions are mentioned below,
(1) BCE Loss calculates probabilities and compares each actual class output with predicted probabilities which ca... | {
"title": "HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images",
"url": "https://paperswithcode.com/paper/histoseg-quick-attention-with-multi-loss"
} | 2,000 | https://arxiv.org/abs/2209.00729v1 | HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images | 2 | [
{
"area": "General",
"area_id": "general",
"collection": "Loss Functions"
}
] | |
https://paperswithcode.com/method/contact-usa-what-is-the-phone-number-for-3 | [[Contact!!USA]] What is the phone number for American Express cruises? | [[Contact!!USA]] What is the phone number for American Express cruises? | You can cancel and rebook your reservation on AmexTravel.com or by calling a representative of AmexTravel.com at +1-855-732-4023 (USA) OR +44-289-708-0062 (UK).
If you booked through American Express Cruises: Call +1-855-732-4023 (USA) OR +44-289-708-0062 (UK).
To get in touch with an American Express Travel Consulta... | {
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
https://paperswithcode.com/method/faqs-support-how-do-i-contact-american | {{FAQs_Support}} How do I contact American Express travel services? | {{FAQs_Support}} How do I contact American Express travel services? | You can cancel and rebook your reservation on AmexTravel.com or by calling a representative of AmexTravel.com at +1-855-732-4023 (USA) OR +44-289-708-0062 (UK).
If you booked through American Express Cruises: Call +1-855-732-4023 (USA) OR +44-289-708-0062 (UK).
To get in touch with an American Express Travel Consulta... | {
"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/fixmatch | FixMatch | FixMatch | FixMatch is an algorithm that first generates pseudo-labels using the model's predictions on weakly-augmented unlabeled images. For a given image, the pseudo-label is only retained if the model produces a high-confidence prediction. The model is then trained to predict the pseudo-label when fed a strongly-augmented ver... | {
"title": "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence",
"url": "https://paperswithcode.com/paper/fixmatch-simplifying-semi-supervised-learning"
} | 2,000 | https://arxiv.org/abs/2001.07685v2 | FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence | 85 | [
{
"area": "General",
"area_id": "general",
"collection": "Semi-Supervised Learning Methods"
}
] | |
https://paperswithcode.com/method/faqs-live-how-do-i-contact-holland-america | [[FAQS-Live]] ~How do I contact Holland America customer service? | [[FAQS-Live]] ~How do I contact Holland America customer service? | You can reach Holland America Line's customer service through various channels:
1. General Reservation Assistance:
Phone: +1–855–732–4023
Office Hours: Monday – Friday, 6 a.m. to 6 p.m. (PT), Saturday & Sunday, 7 a.m. to 4 p.m. (PT)
Customer Support Icon (website): Ask to "connect with a live agent"
2. Group... | {
"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/quick-help-what-is-the-cheapest-day-to-book-a | {{Quick Help}} What is the cheapest day to book a United Airlines flight? | {{Quick Help}} What is the cheapest day to book a United Airlines flight? | If you're looking ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ for the cheapest day to fly with United Airline, statistics show that Tuesdays, Wednesdays, and Saturdays [☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅] are typically the best days to book and travel. By choosing to fly on these days, you can take advanta... | {
"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/hypersa | HyperSA | HyperGraph Self-Attention | An extension of Self-Attention to hypergraph
Skeleton-based action recognition aims to recognize human actions given human joint coordinates with skeletal interconnections. By defining a graph with joints as vertices and their natural connections as edges, previous works successfully adopted Graph Convolutional networ... | {
"title": "Hypergraph Transformer for Skeleton-based Action Recognition",
"url": "https://paperswithcode.com/paper/hypergraph-transformer-for-skeleton-based"
} | 2,000 | https://arxiv.org/abs/2211.09590v5 | Hypergraph Transformer for Skeleton-based Action Recognition | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Attention Mechanisms"
}
] | |
https://paperswithcode.com/method/gradual-self-training | Gradual Self-Training | Gradual Self-Training | Gradual self-training is a method for semi-supervised domain adaptation. The goal is to adapt an initial classifier trained on a source domain given only unlabeled data that shifts gradually in distribution towards a target domain.
This comes up for example in applications ranging from sensor networks and self-driv... | {
"title": "Understanding Self-Training for Gradual Domain Adaptation",
"url": "https://paperswithcode.com/paper/understanding-self-training-for-gradual"
} | 2,000 | https://arxiv.org/abs/2002.11361v1 | Understanding Self-Training for Gradual Domain Adaptation | 8 | [
{
"area": "General",
"area_id": "general",
"collection": "Semi-Supervised Learning Methods"
}
] | |
https://paperswithcode.com/method/who-is-305-539-6000 | Who is 305 539 6000? | Who is 305 539 6000? | The phone number 305-539-6000 belongs to Royal Caribbean International's corporate headquarters in Miami, Florida (1-855-732-4023 or 1-808-900-8011). It is primarily used for administrative purposes, media inquiries, and high-level customer relations rather than general reservations or support (1-855-732-4023 USA or 44... | {
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] | |
https://paperswithcode.com/method/hmgnn | HMGNN | Heterogeneous Molecular Graph Neural Network | As they carry great potential for modeling complex interactions, graph neural network (GNN)-based methods have been widely used to predict quantum mechanical properties of molecules. Most of the existing methods treat molecules as molecular graphs in which atoms are modeled as nodes. They characterize each atom's chemi... | {
"title": "Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties",
"url": "https://paperswithcode.com/paper/heterogeneous-molecular-graph-neural-networks"
} | 2,000 | https://arxiv.org/abs/2009.12710v1 | Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties | 1 | [
{
"area": "Graphs",
"area_id": "graphs",
"collection": "Graph Models"
}
] | |
https://paperswithcode.com/method/snn | SNN | Spiking Neural Networks | **Spiking Neural Networks** (**SNNs**) are a class of artificial neural networks inspired by the structure and functioning of the brain's neural networks. Unlike traditional artificial neural networks that operate based on continuous firing rates, SNNs simulate the behavior of individual neurons through discrete spike... | {
"title": "Self-Normalizing Neural Networks",
"url": "https://paperswithcode.com/paper/self-normalizing-neural-networks"
} | 2,000 | http://arxiv.org/abs/1706.02515v5 | Self-Normalizing Neural Networks | 363 | [] | |
https://paperswithcode.com/method/cvrl | CVRL | Contrastive Video Representation Learning | **Contrastive Video Representation Learning**, or **CVRL**, is a self-supervised contrastive learning framework for learning spatiotemporal visual representations from unlabeled videos. Representations are learned using a contrastive loss, where two clips from the same short video are pulled together in the embedding s... | {
"title": "Spatiotemporal Contrastive Video Representation Learning",
"url": "https://paperswithcode.com/paper/spatiotemporal-contrastive-video"
} | 2,000 | https://arxiv.org/abs/2008.03800v4 | Spatiotemporal Contrastive Video Representation Learning | 3 | [
{
"area": "General",
"area_id": "general",
"collection": "Self-Supervised Learning"
},
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Generative Video Models"
}
] | |
https://paperswithcode.com/method/sycoca | SyCoCa | Symmetrizing Contrastive Captioners with Attentive Masking for Multimodal Alignment | Multimodal alignment between language and vision is the fundamental topic in current vision-language model research. Contrastive Captioners (CoCa), as a representative method, integrates Contrastive Language-Image Pretraining (CLIP) and Image Caption (IC) into a unified framework, resulting in impressive results. CLIP ... | {
"title": "SyCoCa: Symmetrizing Contrastive Captioners with Attentive Masking for Multimodal Alignment",
"url": "https://paperswithcode.com/paper/sycoca-symmetrizing-contrastive-captioners"
} | 2,000 | https://arxiv.org/abs/2401.02137v1 | SyCoCa: Symmetrizing Contrastive Captioners with Attentive Masking for Multimodal Alignment | null | 2 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Multi-Modal Methods"
}
] |
https://paperswithcode.com/method/24-hour-policy-latam-what-is-the-24-hour-rule | 24-hour policy-LATAm---What is the 24-hour rule for Latam? | 24-hour policy-LATAm---What is the 24-hour rule for Latam? | What is the 24 hour rule for Latam? Latam 𝙰irlines' 24-hour policy allows travelers to cancel or modify their booking for free within 24 hours of purchase,++𝟙--𝟠𝟘𝟙--𝟠𝟝𝟝--𝟝𝟡𝟘𝟝. US ++𝟙--𝟠𝟘𝟙--𝟠𝟝𝟝--𝟝𝟡𝟘𝟝. UK. as long as the 𝙩𝙞𝙘𝙠𝙚𝙩 was bought at least seven days 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/your-carnival-booking-number-customer-service | Your carnival booking number? @Customer Service | Your carnival booking number? @Customer Service | Your carnival booking number?
Your Carnival booking number should be available on your booking confirmation or cruise card
1-855-732-4023. If you have a booking but cannot locate the number, you can try contacting Carnival Cruise Line directly for assistance.
Here are some ways to contact them:
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": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
https://paperswithcode.com/method/guest-care-way-to-contact-carnival | (Guest Care): Way to contact carnival reservation number? | (Guest Care): Way to contact carnival reservation number? | To contact Carnival Cruise Line about your reservation, you can call their Customer Service team at
1-855-732-4023. They are available Monday – Friday from 9:00 a.m. to 10:00 p.m. ET and Saturday – Sunday from 9:00 a.m. to 6:00 p.m. ET.
If you are calling from the United Kingdom, you can reach them at 0855-732-4023.
... | {
"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/neural-cache | Neural Cache | Neural Cache | A **Neural Cache**, or a **Continuous Cache**, is a module for language modelling which stores previous hidden states in memory cells. They are then used as keys to retrieve their corresponding word, that is the next word. There is no transformation applied to the storage during writing and reading.
More formally it... | {
"title": "Improving Neural Language Models with a Continuous Cache",
"url": "https://paperswithcode.com/paper/improving-neural-language-models-with-a"
} | 2,000 | http://arxiv.org/abs/1612.04426v1 | Improving Neural Language Models with a Continuous Cache | 4 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Language Model Components"
}
] | |
https://paperswithcode.com/method/resnext-elastic | ResNeXt-Elastic | ResNeXt-Elastic | **ResNeXt-Elastic** is a convolutional neural network that is a modification of a [ResNeXt](https://paperswithcode.com/method/resnext) with elastic blocks (extra upsampling and downsampling). | {
"title": "ELASTIC: Improving CNNs with Dynamic Scaling Policies",
"url": "https://paperswithcode.com/paper/elastic-improving-cnns-with-instance-specific"
} | 2,000 | http://arxiv.org/abs/1812.05262v2 | ELASTIC: Improving CNNs with Dynamic Scaling Policies | https://github.com/allenai/elastic/blob/57345c600c63fbde163c41929d6d6dd894d408ce/models/resnext.py#L173 | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Convolutional Neural Networks"
}
] |
https://paperswithcode.com/method/how-do-i-reserve-a-seat-on-air-france | How do I reserve a seat on Air France? | How do I reserve a seat on Air France? | To reserve a seat on Air France, visit their official website or mobile app, log in to your booking 1-833-705-0001, and select “Choose My Seat” under the “My Bookings” section. Seat selection is free for many fare types within 30 hours of departure during online check-in 1-833-705-0001, but advance seat selection may i... | {
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/can-you-cancel-a-royal-caribbean-cruise-and | Can you cancel a Royal Caribbean cruise and get your deposit back? @Quick-Help | Can you cancel a Royal Caribbean cruise and get your deposit back? @Quick-Help | The Royal Caribbean cancellation phone number is not provided in the search results. However, it's recommended to call their customer service line at +1-855-732-4023 or +44-289-708-0062. You can also manage your booking and potentially find cancellation information on their website.
To cancel a Royal Caribbean cruis... | {
"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/lufthansa-l-what-is-the-24-hour-rule-with | [@LUFTHANSA~L@] (𝑳𝒊𝒗𝒆 𝑯𝒖𝒎𝒂𝒏)What is the 24-hour rule with Lufthansa? | [@LUFTHANSA~L@] (𝑳𝒊𝒗𝒆 𝑯𝒖𝒎𝒂𝒏)What is the 24-hour rule with Lufthansa? | Lufthansa's 24-hour rule ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ allows for free cancellations and refunds on flight bookings made directly with the airline, provided the booking ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ is made at least seven days before the departure date and originates in or is flying to 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/tsrus | TSRUs | TSRUs | **TSRUs**, or **Transformation-based Spatial Recurrent Unit p**, is a modification of a [ConvGRU](https://paperswithcode.com/method/cgru) used in the [TriVD-GAN](https://paperswithcode.com/method/trivd-gan) architecture for video generation.
It largely follows [TSRUc](https://paperswithcode.com/method/tsruc), but co... | {
"title": "Transformation-based Adversarial Video Prediction on Large-Scale Data",
"url": "https://paperswithcode.com/paper/transformation-based-adversarial-video"
} | 2,000 | https://arxiv.org/abs/2003.04035v3 | Transformation-based Adversarial Video Prediction on Large-Scale Data | null | 1 | [
{
"area": "Sequential",
"area_id": "sequential",
"collection": "Recurrent Neural Networks"
}
] |
https://paperswithcode.com/method/attentional-liquid-warping-gan | Attentional Liquid Warping GAN | Attentional Liquid Warping GAN | **Attentional Liquid Warping GAN** is a type of generative adversarial network for human image synthesis that utilizes a [AttLWB](https://paperswithcode.com/method/attlwb) block, which is a 3D body mesh recovery module that disentangles pose and shape. To preserve the source information, such as texture, style, color, ... | {
"title": "Liquid Warping GAN with Attention: A Unified Framework for Human Image Synthesis",
"url": "https://paperswithcode.com/paper/liquid-warping-gan-with-attention-a-unified"
} | 2,000 | https://arxiv.org/abs/2011.09055v2 | Liquid Warping GAN with Attention: A Unified Framework for Human Image Synthesis | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Generative Adversarial Networks"
}
] | |
https://paperswithcode.com/method/tacotron | Tacotron | Tacotron | **Tacotron** is an end-to-end generative text-to-speech model that takes a character sequence as input and outputs the corresponding spectrogram. The backbone of Tacotron is a seq2seq model with attention. The Figure depicts the model, which includes an encoder, an attention-based decoder, and a post-processing net. At... | {
"title": "Tacotron: Towards End-to-End Speech Synthesis",
"url": "https://paperswithcode.com/paper/tacotron-towards-end-to-end-speech-synthesis"
} | 2,000 | http://arxiv.org/abs/1703.10135v2 | Tacotron: Towards End-to-End Speech Synthesis | null | 65 | [
{
"area": "Sequential",
"area_id": "sequential",
"collection": "Sequence To Sequence Models"
},
{
"area": "Audio",
"area_id": "audio",
"collection": "Text-to-Speech Models"
}
] |
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/m3l | M3L | Multi-modal Teacher for Masked Modality Learning | {
"title": "Missing Modality Robustness in Semi-Supervised Multi-Modal Semantic Segmentation",
"url": "https://paperswithcode.com/paper/missing-modality-robustness-in-semi"
} | 2,000 | https://arxiv.org/abs/2304.10756v1 | Missing Modality Robustness in Semi-Supervised Multi-Modal Semantic Segmentation | null | 2 | [
{
"area": "General",
"area_id": "general",
"collection": "Semi-Supervised Learning Methods"
}
] | |
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{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
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} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
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{
"area": "General",
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"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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{
"area": "General",
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"collection": "2D Parallel Distributed Methods"
}
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
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{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "2020 CATARACTS Semantic Segmentation Challenge",
"url": "https://paperswithcode.com/paper/2020-cataracts-semantic-segmentation"
} | 2,000 | https://arxiv.org/abs/2110.10965v2 | 2020 CATARACTS Semantic Segmentation Challenge | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
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} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
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} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/target-policy-smoothing | Target Policy Smoothing | Target Policy Smoothing | **Target Policy Smoothing** is a regularization strategy for the value function in reinforcement learning. Deterministic policies can overfit to narrow peaks in the value estimate, making them highly susceptible to functional approximation error, increasing the variance of the target. To reduce this variance, target po... | {
"title": "Addressing Function Approximation Error in Actor-Critic Methods",
"url": "https://paperswithcode.com/paper/addressing-function-approximation-error-in"
} | 2,000 | http://arxiv.org/abs/1802.09477v3 | Addressing Function Approximation Error in Actor-Critic Methods | https://github.com/sfujim/TD3/blob/ade6260da88864d1ab0ed592588e090d3d97d679/main.py#L86 | 116 | [
{
"area": "General",
"area_id": "general",
"collection": "Regularization"
}
] |
https://paperswithcode.com/method/hitnet | HITNet | HITNet | **HITNet** is a framework for neural network based depth estimation which overcomes the computational disadvantages of operating on a 3D volume by integrating image warping, spatial propagation and a fast high resolution initialization step into the network architecture, while keeping the flexibility of a learned repre... | {
"title": "HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching",
"url": "https://paperswithcode.com/paper/hitnet-hierarchical-iterative-tile-refinement"
} | 2,000 | https://arxiv.org/abs/2007.12140v5 | HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Stereo Depth Estimation Models"
}
] | |
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
"url": "https://paperswithcode.com/paper/0-1-laws-for-pattern-occurrences-in"
} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/help-support-how-to-speak-directly-on-latam | Help & Support - How to speak directly on LATAM Airlines? | Help & Support - How to speak directly on LATAM Airlines? | To speak directly with a live representative at LATAM Airlines, you can call ☎️+1-801-(855)-(5905)or +1-804-853-9001✅ (UK). You can also contact LATAM Airlines through their website, mobile app, or customer service hotline at ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. | {
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
"url": "https://paperswithcode.com/paper/0-1-laws-for-pattern-occurrences-in"
} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
https://paperswithcode.com/method/cross-encoder-reranking | Cross-encoder Reranking | Cross-encoder Reranking | Cross-encoder Reranking | {
"title": "ReRankMatch: Semi-Supervised Learning with Semantics-Oriented Similarity Representation",
"url": "https://paperswithcode.com/paper/rerankmatch-semi-supervised-learning-with"
} | 2,000 | https://arxiv.org/abs/2102.06328v2 | ReRankMatch: Semi-Supervised Learning with Semantics-Oriented Similarity Representation | null | 4 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Language Models"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/pafs | PAFs | Part Affinity Fields | {
"title": "Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields",
"url": "https://paperswithcode.com/paper/realtime-multi-person-2d-pose-estimation"
} | 2,000 | http://arxiv.org/abs/1611.08050v2 | Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields | null | 6 | [
{
"area": "General",
"area_id": "general",
"collection": "Output Functions"
}
] | |
https://paperswithcode.com/method/disentangled-attribution-curves | Disentangled Attribution Curves | Disentangled Attribution Curves | **Disentangled Attribution Curves (DAC)** provide interpretations of tree ensemble methods in the form of (multivariate) feature importance curves. For a given variable, or group of variables, [DAC](https://paperswithcode.com/method/dac) plots the importance of a variable(s) as their value changes.
The Figure to the... | {
"title": "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees",
"url": "https://paperswithcode.com/paper/disentangled-attribution-curves-for"
} | 2,000 | https://arxiv.org/abs/1905.07631v1 | Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees | https://github.com/csinva/disentangled-attribution-curves | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Interpretability"
}
] |
https://paperswithcode.com/method/advprop | AdvProp | AdvProp | **AdvProp** is an adversarial training scheme which treats adversarial examples as additional examples, to prevent overfitting. Key to the method is the usage of a separate auxiliary batch norm for adversarial examples, as they have different underlying distributions to normal examples. | {
"title": "Adversarial Examples Improve Image Recognition",
"url": "https://paperswithcode.com/paper/adversarial-examples-improve-image"
} | 2,000 | https://arxiv.org/abs/1911.09665v2 | Adversarial Examples Improve Image Recognition | null | 6 | [
{
"area": "General",
"area_id": "general",
"collection": "Adversarial Training"
}
] |
https://paperswithcode.com/method/alphastar | AlphaStar | DeepMind AlphaStar | **AlphaStar** is a reinforcement learning agent for tackling the game of Starcraft II. It learns a policy $\pi\_{\theta}\left(a\_{t}\mid{s\_{t}}, z\right) = P\left[a\_{t}\mid{s\_{t}}, z\right]$ using a neural network for parameters $\theta$ that receives observations $s\_{t} = \left(o\_{1:t}, a\_{1:t-1}\right)$ as inpu... | null | 2,000 | null | null | https://github.com/google-deepmind/alphastar | 10 | [
{
"area": "Reinforcement Learning",
"area_id": "reinforcement-learning",
"collection": "Video Game Models"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/ts | TS | Spatio-temporal stability analysis | Spatio-temporal features extraction that measure the stabilty. The proposed method is based on a compression algorithm named Run Length Encoding. The workflow of the method is presented bellow. | null | 2,000 | null | null | https://github.com/mchelali/TemporalStability | 242 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Feature Extractors"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/guia-de-viaje-como-hablo-con-una-persona-en | {Guía de viaje}¿Cómo hablo con una persona en American Airlines? | {Guía de viaje}¿Cómo hablo con una persona en American Airlines? | Para hablar con una persona en 𝐀𝐦𝐞𝐫𝐢𝐜𝐚𝐧 𝐀𝐢𝐫𝐥𝐢𝐧𝐞𝐬 +1-808-(470)-(7107), puedes llamar al número de servicio al cliente en español: +1-808-(470)-(7107). También puedes comunicarte con ellos en línea a través de su sitio web o aplicación +1-808-(470)-(7107). | {
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/tildev2 | TILDEv2 | TILDEv2 | **TILDEv2** is a [BERT](https://paperswithcode.com/method/bert)-based re-ranking method that stems from [TILDE](https://dl.acm.org/doi/abs/10.1145/3404835.3462922) but that addresses its limitations. It relies on contextualized exact term matching with expanded passages. This requires to only store in the index the sco... | {
"title": "Fast Passage Re-ranking with Contextualized Exact Term Matching and Efficient Passage Expansion",
"url": "https://paperswithcode.com/paper/fast-passage-re-ranking-with-contextualized"
} | 2,000 | https://arxiv.org/abs/2108.08513v2 | Fast Passage Re-ranking with Contextualized Exact Term Matching and Efficient Passage Expansion | 3 | [
{
"area": "General",
"area_id": "general",
"collection": "Information Retrieval Methods"
},
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Passage Re-Ranking Models"
}
] | |
https://paperswithcode.com/method/3d-resnet-rs | 3D ResNet-RS | 3D ResNet-RS | **3D ResNet-RS** is an architecture and scaling strategy for 3D ResNets for video recognition. The key additions are:
- **3D ResNet-D stem**: The [ResNet-D](https://paperswithcode.com/method/resnet-d) stem is adapted to 3D inputs by using three consecutive [3D convolutional layers](https://paperswithcode.com/method/... | {
"title": "Revisiting 3D ResNets for Video Recognition",
"url": "https://paperswithcode.com/paper/revisiting-3d-resnets-for-video-recognition"
} | 2,000 | https://arxiv.org/abs/2109.01696v1 | Revisiting 3D ResNets for Video Recognition | 3 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Video Recognition Models"
}
] | |
https://paperswithcode.com/method/skep | SKEP | SKEP | **SKEP** is a self-supervised pre-training method for sentiment analysis. With the help of automatically-mined knowledge, SKEP conducts sentiment masking and constructs three sentiment knowledge prediction objectives, so as to embed sentiment information at the word, polarity and aspect level into pre-trained sentiment... | {
"title": "SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis",
"url": "https://paperswithcode.com/paper/skep-sentiment-knowledge-enhanced-pre"
} | 2,000 | https://arxiv.org/abs/2005.05635v2 | SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Semi-Supervised Learning Methods"
}
] |
https://paperswithcode.com/method/focal-transformers | Focal Transformers | Focal Transformers | The **focal self-attention** is built to make Transformer layers scalable to high-resolution inputs. Instead of attending all tokens at fine-grain, the approach attends the fine-grain tokens only locally, but the summarized ones globally. As such, it can cover as many regions as standard self-attention but with much l... | {
"title": "Focal Self-attention for Local-Global Interactions in Vision Transformers",
"url": "https://paperswithcode.com/paper/focal-self-attention-for-local-global"
} | 2,000 | https://arxiv.org/abs/2107.00641v1 | Focal Self-attention for Local-Global Interactions in Vision Transformers | 4 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Vision Transformers"
}
] | |
https://paperswithcode.com/method/understanding-process-how-to-book-a-cruise-on | [[Understanding--Process]] How to book a cruise on Norwegian Cruise Line? | [[Understanding--Process]] How to book a cruise on Norwegian Cruise Line? | To call someone on a Norwegian cruise+1-855-732-4023, you have a few options: use the ship's phone system to call their stateroom, utilize the Cruise Norwegian app for calls over Wi-Fi, or enable cellular service on your phone once you reach international waters+1-855-732-4023. The ship's phone system is the most strai... | {
"title": "0-1 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/cascade-r-cnn | Cascade R-CNN | Cascade R-CNN | **Cascade R-CNN** is an object detection architecture that seeks to address problems with degrading performance with increased IoU thresholds (due to overfitting during training and inference-time mismatch between IoUs for which detector is optimal and the inputs). It is a multi-stage extension of the [R-CNN](https://p... | {
"title": "Cascade R-CNN: Delving into High Quality Object Detection",
"url": "https://paperswithcode.com/paper/cascade-r-cnn-delving-into-high-quality"
} | 2,000 | http://arxiv.org/abs/1712.00726v1 | Cascade R-CNN: Delving into High Quality Object Detection | https://github.com/open-mmlab/mmdetection/blob/588536de9905feb7f37c2c977d146a64c74ef28e/mmdet/models/detectors/cascade_rcnn.py#L6 | 34 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Object Detection Models"
}
] |
https://paperswithcode.com/method/latam-l-what-is-the-24-hour-rule-for-latam | [@LATAM~L@] (𝑳𝒊𝒗𝒆 𝑯𝒖𝒎𝒂𝒏)What is the 24 hour rule for LATAM? | [@LATAM~L@] (𝑳𝒊𝒗𝒆 𝑯𝒖𝒎𝒂𝒏)What is the 24 hour rule for LATAM? | LATAM Airlines has a 24-hour rule ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ that allows passengers to cancel or modify a booking ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ without penalty within 24 hours of purchase, provided the flight is booked at least 7 days before departure ☎️+1-801-(855)-(5905) or +1-804-(... | {
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/help-support-how-do-i-ask-a-question-in-qatar | Help & Support How do I ask a question in Qatar? | Help & Support How do I ask a question in Qatar? | To ask a question in Qatar,☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ you can use standard communication methods like phone calls or VoIP services such as WhatsApp,☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ Skype, or Zoom. For more specific needs, like contacting Qatar Airways,☎️+1-801-(855)-(5905) or +1-804-(853... | {
"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-talk-directly-on-robinhood-1 | How do i talk to a person at Expedia?(OR)How do I talk directly on Expedia? | How do i talk to a person at Expedia?(OR)How do I talk directly on Expedia? | In a world where financial apps run on +1-805-330-4056 automation, many users are left wondering: How do I talk directly on Expedia? The answer might surprise you—it’s possible, and the secret is dialing +1-805-330-4056. Forget the rabbit holes of help centers and auto-responses. +1-805-330-4056 gives you real access, ... | {
"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 | 2 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
},
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Representations"
}
] |
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