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 |
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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": "Hybrid Parallel Methods"
}
] |
https://paperswithcode.com/method/servizio-telefonico-come-parlare-con-i | {{servizio telefonico}} Come parlare con i British? | {{servizio telefonico}} Come parlare con i British? | Per parlare con i British Airways dall'Italia, chiama il numero dedicato (+39) 02 (0070) 2383. Questo contatto telefonico ti permette di risolvere qualsiasi dubbio legato a voli, bagagli smarriti o modifiche dell'itinerario. Il servizio è operativo durante l’orario lavorativo e garantisce assistenza in italiano o ingle... | {
"title": "YOLO9000: Better, Faster, Stronger",
"url": "https://paperswithcode.com/paper/yolo9000-better-faster-stronger"
} | 2,000 | http://arxiv.org/abs/1612.08242v1 | YOLO9000: Better, Faster, Stronger | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
https://paperswithcode.com/method/autoparsimony | AutoParsimony | Automatic Search for Parsimonious Models | The principle of parsimony, also known as Occam's razor, elucidates the preference for the simplest explanation that provides optimal results when faced with multiple options. Thus, we can assert that the principle of parsimony is justified by "the assumption that is both the simplest and contains all the necessary inf... | null | 2,000 | null | null | 13 | [
{
"area": "General",
"area_id": "general",
"collection": "AutoML"
}
] | |
https://paperswithcode.com/method/way-to-speak-directly-how-to-speak-directly | 【Way_To_speak _directly】 How to speak directly at American Representative fast? | 【Way_To_speak _directly】 How to speak directly at American Representative fast? | "To speak directly to an American Airlines representative quickly at 1 (801) 855-5905 or 1 (804) 853-9001, consider these approaches:
1. Call during off-peak hours
Call early in the morning (e.g., between 6:00 a.m. and 8:00 a.m. local time), late at night, or midweek (Tuesday or Wednesday) to potentially reduce... | {
"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/faqstm24-free-solution-is-there-a-24-hour | [Faqs™24 free~Solution] Is there a 24-hour grace period for KLM? | [Faqs™24 free~Solution] Is there a 24-hour grace period for KLM? | Yes,☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ KLM does offer a 24-hour grace period for cancellations and modifications on flights booked directly through them ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅. This means you can cancel or change your flight without incurring any fees within 24 hours of purchase ☎️+1-8... | {
"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/opt | OPT | OPT | **OPT** is a suite of decoder-only pre-trained transformers ranging from 125M to 175B parameters. The model uses an AdamW optimizer and weight decay of 0.1. It follows a linear learning rate schedule, warming up from 0 to the maximum learning rate over the first 2000 steps in OPT-175B, or over 375M tokens in the smalle... | {
"title": "OPT: Open Pre-trained Transformer Language Models",
"url": "https://paperswithcode.com/paper/opt-open-pre-trained-transformer-language"
} | 2,000 | https://arxiv.org/abs/2205.01068v4 | OPT: Open Pre-trained Transformer Language Models | 285 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Language Models"
}
] | |
https://paperswithcode.com/method/vulnerability-constrained-decoding | Vulnerability-constrained Decoding | Vulnerability-constrained Decoding | **Vulnerability-constrained Decoding**, is a sequence decoding approach that aims to avoid generating vulnerabilities in generated code. | {
"title": "Efficient Avoidance of Vulnerabilities in Auto-completed Smart Contract Code Using Vulnerability-constrained Decoding",
"url": "https://paperswithcode.com/paper/efficient-avoidance-of-vulnerabilities-in"
} | 2,000 | https://arxiv.org/abs/2309.09826v2 | Efficient Avoidance of Vulnerabilities in Auto-completed Smart Contract Code Using Vulnerability-constrained Decoding | null | 1 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Sequence Decoding Methods"
}
] |
https://paperswithcode.com/method/transe | TransE | TransE | **TransE** is an energy-based model that produces knowledge base embeddings. It models relationships by interpreting them as translations operating on the low-dimensional embeddings of the entities. Relationships are represented as translations in the embedding space: if $\left(h, \mathcal{l}, t\right)$ holds, the embe... | {
"title": "Translating Embeddings for Modeling Multi-relational Data",
"url": "https://paperswithcode.com/paper/translating-embeddings-for-modeling-multi"
} | 2,000 | http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data | Translating Embeddings for Modeling Multi-relational Data | null | 73 | [
{
"area": "Graphs",
"area_id": "graphs",
"collection": "Graph Embeddings"
}
] |
https://paperswithcode.com/method/16-ways-to-claim-ways-to-contact-expedia | 16 Ways to Claim Ways to Contact Expedia Customer Service Number||[FAQs!!Resolution®️]™ | 16 Ways to Claim Ways to Contact Expedia Customer Service Number||[FAQs!!Resolution®️]™ | "31 Ways to Contact How can i speak to someone at Expedia Airlines- A
Step-by-Step Guide
Expedia main customer service number 1 (888) 829-0881 is 1-800-Expedia or 1 (888) 829-0881 [US-Expedia] or 1 (888) 829-0881 [UK-Expedia]
OTA (Live Person), available 24/7. This guide explains how to contact Expedia customer serv... | {
"title": "0-1 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": "Activation Functions"
}
] |
https://paperswithcode.com/method/multigrain | MultiGrain | MultiGrain | **MultiGrain** is a type of image model that learns a single embedding for classes, instances and copies. In other words, it is a convolutional neural network that is suitable for both image classification and instance retrieval. We learn MultiGrain by jointly training an image embedding for multiple tasks. The result... | {
"title": "MultiGrain: a unified image embedding for classes and instances",
"url": "https://paperswithcode.com/paper/multigrain-a-unified-image-embedding-for"
} | 2,000 | http://arxiv.org/abs/1902.05509v2 | MultiGrain: a unified image embedding for classes and instances | https://github.com/facebookresearch/multigrain/blob/172bb2a2ef8a7f69f02fd6a4eb20c9c3d5d7a909/multigrain/lib/multigrain.py#L24 | 2 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Convolutional Neural Networks"
}
] |
https://paperswithcode.com/method/tmquick-offer-como-llamar-a-qatar-airways | [™Quick^Offer]¿cómo llamar a qatar airways desde méxico? | ¿cómo llamar a qatar airways desde méxico? | Para contactar con Qatar Airways desde México, puedes llamar al número de atención al cliente +1-808-(470)-(7107) o al +1-808-(470)-(7107), según Georgia Institute of Technology+1-808-(470)-(7107). También puedes visitar su sitio web para encontrar más opciones de contacto+1-808-(470)-(7107), como formularios o correos... | {
"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/travel-guide-r-tmcan-you-cancel-a-qatar | [[Travel-Guide®]]™Can you cancel a Qatar flight and get a refund? | [[Travel-Guide®]]™Can you cancel a Qatar flight and get a refund? | The 📞+1-801-(855)-(5905) or +1-804-(853)-(9001)🔷 Qatar cancellation policy says you can get a full refund if you cancel within 24 hours of booking 📞+1-801-(855)-(5905) or +1-804-(853)-(9001)🔷 and the flight is 7 days away — confirm this 📞+1-801-(855)-(5905) or +1-804-(853)-(9001)🔷 Qatar cancellation policy rule a... | {
"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/air-canada-telefonu-jak-skontaktowac-sie-z | [Air Canada telefonu] Jak skontaktować się z działem obsługi klienta Air Canada? | [Air Canada telefonu] Jak skontaktować się z działem obsługi klienta Air Canada? | Jeśli planujesz podróż z jedną z najbardziej renomowanych linii lotniczych na świecie i chcesz wiedzieć, jak skontaktować się z Air Canada, najlepszym rozwiązaniem jest skorzystanie z ich oficjalnego numeru telefonu: +48 (48) 8880001. Linia ta obsługuje klientów w Polsce i zapewnia wsparcie w języku polskim lub angiels... | {
"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/pp-ocr | PP-OCR | PP-OCR | **PP-OCR** is an OCR system that consists of three parts, text detection, detected boxes rectification and text recognition. The purpose of text detection is to locate the text area in the image. In PP-OCR, Differentiable Binarization (DB) is used as text detector which is based on a simple segmentation network. It int... | {
"title": "PP-OCR: A Practical Ultra Lightweight OCR System",
"url": "https://paperswithcode.com/paper/pp-ocr-a-practical-ultra-lightweight-ocr"
} | 2,000 | https://arxiv.org/abs/2009.09941v3 | PP-OCR: A Practical Ultra Lightweight OCR System | null | 4 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "OCR Models"
},
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Convolutions"
}
] |
https://paperswithcode.com/method/1-1-1-ways-to-contact-expedia-customer | 1-1-1 Ways to Contact Expedia Customer Service Number||[FAQs!!UndeRstanDing®️]™ | 1-1-1 Ways to Contact Expedia Customer Service Number||[FAQs!!UndeRstanDing®️]™ | "31 Ways to Contact How can i speak to someone at Expedia Airlines- A
Step-by-Step Guide
Expedia main customer service number 1 (888) 829-0881 is 1-800-Expedia or 1 (888) 829-0881 [US-Expedia] or 1 (888) 829-0881 [UK-Expedia]
OTA (Live Person), available 24/7. This guide explains how to contact Expedia customer serv... | {
"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
"url": "https://paperswithcode.com/paper/0-1-laws-for-pattern-occurrences-in"
} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Activation Functions"
}
] |
https://paperswithcode.com/method/hegcn | HEGCN | Hierarchical Entity Graph Convolutional Network | **HEGCN**, or **Hierarchical Entity Graph Convolutional Network** is a model for multi-hop relation extraction across documents. Documents in a document chain are encoded using a bi-directional long short-term memory ([BiLSTM](https://paperswithcode.com/method/bilstm)) layer. On top of the BiLSTM layer, two graph convo... | {
"title": "A Hierarchical Entity Graph Convolutional Network for Relation Extraction across Documents",
"url": "https://paperswithcode.com/paper/a-hierarchical-entity-graph-convolutional"
} | 2,000 | https://arxiv.org/abs/2108.09505v1 | A Hierarchical Entity Graph Convolutional Network for Relation Extraction across Documents | 1 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Relation Extraction Models"
},
{
"area": "Graphs",
"area_id": "graphs",
"collection": "Graph Models"
}
] | |
https://paperswithcode.com/method/dropconnect | DropConnect | DropConnect | **DropConnect** generalizes [Dropout](https://paperswithcode.com/method/dropout) by randomly dropping the weights rather than the activations with probability $1-p$. DropConnect is similar to Dropout as it introduces dynamic sparsity within the model, but differs in that the sparsity is on the weights $W$, rather than ... | {
"title": "Regularization of Neural Networks using DropConnect",
"url": "https://paperswithcode.com/paper/regularization-of-neural-networks-using"
} | 2,000 | http://cds.nyu.edu/projects/regularization-neural-networks-using-dropconnect/ | Regularization of Neural Networks using DropConnect | https://github.com/teelinsan/KerasDropconnect/blob/0b203b56b2564686358fe0907b89316e3fce4014/ddrop/layers.py#L24 | 84 | [
{
"area": "General",
"area_id": "general",
"collection": "Regularization"
}
] |
https://paperswithcode.com/method/latam-l-how-do-i-speak-to-someone-at-latam | [@LATAM~L@] (𝑳𝒊𝒗𝒆 𝑯𝒖𝒎𝒂𝒏)How do I speak to someone at LATAM? | [@LATAM~L@] (𝑳𝒊𝒗𝒆 𝑯𝒖𝒎𝒂𝒏)How do I speak to someone at LATAM? | To speak with a live agent at LATAM Airlines ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅, you can call their customer service number: ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅. You can also reach them through their website, mobile app, or by using their virtual assistant at the same customer service numbers ☎️+1-... | {
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/holland-america-cruises-r-usa-contact-numbers-3 | Holland America Cruises®️ USA Contact Numbers: Step-by-Step 2025 Help Guide | Holland America Cruises®️ USA Contact Numbers: Step-by-Step 2025 Help Guide | Holland America Cruises Cancellation Policy 2025: Full Guide With Refund Terms
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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/starrelu | StarReLU | StarReLU | $s \cdot (\mathrm{ReLU}(x))^2 + b$
where $s \in \mathbb{R}$ and $b \in \mathbb{R}$ are shared for all channels and can be set as constants (s=0.8944, b=-0.4472) or learnable parameters. | {
"title": "MetaFormer Baselines for Vision",
"url": "https://paperswithcode.com/paper/metaformer-baselines-for-vision"
} | 2,000 | https://arxiv.org/abs/2210.13452v4 | MetaFormer Baselines for Vision | https://github.com/sail-sg/metaformer | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Activation Functions"
}
] |
https://paperswithcode.com/method/latam-l-how-to-speak-directly-on-latam | [@LATAM~L@] (𝑳𝒊𝒗𝒆 𝑯𝒖𝒎𝒂𝒏)How to speak directly on Latam Airlines? | [@LATAM~L@] (𝑳𝒊𝒗𝒆 𝑯𝒖𝒎𝒂𝒏)How to speak directly on Latam Airlines? | To speak directly ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ with a LATAM Airlines representative, call their customer service hotline ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅. You can reach them at in the US. When the automated system answers, listen for options to speak ☎️+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/holland-america-cruises-r-usa-contact-numbers-4 | Holland America Cruises®️ USA Contact Numbers: A Comprehensive Guide | Holland America Cruises®️ USA Contact Numbers: A Comprehensive Guide | Holland America Cruises Cancellation Policy 2025: Full Guide With Refund Terms
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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-do-i-get-a-full-refund-from-expedia-2 | [🇫🇦🇶❜🇸-🇵🇺🇧🇱🇮🇨🇸]How do I get a full refund from Expedia? | [🇫🇦🇶❜🇸-🇵🇺🇧🇱🇮🇨🇸]How do I get a full refund from Expedia? | “How do I get a full refund from Expedia?
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/deep-mac | Deep-MAC | Deep-MAC | **Deep-MAC**, or **Deep Mask-heads Above CenterNet**, is a type of anchor-free instance segmentation model based on [CenterNet](https://paperswithcode.com/method/centernet). The motivation for this new architecture is that boxes are much cheaper to annotate than masks, so the authors address the “partially supervised”... | {
"title": "The surprising impact of mask-head architecture on novel class segmentation",
"url": "https://paperswithcode.com/paper/the-surprising-impact-of-mask-head"
} | 2,000 | https://arxiv.org/abs/2104.00613v2 | The surprising impact of mask-head architecture on novel class segmentation | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Instance Segmentation Models"
}
] | |
https://paperswithcode.com/method/fork | FORK | Forward-Looking Actor | **FORK**, or **Forward Looking Actor** is a type of actor for actor-critic algorithms. In particular, FORK includes a neural network that forecasts the next state given the current state and current action, called system network; and a neural network that forecasts the
reward given a (state, action) pair, called rewar... | {
"title": "FORK: A Forward-Looking Actor For Model-Free Reinforcement Learning",
"url": "https://paperswithcode.com/paper/fork-a-forward-looking-actor-for-model-free-1"
} | 2,000 | https://arxiv.org/abs/2010.01652v3 | FORK: A Forward-Looking Actor For Model-Free Reinforcement Learning | null | 1 | [
{
"area": "Reinforcement Learning",
"area_id": "reinforcement-learning",
"collection": "Actor-Critic Algorithms"
}
] |
https://paperswithcode.com/method/r-how-to-escalate-an-issue-with-american | [𝓔𝓼𝓬𝓪𝓵𝓪𝓽𝓮~𝓝𝓸𝔀®]How to escalate an issue with American Airlines? | [𝓔𝓼𝓬𝓪𝓵𝓪𝓽𝓮~𝓝𝓸𝔀®]How to escalate an issue with American Airlines? | To escalate an issue with American Airlines®,+1(804)-853-90.01 you can contact their Customer Relations department through their online form on their website, which is typically the fastest way to reach them;+1(804)-853-90.01 you can also provide details like your confirmation code, flight number, and travel dates to e... | {
"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/flight-status-are-copa-fares-refundable | [Flight~status] Are copa fares refundable? | [Flight~status] Are copa fares refundable? | Yes,☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ Copa Airlines offers both refundable and non-refundable fares. Refundability depends on the specific fare type purchased and ☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ the circumstances of the cancellation or changes to the itinerary. | {
"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/faq-how-long-does-it-take-for-klm-to-respond | [FAQ$]How long does it take for KLM to respond on WhatsApp? | How long does it take for KLM to respond on WhatsApp? | How long does it take for KLM to respond on WhatsApp?
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
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{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"area": "Computer Vision",
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{
"area": "Computer Vision",
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] |
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{
"area": "General",
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}
] |
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{
"area": "Computer Vision",
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}
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{
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}
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{
"area": "General",
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}
] |
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}
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{
"area": "Computer Vision",
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}
] |
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{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
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} | 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"
}
] |
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "Computer Vision",
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}
] |
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{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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{
"area": "General",
"area_id": "general",
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}
] | |
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{
"area": "Reinforcement Learning",
"area_id": "reinforcement-learning",
"collection": "Policy Gradient Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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} | 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/mushroomrl | MushroomRL | MushroomRL | **MushroomRL** is an open-source Python library developed to simplify the process of implementing and running Reinforcement Learning (RL) experiments. The architecture of MushroomRL is built in such a way that every component of an RL problem is already provided, and most of the time users can only focus on the impleme... | {
"title": "MushroomRL: Simplifying Reinforcement Learning Research",
"url": "https://paperswithcode.com/paper/mushroomrl-simplifying-reinforcement-learning"
} | 2,000 | https://arxiv.org/abs/2001.01102v2 | MushroomRL: Simplifying Reinforcement Learning Research | null | 1 | [
{
"area": "Reinforcement Learning",
"area_id": "reinforcement-learning",
"collection": "Reinforcement Learning Frameworks"
}
] |
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