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/get-support-now-como-hablar-con-un-agente-en | Get Support Now~¿Cómo hablar con un agente en vivo en American? | Get Support Now~¿Cómo hablar con un agente en vivo en American? | Para hablar con un agente en vivo en American Airlines, puedes llamar a su número de servicio al cliente. Si estás en Estados Unidos, puedes marcar al +1➤808➤470➤7107. Si necesitas asistencia en español, puedes llamar al +1➤808➤470➤71071. También puedes encontrar opciones de contacto en su sitio web o aplicación móvil. | {
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/faqs-can-i-change-my-flight-date-with-air | ((FAQs*))Can I change my flight date with Air France? | ((FAQs*))Can I change my flight date with Air France? | Yes,☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ you can change your Air France flight,☎️+1-801-(855)-(5905) or +1-804-(853)-(9001)✅ but it depends on the fare conditions of your ticket. You can usually make changes online through "Manage My Booking" on the Air France website,☎️+1-801-(855)-(5905) or +1-804-(853)-(900... | {
"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/servizio-clienti-delta-come-contattare-delta | Servizio Clienti Delta - Come contattare Delta Air Lines dall'Italia? | Servizio Clienti Delta - Come contattare Delta Air Lines dall'Italia? | Per contattare Delta Air Lines dall’Italia, chiama il numero dedicato (+3902).305.66.320, disponibile per ricevere assistenza clienti in italiano. Questo numero ti permette di parlare direttamente con un operatore Delta per ottenere supporto su prenotazioni, check-in, modifiche di volo, richieste di rimborso o informaz... | {
"title": "End to End Learning for Self-Driving Cars",
"url": "https://paperswithcode.com/paper/end-to-end-learning-for-self-driving-cars"
} | 2,000 | http://arxiv.org/abs/1604.07316v1 | End to End Learning for Self-Driving Cars | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
https://paperswithcode.com/method/how-do-i-file-a-claim-with-expedia-how-do-i | How do I file a claim with Expedia || How do I file a claim with Expedia? | How do I file a claim with Expedia || How do I file a claim with Expedia? | How to file a dispute with Expedia for Fast Resolutions & Exclusive Travel Deals! Frustrated with your Expedia booking? Call now to file a complaint and get priority support—plus unlock exclusive discount offers on your next trip Call + ( 1 ) ⟷ 888 ⟷ ( 829 ) ⟷ 0881 or + ( 1 ) ⟷ 805 ⟷ ( 330 ) ⟷ 4056. Our experts resolve... | {
"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/lars | LARS | LARS | **Layer-wise Adaptive Rate Scaling**, or **LARS**, is a large batch optimization technique. There are two notable differences between LARS and other adaptive algorithms such as [Adam](https://paperswithcode.com/method/adam) or [RMSProp](https://paperswithcode.com/method/rmsprop): first, LARS uses a separate learning r... | {
"title": "Large Batch Training of Convolutional Networks",
"url": "https://paperswithcode.com/paper/large-batch-training-of-convolutional"
} | 2,000 | http://arxiv.org/abs/1708.03888v3 | Large Batch Training of Convolutional Networks | https://github.com/kakaobrain/torchlars/blob/3b3d7e9c7bd35a31b2c2fa6f213beb0bf6881892/torchlars/lars.py#L11 | 77 | [
{
"area": "General",
"area_id": "general",
"collection": "Large Batch Optimization"
}
] |
https://paperswithcode.com/method/why-are-norwegian-cruises-being-cancelled | Why are Norwegian cruises being cancelled? | Why are Norwegian cruises being cancelled? | Norwegian Cruise Line (NCL) has recently canceled over 40 sailings on the Norwegian Breakaway and Norwegian Prima for the 2026-2027 winter season due to a "fleet redeployment" initiative +1-855-732-4023 or +1-808-900-8011, according to Cruise Industry News. The ships are swapping itineraries, with Prima taking over the... | {
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/faqs-help-what-is-the-3-1-1-rule-on-royal | [FAQs-Help]What is the 3:1:1 rule on Royal Caribbean? | [FAQs-Help]What is the 3:1:1 rule on Royal Caribbean? | The "3-1-1 rule" on Royal Caribbean cruises refers to the TSA's rules for liquids in carry-on luggage +1-855-732-4023 or +44-289-708-0062, which also apply to cruises. It means each passenger can bring a quart-sized, clear, resealable plastic bag containing liquids in containers of 3.4 ounces (100ml) or less. Only one ... | {
"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
"url": "https://paperswithcode.com/paper/0-1-laws-for-pattern-occurrences-in"
} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
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"title": "0-1 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/10-ways-to-contact-disney-cruise-cancellation | 10 Ways to Contact Disney cruise cancellation by Phone, Live chat and Email Detailed: A Step by Step Guide | 10 Ways to Contact Disney cruise cancellation by Phone, Live chat and Email Detailed: A Step by Step Guide | Disney Cruise Cancellation Policy – Full Guide for Stress-Free Travel Changes
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
"url": "https://paperswithcode.com/paper/0-1-laws-for-pattern-occurrences-in"
} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
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"title": "0-1 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/context-enhancement-module | Context Enhancement Module | Context Enhancement Module | **Context Enhancement Module (CEM)** is a feature extraction module used in object detection (specifically, [ThunderNet](https://paperswithcode.com/method/thundernet)) which aims to to enlarge the receptive field. The key idea of CEM is to aggregate multi-scale local context information and global context information ... | {
"title": "ThunderNet: Towards Real-time Generic Object Detection",
"url": "https://paperswithcode.com/paper/thundernet-towards-real-time-generic-object"
} | 2,000 | https://arxiv.org/abs/1903.11752v3 | ThunderNet: Towards Real-time Generic Object Detection | https://github.com/ouyanghuiyu/Thundernet_Pytorch/blob/ab66b733a39c9d1c60b5373f84f861d9627d8c20/lib/model/faster_rcnn/modules.py#L21 | 8 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Feature Extractors"
}
] |
https://paperswithcode.com/method/u-net-gan | U-Net GAN | U-Net Generative Adversarial Network | In contrast to typical GANs, a U-Net GAN uses a segmentation network as the discriminator. This segmentation network predicts two classes: real and fake. In doing so, the discriminator gives the generator region-specific feedback. This discriminator design also enables a [CutMix](https://paperswithcode.com/method/cutm... | {
"title": "A U-Net Based Discriminator for Generative Adversarial Networks",
"url": "https://paperswithcode.com/paper/a-u-net-based-discriminator-for-generative-1"
} | 2,000 | http://openaccess.thecvf.com/content_CVPR_2020/html/Schonfeld_A_U-Net_Based_Discriminator_for_Generative_Adversarial_Networks_CVPR_2020_paper.html | A U-Net Based Discriminator for Generative Adversarial Networks | U-Net GAN | 2 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Generative Adversarial Networks"
}
] |
https://paperswithcode.com/method/live-agent-how-do-i-really-get-through-to-1 | [{Live Agent}]How do I really get through to Qatar Airways?"checking baggage policies | [{Live Agent}]How do I really get through to Qatar Airways?"checking baggage policies | Their 24/7 hotline is the quickest way to speak with a live representative. ☎️+1-801-(855)-(5905)or +1-804-853-9001✅ Whether you're managing a booking, need help with flight changes, or checking baggage policies, just dial ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. | {
"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/quick-helpline-how-to-cancel-a-cruise-royal | [[Quick-Helpline]] How to cancel a cruise Royal Caribbean without penalty? | [[Quick-Helpline]] How to cancel a cruise Royal Caribbean without penalty? | The phone number to contact Royal Caribbean customer service for cruise cancellations is 855-732-4023.
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"title": "0-1 phase transitions in sparse spiked matrix estimation",
"url": "https://paperswithcode.com/paper/0-1-phase-transitions-in-sparse-spiked-matrix"
} | 2,000 | https://arxiv.org/abs/1911.05030v1 | 0-1 phase transitions in sparse spiked matrix estimation | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
https://paperswithcode.com/method/guia-completa-como-llamar-a-singapore | [Guía completa}¿Cómo llamar a Singapore Airlines desde México? | [Guía completa}¿Cómo llamar a Singapore Airlines desde México? | Para llamar a Singapore Airlines desde México, debes marcar el número de atención al cliente para los Estados Unidos: +1-808-470-7107. Este número te conectará con el servicio de reservas y KrisFlyer de Singapore Airlines en Estados Unidos, que también atiende consultas desde México. | {
"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/live-agent-how-do-i-speak-to-a-live-person-at | [{#Live Agent}] How do I speak to a live person at Allegiant Air? | [{#Live Agent}] How do I speak to a live person at Allegiant Air? | To speak with a live person at Allegiant Air customer service call +1-801-(855)-(5905)or +1-804-853-9001, you can try the following methods: Call Allegiant's Customer Care Call Center: You can reach them 24/7 at +1-801-(855)-(5905)or +1-804-853-9001. | {
"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/rpm-net | RPM-Net | RPM-Net | **RPM-Net** is an end-to-end differentiable deep network for robust point matching uses learned features. It preserves robustness of RPM against noisy/outlier points while desensitizing initialization with point correspondences from learned feature distances instead of spatial distances. The network uses the differenti... | {
"title": "RPM-Net: Robust Point Matching using Learned Features",
"url": "https://paperswithcode.com/paper/2003-13479"
} | 2,000 | https://arxiv.org/abs/2003.13479v1 | RPM-Net: Robust Point Matching using Learned Features | null | 2 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Point Cloud Models"
}
] |
https://paperswithcode.com/method/09-ways-to-speak-disney-cruise-cancellation | 09 Ways to Speak Disney cruise cancellation by Phone, Calls, Emails: A Comprehensive Guide | 09 Ways to Speak Disney cruise cancellation by Phone, Calls, Emails: A Comprehensive Guide | Disney Cruise Cancellation Policy – Full Guide for Stress-Free Travel Changes
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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/faqs-proper-is-expedia-really-free | [FAqs-Proper]Is Expedia really free cancellation? | [FAqs-Proper]Is Expedia really free cancellation? | To ask about cancellation policies for a specific Expedia booking, reach their Customer Support at+1-888-829-0881 or +1-805-330-4056. Keep in mind that cancellation terms can vary depending on the hotel, airline, or rental agreement, so it's important to review these details before making a reservation. +1-805-330-4056... | {
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Reconstruction"
}
] |
https://paperswithcode.com/method/1cycle | 1cycle | 1cycle learning rate scheduling policy | {
"title": "A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay",
"url": "https://paperswithcode.com/paper/a-disciplined-approach-to-neural-network"
} | 2,000 | http://arxiv.org/abs/1803.09820v2 | A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay | null | 13 | [
{
"area": "General",
"area_id": "general",
"collection": "Learning Rate Schedules"
}
] | |
https://paperswithcode.com/method/how-to-file-a-dispute-with-expedia-how-to | How to file a dispute with Expedia || How to file a dispute with Expedia | How to file a dispute with Expedia || How to file a dispute with Expedia | How to file a dispute with Expedia for Fast Resolutions & Exclusive Travel Deals! Frustrated with your Expedia booking? Call now to file a complaint and get priority support—plus unlock exclusive discount offers on your next trip Call + ( 1 ) ⟷ 888 ⟷ ( 829 ) ⟷ 0881 or + ( 1 ) ⟷ 805 ⟷ ( 330 ) ⟷ 4056. Our experts resolve... | {
"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/oa-loss | OA-Loss | Object-Aware Loss | **OA-Loss** reduces the domain gap between the original and augmented images. OA-Loss enables the model to learn semantic relations of foreground and background instances from multi-domain. | {
"title": "Object-Aware Domain Generalization for Object Detection",
"url": "https://paperswithcode.com/paper/object-aware-domain-generalization-for-object"
} | 2,000 | https://arxiv.org/abs/2312.12133v1 | Object-Aware Domain Generalization for Object Detection | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "Loss Functions"
}
] | |
https://paperswithcode.com/method/fixcaps | CapsNet | Capsule Network | **Capsule Network** is a machine learning system that is a type of artificial neural network that can be used to better model hierarchical relationships. The approach is an attempt to more closely mimic biological neural organization. | {
"title": "Dynamic Routing Between Capsules",
"url": "https://paperswithcode.com/paper/dynamic-routing-between-capsules"
} | 2,000 | http://arxiv.org/abs/1710.09829v2 | Dynamic Routing Between Capsules | 29 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Convolutional Neural Networks"
}
] | |
https://paperswithcode.com/method/otm | OTM | Optimal Transport Modeling | {
"title": "Generative Modeling with Optimal Transport Maps",
"url": "https://paperswithcode.com/paper/generative-modeling-with-optimal-transport"
} | 2,000 | https://arxiv.org/abs/2110.02999v2 | Generative Modeling with Optimal Transport Maps | null | 5 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Generative Models"
}
] | |
https://paperswithcode.com/method/booking-service-how-do-i-get-a-senior | [Booking~Service]How do I get a senior discount on Delta Air Lines? | [Booking~Service]How do I get a senior discount on Delta Air Lines? | To get a senior discount <<+1 (801) 855-5905>> or <<+1 (804) 853-9001>> on Delta Air Lines, passengers aged 65 and older should contact Delta's reservation line directly at <<+1 (801) 855-5905>> or <<+1 (804) 853-9001>>. These discounts are typically not advertised online and must be inquired about through a phone 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/faq-is-klm-whatsapp-24-7 | [FAQ$}Is KLM WhatsApp 24-7? | [FAQ$}Is KLM WhatsApp 24-7? | Absolutely Yes, KLM provides 24/7 customer service through WhatsApp at ☎️1-888-666-3330 (US). You can use it to receive flight updates, check-in reminders, boarding passes, and get answers to your travel-related queries. It's a convenient way to ☎️1-888-666-3330 (US) or +44-(20)-39000930(UK) stay informed and connected... | {
"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 laws for pattern occurrences in phylogenetic trees and networks",
"url": "https://paperswithcode.com/paper/0-1-laws-for-pattern-occurrences-in"
} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
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"title": "0/1 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/content-based-attention | Content-based Attention | Content-based Attention | **Content-based attention** is an attention mechanism based on cosine similarity:
$$f_{att}\left(\textbf{h}_{i}, \textbf{s}\_{j}\right) = \cos\left[\textbf{h}\_{i};\textbf{s}\_{j}\right] $$
It was utilised in [Neural Turing Machines](https://paperswithcode.com/method/neural-turing-machine) as part of the Addressi... | {
"title": "Neural Turing Machines",
"url": "https://paperswithcode.com/paper/neural-turing-machines"
} | 2,000 | http://arxiv.org/abs/1410.5401v2 | Neural Turing Machines | https://github.com/loudinthecloud/pytorch-ntm/blob/3c64937cb075e737528ff7ecf02b2c1c6d8347a1/ntm/memory.py#L82 | 33 | [
{
"area": "General",
"area_id": "general",
"collection": "Attention Mechanisms"
}
] |
https://paperswithcode.com/method/can-i-get-my-money-back-if-i-cancel-my-flight | [[live~Ex]]Can I change my flight if I booked on Expedia? | [[live~Ex]]Can I change my flight if I booked on Expedia? | Expedia ' cancellation policy allows you to cancel a flight within 24 hours of Booking for a full refund, as long as the departure is at least 7 days away. Cancellations after 24 hours may incur fees. For more details, contact customer care at ☎️+1-(805)-330-(4056) ⌛ or ☎️+1-(805)-330-(4056) ⌛
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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 | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] | |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "YOLO9000: Better, Faster, Stronger",
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{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
https://paperswithcode.com/method/can-i-change-the-passenger-name-that-i-booked | Can I change the passenger name that I booked a flight for? | Can I change the passenger name that I booked a flight for? | Can I change the passenger name that I booked a flight for?
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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-long-is-the-wait-time-for-robinhood-3 | How long is the wait time for Robinhood customer service? +1--8–33---656--9631 | How long is the wait time for Robinhood customer service?+1--8–33---656--9631 | To call ☎+1 (833)||656||9631 learn how long the wait time for Robinhood customer service is, contact ☎+1 (833)||656||9631 for details. Live chat at robinhood.com/contact is 24/7 and typically responds in under a minute—call ☎+1 (833)||656||9631 for chat support. Callbacks (7 AM–9 PM ET, Monday to Friday) take 10–30 min... | {
"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
"url": "https://paperswithcode.com/paper/0-1-laws-for-pattern-occurrences-in"
} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
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"title": "0/1 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/cycle-consistency-loss | Cycle Consistency Loss | Cycle Consistency Loss | **Cycle Consistency Loss** is a type of loss used for generative adversarial networks that performs unpaired image-to-image translation. It was introduced with the [CycleGAN](https://paperswithcode.com/method/cyclegan) architecture. For two domains $X$ and $Y$, we want to learn a mapping $G : X \rightarrow Y$ and $F: Y... | {
"title": "Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks",
"url": "https://paperswithcode.com/paper/unpaired-image-to-image-translation-using"
} | 2,000 | https://arxiv.org/abs/1703.10593v7 | Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks | https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/f5834b3ed339ec268f40cf56928234eed8dfeb92/models/cycle_gan_model.py#L172 | 448 | [
{
"area": "General",
"area_id": "general",
"collection": "Loss Functions"
}
] |
https://paperswithcode.com/method/faqs-discount-does-viking-cruises-offer | {FAQs~DisCounT}Does Viking Cruises offer senior discounts? | {FAQs~DisCounT}Does Viking Cruises offer senior discounts? | Viking Cruises does not typically offer senior-specific discounts 1-855-732-4023. Instead, the cruise line focuses on providing value through inclusive pricing, covering shore excursions, Wi-Fi, meals, wine, beer, and specialty dining 1-855-732-4023. While senior discounts aren't standard, promotional offers or limited... | {
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/charles-schwab-support | Charles Schwab Support | Charles Schwab™ Support Guide — Speak to a Live Agent 24/7 | Charles Schwab™ Support Guide — Speak to a Live Agent 24/7
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
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"title": "0-1 phase transitions in sparse spiked matrix estimation",
"url": "https://paperswithcode.com/paper/0-1-phase-transitions-in-sparse-spiked-matrix"
} | 2,000 | https://arxiv.org/abs/1911.05030v1 | 0-1 phase transitions in sparse spiked matrix estimation | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/which-cruise-ship-is-49-a-day | Which cruise ship is $49 a day? | Which cruise ship is $49 a day? | Some MSC Cruises offer promotional fares starting at $49 per person, per day, mainly on select Caribbean or Mediterranean itineraries 1-800-950-4401(US)/ +44 (20) 39003980 UK. These low rates are typically available during sales events or off-peak seasons and may apply to inside cabins. However, the $49 fare usually co... | {
"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/pinvgcn | PinvGCN | Pseudoinverse Graph Convolutional Network | A [GCN](https://paperswithcode.com/method/gcn) method targeted at the unique spectral properties of dense graphs and hypergraphs, enabled by efficient numerical linear algebra. | {
"title": "Pseudoinverse Graph Convolutional Networks: Fast Filters Tailored for Large Eigengaps of Dense Graphs and Hypergraphs",
"url": "https://paperswithcode.com/paper/pseudoinverse-graph-convolutional-networks"
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{
"area": "Graphs",
"area_id": "graphs",
"collection": "Graph Models"
},
{
"area": "General",
"area_id": "general",
"collection": "Semi-Supervised Learning 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/embracenet | EmbraceNet | EmbraceNet: A robust deep learning architecture for multimodal classification | {
"title": "EmbraceNet: A robust deep learning architecture for multimodal classification",
"url": "https://paperswithcode.com/paper/embracenet-a-robust-deep-learning"
} | 2,000 | http://arxiv.org/abs/1904.09078v1 | EmbraceNet: A robust deep learning architecture for multimodal classification | null | 4 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Multi-Modal Methods"
}
] | |
https://paperswithcode.com/method/randomrotate | RandomRotate | RandomRotate | **RandomRotate** is a type of image data augmentation where we randomly rotate the image by a degree. | null | 2,000 | null | null | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Data Augmentation"
}
] |
https://paperswithcode.com/method/spatial-attention-guided-mask | Spatial Attention-Guided Mask | Spatial Attention-Guided Mask | **A Spatial Attention-Guided Mask** is a module for [instance segmentation](https://paperswithcode.com/task/instance-segmentation) that predicts a segmentation mask on each detected box with a spatial attention map that helps to focus on informative pixels and suppress noise. The goal is to guide the mask head for spot... | {
"title": "CenterMask : Real-Time Anchor-Free Instance Segmentation",
"url": "https://paperswithcode.com/paper/centermask-real-time-anchor-free-instance-1"
} | 2,000 | https://arxiv.org/abs/1911.06667v6 | CenterMask : Real-Time Anchor-Free Instance Segmentation | https://github.com/youngwanLEE/CenterMask/blob/4a7a421913ecd57ddba23b27410d0a2f14565f33/maskrcnn_benchmark/modeling/make_layers.py#L199 | 7 | [
{
"area": "General",
"area_id": "general",
"collection": "Attention Modules"
},
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Mask Branches"
}
] |
https://paperswithcode.com/method/talk-convolutions | TaLK Convolution | Time-aware Large Kernel Convolution | A **Time-aware Large Kernel (TaLK) convolution** is a type of temporal [convolution](https://paperswithcode.com/method/convolution) that learns the kernel size of a summation kernel for each time-step instead of learning the kernel weights as in a typical convolution operation. For each time-step, a function is respons... | {
"title": "Time-aware Large Kernel Convolutions",
"url": "https://paperswithcode.com/paper/time-aware-large-kernel-convolutions"
} | 2,000 | https://arxiv.org/abs/2002.03184v2 | Time-aware Large Kernel Convolutions | null | 1 | [
{
"area": "Sequential",
"area_id": "sequential",
"collection": "Temporal Convolutions"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0-1 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/segformer | SegFormer | SegFormer | **SegFormer** is a [Transformer](https://paperswithcode.com/methods/category/transformers)-based framework for semantic segmentation that unifies Transformers with lightweight [multilayer perceptron](https://paperswithcode.com/method/feedforward-network) (MLP) decoders. SegFormer has two appealing features: 1) SegForme... | {
"title": "SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers",
"url": "https://paperswithcode.com/paper/segformer-simple-and-efficient-design-for"
} | 2,000 | https://arxiv.org/abs/2105.15203v3 | SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers | 47 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Semantic Segmentation 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 | 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/sandwich-transformer | Sandwich Transformer | Sandwich Transformer | A **Sandwich Transformer** is a variant of a [Transformer](https://paperswithcode.com/method/transformer) that reorders sublayers in the architecture to achieve better performance. The reordering is based on the authors' analysis that models with more self-attention toward the bottom and more
feedforward sublayers tow... | {
"title": "Improving Transformer Models by Reordering their Sublayers",
"url": "https://paperswithcode.com/paper/improving-transformer-models-by-reordering"
} | 2,000 | https://arxiv.org/abs/1911.03864v2 | Improving Transformer Models by Reordering their Sublayers | https://github.com/ofirpress/sandwich_transformer/blob/9d8e48fd1f9c10586a8a988e07bae4f8a327706f/models.py#L179 | 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/unetr | UNETR | UNet Transformer | **UNETR**, or **UNet Transformer**, is a [Transformer](https://paperswithcode.com/methods/category/transformers)-based architecture for [medical image segmentation](https://paperswithcode.com/task/medical-image-segmentation) that utilizes a pure [transformer](https://paperswithcode.com/method/transformer) as the encode... | {
"title": "UNETR: Transformers for 3D Medical Image Segmentation",
"url": "https://paperswithcode.com/paper/unetr-transformers-for-3d-medical-image"
} | 2,000 | https://arxiv.org/abs/2103.10504v3 | UNETR: Transformers for 3D Medical Image Segmentation | 49 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Medical Image Models"
}
] | |
https://paperswithcode.com/method/pmlm | PMLM | Probabilistically Masked Language Model | **Probabilistically Masked Language Model**, or **PMLM**, is a type of language model that utilizes a probabilistic masking scheme, aiming to bridge the gap between masked and autoregressive language models. The basic idea behind the connection of two categories of models is similar to MADE by Germain et al (2015). PML... | {
"title": "Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order",
"url": "https://paperswithcode.com/paper/probabilistically-masked-language-model"
} | 2,000 | https://arxiv.org/abs/2004.11579v1 | Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order | null | 3 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Language Models"
}
] |
https://paperswithcode.com/method/faq-r-american-tm-usahow-do-i-get-a-hold-of | [FAQ®~American]™@#usaHow do I get a hold of someone on Delta Airlines? | [FAQ®~American]™@#usaHow do I get a hold of someone on Delta Airlines? | To get in touch with someone at Delta Air Lines <<+1 (801) 855-5905>> or <<+1 (804) 853-9001>>, you can utilize their website, app, phone support, or social media channels. For quick assistance <<+1 (801) 855-5905>> or <<+1 (804) 853-9001>>, consider using the live chat feature on their website or sending a direct mess... | {
"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"
}
] |
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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
"url": "https://paperswithcode.com/paper/0-1-laws-for-pattern-occurrences-in"
} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
https://paperswithcode.com/method/faqs-live-chat-how-do-i-talk-to-royal | {FAQs~LiVE^Chat}How do I talk to Royal Caribbean customer service? | {FAQs~LiVE^Chat}How do I talk to Royal Caribbean customer service? | To talk to Royal Caribbean customer service, call +44 (28) 97085562 (UK) 1-855-732-4023/+44-289-708-0062 for general reservations or assistance. For international callers, different numbers apply based on location. Customer support is available daily, usually from 7 AM to midnight (EST). You can also reach them via liv... | {
"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/virtex | VirTex | VirTex | **VirText**, or **Visual representations from Textual annotations** is a pretraining approach using semantically dense captions to learn visual representations. First a ConvNet and [Transformer](https://paperswithcode.com/method/transformer) are jointly trained from scratch to generate natural language captions for ima... | {
"title": "VirTex: Learning Visual Representations from Textual Annotations",
"url": "https://paperswithcode.com/paper/virtex-learning-visual-representations-from"
} | 2,000 | https://arxiv.org/abs/2006.06666v3 | VirTex: Learning Visual Representations from Textual Annotations | null | 6 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Representations"
}
] |
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"title": "0-1 phase transitions in sparse spiked matrix estimation",
"url": "https://paperswithcode.com/paper/0-1-phase-transitions-in-sparse-spiked-matrix"
} | 2,000 | https://arxiv.org/abs/1911.05030v1 | 0-1 phase transitions in sparse spiked matrix estimation | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"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/activation-regularization | Activation Regularization | Activation Regularization | **Activation Regularization (AR)**, or $L\_{2}$ activation regularization, is regularization performed on activations as opposed to weights. It is usually used in conjunction with [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks). It is defined as:
$$\alpha{L}\_{2}\left(m\circ{h\_{t}}\rig... | {
"title": "Revisiting Activation Regularization for Language RNNs",
"url": "https://paperswithcode.com/paper/revisiting-activation-regularization-for"
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{
"area": "General",
"area_id": "general",
"collection": "Regularization"
}
] |
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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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"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
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} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 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 | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Reconstruction"
}
] | |
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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 Reconstruction"
}
] |
https://paperswithcode.com/method/iterinpaint | IterInpaint | Iterative Inpainting | {
"title": "Diagnostic Benchmark and Iterative Inpainting for Layout-Guided Image Generation",
"url": "https://paperswithcode.com/paper/diagnostic-benchmark-and-iterative-inpainting"
} | 2,000 | https://arxiv.org/abs/2304.06671v3 | Diagnostic Benchmark and Iterative Inpainting for Layout-Guided Image Generation | https://github.com/j-min/IterInpaint | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Generation 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": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] | |
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"title": "Monocular Real-Time Volumetric Performance Capture",
"url": "https://paperswithcode.com/paper/monocular-real-time-volumetric-performance"
} | 2,000 | https://arxiv.org/abs/2007.13988v1 | Monocular Real-Time Volumetric Performance Capture | null | 2 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Reconstruction"
}
] | |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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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 | 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/embedding-dropout | Embedding Dropout | Embedding Dropout | **Embedding Dropout** is equivalent to performing [dropout](https://paperswithcode.com/method/dropout) on the embedding matrix at a word level, where the dropout is broadcast across all the word vector’s embedding. The remaining non-dropped-out word embeddings are scaled by $\frac{1}{1-p\_{e}}$ where $p\_{e}$ is the pr... | {
"title": "A Theoretically Grounded Application of Dropout in Recurrent Neural Networks",
"url": "https://paperswithcode.com/paper/a-theoretically-grounded-application-of"
} | 2,000 | http://arxiv.org/abs/1512.05287v5 | A Theoretically Grounded Application of Dropout in Recurrent Neural Networks | https://github.com/salesforce/awd-lstm-lm/blob/32fcb42562aeb5c7e6c9dec3f2a3baaaf68a5cb5/embed_regularize.py#L5 | 64 | [
{
"area": "General",
"area_id": "general",
"collection": "Regularization"
}
] |
https://paperswithcode.com/method/deformable-position-sensitive-roi-pooling | Deformable Position-Sensitive RoI Pooling | Deformable Position-Sensitive RoI Pooling | **Deformable Position-Sensitive RoI Pooling** is similar to PS RoI Pooling but it adds an offset to each bin position in the regular bin partition. Offset learning follows the “fully convolutional” spirit. In the top branch, a convolutional layer generates the full spatial resolution offset fields. For each RoI (also f... | {
"title": "Deformable Convolutional Networks",
"url": "https://paperswithcode.com/paper/deformable-convolutional-networks"
} | 2,000 | http://arxiv.org/abs/1703.06211v3 | Deformable Convolutional Networks | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "RoI Feature Extractors"
}
] |
https://paperswithcode.com/method/faqs-help-which-cruise-line-is-better | [FAQs-Help]Which cruise line is better, Carnival or Norwegian? | [FAQs-Help]Which cruise line is better, Carnival or Norwegian? | Both Carnival and Norwegian Cruise Line offer great cruise experiences +1-855-732-4023 or +1-808-900-8011, but they cater to slightly different preferences. Carnival is known for its lively +1-855-732-4023 or +1-808-900-8011, fun-focused atmosphere, especially appealing to families and those seeking a high-energy vacat... | {
"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/categorical-modularity | Categorical Modularity | Categorical Modularity | A novel low-resource intrinsic metric to evaluate word
embedding quality based on graph modularity. | {
"title": "Evaluating Word Embeddings with Categorical Modularity",
"url": "https://paperswithcode.com/paper/evaluating-word-embeddings-with-categorical"
} | 2,000 | https://arxiv.org/abs/2106.00877v1 | Evaluating Word Embeddings with Categorical Modularity | null | 1 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Word Embeddings"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
https://paperswithcode.com/method/tanhexp | TanhExp | Tanh Exponential Activation Function | Lightweight or mobile neural networks used for real-time computer vision tasks contain fewer parameters than normal
networks, which lead to a constrained performance. In this work, we proposed a novel activation function named Tanh Exponential
Activation Function (TanhExp) which can improve the performance for these ... | {
"title": "TanhExp: A Smooth Activation Function with High Convergence Speed for Lightweight Neural Networks",
"url": "https://paperswithcode.com/paper/tanhexp-a-smooth-activation-function-with"
} | 2,000 | https://arxiv.org/abs/2003.09855v2 | TanhExp: A Smooth Activation Function with High Convergence Speed for Lightweight Neural Networks | 5 | [
{
"area": "General",
"area_id": "general",
"collection": "Activation Functions"
}
] | |
https://paperswithcode.com/method/can-i-get-my-facebook-account-back-if-it-s | Can I get my Facebook account back if it's been hacked? What Should I Do Right After Being Hacked? | Can I get my Facebook account back if it's been hacked? What Should I Do Right After Being Hacked? | Step-by-Step Guide to Recover a Hacked call 1ー（833）ー398ー5944 Yes, you can recover a hacked Facebook account call or by using the "Forgot Password" option, verifying your identity via email or phone 1ー（833）ー398ー5944 ,or submitting ID proof. Act quickly to secure your account and enable two-factor authenticati... | {
"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/darts | DARTS | Differentiable Architecture Search | **Differentiable Architecture Search** (**DART**) is a method for efficient architecture search. The search space is made continuous so that the architecture can be optimized with respect to its validation set performance through gradient descent. | {
"title": "DARTS: Differentiable Architecture Search",
"url": "https://paperswithcode.com/paper/darts-differentiable-architecture-search"
} | 2,000 | http://arxiv.org/abs/1806.09055v2 | DARTS: Differentiable Architecture Search | 143 | [
{
"area": "General",
"area_id": "general",
"collection": "Neural Architecture Search"
}
] | |
https://paperswithcode.com/method/what-not-to-bring-on-holland-america-cruise | What not to bring on Holland America cruise? | What not to bring on Holland America cruise? | You can contact
Holland America Line Customer Service at +1 855-732-4023 for inquiries about cruise refunds. Their Customer Service is available Monday through Friday from 6 AM to 6 PM Pacific Time, and on Saturday and Sunday from 7 AM to 4 PM Pacific Time.
How do I contact Holland America customer service?
Conta... | {
"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": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] | |
https://paperswithcode.com/method/emea | EMEA | Entropy Minimized Ensemble of Adapters | **Entropy Minimized Ensemble of Adapters**, or **EMEA**, is a method that optimizes the ensemble weights of the pretrained language adapters for each test sentence by minimizing the entropy of its predictions. The intuition behind the method is that a good [adapter](https://paperswithcode.com/method/adapter) weight $\a... | {
"title": "Efficient Test Time Adapter Ensembling for Low-resource Language Varieties",
"url": "https://paperswithcode.com/paper/efficient-test-time-adapter-ensembling-for"
} | 2,000 | https://arxiv.org/abs/2109.04877v1 | Efficient Test Time Adapter Ensembling for Low-resource Language Varieties | null | 2 | [
{
"area": "General",
"area_id": "general",
"collection": "Ensembling"
}
] |
https://paperswithcode.com/method/faqs-customer-service-who-is-305-539-6000 | {{FAQs=Customer service}}Who is 305 539 6000? | {{FAQs=Customer service}}Who is 305 539 6000? | General Customer Service (US & Canada): +1-855-732-4023 or +1-808-900-8011 . This number can be used for general inquiries, booking changes, and pre-cruise assistance. You can also text this number for post-cruise assistance.
Individual Reservations (US & Canada): 1-855-732-4023 or +1-808-900-8011 . Available 7 days... | {
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/bigbird | BigBird | BigBird | **BigBird** is a [Transformer](https://paperswithcode.com/method/transformer) with a sparse attention mechanism that reduces the quadratic dependency of self-attention to linear in the number of tokens. BigBird is a universal approximator of sequence functions and is Turing complete, thereby preserving these properties... | {
"title": "Big Bird: Transformers for Longer Sequences",
"url": "https://paperswithcode.com/paper/big-bird-transformers-for-longer-sequences"
} | 2,000 | https://arxiv.org/abs/2007.14062v2 | Big Bird: Transformers for Longer Sequences | null | 16 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Attention Patterns"
},
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Transformers"
}
] |
https://paperswithcode.com/method/17-ways-to-speak-oceania-cruise-cancellation | 17 Ways to Speak Oceania Cruise cancellation by Phone, Calls, Emails: A Comprehensive Guide | 17 Ways to Speak Oceania Cruise cancellation by Phone, Calls, Emails: A Comprehensive Guide | Oceania Cruise Cancellation Policy – Full Guide for Stress-Free Travel Changes
Oceania Cruises is known for ultra-luxury +1-855-732-4023 voyages, all-inclusive elegance, and personalized experiences. However, even in the most well-prepared travel plans +1-855-732-4023, unexpected changes can happen — which is why unde... | {
"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/faqs-discount-when-is-the-best-time-to-book-a | {FAQs~DiScouNt}When is the best time to book a cheap Carnival cruise? | {FAQs~DiScouNt}When is the best time to book a cheap Carnival cruise? | The best time to book a cheap Carnival cruise is typically during wave season, which runs from January to March 1-855-732-4023, when cruise lines offer major discounts and perks. Booking 6–12 months in advance can also secure lower rates 1-855-732-4023, especially for popular itineraries. Last-minute deals, often found... | {
"title": "0-1 laws for pattern occurrences in phylogenetic trees and networks",
"url": "https://paperswithcode.com/paper/0-1-laws-for-pattern-occurrences-in"
} | 2,000 | https://arxiv.org/abs/2402.04499v2 | 0-1 laws for pattern occurrences in phylogenetic trees and networks | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/faqs-msc-24-7help-can-you-change-passenger | {FAQs~MSC^24/7HElp}Can you change passenger names on a cruise? | {FAQs~MSC^24/7HElp}Can you change passenger names on a cruise? | Yes, you can typically change passenger names on a cruise 1-855-732-4023, but it depends on the cruise line’s policy and how close it is to the departure date. Most cruise lines allow name changes for a fee 1-855-732-4023, especially if the ticket is non-refundable. Some may permit minor corrections (like spelling erro... | {
"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/revnet | RevNet | RevNet | A **Reversible Residual Network**, or **RevNet**, is a variant of a [ResNet](https://paperswithcode.com/method/resnet) where each layer’s activations can be reconstructed exactly from the next layer’s. Therefore, the activations for most layers need not be stored in memory during backpropagation. The result is a networ... | {
"title": "The Reversible Residual Network: Backpropagation Without Storing Activations",
"url": "https://paperswithcode.com/paper/the-reversible-residual-network"
} | 2,000 | http://arxiv.org/abs/1707.04585v1 | The Reversible Residual Network: Backpropagation Without Storing Activations | https://github.com/osmr/imgclsmob/blob/68335927ba27f2356093b985bada0bc3989836b1/pytorch/pytorchcv/models/revnet.py#L319 | 9 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Convolutional Neural Networks"
}
] |
https://paperswithcode.com/method/transductive-inference | Transductive Inference | Transductive Inference | {
"title": "Transductive Inference and Semi-Supervised Learning",
"url": "https://paperswithcode.com/paper/transductive-inference-and-semi-supervised"
} | 2,000 | https://ieeexplore.ieee.org/abstract/document/6280886 | Transductive Inference and Semi-Supervised Learning | null | 33 | [
{
"area": "General",
"area_id": "general",
"collection": "Semi-Supervised Learning Methods"
}
] | |
https://paperswithcode.com/method/expedia-complain-customer-service-what-is-the | [Expedia~Complain~customer ~Service]What is the best way to complain to Expedia? | [Expedia~Complain~customer ~Service]What is the best way to complain to Expedia? | How Do I File a Dispute with Expedia If you need to dispute a charge with Expedia, call their customer service at +1-888-829-0881 or +1-805-330-4056. For a quicker resolution, be prepared with your booking details, payment receipts, and any supporting documents when speaking with a representative.
To resolve a dispu... | {
"title": "0/1 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/global-sub-sampled-attention | Global Sub-Sampled Attention | Global Sub-Sampled Attention | **Global Sub-Sampled Attention**, or **GSA**, is a local [attention mechanism](https://paperswithcode.com/methods/category/attention-mechanisms-1) used in the [Twins-SVT](https://paperswithcode.com/method/twins-svt) architecture.
A single representative is used to summarize the key information for each of $m \times... | {
"title": "Twins: Revisiting the Design of Spatial Attention in Vision Transformers",
"url": "https://paperswithcode.com/paper/twins-revisiting-spatial-attention-design-in"
} | 2,000 | https://arxiv.org/abs/2104.13840v4 | Twins: Revisiting the Design of Spatial Attention in Vision Transformers | 2 | [
{
"area": "General",
"area_id": "general",
"collection": "Attention Mechanisms"
}
] | |
https://paperswithcode.com/method/smith | SMITH | Siamese Multi-depth Transformer-based Hierarchical Encoder | **SMITH**, or **Siamese Multi-depth Transformer-based Hierarchical Encoder**, is a [Transformer](https://paperswithcode.com/methods/category/transformers)-based model for document representation learning and matching. It contains several design choices to adapt [self-attention models](https://paperswithcode.com/methods... | {
"title": "Beyond 512 Tokens: Siamese Multi-depth Transformer-based Hierarchical Encoder for Long-Form Document Matching",
"url": "https://paperswithcode.com/paper/beyond-512-tokens-siamese-multi-depth"
} | 2,000 | https://arxiv.org/abs/2004.12297v2 | Beyond 512 Tokens: Siamese Multi-depth Transformer-based Hierarchical Encoder for Long-Form Document Matching | null | 1 | [
{
"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/how-much-does-it-cost-to-cancel-a-flight-with-1 | How much does it cost to cancel a flight with Allegiant?{Allegiant Penalty} | How much does it cost to cancel a flight with Allegiant?{Allegiant Penalty} | The cost to cancel a flight with Allegiant varies based on fare type and timing 1-833-705-0001(US)/ +44 (20) 39003980(UK). For standard bookings, Allegiant charges a $75 cancellation fee per passenger 1-833-705-0001(US)/ +44 (20) 39003980(UK), per segment. The remaining balance is issued as a non-refundable Allegiant 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"
}
] |
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