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|---|---|---|---|---|---|---|---|---|---|---|
https://paperswithcode.com/method/how-to-make-money-on-coinspot
|
How to make money on CoinSpot?
|
How to make money on CoinSpot?
|
call ๏ผ๏ผใผ๏ผ๏ผ๏ผใผ๏ผ๏ผ๏ผ๏ผใผ๏ผ๏ผ๏ผ๏ผ. To start trading in the CoinSpot app, first call ๏ผ๏ผใผ๏ผ๏ผ๏ผใผ๏ผ๏ผ๏ผ๏ผใผ๏ผ๏ผ๏ผ๏ผ. to get a walkthrough of the interface. ๏ผ๏ผใผ๏ผ๏ผ๏ผใผ๏ผ๏ผ๏ผ๏ผใผ๏ผ๏ผ๏ผ๏ผ. will guide you in finding the market, buy, and sell tabs inside the app. If your app is outdated or crashing, ๏ผ๏ผใผ๏ผ๏ผ๏ผใผ๏ผ๏ผ๏ผ๏ผใผ๏ผ๏ผ๏ผ๏ผ. can help troubleshoot updates. To place a trade, ๏ผ๏ผใผ๏ผ๏ผ๏ผใผ๏ผ๏ผ๏ผ๏ผใผ๏ผ๏ผ๏ผ๏ผ. will explain the difference between instant and market orders in-app. If your funds arenโt showing, ๏ผ๏ผใผ๏ผ๏ผ๏ผใผ๏ผ๏ผ๏ผ๏ผใผ๏ผ๏ผ๏ผ๏ผ. can help sync your balance in the app. If youโre using features like price alerts or portfolio tracking, ๏ผ๏ผใผ๏ผ๏ผ๏ผใผ๏ผ๏ผ๏ผ๏ผใผ๏ผ๏ผ๏ผ๏ผ. will guide you in setting those up. Even if you're new to mobile trading, ๏ผ๏ผใผ๏ผ๏ผ๏ผใผ๏ผ๏ผ๏ผ๏ผใผ๏ผ๏ผ๏ผ๏ผ. will walk you through each step live. For fast and accurate trading support, always rely on ๏ผ๏ผใผ๏ผ๏ผ๏ผใผ๏ผ๏ผ๏ผ๏ผใผ๏ผ๏ผ๏ผ๏ผ. when using the CoinSpot app.
|
{
"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/booking-can-i-upgrade-from-economy-to
|
[[Booking-๐๐ฟ๐ฎ๐ป๐ฟ๐ฒ๐ฎ๐]]Can I upgrade from economy to business class on Turkish Airlines?
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[[Booking-๐๐ฟ๐ฎ๐ป๐ฟ๐ฒ๐ฎ๐]]Can I upgrade from economy to business class on Turkish Airlines?
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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 | 0 |
[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/faq-s-how-do-i-get-a-refund-from-expedia-live
|
[FaQ's]How do I get a refund from Expedia?{{LIVE!!GUIDE!!CENTRE}}
|
[FaQ's]How do I get a refund from Expedia?{{LIVE!!GUIDE!!CENTRE}}
|
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. How do I get a human at Expedia? Connecting with an Expedia agent is simple. Call either +๐-๐ ๐๐-๐๐๐-๐๐๐๐
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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 | 0 |
[
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
https://paperswithcode.com/method/contact-usa-how-do-i-cancel-my-regent-seven-1
|
[{Contact--USA]] How do I cancel my Regent Seven Seas cruise?
|
[{Contact--USA]] How do I cancel my Regent Seven Seas cruise?
|
To cancel a Regent Seven Seas cruise, you'll need to contact their reservations department directly . You can reach them by phone at +1-855-732-4023 (USA) OR +44-289-708-0062 (UK). Their hours of operation are Monday - Friday: 8 am - 8 pm ET and Saturday and Sunday: 9:30 am - 5:30 pm ET.
To cancel a Regent Seven Seas cruise in the USA, you can contact their customer service or reservations department by phone at +1-855-732-4023 (USA) OR +44-289-708-0062 (UK).
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The cancellation number for Regent Seven Seas Cruises in the USA is 800-285-1835. You can also find this information on their website under "General Reservations Questions".
You can reach Regent Seven Seas Cruises by calling their main customer service line at +1-855-732-4023 (USA) OR +44-289-708-0062 (UK). Customer Service is available 24/7.
To cancel a Regent Seven Seas cruise, you can call their reservations number: +1-855-732-4023 (USA) OR +44-289-708-0062 (UK) on their website.
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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 Reconstruction"
}
] |
https://paperswithcode.com/method/srs
|
SRS
|
Sticker Response Selector
|
**Sticker Response Selector**, or **SRS**, is a model for multi-turn dialog that automatically selects a sticker response. SRS first employs a convolutional based sticker image encoder and a self-attention based multi-turn dialog encoder to obtain the representation of stickers and utterances. Next, deep interaction network is proposed to conduct deep matching between the sticker with each utterance in the dialog history. SRS then learns the short-term and long-term dependency between all interaction results by a fusion network to output the the final matching score.
|
{
"title": "Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog",
"url": "https://paperswithcode.com/paper/learning-to-respond-with-stickers-a-framework"
}
| 2,000 |
https://arxiv.org/abs/2003.04679v1
|
Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog
| null | 45 |
[
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Conversational Models"
}
] |
https://paperswithcode.com/method/population-based-augmentation
|
Population Based Augmentation
|
Population Based Augmentation
|
**Population Based Augmentation**, or **PBA**, is a data augmentation strategy (PBA), which generates nonstationary augmentation policy schedules instead of a fixed augmentation policy. In PBA we consider the augmentation policy search problem as a special case of hyperparameter schedule learning. It leverages [Population Based Training](https://paperswithcode.com/method/population-based-training) (PBT), a hyperparameter search algorithm which
optimizes the parameters of a network jointly with their hyperparameters to maximize performance. The output of PBT is not an optimal hyperparameter configuration but rather a trained model and schedule of hyperparameters.
In PBA, we are only interested in the learned schedule and discard the child model result (similar to [AutoAugment](https://paperswithcode.com/method/autoaugment)). This learned augmentation schedule can then be used to improve the training of different (i.e., larger and costlier to train) models on the same dataset.
PBT executes as follows. To start, a fixed population of models are randomly initialized and trained in parallel. At certain intervals, an โexploit-and-exploreโ procedure is applied to the worse performing population members, where the model clones the weights of a better performing model (i.e., exploitation) and then perturbs the hyperparameters of the cloned model to search in the hyperparameter space (i.e., exploration). Because the weights of the models are cloned and never reinitialized, the total computation required is the computation to train a single model times the population size.
|
{
"title": "Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules",
"url": "https://paperswithcode.com/paper/190505393"
}
| 2,000 |
https://arxiv.org/abs/1905.05393v1
|
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules
|
https://github.com/arcelien/pba
| 3 |
[
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Data Augmentation"
}
] |
https://paperswithcode.com/method/l1-regularization
|
L1 Regularization
|
L1 Regularization
|
**$L_{1}$ Regularization** is a regularization technique applied to the weights of a neural network. We minimize a loss function compromising both the primary loss function and a penalty on the $L\_{1}$ Norm of the weights:
$$L\_{new}\left(w\right) = L\_{original}\left(w\right) + \lambda{||w||}\_{1}$$
where $\lambda$ is a value determining the strength of the penalty. In contrast to [weight decay](https://paperswithcode.com/method/weight-decay), $L_{1}$ regularization promotes sparsity; i.e. some parameters have an optimal value of zero.
Image Source: [Wikipedia](https://en.wikipedia.org/wiki/Regularization_(mathematics)#/media/File:Sparsityl1.png)
| null | 1,986 | null | null | 90 |
[
{
"area": "General",
"area_id": "general",
"collection": "Parameter Norm Penalties"
},
{
"area": "General",
"area_id": "general",
"collection": "Regularization"
}
] |
|
https://paperswithcode.com/method/csgld
|
CSGLD
|
Contour Stochastic Gradient Langevin Dynamics
|
Simulations of multi-modal distributions can be very costly and often lead to unreliable predictions. To accelerate the computations, we propose to sample from a flattened distribution to accelerate the computations and estimate the importance weights between the original distribution and the flattened distribution to ensure the correctness of the distribution.
|
{
"title": "A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions",
"url": "https://paperswithcode.com/paper/a-contour-stochastic-gradient-langevin"
}
| 2,000 |
https://arxiv.org/abs/2010.09800v2
|
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions
|
https://github.com/WayneDW/Contour-Stochastic-Gradient-Langevin-Dynamics
| 2 |
[
{
"area": "General",
"area_id": "general",
"collection": "Markov Chain Monte Carlo"
}
] |
https://paperswithcode.com/method/booking-how-to-reserve-a-seat-on-singapore
|
[[Booking-๐๐ฟ๐ฎ๐ป๐ฟ๐ฒ๐ฎ๐]]How to reserve a seat on Singapore Airlines?
|
[[Booking-๐๐ฟ๐ฎ๐ป๐ฟ๐ฒ๐ฎ๐]]How to reserve a seat on Singapore Airlines?
|
Depending on the fare type you choose (((๐ฐ+1~801~855~5905))) // ((๐ฐ+1~804~853~9001))), you may select a seat in advance for free or a fee. Alternatively, you may select any available Economy Class seat (((๐ฐ+1~801~855~5905))) // ((๐ฐ+1~804~853~9001))) for free when online check-in opens, 48 hours before your flight departs.
|
{
"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 | 0 |
[
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
https://paperswithcode.com/method/faq-s-how-do-i-connect-with-people-on-expedia
|
[FaQ's]How do I connect with people on Expedia?{{LIVE!!GUIDE!!CENTRE}}
|
[FaQ's]How do I connect with people on Expedia?{{LIVE!!GUIDE!!CENTRE}}
|
If you're trying to resolve a travel issue quickly, the most effective way to reach a live human at Expedia is by calling ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ(USA) or +๐-๐ ๐๐-๐๐๐-๐๐๐๐
. How do I get a human at Expedia? Connecting with an Expedia agent is simple. Call either +๐-๐ ๐๐-๐๐๐-๐๐๐๐
(USA) or ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ. to speak directly.30 Jun 2025
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To get a human at Expedia, call their support team directly at๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ๏ผ. After dialing, follow the automated menu prompts and press โ0โ or say โrepresentativeโ to connect faster. +(1):805:(330):4056 If that doesn't work, wait through the menu until the system transfers you to an agent.
|
{
"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 | 0 |
[
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
https://paperswithcode.com/method/impala
|
IMPALA
|
IMPALA
|
**IMPALA**, or the **Importance Weighted Actor Learner Architecture**, is an off-policy actor-critic framework that decouples acting from learning and learns from experience trajectories using [V-trace](https://paperswithcode.com/method/v-trace). Unlike the popular [A3C](https://paperswithcode.com/method/a3c)-based agents, in which workers communicate gradients with respect to the parameters of the policy to a central parameter server, IMPALA actors communicate trajectories of experience (sequences of states, actions, and rewards) to a centralized learner. Since the learner in IMPALA has access to full trajectories of experience we use a GPU to perform updates on mini-batches of trajectories while aggressively parallelising all time independent operations.
This type of decoupled architecture can achieve very high throughput. However, because the policy used to generate a trajectory can lag behind the policy on the learner by several updates at the time of gradient calculation, learning becomes off-policy. The V-trace off-policy actor-critic algorithm is used to correct for this harmful discrepancy.
|
{
"title": "IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures",
"url": "https://paperswithcode.com/paper/impala-scalable-distributed-deep-rl-with"
}
| 2,000 |
http://arxiv.org/abs/1802.01561v3
|
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
| null | 16 |
[
{
"area": "General",
"area_id": "general",
"collection": "Distributed Methods"
},
{
"area": "Reinforcement Learning",
"area_id": "reinforcement-learning",
"collection": "Distributed Reinforcement Learning"
},
{
"area": "Reinforcement Learning",
"area_id": "reinforcement-learning",
"collection": "Policy Gradient Methods"
}
] |
https://paperswithcode.com/method/amsgrad
|
AMSGrad
|
AMSGrad
|
**AMSGrad** is a stochastic optimization method that seeks to fix a convergence issue with [Adam](https://paperswithcode.com/method/adam) based optimizers. AMSGrad uses the maximum of past squared gradients
$v\_{t}$ rather than the exponential average to update the parameters:
$$m\_{t} = \beta\_{1}m\_{t-1} + \left(1-\beta\_{1}\right)g\_{t} $$
$$v\_{t} = \beta\_{2}v\_{t-1} + \left(1-\beta\_{2}\right)g\_{t}^{2}$$
$$ \hat{v}\_{t} = \max\left(\hat{v}\_{t-1}, v\_{t}\right) $$
$$\theta\_{t+1} = \theta\_{t} - \frac{\eta}{\sqrt{\hat{v}_{t}} + \epsilon}m\_{t}$$
|
{
"title": "On the Convergence of Adam and Beyond",
"url": "https://paperswithcode.com/paper/on-the-convergence-of-adam-and-beyond-1"
}
| 2,000 |
http://arxiv.org/abs/1904.09237v1
|
On the Convergence of Adam and Beyond
|
https://github.com/pytorch/pytorch/blob/b7bda236d18815052378c88081f64935427d7716/torch/optim/adam.py#L6
| 49 |
[
{
"area": "General",
"area_id": "general",
"collection": "Stochastic Optimization"
}
] |
https://paperswithcode.com/method/coat
|
CoaT
|
Co-Scale Conv-attentional Image Transformer
|
**Co-Scale Conv-Attentional Image Transformer** (CoaT) is a [Transformer](https://paperswithcode.com/method/transformer)-based image classifier equipped with co-scale and conv-attentional mechanisms. First, the co-scale mechanism maintains the integrity of Transformers' encoder branches at individual scales, while allowing representations learned at different scales to effectively communicate with each other. Second, the conv-attentional mechanism is designed by realizing a relative position embedding formulation in the factorized attention module with an efficient [convolution](https://paperswithcode.com/method/convolution)-like implementation. CoaT empowers image Transformers with enriched multi-scale and contextual modeling capabilities.
|
{
"title": "Co-Scale Conv-Attentional Image Transformers",
"url": "https://paperswithcode.com/paper/co-scale-conv-attentional-image-transformers"
}
| 2,000 |
https://arxiv.org/abs/2104.06399v2
|
Co-Scale Conv-Attentional Image Transformers
| null | 5 |
[
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Vision Transformers"
}
] |
https://paperswithcode.com/method/moco-v2
|
MoCo v2
|
MoCo v2
|
**MoCo v2** is an improved version of the [Momentum Contrast](https://paperswithcode.com/method/moco) self-supervised learning algorithm. Motivated by the findings presented in the [SimCLR](https://paperswithcode.com/method/simclr) paper, authors:
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- Include blur augmentation.
- Use cosine learning rate schedule.
These modifications enable MoCo to outperform the state-of-the-art SimCLR with a smaller batch size and fewer epochs.
|
{
"title": "Improved Baselines with Momentum Contrastive Learning",
"url": "https://paperswithcode.com/paper/improved-baselines-with-momentum-contrastive"
}
| 2,000 |
https://arxiv.org/abs/2003.04297v1
|
Improved Baselines with Momentum Contrastive Learning
|
https://github.com/facebookresearch/moco
| 30 |
[
{
"area": "General",
"area_id": "general",
"collection": "Semi-Supervised Learning Methods"
},
{
"area": "General",
"area_id": "general",
"collection": "Self-Supervised Learning"
}
] |
https://paperswithcode.com/method/residual-srm
|
Residual SRM
|
Residual SRM
|
A **Residual SRM** is a module for convolutional neural networks that uses a [Style-based Recalibration Module](https://paperswithcode.com/method/style-based-recalibration-module) within a [residual block](https://paperswithcode.com/method/residual-block) like structure. The Style-based Recalibration Module (SRM) adaptively recalibrates intermediate feature maps by exploiting their styles.
|
{
"title": "SRM : A Style-based Recalibration Module for Convolutional Neural Networks",
"url": "https://paperswithcode.com/paper/srm-a-style-based-recalibration-module-for"
}
| 2,000 |
http://arxiv.org/abs/1903.10829v1
|
SRM : A Style-based Recalibration Module for Convolutional Neural Networks
|
https://github.com/EvgenyKashin/SRMnet/blob/a7ebdbe47a489c3e604ef44a84d361ef7b679e17/models/layer_blocks.py#L27
| 1 |
[
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Model Blocks"
},
{
"area": "General",
"area_id": "general",
"collection": "Skip Connection Blocks"
}
] |
https://paperswithcode.com/method/booking-what-is-the-difference-between-flight
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[[Booking-๐๐ฟ๐ฎ๐ป๐ฟ๐ฒ๐ฎ๐]]What is the difference between flight booking and reservation?
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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 | 0 |
[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/faq-s-how-do-i-complain-to-expedia-live-guide
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[FaQ's]How do I complain to Expedia?{{LIVE!!GUIDE!!CENTRE}}
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{
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
}
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https://arxiv.org/abs/2206.09379v2
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0/1 Deep Neural Networks via Block Coordinate Descent
| null | 0 |
[
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
https://paperswithcode.com/method/what-is-the-phone-number-for-celebrity
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What is the phone number for Celebrity retreat? [[Refund or cancellation]]
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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/proximity-regularization
|
Proximity Regularization
|
Proximity Regularization
|
{
"title": "Federated Optimization in Heterogeneous Networks",
"url": "https://paperswithcode.com/paper/federated-optimization-for-heterogeneous"
}
| 2,000 |
https://arxiv.org/abs/1812.06127v5
|
Federated Optimization in Heterogeneous Networks
| null | 6 |
[
{
"area": "General",
"area_id": "general",
"collection": "Optimization"
}
] |
|
https://paperswithcode.com/method/12-ways-to-digital-expedia-customer-service
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12 Ways to Digital Expedia Customer Service Telephone Number 805-330-4056
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12 Ways to Digital Expedia Customer Service Telephone Number 805-330-4056
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{
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
}
| 2,000 |
https://arxiv.org/abs/2206.09379v2
|
0/1 Deep Neural Networks via Block Coordinate Descent
| null | 1 |
[
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
https://paperswithcode.com/method/what-is-the-cheapest-day-to-fly-qatar-airways
|
What is the cheapest day to fly Qatar Airways?
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What is the cheapest day to fly Qatar Airways?
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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/united-now-do-seniors-get-discounts-on-united
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{UnItED~NoW} Do seniors get discounts on United flights?
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{UnItED~NoW} Do seniors get discounts on United flights?
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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/basnet
|
BASNet
|
Boundary-Aware Segmentation Network
|
**BASNet**, or **Boundary-Aware Segmentation Network**, is an image segmentation architecture that consists of a predict-refine architecture and a hybrid loss. The proposed BASNet comprises a predict-refine architecture and a hybrid loss, for highly accurate image segmentation. The predict-refine architecture consists of a densely supervised encoder-decoder network and a residual
refinement module, which are respectively used to predict and refine a segmentation probability map. The hybrid loss is a combination of the binary cross entropy, structural similarity and intersection-over-union losses, which guide the network to learn three-level (i.e., pixel-, patch- and map- level) hierarchy representations.
|
{
"title": "Boundary-Aware Segmentation Network for Mobile and Web Applications",
"url": "https://paperswithcode.com/paper/boundary-aware-segmentation-network-for"
}
| 2,000 |
https://arxiv.org/abs/2101.04704v2
|
Boundary-Aware Segmentation Network for Mobile and Web Applications
| null | 4 |
[
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Semantic Segmentation Models"
}
] |
https://paperswithcode.com/method/htcn
|
HTCN
|
Hierarchical Transferability Calibration Network
|
**Hierarchical Transferability Calibration Network** (HTCN) is an adaptive object detector that hierarchically (local-region/image/instance) calibrates the transferability of feature representations for harmonizing transferability and discriminability. The proposed model consists of three components: (1) Importance Weighted Adversarial Training with input Interpolation (IWAT-I), which strengthens the global discriminability by re-weighting the interpolated image-level features; (2) Context-aware Instance-Level Alignment (CILA) module, which enhances the local discriminability by capturing the complementary effect between the instance-level feature and the global context information for the instance-level feature alignment; (3) local feature masks that calibrate the local transferability to provide semantic guidance for the following discriminative pattern alignment.
|
{
"title": "Harmonizing Transferability and Discriminability for Adapting Object Detectors",
"url": "https://paperswithcode.com/paper/harmonizing-transferability-and"
}
| 2,000 |
https://arxiv.org/abs/2003.06297v1
|
Harmonizing Transferability and Discriminability for Adapting Object Detectors
| null | 4 |
[
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Object Detection Models"
}
] |
https://paperswithcode.com/method/company-for-assistance-how-do-i-file-a
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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 | 0 |
[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/faq-s-how-do-i-ask-a-question-at-expedia-live
|
[FaQ's]How do I ask a question at Expedia?{{LIVE!!GUIDE!!CENTRE}}
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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 | 0 |
[
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
https://paperswithcode.com/method/bytescheduler
|
ByteScheduler
|
ByteScheduler
|
**ByteScheduler** is a generic communication scheduler for distributed DNN training acceleration. It is based on analysis that partitioning and rearranging the tensor transmissions can result in optimal results in theory and good performance in real-world even with scheduling overhead.
| null | 2,019 | null | null | 1 |
[
{
"area": "General",
"area_id": "general",
"collection": "Replicated Data Parallel"
},
{
"area": "General",
"area_id": "general",
"collection": "Data Parallel Methods"
},
{
"area": "General",
"area_id": "general",
"collection": "Distributed Methods"
}
] |
|
https://paperswithcode.com/method/teacher-tutor-student-knowledge-distillation
|
Teacher-Tutor-Student Knowledge Distillation
|
Teacher-Tutor-Student Knowledge Distillation
|
**Teacher-Tutor-Student Knowledge Distillation** is a method for image virtual try-on models. It treats fake images produced by the parser-based method as "tutor knowledge", where the artifacts can be corrected by real "teacher knowledge", which is extracted from the real person images in a self-supervised way. Other than using real images as supervisions, knowledge distillation is formulated in the try-on problem as distilling the appearance flows between the person image and the garment image, enabling the finding of dense correspondences between them to produce high-quality results.
|
{
"title": "Parser-Free Virtual Try-on via Distilling Appearance Flows",
"url": "https://paperswithcode.com/paper/parser-free-virtual-try-on-via-distilling"
}
| 2,000 |
https://arxiv.org/abs/2103.04559v2
|
Parser-Free Virtual Try-on via Distilling Appearance Flows
| null | 4 |
[
{
"area": "General",
"area_id": "general",
"collection": "Knowledge Distillation"
}
] |
https://paperswithcode.com/method/fff
|
FFF
|
Fast Feedforward Networks
|
A log-time alternative to feedforward layers outperforming both the vanilla feedforward and mixture-of-experts approaches.
|
{
"title": "Fast Feedforward Networks",
"url": "https://paperswithcode.com/paper/fast-feedforward-networks"
}
| 2,000 |
https://arxiv.org/abs/2308.14711v2
|
Fast Feedforward Networks
| null | 6 |
[
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Backbone Architectures"
}
] |
https://paperswithcode.com/method/decorrelated-batch-normalization
|
Decorrelated Batch Normalization
|
Decorrelated Batch Normalization
|
**Decorrelated Batch Normalization (DBN)**
is a normalization technique which not just centers and scales activations but whitens them. ZCA whitening instead of [PCA](https://paperswithcode.com/method/pca) whitening is employed since PCA whitening causes a problem called *stochastic axis swapping*, which is detrimental to learning.
|
{
"title": "Decorrelated Batch Normalization",
"url": "https://paperswithcode.com/paper/decorrelated-batch-normalization"
}
| 2,000 |
http://arxiv.org/abs/1804.08450v1
|
Decorrelated Batch Normalization
|
https://github.com/bhneo/decorrelated_bn/blob/ded9279387d62209c7fd4d1d21918431c0d6e96d/common/normalization.py#L16
| 4 |
[
{
"area": "General",
"area_id": "general",
"collection": "Normalization"
}
] |
https://paperswithcode.com/method/nas-fcos
|
NAS-FCOS
|
NAS-FCOS
|
**NAS-FCOS** consists of two sub networks, an [FPN](https://paperswithcode.com/method/fpn) $f$ and a set of prediction heads $h$ which have shared structures. One notable difference with other FPN-based one-stage detectors is that our heads have partially shared weights. Only the last several layers of the predictions heads (marked as yellow) are tied by their weights. The number of layers to share is decided automatically by the search algorithm. Note that both FPN and head are in our actual search space; and have more layers than shown in this figure.
|
{
"title": "NAS-FCOS: Fast Neural Architecture Search for Object Detection",
"url": "https://paperswithcode.com/paper/nas-fcos-fast-neural-architecture-search-for"
}
| 2,000 |
https://arxiv.org/abs/1906.04423v4
|
NAS-FCOS: Fast Neural Architecture Search for Object Detection
| 2 |
[
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Object Detection Models"
}
] |
|
https://paperswithcode.com/method/how-do-i-contact-viking-stove-top-customer
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How do I contact Viking stove top customer service?
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How do I contact Viking stove top customer service?
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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/quick-attention
|
Quick Attention
|
Quick Attention
|
\begin{equation}
QA\left( x \right) = \sigma\left( f\left( x \right)^{1x1} \right) + x \end{equation}
Quick Attention takes in the feature map as an input WxHxC (Width x Height x Channels) and creates two instances of the input feature map then it performs the 1x1xC convolution on the first instance and calculates the sigmoid activations after that it is added with the second instance to generate the final attention map as output which is of same dimensions as of input.
|
{
"title": "HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images",
"url": "https://paperswithcode.com/paper/histoseg-quick-attention-with-multi-loss"
}
| 2,000 |
https://arxiv.org/abs/2209.00729v1
|
HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images
| 1 |
[
{
"area": "General",
"area_id": "general",
"collection": "Attention"
}
] |
|
https://paperswithcode.com/method/qs-support-what-is-the-number-for-royal
|
[[^%QS-Support*&^]]What is the number for Royal Caribbean group booking?
|
[[^%QS-Support*&^]]What is the number for Royal Caribbean group booking?
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To make a Royal Caribbean group booking of 8 or more staterooms, call their customer service line at 1-855-732-4023 or 1-808-900-8011. Alternatively, you can work with a local travel advisor. A group number will be assigned during the initial call, according to Royal Caribbean Cruises.
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Day of Sailing help: 1-855-732-4023 or 1-808-900-8011 (domestic) or 808-900-8011 (international). This number can also be used for Air2Sea flight delays and changes, where an Emergency Travel Team can assist with re-accommodation, hotel, and ground transportation.
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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 | 0 |
[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/faqs-guidelinea-r-does-alaska-airlines-offer
|
[[FAQs-Guidelineรยฎ]]Does Alaska Airlines offer senior discounts for seniors?
|
[[FAQs-Guidelineรยฎ]]Does Alaska Airlines offer senior discounts for seniors?
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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/scatnet
|
ScatNet
|
Scattering Transform
|
A wavelet **scattering transform** computes a translation invariant representation, which is stable to deformation, using a deep [convolution](https://paperswithcode.com/method/convolution) network architecture. It computes non-linear invariants with modulus and averaging pooling functions. It helps to eliminate the image variability due to translation and is stable to deformations.
Image source: [Bruna and Mallat](https://arxiv.org/pdf/1203.1513v2.pdf)
|
{
"title": "Invariant Scattering Convolution Networks",
"url": "https://paperswithcode.com/paper/invariant-scattering-convolution-networks"
}
| 2,000 |
https://arxiv.org/abs/1203.1513v2
|
Invariant Scattering Convolution Networks
| 5 |
[
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Representations"
}
] |
|
https://paperswithcode.com/method/pytorch-ddp
|
PyTorch DDP
|
PyTorch DDP
|
**PyTorch DDP** (Distributed Data Parallel) is a distributed data parallel implementation for PyTorch. To guarantee mathematical equivalence, all replicas start from the same initial values for model parameters and synchronize gradients to keep parameters consistent across training iterations. To minimize the intrusiveness, the implementation exposes the same forward API as the user model, allowing applications to seamlessly replace subsequent occurrences of a user model with the distributed data parallel model object with no additional code changes. Several techniques are integrated into the design to deliver high-performance training, including bucketing gradients, overlapping communication with computation, and skipping synchronization.
|
{
"title": "PyTorch Distributed: Experiences on Accelerating Data Parallel Training",
"url": "https://paperswithcode.com/paper/pytorch-distributed-experiences-on"
}
| 2,000 |
https://arxiv.org/abs/2006.15704v1
|
PyTorch Distributed: Experiences on Accelerating Data Parallel Training
| 3 |
[
{
"area": "General",
"area_id": "general",
"collection": "Replicated Data Parallel"
},
{
"area": "General",
"area_id": "general",
"collection": "Data Parallel Methods"
},
{
"area": "General",
"area_id": "general",
"collection": "Distributed Methods"
}
] |
|
https://paperswithcode.com/method/lbl2vec
|
Lbl2Vec
|
Lbl2Vec
|
{
"title": "Lbl2Vec: An Embedding-Based Approach for Unsupervised Document Retrieval on Predefined Topics",
"url": "https://paperswithcode.com/paper/lbl2vec-an-embedding-based-approach-for"
}
| 2,000 |
https://arxiv.org/abs/2210.06023v1
|
Lbl2Vec: An Embedding-Based Approach for Unsupervised Document Retrieval on Predefined Topics
| null | 2 |
[
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Text Classification Models"
}
] |
|
https://paperswithcode.com/method/eend
|
EEND
|
End-to-End Neural Diarization
|
**End-to-End Neural Diarization** is a neural network for speaker diarization in which a neural network directly outputs speaker diarization results given a multi-speaker recording. To realize such an end-to-end model, the speaker diarization problem is formulated as a multi-label classification problem and a permutation-free objective function is introduced to directly minimize diarization errors. The EEND method can explicitly handle speaker overlaps during training and inference. Just by feeding multi-speaker recordings with corresponding speaker segment labels, the model can be adapted to real conversations.
|
{
"title": "End-to-End Neural Diarization: Reformulating Speaker Diarization as Simple Multi-label Classification",
"url": "https://paperswithcode.com/paper/end-to-end-neural-diarization-reformulating"
}
| 2,000 |
https://arxiv.org/abs/2003.02966v1
|
End-to-End Neural Diarization: Reformulating Speaker Diarization as Simple Multi-label Classification
| null | 26 |
[
{
"area": "Audio",
"area_id": "audio",
"collection": "Speaker Diarization"
}
] |
https://paperswithcode.com/method/trans-encoder
|
Trans-Encoder
|
Trans-Encoder
|
Unsupervised knowledge distillation from a pretrained language model to *itself*, by alternating between its bi- and cross-encoder forms.
|
{
"title": "Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations",
"url": "https://paperswithcode.com/paper/trans-encoder-unsupervised-sentence-pair"
}
| 2,000 |
https://arxiv.org/abs/2109.13059v4
|
Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations
| 1 |
[
{
"area": "General",
"area_id": "general",
"collection": "Self-Supervised Learning"
},
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Sentence Embeddings"
}
] |
|
https://paperswithcode.com/method/patching
|
Patching
|
Activation Patching
|
Activation patching studies the model's computation by altering its latent representations, the token embeddings in transformer-based language models, during the inference process
|
{
"title": "Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Models",
"url": "https://paperswithcode.com/paper/patchscope-a-unifying-framework-for"
}
| 2,000 |
https://arxiv.org/abs/2401.06102v4
|
Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Models
| null | 102 |
[
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Inference Extrapolation"
}
] |
https://paperswithcode.com/method/what-is-the-average-age-of-viking-river
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What is the average age of Viking river cruise customers?
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What is the average age of Viking river cruise customers?
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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/linear-warmup-with-linear-decay
|
Linear Warmup With Linear Decay
|
Linear Warmup With Linear Decay
|
**Linear Warmup With Linear Decay** is a learning rate schedule in which we increase the learning rate linearly for $n$ updates and then linearly decay afterwards.
| null | 2,000 | null | null | null | 7,076 |
[
{
"area": "General",
"area_id": "general",
"collection": "Learning Rate Schedules"
}
] |
https://paperswithcode.com/method/anycost-gan
|
Anycost GAN
|
Anycost GAN
|
**Anycost GAN** is a type of generative adversarial network for image synthesis and editing. Given an input image, we project it into the latent space with encoder $E$ and backward optimization. We can modify the latent code with user input to edit the image. During editing, a sub-generator of small cost is used for fast and interactive preview; during idle time, the full cost generator renders the final, high-quality output. The outputs from the full and sub-generators are visually consistent during projection and editing.
|
{
"title": "Anycost GANs for Interactive Image Synthesis and Editing",
"url": "https://paperswithcode.com/paper/anycost-gans-for-interactive-image-synthesis"
}
| 2,000 |
https://arxiv.org/abs/2103.03243v1
|
Anycost GANs for Interactive Image Synthesis and Editing
| null | 2 |
[
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Generative Adversarial Networks"
}
] |
https://paperswithcode.com/method/faq-s-how-do-i-get-a-human-at-expedia-live
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[FaQ's]How do I get a human at Expedia?{{LIVE!!GUIDE!!CENTRE}}
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[FaQ's]How do I get a human at Expedia?{{LIVE!!GUIDE!!CENTRE}}
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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 | 0 |
[
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
https://paperswithcode.com/method/tm-call-support-tm-how-much-is-the-fee-to
|
@#โข{[CALL & SuPPorT]}โข#@How much is the fee to change a flight on United Airlines?
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@#โข{[CALL & SuPPorT]}โข#@How much is the fee to change a flight on United Airlines?
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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 | 0 |
[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/booking-what-is-the-baggage-allowance-for
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[[Booking-๐๐ฟ๐ฎ๐ป๐ฟ๐ฒ๐ฎ๐]]What is the baggage allowance for Turkish Airlines international flights?
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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 | 0 |
[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/ways-to-support-how-can-i-speak-to-someone-at-2
|
Ways to Support How can i speak to someone at Silversea Cruises (live chat email Option)
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Ways to Support How can i speak to someone at Silversea Cruises (live chat email Option)
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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 | 0 |
[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/vilt
|
ViLT
|
Vision-and-Language Transformer
|
ViLT is a minimal vision-and-language pre-training transformer model where processing of visual inputs is simplified to just the same convolution-free manner that text inputs are processed. The model-specific components of ViLT require less computation than the transformer component for multimodal interactions. ViLTThe model is pre-trained on the following objectives: image text matching, masked language modeling, and word patch alignment.
|
{
"title": "ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision",
"url": "https://paperswithcode.com/paper/vilt-vision-and-language-transformer-without"
}
| 2,000 |
https://arxiv.org/abs/2102.03334v2
|
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
| 6 |
[
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Vision and Language Pre-Trained Models"
}
] |
|
https://paperswithcode.com/method/cubert
|
CuBERT
|
CuBERT
|
**CuBERT**, or **Code Understanding BERT**, is a [BERT](https://paperswithcode.com/method/bert) based model for code understanding. In order to achieve this, the authors curate a massive corpus of Python programs collected from GitHub. GitHub projects are known to contain a large amount of duplicate code. To avoid biasing the model to such duplicated code, authors perform deduplication using the method of [Allamanis (2018)](https://arxiv.org/abs/1812.06469). The resulting corpus has 7.4 million files with a total of 9.3 billion tokens (16 million unique).
|
{
"title": "Learning and Evaluating Contextual Embedding of Source Code",
"url": "https://paperswithcode.com/paper/pre-trained-contextual-embedding-of-source-1"
}
| 2,000 |
https://arxiv.org/abs/2001.00059v3
|
Learning and Evaluating Contextual Embedding of Source Code
| 3 |
[
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Code Generation Transformers"
},
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Autoencoding Transformers"
},
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Language Models"
}
] |
|
https://paperswithcode.com/method/lightautoml
|
LightAutoML
|
LightAutoML
|
**LightAutoML** is an AutoML solution targeted for financial services companies. A typical LightAutoML pipeline scheme is presented in the Figure, each pipeline containing:
- Reader: object that receives raw data and task as input, calculates some useful metadata, performs initial data cleaning and decides about data manipulations that should be done before fitting different model types.
- LightAutoML inner datasets that contains metadata and CV iterators that implements validation scheme for the datasets.
- Multiple ML Pipelines that are stacked and/or blended to get a single prediction.
An ML pipeline in LightAutoML is one or multiple ML models that share a single data preprocessing and validation scheme. The preprocessing step may have up to two feature selection steps, a feature engineering step or even just be empty if no preprocessing is needed. The ML pipelines can be computed independently on the same datasets and then blended together using averaging (or weighted averaging). Alternatively, a stacking ensemble scheme can be used to build multi level ensemble architectures.
|
{
"title": "LightAutoML: AutoML Solution for a Large Financial Services Ecosystem",
"url": "https://paperswithcode.com/paper/lightautoml-automl-solution-for-a-large"
}
| 2,000 |
https://arxiv.org/abs/2109.01528v2
|
LightAutoML: AutoML Solution for a Large Financial Services Ecosystem
| 1 |
[
{
"area": "General",
"area_id": "general",
"collection": "AutoML"
}
] |
|
https://paperswithcode.com/method/what-is-the-cheapest-day-to-book-a-jetblue
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[{FAQ}]What is the cheapest day to book a JetBlue flight?
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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/tm-call-support-tm-how-do-i-select-seats-on
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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 | 0 |
[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/hablo-con-klm-telefono-como-hablo-con-klm-en
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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 | 0 |
[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/prioritized-sweeping
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Prioritized Sweeping
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Prioritized Sweeping
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Source: Sutton and Barto, Reinforcement Learning, 2nd Edition
| null | 2,000 | null | null | null | 6 |
[
{
"area": "Reinforcement Learning",
"area_id": "reinforcement-learning",
"collection": "Efficient Planning"
}
] |
https://paperswithcode.com/method/realformer
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RealFormer
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{
"title": "RealFormer: Transformer Likes Residual Attention",
"url": "https://paperswithcode.com/paper/informer-transformer-likes-informed-attention"
}
| 2,000 |
https://arxiv.org/abs/2012.11747v3
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RealFormer: Transformer Likes Residual Attention
| 1 |
[
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Transformers"
}
] |
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"title": "0.8% Nyquist computational ghost imaging via non-experimental deep learning",
"url": "https://paperswithcode.com/paper/0-8-nyquist-computational-ghost-imaging-via"
}
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https://arxiv.org/abs/2108.07673v1
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0.8% Nyquist computational ghost imaging via non-experimental deep learning
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[
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
https://paperswithcode.com/method/adversarial-color-enhancement
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Adversarial Color Enhancement
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**Adversarial Color Enhancement** is an approach to generating unrestricted adversarial images by optimizing a color filter via gradient descent.
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{
"title": "Adversarial Color Enhancement: Generating Unrestricted Adversarial Images by Optimizing a Color Filter",
"url": "https://paperswithcode.com/paper/a-differentiable-color-filter-for-generating"
}
| 2,000 |
https://arxiv.org/abs/2002.01008v3
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Adversarial Color Enhancement: Generating Unrestricted Adversarial Images by Optimizing a Color Filter
| null | 3 |
[
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Adversarial Image Data Augmentation"
}
] |
https://paperswithcode.com/method/way-to-contact-disney-cruise-line-by-phone
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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
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0-1 laws for pattern occurrences in phylogenetic trees and networks
| null | 0 |
[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/self-service-communicate-how-do-i-communicate
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[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
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{
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}
| 2,000 |
https://arxiv.org/abs/2206.09379v2
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0/1 Deep Neural Networks via Block Coordinate Descent
| null | 0 |
[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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{
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"url": "https://paperswithcode.com/paper/0-dimensional-homology-preserving"
}
| 2,000 |
https://openreview.net/forum?id=zrDNDWjOGwH
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0-dimensional Homology Preserving Dimensionality Reduction with TopoMap
| 0 |
[
{
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}
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https://arxiv.org/abs/2206.09379v2
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0/1 Deep Neural Networks via Block Coordinate Descent
| null | 1 |
[
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[
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{
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
}
| 2,000 |
https://arxiv.org/abs/2206.09379v2
|
0/1 Deep Neural Networks via Block Coordinate Descent
| null | 0 |
[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/experts-dial-how-do-i-get-american-airlines-1
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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 | 0 |
[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/reservation-s-refund-policy-how-do-i-escalate
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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 | 0 |
[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/como-puedo-llamar-a-latam-desde-mexico-ltm
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ยฟCรณmo puedo llamar a LATAM desde Mรฉxico?LTM-Call
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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 | 0 |
[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/como-hablo-con-una-persona-real-en-klm
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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 | 0 |
[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/ssds
|
SSDS
|
Self-Supervised Deep Supervision
|
The method exploits the finding that high correlation of segmentation performance among each U-Net's decoder layer -- with discriminative layer attached -- tends to have higher segmentation performance in the final segmentation map. By introducing an "Inter-layer Divergence Loss", based on Kulback-Liebler Divergence, to promotes the consistency between each discriminative output from decoder layers by minimizing the divergence.
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$Decoder_i(x;\theta_i) \equiv PDE(x;\theta_i)$
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$PDE(x; \theta_d) \sim PDE(x; \theta_i);\text{ } 0 \leq i < d$
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|
{
"title": "OCTAve: 2D en face Optical Coherence Tomography Angiography Vessel Segmentation in Weakly-Supervised Learning with Locality Augmentation",
"url": "https://paperswithcode.com/paper/octave-2d-en-face-optical-coherence"
}
| 2,000 |
https://arxiv.org/abs/2207.12238v1
|
OCTAve: 2D en face Optical Coherence Tomography Angiography Vessel Segmentation in Weakly-Supervised Learning with Locality Augmentation
| 10 |
[
{
"area": "General",
"area_id": "general",
"collection": "Self-Supervised Learning"
}
] |
|
https://paperswithcode.com/method/experts-dial-how-do-i-speak-to-someone-at
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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 | 0 |
[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/faq-what-number-is-1-800-237-2747
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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 | 0 |
[
{
"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 | 0 |
[
{
"area": "General",
"area_id": "general",
"collection": "Hybrid Parallel Methods"
}
] |
https://paperswithcode.com/method/how-much-are-united-miles-worth
|
How much are United miles worth?
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How much are United miles worth?
|
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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 | 0 |
[
{
"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 | 0 |
[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/16-ways-to-call-how-do-i-cancel-my-royal
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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/complain-now-where-can-i-file-a-complaint
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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 | 0 |
[
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Reconstruction"
}
] |
https://paperswithcode.com/method/expedia-free-is-expedia-really-free
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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 | 0 |
[
{
"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 | 0 |
[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/ways-to-contact-disney-cruise-customer
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Ways to Contact Disney cruise Customer Service by Phone, Live chat and Email Detailed: ยฎ๏ธStep by Step Guide
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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 | 0 |
[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/oficial-agente-de-avianca-telefono-como-puedo
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{Oficial~ agente de Avianca.Telefรณno}ยฟCรณmo puedo hablar con un agente de Avianca?
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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 | 0 |
[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/faqs-24-hours-guide-what-is-the-24-hour-rule
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{[(Faqs/24 hours/Guide-)]}what is the 24-hour rule with lufthansa
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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 | 0 |
[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/tm-call-support-tm-is-the-united-economy-the
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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 | 0 |
[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/quick-call-what-is-the-best-day-to-book-a
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[((Quick~CaLL))]~What is the best day to book a flight on Alaska Airlines?
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[((Quick~CaLL))]~What is the best day to book a flight on Alaska Airlines?
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{
"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 | 0 |
[
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Reconstruction"
}
] |
https://paperswithcode.com/method/representative-q-a-what-is-the-24-hour-rule
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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 | 0 |
[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/hum-n-help-support-how-do-i-get-a-human-at
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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}
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https://arxiv.org/abs/2206.09379v2
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0/1 Deep Neural Networks via Block Coordinate Descent
| null | 0 |
[
{
"area": "General",
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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}
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https://arxiv.org/abs/2206.09379v2
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0/1 Deep Neural Networks via Block Coordinate Descent
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[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/ways-to-contact-msc-cruise-customer-service
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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 | 0 |
[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/gan-tts
|
GAN-TTS
|
GAN-TTS
|
**GAN-TTS** is a generative adversarial network for text-to-speech synthesis. The architecture is composed of a conditional feed-forward generator producing raw speech audio, and an ensemble of discriminators which operate on random windows of different sizes. The discriminators analyze the audio both in terms of general realism, as well as how well the audio corresponds to the utterance that should be pronounced.
The generator architecture consists of several GBlocks, which are residual based (dilated) [convolution](https://paperswithcode.com/method/convolution) blocks. GBlocks 3โ7 gradually upsample the temporal dimension of hidden representations by factors of 2, 2, 2, 3, 5, while the number of channels is reduced by GBlocks 3, 6 and 7 (by a factor of 2 each). The final convolutional layer with [Tanh activation](https://paperswithcode.com/method/tanh-activation) produces a single-channel audio waveform.
Instead of a single discriminator, GAN-TTS uses an ensemble of Random Window Discriminators (RWDs) which operate on randomly sub-sampled fragments of the real or generated samples. The ensemble allows for the evaluation of audio in different complementary ways.
|
{
"title": "High Fidelity Speech Synthesis with Adversarial Networks",
"url": "https://paperswithcode.com/paper/high-fidelity-speech-synthesis-with-1"
}
| 2,000 |
https://arxiv.org/abs/1909.11646v2
|
High Fidelity Speech Synthesis with Adversarial Networks
| null | 2 |
[
{
"area": "Sequential",
"area_id": "sequential",
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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
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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 | 0 |
[
{
"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 | 0 |
[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/msc-cruises-r-usa-contact-numbers-a-complete
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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 | 0 |
[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
https://paperswithcode.com/method/can-you-phone-someone-on-a-cruise-ship
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{
"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
}
| 2,000 |
https://arxiv.org/abs/2206.09379v2
|
0/1 Deep Neural Networks via Block Coordinate Descent
| null | 1 |
[
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
https://paperswithcode.com/method/get-help-how-do-i-really-get-through-to
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"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
}
| 2,000 |
https://arxiv.org/abs/2206.09379v2
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0/1 Deep Neural Networks via Block Coordinate Descent
| null | 0 |
[
{
"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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}
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https://arxiv.org/abs/2206.09379v2
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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
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0/1 Deep Neural Networks via Block Coordinate Descent
| null | 0 |
[
{
"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 | 0 |
[
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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