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Found 13 papers, 6 papers with code
Collaborative Learning of Bidirectional Decoders for Unsupervised Text Style Transfer
In this paper, we propose a collaborative learning framework for unsupervised text style transfer using a pair of bidirectional decoders, one decoding from left to right while the other decoding from right to left.
Exploring Non-Autoregressive Text Style Transfer
In this paper, we explore Non-AutoRegressive (NAR) decoding for unsupervised text style transfer.
LLM-Powered Test Case Generation for Detecting Tricky Bugs
Conventional automated test generation tools struggle to generate test oracles and tricky bug-revealing test inputs.
On the best approximation by finite Gaussian mixtures
We consider the problem of approximating a general Gaussian location mixture by finite mixtures.
Exploring the Impact of In-Browser Deep Learning Inference on Quality of User Experience and Performance
Additionally, we noticed that in-browser inference increases the time it takes for graphical user interface (GUI) components to load in web browsers by a significant 67. 2\%, which severely impacts the overall QoE for users of web applications that depend on this technology.
DeFL: Decentralized Weight Aggregation for Cross-silo Federated Learning
An important type of FL is cross-silo FL, which enables a small scale of organizations to cooperatively train a shared model by keeping confidential data locally and aggregating weights on a central parameter server.
Benchmarking of DL Libraries and Models on Mobile Devices
Deploying deep learning (DL) on mobile devices has been a notable trend in recent years.
Demystifying Swarm Learning: A New Paradigm of Blockchain-based Decentralized Federated Learning
Federated learning (FL) is an emerging promising privacy-preserving machine learning paradigm and has raised more and more attention from researchers and developers.
Multi-Facet Recommender Networks with Spherical Optimization
Metric learning has been proposed to capture user-item interactions from implicit feedback, but existing methods only represent users and items in a single metric space, ignoring the fact that users can have multiple preferences and items can have multiple properties, which leads to potential conflicts limiting their performance in recommendation.
$C^3DRec$: Cloud-Client Cooperative Deep Learning for Temporal Recommendation in the Post-GDPR Era
The reason is that state-of-the-art recommendation systems require to gather and process the user data in centralized servers but the interaction behaviors data used for temporal recommendation are usually non-transactional data that are not allowed to gather without the explicit permission of users according to GDPR.
MetaMix: Improved Meta-Learning with Interpolation-based Consistency Regularization
MetaMix can be integrated with any of the MAML-based algorithms and learn the decision boundaries generalizing better to new tasks.
Virtual Mixup Training for Unsupervised Domain Adaptation
Existing methods only impose the locally-Lipschitz constraint around the training points while miss the other areas, such as the points in-between training data.
Moving Deep Learning into Web Browser: How Far Can We Go?
Recently, several JavaScript-based deep learning frameworks have emerged, making it possible to perform deep learning tasks directly in browsers.
Software Engineering
