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Found 5 papers, 0 papers with code
Incremental training of multi-generative adversarial networks
Generative neural networks map a standard, possibly distribution to a complex high-dimensional distribution, which represents the real world data set.
Demystifying Deep Learning in Predictive Spatio-Temporal Analytics: An Information-Theoretic Framework
Deep learning has achieved incredible success over the past years, especially in various challenging predictive spatio-temporal analytics (PSTA) tasks, such as disease prediction, climate forecast, and traffic prediction, where intrinsic dependency relationships among data exist and generally manifest at multiple spatio-temporal scales.
Item Cold Start Recommendation via Adversarial Variational Auto-encoder Warm-up
The gap between the randomly initialized item ID embedding and the well-trained warm item ID embedding makes the cold items hard to suit the recommendation system, which is trained on the data of historical warm items.
Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach
According to the channel model, we propose algorithms to constrain the information transmitted in a single round of local training.
