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Folded Hamiltonian Monte Carlo for Bayesian Generative Adversarial Networks
Generative Adversarial Networks (GANs) can learn complex distributions over images, audio, and data that are difficult to model.
When Complexity Is Good: Do We Need Recurrent Deep Learning For Time Series Outlier Detection?
Recurrent deep learning methods have a larger capacity for learning complex representations in time series data.
Continual Learning Using Task Conditional Neural Networks
The proposed model can continually learn and embed new tasks into the model without losing the information about previously learned tasks.
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