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Found 4 papers, 2 papers with code
Synthesizing EEG Signals from Event-Related Potential Paradigms with Conditional Diffusion Models
Data scarcity in the brain-computer interface field can be alleviated through the use of generative models, specifically diffusion models.
Deterministic training of generative autoencoders using invertible layers
In this work, we provide a deterministic alternative to the stochastic variational training of generative autoencoders.
Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling
We also introduce gated structured layers, which allow bypassing the parts of the models that fail to capture the statistics of the data.
Automatic variational inference with cascading flows
We evaluate the performance of the new variational programs in a series of structured inference problems.
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