We propose a Crystal Diffusion Variational Autoencoder (CDVAE) that captures the physical inductive bias of material stability. By learning from the data distribution of stable materials, the decoder ...

EEG-based seizure prediction enables timely treatment for patients, but its performance is limited by the difficulty in effectively characterizing the temporal dynamics of epileptic brain networks.

Through adversarial feature learning, we then impose the metastability prior on the latent embedding space encoded via a variational autoencoder (VAE), while leveraging the maximum mean discrepancy ...