Recent developments in Generative Deep Learning have fostered new engineering methods for protein design. Although deep generative models trained on protein sequence can learn biologically meaningful ...

This paper presents a novel approach for reducing noise in electroencephalography (EEG) signals using the Variational AutoEncoders (VAE) algorithm. VAE's are attractive as they are designed on top of ...

Variational Autoencoders (VAEs) are expressive latent variable models that can be used to learn complex probability distributions from training data. However, the quality of the resulting model ...

This is a re-implementation of the Graph Auto-encoder and Variational Graph Auto-encoder presented here.. This implementation is based on Tensorflow 2 and Spektral.Compared to the original ...

Soft-IntroVAE: Analyzing and Improving Introspective Variational Autoencoders Tal Daniel, Aviv Tamar. Abstract: The recently introduced introspective variational autoencoder (IntroVAE) exhibits ...