PNAS

ProT-VAE: Protein Transformer Variational AutoEncoder for functional protein design

The sequence of amino acids within a protein dictates its structure and function. Protein engineering campaigns seek to discover protein sequences with desired functions. Data-driven models of the ...
IEEE

TVAE: Transformer based Variational autoencoder for the design of drug molecules

Abstract: Deep learning has transformed drug design by enabling the generation of novel molecular structures, with Variational Autoencoders (VAEs) playing a key role in learning latent representations ...
GitHub

VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech

In our recent paper, we propose VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech. Several recent end-to-end text-to-speech (TTS) models enabling single ...
GitHub

paisatvik/Variational-Auto-Encoder-from-Scratch

This project implements a Variational Autoencoder (VAE) for generating handwritten digits using the MNIST dataset. The code is organized into modular files for model definition, training, inference, ...
EurekAlert!

New AI method makes materials design more efficient and transparent

Disentangled variational autoencoder (D-VAE) separates materials properties from the latent space by conditioning to make inverse materials design more efficient and transparent. It combines labeled ...
IEEE

An Explainable AI Framework Integrating Variational Sparse Autoencoder and Random Forest for EEG-Based Epilepsy Detection

Abstract: Epilepsy is a medical condition characterized by sudden and frequent sensory disruptions which is commonly detected by electroencephalogram (EEG) analysis. However, analyzing these signals ...