IEEE

CVA 2 E: A Conditional Variational Autoencoder With an Adversarial Training Process for Hyperspectral Imagery Classification

Abstract: Deep generative models such as the generative adversarial network (GAN) and the variational autoencoder (VAE) have obtained increasing attention in a wide variety of applications.
Hosted on MSN

Smarter search for fuel-cell catalysts uses machine learning

A computational method combining generative AI with atomistic simulations can identify promising platinum alloy catalyst structures for hydrogen fuel cells, report researchers from Science Tokyo.
IEEE

Semianalytical Bounded Formation Configuration Screening Method Based on Poincaré Contraction Mapping

Abstract: This article proposes a semianalytical method combining Poincaré Contraction Mapping (PCM) with conditional variational autoencoder (CVAE) for efficient screening of bounded relative orbit ...
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

MAGI-1: Autoregressive Video Generation at Scale

We present MAGI-1, a world model that generates videos by autoregressively predicting a sequence of video chunks, defined as fixed-length segments of consecutive frames. Trained to denoise per-chunk ...