To understand what, how and when these unlikely incidents might occur, the researchers have used an artificial neural network they call a variational autoencoder (VAE).

Variational autoencoder (VAE)-based methods excel at integrating large datasets but struggle with cross-atlas comparisons and semi-supervised training across diverse annotation criteria.

A technical paper titled “Improving Semiconductor Device Modeling for Electronic Design Automation by Machine Learning Techniques” was published by researchers at Commonwealth Scientific and ...

A variational autoencoder (VAE) is a deep neural system that can be used to generate synthetic data. VAEs share some architectural similarities with regular neural autoencoders (AEs) but an AE is not ...

Learn how generative models, like variational autoencoders, extract natural features from unlabeled data. Discover how combining variational ladder autoencoders and MMD variational autoencoders can ...