Autoencoder models of source code are an emerging alternative to autoregressive large language models with important benefits for genetic improvement of software. We hypothesize that encoder-decoder ...

Exploring variational auto-encoder architectures, configurations, and datasets for generative music explainable AI Peer-Reviewed Publication Beijing Zhongke Journal Publising Co. Ltd.

Variational Autoencoders (VAEs) are an artificial neural network architecture to generate new data which consist of an encoder and decoder.

Here we develop novel models that build on variational graph auto-encoders and can integrate diverse types of data to provide high quality predictions of genetic interactions, cell line dependencies ...

Conceptual overview about Variational Autoencoder Modular Bayesian Network VAMBN) approach: In a first step, a low dimensional representation of known modules of variables is learned via HI-VAEs.