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 ...

Jianwei Shuai's team and Jiahuai Han's team at Xiamen University have developed a deep autoencoder-based data-independent acquisition data analysis software for protein mass spectrometry, which ...

Building a Variational Autoencoder from Scratch Overview This repository contains an implementation of a Variational Autoencoder (VAE) built from scratch using PyTorch. The VAE is a type of generative ...

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 ...

Variational Autoencoder for Aerial Agriculture This repository contains implementations of Variational Autoencoder (VAE) variants and Autoencoders for learning compact representations from aerial ...