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 ...

In this paper we present a new implementation of a Variational Autoencoder (VAE) for the calibration of sensors. We propose that the VAE can be used to calibrate sensor data by training the latent ...

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 ...

Latent Diffusion Model Implementation is a Python project exploring the implementation of a latent diffusion model using a variational autoencoder (VAE) and a conditional U-Net. It currently targets ...

MIT researchers used AI to develop novel antibiotics NG1 and DN1, effective against drug-resistant gonorrhoea and MRSA, ...

Variational Autoencoder(VAE) combines the ideas of autoencoders and variational inference, introducing the concept of latent space and variational inference to endow autoencoders to generate new ...