A transfer-learned hierarchical variational autoencoder model for computational design of anticancer peptides.. If you have the appropriate software installed, you can download article citation data ...

The study introduces a novel hybrid Variational Autoencoder-SURF (VAE-SURF) model for anomaly detection in crowded environments, addressing critical challenges such as scale variance and temporal ...

This paper presents an unsupervised learning method to classify and label transients observed in the distribution grid. A Convolutional Variational Autoencoder (CVAE) was developed for this purpose.

Variational Autoencoder in tensorflow and pytorch Reference implementation for a variational autoencoder in TensorFlow and PyTorch. I recommend the PyTorch version. It includes an example of a more ...

In this article, we propose a self-augmentation strategy for improving ML-based device modeling using variational autoencoder (VAE)-based techniques. These techniques require a small number of ...

Then, MSM and TPT are constructed to obtain the ensemble of pathways, and a deep learning architecture named the variational autoencoder (VAE) is used to learn the spatial distributions of kinetic ...

Keywords: protein system, conformational space, variational autoencoder, molecular dynamics, deep learning Citation: Tian H, Jiang X, Trozzi F, Xiao S, Larson EC and Tao P (2021) Explore Protein ...