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 ...

This paper proposes a novel hybrid model that synergistically integrates Variational Autoencoders (VAEs) and Speeded-Up Robust Features (SURF) to address these challenges. The VAE component captures ...

Yet, physics knowledge and the spatio-temporal data correlations are not exploited by these deep learning models. In this work, we propose a physics-guided variational graph autoencoder whose graph ...

Specifically, we demonstrate how exploring a variational autoencoder (VAE) latent space, trained on purely normal (valid) data, can effectively fuzz-test representational robustness by anomaly ...

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 ...

Background: Artificial patient technology could transform health care by accelerating diagnosis, treatment, and mapping clinical pathways. Deep learning methods for generating artificial data in ...

Generic Deep Autoencoder for Time-Series This toolbox enables the simple implementation of different deep autoencoder. The primary focus is on multi-channel time-series analysis. Each autoencoder ...

Thanks to recent advances in optical imaging techniques, calcium imaging can now record the activities of thousands of neurons simultaneously, through several sessions and over long periods. Neuron ...