cdedonno

CPA

The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell level. CPA performs OOD predictions of unseen combinations of drugs, learns interpretable embeddings, estimates dose-response curves, and provides uncertainty estimates.

comp-neuro

Code and notebooks for Computational Neuroscience class at TUM

ConvAge

Convolutional Neural Network for prediction of future appearances on human faces.

deep-face-saliency

gitignore

A collection of useful .gitignore templates

scgen

Single cell perturbation prediction

Variational-Autoencoder-PyTorch

Variational Autoencoder implemented with PyTorch, Trained over CelebA Dataset