Zero-1-to-3: Zero-shot One Image to 3D Object (ICCV 2023)

Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

Master the command line, in one page

Likelihood-free AMortized Posterior Estimation with PyTorch

Code for "Autoregressive flow-based causal discovery and inference" - ICML INNF workshop, 2020

"PHOTORAMA" template for Jekyll

Some cool Python tricks

(Pytorch ver) Code for "Fully Neural Network based Model for General Temporal Point Process"

PyTorch implementation of the paper "Neural Decomposition: Functional ANOVA with Variational Autoencoders"

Code for the Paper 'Learning Generative Models across Incomparable Spaces'

Disentanglement library for PyTorch

Code for the paper, Neural Network Attributions: A Causal Perspective (ICML 2019).

Starter Kit for the NeurIPS 2019 Disentanglement Challenge

Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.

Code for paper Causal Confusion in Imitation Learning

Collection of notebooks about quantitative finance, with interactive python code.

Code for NeurIPS 2019 paper: "Symmetry-Based Disentangled Representation Learning requires Interaction with Environments" by H. Caselles-Dupré, M. Garcia-Ortiz and D. Filliat.

Experiments for understanding disentanglement in VAE latent representations

🎓 Path to a free self-taught education in Computer Science!