Prince's repositories
benchmark_VAE
Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
kingofspace0wzz.github.io
Prince's academic webpage
the-art-of-command-line
Master the command line, in one page
lampe
Likelihood-free AMortized Posterior Estimation with PyTorch
proposals
Here stores some proposals I wrote in the past for my projects
AffineFlowCausalInf
Code for "Autoregressive flow-based causal discovery and inference" - ICML INNF workshop, 2020
photorama
"PHOTORAMA" template for Jekyll
python-tricks
Some cool Python tricks
torch-neuralpointprocess
(Pytorch ver) Code for "Fully Neural Network based Model for General Temporal Point Process"
NeuralDecomposition
PyTorch implementation of the paper "Neural Decomposition: Functional ANOVA with Variational Autoencoders"
wae-rnf-lm
Pytorch Implemetation for our NAACL2019 Paper "Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling" https://arxiv.org/abs/1904.02399
gw_gan
Code for the Paper 'Learning Generative Models across Incomparable Spaces'
disentanglement-pytorch
Disentanglement library for PyTorch
uai2020-fair
UAI2020 Fairness paper
ACE
Code for the paper, Neural Network Attributions: A Causal Perspective (ICML 2019).
neurips2019_disentanglement_challenge_starter_kit
Starter Kit for the NeurIPS 2019 Disentanglement Challenge
cxplain
Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.
causal-confusion
Code for paper Causal Confusion in Imitation Learning
Financial-Models-Numerical-Methods
Collection of notebooks about quantitative finance, with interactive python code.
NeurIPS19-SBDRL
Code for NeurIPS 2019 paper: "Symmetry-Based Disentangled Representation Learning requires Interaction with Environments" by H. Caselles-Dupré, M. Garcia-Ortiz and D. Filliat.
disentangling-vae
Experiments for understanding disentanglement in VAE latent representations
computer-science
:mortar_board: Path to a free self-taught education in Computer Science!