Stars
modular domain generalization: https://pypi.org/project/domainlab/
Causal Discovery in Python. It also includes (conditional) independence tests and score functions.
A modular, easy to extend GFlowNet library
A collection of visual guides to help applied scientists learn causal inference.
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
A collection of resources and papers on Diffusion Models
Official Implementation of "Transformers Can Do Bayesian Inference", the PFN paper
MAGIC (Markov Affinity-based Graph Imputation of Cells), is a method for imputing missing values restoring structure of large biological datasets.
Figure sizes, font sizes, fonts, and more configurations at minimal overhead. Fix your journal papers, conference proceedings, and other scientific publications.
Examples of how to create colorful, annotated equations in Latex using Tikz.
Neural Networks and Deep Learning, NUS CS5242, 2021
A every-so-often-updated collection of every causality + machine learning paper submitted to arXiv in the recent past.
Multi-Joint dynamics with Contact. A general purpose physics simulator.
Official data and code for our paper Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning
This is the repository for the distill web framework
Uplift modeling and causal inference with machine learning algorithms
Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.
code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm"
Express & compile probabilistic programs for performant inference on CPU & GPU. Powered by JAX.
Evaluation Framework for Probabilistic Programming Languages
Pytorch implementations of density estimation algorithms: BNAF, Glow, MAF, RealNVP, planar flows
An efficient PyTorch library for deep generative modeling.