An educational resource to help anyone learn deep reinforcement learning.

Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.

An interactive NVIDIA-GPU process viewer and beyond, the one-stop solution for GPU process management.

A highly efficient implementation of Gaussian Processes in PyTorch

Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.

Official implementation of "Implicit Neural Representations with Periodic Activation Functions"

A JAX research toolkit for building, editing, and visualizing neural networks.

A Graph Neural Network Library in Jax

A Python module for getting the GPU status from NVIDA GPUs using nvidia-smi programmically in Python

Manipulate JSON-like data with NumPy-like idioms.

Really Fast End-to-End Jax RL Implementations

A generative model for programmable protein design

Fast Differentiable Sorting and Ranking

A library to model multivariate data using copulas.

Gaussian processes in JAX.

Linear solvers in JAX and Equinox. https://docs.kidger.site/lineax

Plausibility checks for generated molecule poses.

gReLU is a python library to train, interpret, and apply deep learning models to DNA sequences.

A package for statistically rigorous scientific discovery using machine learning. Implements prediction-powered inference.

A generative latent variable model for biological sequence families.

Framework for Data-Driven Design & Analysis of Structures & Materials (F3DASM)

Benchmarks for Model-Based Optimization

Repository for "Differentiable Causal Discovery from Interventional Data"

Machine Learning eXperiment Utilities

Code for A General Recipe for Likelihood-free Bayesian Optimization, ICML 2022

Deep Bayesian Optimization for Problems with High-Dimensional Structure