Stars
Example codes for the book Applied Stochastic Differential Equations
Simple Conditional Flow Matching in MLX with ODE Solvers
A Python library for amortized Bayesian workflows using generative neural networks.
A lightweight library for portable low-level GPU computation using WebGPU.
Code to reproduce experiments in Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows
GPU programming related news and material links
Repository for Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learning
An extremely fast Python package and project manager, written in Rust.
A Functional Programming Approach to Composable Bayesian Workflow
Official implementation of Transformer Neural Processes
Code for paper "Compositional Sculpting of Iterative Generative Processes"
🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
A multi-variate transport-optimized diffusion models accelerated by simulation-free properties (ICML'24)
Deep generative models for semi-supervised learning.
A variational inference method with accurate uncertainty estimation. It uses a new semi-implicit variational family built on neural networks and hierarchical distribution (ICML 2018).
Pytorch implementation of "Entropic Neural Optimal Transport via Diffusion Processes" (NeurIPS 2023, oral).
Oryx is a library for probabilistic programming and deep learning built on top of Jax.
State of the art inference for your bayesian models.
Functional tensors for probabilistic programming
A JAX research toolkit for building, editing, and visualizing neural networks.
Simple (and cheap!) neural network uncertainty estimation
Official Implementations of "Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space""