Exoplanet Detection using Machine Learning and Artificial Neural Networks to classify stellar light curves, leveraging advanced preprocessing, PCA, and classification techniques for accurate identi…

Finding new worlds from Kepler/TESS data with PyTorch-- A fork of ExoNet from Ansdell et al. 2018

Implementation of Autoregressive Diffusion in Pytorch

Deep Probabilistic Imaging (DPI): Uncertainty Quantification and Multi-modal Solution Characterization for Computational Imaging

Active learning for simulation-based inference

Cosmological parameter inference using normalizing flows and GW events

Neural network model for gravitational wave sky localization

Repository for the paper for fast parameter estimation of BNS GW signals.

This package implements various flow-based MCMC algorithms for statistical analyses and sampling.

Shortcut flow matching Pytorch implementation

State of the art inference for your bayesian models.

A PyTorch library for implementing flow matching algorithms, featuring continuous and discrete flow matching implementations. It includes practical examples for both text and image modalities.

A Python library for amortized Bayesian workflows using generative neural networks.

A Metropolis-Hastings MCMC sampler accelerated via diffusion models

Code of the paper "Listening to the Noise: Blind Denoising with Gibbs Diffusion"

Dingo: Deep inference for gravitational-wave observations

Diffusion Generative Modeling and Posterior Sampling in Simulation-Based Inference

Unofficial implementation of "Simplifying, Stabilizing & Scaling Continuous-Time Consistency Models" for MNIST

Amortized Probabilistic Conditioning for Optimization, Simulation and Inference (Chang et al., AISTATS 2025)

Neural Network-Boosted Importance Nested Sampling for Bayesian Statistics

Flow matching for atmospheric retrievals

Official implementation of Metric Flow Matching (NeurIPS 2024)

Minimal implementation of flow matching with JAX

Flow and consistency matching in Flax