Popular GitHub Repositories for Langevin

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

Ying Nian Wu's UCLA Statistical Machine Learning Tutorial on generative modeling.

A primer on Bayesian Neural Networks. The aim of this reading list is to facilitate the entry of new researchers into the field of Bayesian Deep Learning, by providing an overview of key papers. More details: "A Primer on Bayesian Neural Networks: Review and Debates"

? Energy-Based Modeling library for PyTorch, offering tools for sampling, inference, and learning in complex distributions.

[NeurIPS 2021] SNIPS: Solving Noisy Inverse Problems Stochastically

Variational Inference for Langevin Equations

The code enables to perform Bayesian inference in an efficient manner through the use of Hamiltonian Neural Networks (HNNs), Deep Neural Networks (DNNs), Neural ODEs, and Symplectic Neural Networks (SympNets) used with state-of-the-art sampling schemes like Hamiltonian Monte Carlo (HMC) and the No-U-Turn-Sampler (NUTS).

Software platform for Thermodynamic Sampling Units - probabilistic computing emulator

Bayesian Structured Time Series Analysis with Parallel Tempering for Stock Market Prediction

Code for "Sufficient conditions for offline reactivation in recurrent neural networks" (ICLR 2024)

Multi-agent architecture with nine heterogeneous agents coupled on spherical and hyperbolic manifolds via Kuramoto synchronization, Langevin dynamics, and Riemannian consensus.

Interactive stochastic simulation for learning the implications of different integrators in the presence of position- and velocity-dependent friction