Popular GitHub Repositories for Stochastic

NMA Computational Neuroscience course

PyTorch implementation for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)

Gaussian processes in TensorFlow

Official code for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)

Differentiable SDE solvers with GPU support and efficient sensitivity analysis.

An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations

[NeurIPS 2023 Spotlight] LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios (awesome MCTS)

PyTorch implementation for SDEdit: Image Synthesis and Editing with Stochastic Differential Equations

Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

[ICCV 2023] A latent space for stochastic diffusion models

Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization

A PyTorch implementation of the paper "ZigMa: A DiT-Style Mamba-based Diffusion Model" (ECCV 2024)

Stochastic Adversarial Video Prediction

Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem

Machine learning algorithms in Dart programming language

[CVPR2023] Leapfrog Diffusion Model for Stochastic Trajectory Prediction

Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"

[ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.

Representation learning on large graphs using stochastic graph convolutions.