ludwigwinkler Goto Github PK

18337

18.337 - Parallel Computing and Scientific Machine Learning

18s191

Course 18.S191 at MIT, fall 2020 - Introduction to computational thinking with Julia:

bayesianoptimization

Bayesian Optimization with Gaussian Processes

csgmcmc

Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning

diffsim

diffusion_schrodinger_bridge

PyTorch Implementation of DSB for Score Based Generative Modeling. Experiments managed using Hydra.

hamiltorch

PyTorch-based library for Riemannian Manifold Hamiltonian Monte Carlo (RMHMC)

ibexperiments

Reinforcement Learning in Highly Stochastic Environment with the Industrial Benchmark Environment

improved-diffusion

Release for Improved Denoising Diffusion Probabilistic Models

jaxlightning

Running Jax in PyTorch Lightning

ludwigwinkler.github.io

Ludwig Winklers Github.io

mcmc-with-uncertain-energies

mlmd

mltalks

Machine Learning Talks (Difficulty of Training RNNs, Uncertain MCMC Sampling)

ncsn

Noise Conditional Score Networks (NeurIPS 2019, Oral)

offshoreleaks-data-packages

Tools to produce and share the downloadable Neo4j packages and guides

pytorch-fid

Compute FID scores with PyTorch.

pytorch-mcmc

Little Markov Chain Monte Carlo library built on PyTorch

pytorch_mcmc

Lightweight MCMC sampling for PyTorch Models aka My Corona Project

pytorch_probabilisticlayers

Bayesian Neural Networks with Parallelized Sampling of LogLikelihood

riemannian-score-sde

Score-based generative models for compact manifolds

score_sde_pytorch

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

simple-diffusion-model

Pedagogical codebase for a simplified score-based generative model design, with training loop

simpleparsing

Simple, Elegant, Typed Argument Parsing with argparse

sinkhornautodiff

Toolbox to integrate optimal transport loss functions using automatic differentiation and Sinkhorn's algorithm

sliced_score_matching

Code for reproducing results in the sliced score matching paper (UAI 2019)

tauldr

Code for the paper https://arxiv.org/abs/2205.14987v2

torch-fidelity

High-fidelity performance metrics for generative models in PyTorch

wassdistance

Approximating Wasserstein distances with PyTorch