probablisticmachinelearning

3.6. Probablistic Machine Learning and Deep Learning

Julia:

mcosovic/FactorGraph.jl: The FactorGraph package provides the set of different functions to perform inference over the factor graph with continuous or discrete random variables using the belief propagation algorithm.

stefan-m-lenz/BoltzmannMachines.jl: A Julia package for training and evaluating multimodal deep Boltzmann machines

BIASlab

biaslab/ReactiveMP.jl: Julia package for automatic Bayesian inference on a factor graph with reactive message passing

mroavi/JunctionTrees.jl: A metaprogramming-based implementation of the junction tree algorithm.

pat-alt/LaplaceRedux.jl: Small library for using Laplace Redux with Flux Neural Networks for effortless Bayesian Deep Learning.

Python:

Probabilistic machine learning

thu-ml/zhusuan: A probabilistic programming library for Bayesian deep learning, generative models, based on Tensorflow

OATML/bdl-benchmarks: Bayesian Deep Learning Benchmarks

pgmpy/pgmpy: Python Library for learning (Structure and Parameter) and inference (Probabilistic and Causal) in Bayesian Networks.

scikit-learn-contrib/imbalanced-learn: A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

3.6.1. GAN

Julia:

Python:

torchgan/torchgan: Research Framework for easy and efficient training of GANs based on Pytorch

kwotsin/mimicry: [CVPR 2020 Workshop] A PyTorch GAN library that reproduces research results for popular GANs.

3.6.2. Normilization Flows

Julia:

TuringLang/Bijectors.jl: Implementation of normalising flows and constrained random variable transformations

slimgroup/InvertibleNetworks.jl: A Julia framework for invertible neural networks

FFJord is impleted in DiffEqFlux.jl

Python:

Surveyjanosh/awesome-normalizing-flows: A list of awesome resources on normalizing flows.

RameenAbdal/StyleFlow: StyleFlow: Attribute-conditioned Exploration of StyleGAN-generated Images using Conditional Continuous Normalizing Flows (ACM TOG 2021)

3.6.3. VAE

Julia:

Python:

Variational Autoencoders — Pyro Tutorials 1.7.0 documentation

AntixK/PyTorch-VAE: A Collection of Variational Autoencoders (VAE) in PyTorch.

timsainb/tensorflow2-generative-models: Implementations of a number of generative models in Tensorflow 2. GAN, VAE, Seq2Seq, VAEGAN, GAIA, Spectrogram Inversion. Everything is self contained in a jupyter notebook for easy export to colab.

altosaar/variational-autoencoder: Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)

subinium/Pytorch-AutoEncoders at pythonrepo.com

Ritvik19/pyradox-generative at pythonrepo.com

3.6.4 BNN

JavierAntoran/Bayesian-Neural-Networks: Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

RajDandekar/MSML21_BayesianNODE

bayesian-neural-networks · GitHub Topics

3.6.5 Diffusion-Models

heejkoo/Awesome-Diffusion-Models: A collection of resources and papers on Diffusion Models and Score-based Models, a darkhorse in the field of Generative Models

3.11. Optimal Transportation

Julia:

Optimal transport in Julia

JuliaOptimalTransport/OptimalTransport.jl: Optimal transport algorithms for Julia

JuliaOptimalTransport/ExactOptimalTransport.jl: Solving unregularized optimal transport problems with Julia

Python:

PythonOT/POT: POT : Python Optimal Transport

ott-jax/ott