POT : Python Optimal Transport

Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN

Tensorflow implementation of Wasserstein GAN - arxiv: https://arxiv.org/abs/1701.07875

Optimal transport algorithms for Julia

Code for Supervised Word Mover's Distance (SWMD)

DCGAN and WGAN implementation on Keras for Bird Generation

Wasserstein Introspective Neural Networks (CVPR 2018 Oral)

The Wasserstein Distance and Optimal Transport Map of Gaussian Processes

A Python implementation of Monge optimal transportation

Torch implementation of Wasserstein GAN https://arxiv.org/abs/1701.07875

A materials discovery algorithm geared towards exploring high-performance candidates in new chemical spaces.

FML (Francis' Machine-Learnin' Library) - A collection of utilities for machine learning tasks

Word Mover's Distance (WMD) in Python

Source code for the ICML2019 paper "Subspace Robust Wasserstein Distances"

Tensorflow Implementation of Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation (NAACL 2019).

Code for the article "Learning to solve inverse problems using Wasserstein loss"

GANs Implementations in Keras

code for "Determining Gains Acquired from Word Embedding Quantitatively Using Discrete Distribution Clustering" ACL 2017

Source code for "Training Generative Adversarial Networks Via Turing Test".

Python and MATLAB codes for Density-Driven Optimal Control (D2OC) using Optimal Transport and Wasserstein distance, enabling decentralized multi-agent / multi-robot non-uniform area coverage.