POT : Python Optimal Transport
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Updated
Jun 22, 2024 - Python
POT : Python Optimal Transport
Tensorflow implementation of Wasserstein GAN - arxiv: https://arxiv.org/abs/1701.07875
DCGAN and WGAN implementation on Keras for Bird Generation
A Python implementation of Monge optimal transportation
The Wasserstein Distance and Optimal Transport Map of Gaussian Processes
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
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
Python package for the ICML 2022 paper "Unsupervised Ground Metric Learning Using Wasserstein Singular Vectors".
Improving word mover’s distance by leveraging self-attention matrix
Pytorch Implementation for Topic Modeling with Wasserstein Autoencoders
TensorFlow implementation of Wasserstein GAN (WGAN) with MNIST dataset.
Optimal transport for comparing short brain connectivity between individuals | Optimal transport | Wasserstein distance | Barycenter | K-medoids | Isomap| Sulcus | Brain
Code for our TMLR '24 Journal: MMD-Regularized UOT.
Implementation and results from "Beyond GOTEX: Using Multiple Feature Detectors for Better Texture Synthesis"
Demonstration of Wasserstein GAN. Using Earth Mover's distance to measure similarity between two distributions
Wasserstein barycenter research for images
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