[Deprecated; use POT instead] Fast EMD for Python: a wrapper for Pele and Werman's C++ implementation of the Earth Mover's Distance metric

Morphing and Sampling Network for Dense Point Cloud Completion (AAAI2020)

Everything you could wish for in a library called RoboPoker. Full suite of data structures, algorithms, solvers, ML models, and more.

Code for CVPR 2022 https://arxiv.org/abs/2112.04016 DeepFace-EMD Re-ranking Using Patch-wise Earth Movers Distance Improves Out-Of-Distribution Face Identification

Measure the distance between two spectra/signals using optimal transport and related metrics

Compact Matlab code for the computation of the 1- and 2-Wasserstein distances in 1D

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

An implementation of Squared Earth-Mover's Distance loss for Neural Networks.

PyTorch code for ACL 2022 paper: RoMe: A Robust Metric for Evaluating Natural Language Generation https://aclanthology.org/2022.acl-long.387/

Dot Lib

Earth-movers distance based graph distance metric for financial statements.

Calculating Pairwise Similarity of Polymer Ensembles via Earth Mover’s Distance

Jupyter notebook for my research in Document Similarity.

Pillars is a library that contains fast algorithms for Python implemented in Rust.

Autoencoder dimensionality reduction, EMD-Manhattan metrics comparison and classifier based clustering on MNIST dataset

PyTorch Wrapper for Earth-Mover-Distance (EMD) for 3D point cloud regression

3 part project: A. bottleneck autoencoder, B. manhattan distance, C. earth mover's distance

Comparison of multiple methods for calculating MNIST hand-written digits similarity.

Reducing MNIST image data dimensionality by extracting the latent space representations of an Autoencoder model. Comparing these latent space representations to the default MNIST representation