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1d-optimal-transport

Implementation of the optimal transport problem in 1D. For $L^2$ also known as Earth Mover's distance or Wasserstein distance.

1d-optimal-transport-and-dtw

Implementation of one-dimensional optimal transport

2013-siims-regularized-ot

S. Ferradans, N. Papadakis, G. Peyré, J-F. Aujol. Regularized Discrete Optimal Transport. SIAM Journal on Imaging Sciences, 7(3), pp. 1853–1882, 2014.

2014

Official content for the Fall 2014 Harvard CS109 Data Science course

2015lab1

2015lab4

Lab for Linear and Logistic Regression, SciKit Learn

2016-icml-gromov-wasserstein-master

Gabriel Peyré, Marco Cuturi, Justin Solomon, Gromov-Wasserstein Averaging of Kernel and Distance Matrices, Proc. of ICML 2016.

2017-jmlr-optimaltransport

Multiscale Strategies for Computing Optimal Transport

2020-jmiv-ground-metric-learning-graphs

Matthieu Heitz, Nicolas Bonneel, David Coeurjolly, Marco Cuturi, Gabriel Peyré, Ground Metric Learning on Graphs

3forthoptimaltransport

Numerical optimal transport computation between a measure carried by a set of line segments and a measure carried by a point cloud.

accelerated-coordinate-descent-method

aco-in-urban-transport

Apllication of Ant Colony Optimization (ACO) technique to determine the approximately optimal load distribution given the model of an urban street network as a graph

adaptive-relatively-smooth-optimization

adv-risk-optimal-transport

adv-sat

Sinkhorn Adversarial Training (SAT): Optimal Transport as a Defense Against Adversarial Attacks

advance-machine-learning-kernel-method

Advance machine Learning: Kernel methods implemented for PCA, KMeans, Logistic Regression, Support Vector Machine (SVM) and Support Vector Data Description (SVDD)

advanced-machine-learning-projects

[ETH Zurich] My projects for the module "Advanced Machine Learning" at ETH Zürich (Swiss Federal Institute of Technology in Zurich) during the academic year 2019-2020.

advanced-machine-learning-projects-1

Projects of Advanced Machine Learning, ETH Zürich, Fall 2018

advanced-machine-learning-summary

A not-completed summary of things covered in the Advanced Machine Learning Course at the ETH Zürich, 2018-2019.

advanced_machine_learning

Projects from the 252-0535-00L Advanced Machine Learning at ETH Zürich Fall Semester 2019

adventures-in-deep-learning

State-of-the-art Deep Learning publications, frameworks & resources

aggregate-inference-mot

Inference with Aggregate Data: An Optimal Transport Approach

ais-impute

Accelerated Inexact Soft-Impute for Fast Large-Scale Matrix Completion, matlab code

allstatesforecast

Code used in Springer Nature/Apress Video Tutorial

altegrad2019

Assignments for the course ALTEGRAD (Advanced learning for text and graph data) at ENS Paris-Saclay Masters MVA

am207

Advanced Scientific Computing: Stochastic Optimization Methods. Monte Carlo Methods for Inference and Data Analysis

aml-2018-cheatsheet

Cheatsheet for Advanced Machine Learning exam @ ETH Zürich, 2018-2019.

aml_project_one

Advanced Machine Learning class @ ETH Zürich

analysis_im

Code for the analysis of 21 cm intensity mapping data.

aot

AOT: Appearance Optimal Transport Based Identity Swapping for Forgery Detection (NeurIPS 2020)