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Implementation of the optimal transport problem in 1D. For $L^2$ also known as Earth Mover's distance or Wasserstein distance.
Implementation of one-dimensional optimal transport
S. Ferradans, N. Papadakis, G. Peyré, J-F. Aujol. Regularized Discrete Optimal Transport. SIAM Journal on Imaging Sciences, 7(3), pp. 1853–1882, 2014.
Official content for the Fall 2014 Harvard CS109 Data Science course
Lab for Linear and Logistic Regression, SciKit Learn
Gabriel Peyré, Marco Cuturi, Justin Solomon, Gromov-Wasserstein Averaging of Kernel and Distance Matrices, Proc. of ICML 2016.
Multiscale Strategies for Computing Optimal Transport
Matthieu Heitz, Nicolas Bonneel, David Coeurjolly, Marco Cuturi, Gabriel Peyré, Ground Metric Learning on Graphs
Numerical optimal transport computation between a measure carried by a set of line segments and a measure carried by a point cloud.
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
Sinkhorn Adversarial Training (SAT): Optimal Transport as a Defense Against Adversarial Attacks
Advance machine Learning: Kernel methods implemented for PCA, KMeans, Logistic Regression, Support Vector Machine (SVM) and Support Vector Data Description (SVDD)
[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.
Projects of Advanced Machine Learning, ETH Zürich, Fall 2018
A not-completed summary of things covered in the Advanced Machine Learning Course at the ETH Zürich, 2018-2019.
Projects from the 252-0535-00L Advanced Machine Learning at ETH Zürich Fall Semester 2019
State-of-the-art Deep Learning publications, frameworks & resources
Inference with Aggregate Data: An Optimal Transport Approach
Accelerated Inexact Soft-Impute for Fast Large-Scale Matrix Completion, matlab code
Code used in Springer Nature/Apress Video Tutorial
Assignments for the course ALTEGRAD (Advanced learning for text and graph data) at ENS Paris-Saclay Masters MVA
Advanced Scientific Computing: Stochastic Optimization Methods. Monte Carlo Methods for Inference and Data Analysis
Cheatsheet for Advanced Machine Learning exam @ ETH Zürich, 2018-2019.
Advanced Machine Learning class @ ETH Zürich
Code for the analysis of 21 cm intensity mapping data.
AOT: Appearance Optimal Transport Based Identity Swapping for Forgery Detection (NeurIPS 2020)
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.