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CycleGAN with a Blur Kernel for Deconvolution Microscopy: Optimal Transport Geometry
Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101
Project on Deep Gaussian processes for the MVA course "Bayesian Machine Learning"
Repository for the development of the projects for the Deep Learning for Scientific Computing course taught by Professor Mishra at ETH Zürich in the spring semester 2021
Deep Learning Tutorial notes and code. See the wiki for more info.
Mini-Projects for the course "Deep Learning" at MVA
Demos and tutorials for ORIE 4741
Optimal Transport and barycenters for dendritic measures
A mathematical model for choosing the optimal transport network route. Based on NBA* method
Implementation of the dirichlet process for multivariate clustering
Code for the project in the Discrete Inference class
R Scripts used for my dissertation : "Modelling and analysis of social network data from rank preferences"
Formulate an approach via Optimal Transport Theory that provides a robust comparison of datasets, defined on both the same and different topological space that correlates well within the domain adaptation and task similarity context.
A PySpark Implementation of "Distributed Frank-Wolfe Framework for Learning Low-Rank Matrices with the Trace Norm".
deep learning low rank
An Exploration of Optimization Alternatives for Deep Reinforcement Learning (Deep Learning 2018, ETH Zurich)
Dual Regularized Optimal Transport
Independent Study for Graph Database using Neo4J
Distributional Sliced-Wasserstein distance code
Dold-Thom, Homology and Optimal Transport.
Finite element discretization of dynamical optimal transport using Firedrake. This repo contains the code from the paper: A. Natale, and G. Todeschi. "A mixed finite element discretization of dynamical optimal transport." arXiv preprint arXiv:2003.04558 (2020).
EE227C (Spring 2018) Course page
IPython 2.x/3.x notebook client in Emacs
A general convex solver based on functions with efficient proximal operators.
The codes that detect error feedback when the participants receive visual or somato-sensory feedback . The methodology implements optimal transport to transport source data samples within the distribution of the target data samples. This allows for cross-subject training and hence, does not require re-training for new participants or sessions.
PyTorch GPU implementation of the ES-RNN model for time series forecasting
Cheat sheet for the "Deep Learning" course at ETH Zürich
These projects were part of the Introduction to Machine Learning course at ETH Zürich.
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.