MVA Labs

This repository contains all the labs I have done during my time at the MVA Master at ENS Paris-Saclay.

Course	Lab
Advanced learning for text and graph data	Neural machine translation
	Graph mining
	Transfer learning in NLP
	NLP frameworks
	Deep learning for graphs 1
	Deep learning for graphs 2
	Learning on sets and graph generative models
Computational statistics	Stochastic gradient descent
	Expectation maximization, importance sampling
	Hasting-Metropolis, Gibbs
	Adaptative samplers, parallel tempering
Distributed and large scale computing optimization	Proximal minimization methods for spectroscopy
	Parallel MM algorithms for tomography image reconstruction
Time series	Convolution dictionary learning, spectral analysis
	Autoregressive processes, orthogonal matching pursuit
	Graphs signals processing

The labs from the Distributed and large scale computing optimization course were done with @TheiloT, and the labs from the Time series course were done with @s89ne.