Deep Learning

Variational Autoencoders with Missing Data

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Variational Autoencoders with Missing Data

This is a project in collaboration with Jill-Jênn Vie from Inria-Lille, France. Autoencoders Dimensionality Reduction In some applications like data visulization, data storage or when the dimmensionality of our data is to large, we’d like to reduce its dimmensionality of the data, keeping as much information as possible. So we’d like to construct an encoder that takes the original data and transform it into a latent variable of lower dimmensionality.

Poverty in Mexico 2018

Analysis of poverty in Mexico using public datasets and development of a poverty index using autoencoders.

World Happines Report (WHR) 2017

Multivariate analysis of the data from the WHR 2017