MyDataModels contract on the topic “Semi supervised variational autoencoders for versatile data” (June 2019 – May 2022)
Participants: Konstantin Avrachenkov, Mikhail Kamalov.
Collaborators: Denis Bastiment, Carlo Fanara
Variational autoencoders are highly flexible machine learning techniques for learning latent dimension representation. This model is applicable for denoising data as well as for classification purposes. In this thesis we plan to add semi-supervision component to the variational autoencoder techniques. We plan to develop methods which are universally applicable to versatile data such as categorical data, images, texts, etc. Initially starting from static data we aim to extend the methods to time-varying data such as audio, video, time-series, etc. The proposed algorithms can be integrated into the internal engine of MyDataModels company and tested on use cases of MyDataModels.