I have spent four wonderful years diverting neural networks (with so-called deep learning techniques) from their normal use:
Clustering with generative and discriminative and Wasserstein distances criteria through Generative Adversarial Networks (GANs)
Unsupervised feature importance with infinitesimal Wasserstein maximal distortion
Prediction with uncertainty
In layman's terms, this Ph.D. work was respectively about:
how to make a few groups among a dataset that are both very numerous and big. For example, we explore the feasibility of grouping hundreds of thousands (or more) photographs each made of millions pixels a.k.a color dots
how to show the distinctive attributes of a dataset. Indeed, we investigate the opportunity of interpreting raw data attributes (or coordinates) without annotations nor labels
how to estimate the confidence of an automatic decision. In real-world applications (e.g. industrial constraints, health, security, even justice), a stastical approach to confidence in decision making is crucial for giving back the actual responsibility to human beings for difficult cases typically.
These problems share a common scientific questioning: how should we represent data? For that purpose, we revisit mathematical concept called Optimal Transport with a widely known algorithmical tool called Neural Networks (nicknamed “Deep Learning” since 2010 approximately).
Unpublished works have begun during this thesis without appearing in the manuscript (and are currently in progress):
Grammar and Deep Learning, project in Natural Language Processing towards the reconciliation between grammar from linguistics and neural networks via auto-encoders and modern embedding techniques, as an extension of a research internship that I designed and supervised for Maxime Haddouche.
GaDeMI: Gaussian and Decorrelated Means Independence, project for advanced Data Analysis with Dr. Joan Alexis Glaunès regarding a new kind of auto-encoders built in successive layers to extract a representation whose coordinates are quasi-gaussian and decorrelated (and therefore approximately independent) Extension of the Auto-encoder-based GAN Initialization appendix
StaReLefOU: State Representation Learning from Observation Uncertainty, project in Robotics with Astrid Merckling towards the construction of a state representation built without reward while still being useful for any unknown task in the same environment thanks to exploration
Extension of the Prediction with Uncertainty chapter
Wasserstein Co-Clustering, project in Computatinal Biology for studying the Huttington disease using co-clustering for pairing RNA molecules of different kinds with Optimal Transport with Thi Thanh Yen Nguyen, Pr. Olivier Bouaziz and Pr. Antoine Chambaz Extension of the Wasserstein Clustering chapter
i2nn: An Invitation to Neural Networks, talk given several times to convince people working both in Statistics and Programming to use Deep Learning Extension of the State of the Art chapter