Hello,

Tristan Sylvain will present his thesis proposal Monday 9:30-11:30 at PAA 3195. Everyone is welcome.

Jury:

President: Simon Lacoste-Julien

Supervisor: Yoshua Bengio

Member: Ioannis Mitliagkas

Deep Neural Networks applied to medical data and Zero-Shot learning

This presentation details two ongoing projects, applying deep learning to the problem of survival analysis for medical data, and using an adversarial approach to zero-shot learning. These very different problems have in common the fact that deep learning allows the learning of efficient representations of data that permit generalization when dealing with both highly noisy (in the medical case) and weakly supervised data (the zero-shot learning case).

For the survival data, we used a multi-objective training procedure that allowed us to attain state of the art on many different public datasets, when compared to classical methods such as the Cox model. This presentation will provide both insights on the architecture used, and the medical context. Overall this problem is an interesting instance of applying deep learning to medical problems. 

Zero-shot learning is an active field of machine learning characterized by the fact that the classes we perform test time classification on are all unseen during the training procedure. To compensate for the lack of information on these classes, we are usually given a set of semantic attributes to allow some form of transfer from a model learned on seen train classes. This part of the presentation will present a current project aiming to use conditional generative adversarial networks to generate fake prototypes for unseen classes in a zero-shot learning context. I will present current results, and some of the challenges that lie ahead.