[Lisa_seminaires] Thesis proposal by Tristan Sylvain, Monday 9:30 @ 3195

Yoshua Bengio yoshua.umontreal at gmail.com
Jeu 14 Déc 17:23:00 EST 2017


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.
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