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

Yoshua Bengio yoshua.umontreal at gmail.com
Lun 18 Déc 09:26:36 EST 2017


Starting in 3 minutes

2017-12-14 17:23 GMT-05:00 Yoshua Bengio <yoshua.umontreal at gmail.com>:

> 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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