ML job (faculty) talk! Mark on your calendar.

---------- Forwarded message ---------- From: Pierre McKenzie mckenzie@iro.umontreal.ca Date: 2017-02-03 11:45 GMT-05:00 Subject: Colloque DIRO, lundi 6 février, Devon Hjelm (UdeM) To: seminaires@iro.umontreal.ca

*Learning underlying structure with neuroimaging data and training generative models with iterative refinement*

par

* Devon Hjelm *

Université de Montréal

*Lundi 6 février, 15:30-16:30*, *Salle 6214*, Pavillon André-Aisenstadt

Université de Montréal, 2920 Chemin de la Tour

Café avant 15:00-15:30

*Résumé:*

Generative models can be used to infer latent structure of observed data for the purpose of advancing domain-specific goals. This is demonstrable with functional and structural magnetic resonance imaging (fMRI / sMRI), where inferred structure can reveal brain function and aid in diagnosis of disease. Further advances in training and inference will increase the applicability of machine learning as a tool for scientific analysis, and the considerable flexibility and capacity allowed by deep learning greatly favor these goals. While deep, continuously-differentiable functions trained by back-propagation have been very successful, local, iterative inference in directed graphical models and generative adversarial networks (GANs) can aid in training, expanding beyond the model's default capacity.