Event
Devon Hjelm, Universit茅 de Montr茅al
Monday, February 6, 2017 15:30
room 6214, Pavillon Andr茅-Aisenstadt, 2920, Chemin de la tour, 5th floor, Montreal, QC, H3T 1J4, CA
Learning underlying structure with neuroimaging data and training generative models with iterative refinement.
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.