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

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.