Another ML professor job talk on Thursday.
There might be some time for students or postdocs interested in meeting our
visitor, so let Linda know if you are interested.
---------- Forwarded message ----------
From: Pierre McKenzie <mckenzie(a)iro.umontreal.ca>
Date: 2017-02-14 14:23 GMT-05:00
Subject: Colloque DIRO, jeudi 16 février, Ted Meeds (Vrije U, Amsterdam)
To: seminaires(a)iro.umontreal.ca
Cc: veronique.sage(a)umontreal.ca
*Modeling science with machine learning: from inference in
simulation-based models to understanding cancer.*
par
* Ted Meeds *
Vrije Universiteit Amsterdam
*Jeudi 16 février, 15:30-16:30*, *Salle 6214*, Pavillon André-Aisenstadt
Université de Montréal, 2920 Chemin de la Tour
Café avant 15:00-15:30
* Cette conférence sera donnée en anglais.*
*Résumé:*
In this talk I will explore the role that machine learning models,
algorithms, and inference procedures play in several areas of science.
Likelihood-free inference, or approximate Bayesian computation (ABC) is a
framework for performing Bayesian inference in a wide variety of
simulation-based sciences, from population genetics to computational
psychology. I will first describe recent algorithmic improvements in ABC
inference which are due in part from viewing inference from a machine
learning perspective. I will then describe ongoing work using deep neural
networks to model heterogeneous pan-cancer data and challenging tumour
segmentation tasks whose long term goal is targeted immunotherapy
treatment. Finally I will touch upon recent work on compressing neural
networks using simple machine learning techniques, resulting in sparse and
quantized parameters which, when applied to scientific domains, provides
parsimonious models that are simple enough to be understood by experts.