[Lisa_seminaires] [Tea Talk] Nick Pawlowski (ICL) Fri October 19 2018 10:30 JC S1-111

Michael Noukhovitch mnoukhov at gmail.com
Mar 16 Oct 13:21:44 EDT 2018


This week we have *Nick Pawlowski * from * ICL (interning at FAIR) * giving
a talk on *Fri October 19 2018* at *10:30* in *Jean Coutu S1-111*

Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>?
Yes
Nick has to be back at FAIR at 2pm so I'd suggest if you want to meet with
him then come out to lunch and you can arrange to meet Nick after.

*Prior* to seeing this ad, I would have been very *uncertain* about coming.
But now it seem like just what the doctor ordered!
Michael

*TITLE* Bayesian Deep Learning and Applications to Medical Imaging

*KEYWORDS *bayesian deep learning, medical applications

*ABSTRACT*
Deep learning revolutionised the way we approach computer vision and
medical image analysis. Regardless of improved accuracy scores and other
metrics, deep learning methods tend to be overconfident on unseen data or
even when predicting the wrong label. Bayesian deep learning offers a
framework to alleviate some of these concerns by modelling the uncertainty
over the weights generating those predictions. This talk will review some
previous achievements of the field and introduce Bayes by Hypernet (BbH).
BbH uses neural networks to parametrise the variational approximation of
the distribution of the parameters. We present more complex parameter
distribution, better robustness to adversarial examples, and improved
uncertainties. Lastly, we present the use of Bayesian NNs for outlier
detection in the medical imaging domain, particularly the application of
Brain lesion detection.

*BIO*
Nick is a PhD student in the Biomedical Image Analysis group at Imperial
College London, supervised by Ben Glocker. He works on methods to integrate
and use uncertainty with deep learning methods. He focuses on Bayesian
neural networks and their use for outlier detection. Nick is currently a
Research Intern at FAIR Montreal and a main developer of DLTK, a toolkit
for deep learning for medical imaging. During this summer he was a Machine
Learning resident at Google X.
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