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

Michael Noukhovitch mnoukhov at gmail.com
Ven 19 Oct 09:57:49 EDT 2018


Reminder this is in 30 minutes!

On Tue, Oct 16, 2018 at 1:21 PM Michael Noukhovitch <mnoukhov at gmail.com>
wrote:

> 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.
>


-- 
Thanks,
Michael
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