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

Joseph Paul Cohen joseph at josephpcohen.com
Ven 19 Oct 10:30:53 EDT 2018


What room?

On Fri, Oct 19, 2018, 09:58 Michael Noukhovitch <mnoukhov at gmail.com> wrote:

> 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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