[Lisa_seminaires] [mila-tous] [Tea Talk] Nick Pawlowski (ICL) Fri October 19 2018 10:30 MOVED to CM Z315

Michael Noukhovitch mnoukhov at gmail.com
Ven 19 Oct 10:33:27 EDT 2018


Tea talk has been moved to Z315 in Claire-McNicoll

On Fri, Oct 19, 2018 at 10:31 AM Joseph Paul Cohen <joseph at josephpcohen.com>
wrote:

> 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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>

-- 
Thanks,
Michael
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