[Tea Talk] Nick Pawlowski (ICL) Fri October 19 2018 10:30 JC S1-111
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.
Reminder this is in 30 minutes! On Tue, Oct 16, 2018 at 1:21 PM Michael Noukhovitch <mnoukhov@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
What room? On Fri, Oct 19, 2018, 09:58 Michael Noukhovitch <mnoukhov@gmail.com> wrote:
Reminder this is in 30 minutes!
On Tue, Oct 16, 2018 at 1:21 PM Michael Noukhovitch <mnoukhov@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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It is Z-315! On Fri, Oct 19, 2018, 10:30 Joseph Paul Cohen <joseph@josephpcohen.com> wrote:
What room?
On Fri, Oct 19, 2018, 09:58 Michael Noukhovitch <mnoukhov@gmail.com> wrote:
Reminder this is in 30 minutes!
On Tue, Oct 16, 2018 at 1:21 PM Michael Noukhovitch <mnoukhov@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
-- You received this message because you are subscribed to the Google Groups "MILA Tous" group. To unsubscribe from this group and stop receiving emails from it, send an email to mila-tous+unsubscribe@mila.quebec.
Tea talk has been moved to Z315 in Claire-McNicoll On Fri, Oct 19, 2018 at 10:31 AM Joseph Paul Cohen <joseph@josephpcohen.com> wrote:
What room?
On Fri, Oct 19, 2018, 09:58 Michael Noukhovitch <mnoukhov@gmail.com> wrote:
Reminder this is in 30 minutes!
On Tue, Oct 16, 2018 at 1:21 PM Michael Noukhovitch <mnoukhov@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
-- You received this message because you are subscribed to the Google Groups "MILA Tous" group. To unsubscribe from this group and stop receiving emails from it, send an email to mila-tous+unsubscribe@mila.quebec.
-- Thanks, Michael
participants (2)
-
Joseph Paul Cohen -
Michael Noukhovitch