[Lisa_seminaires] [Tea Talk] Brady Neal (MILA) Fri Apr 4 10:30AM AA1360

Michael Noukhovitch mnoukhov at gmail.com
Ven 6 Avr 10:07:36 EDT 2018


Reminder this is in 20 minutes!

On Wed, Apr 4, 2018, 12:35 Michael Noukhovitch <mnoukhov at gmail.com> wrote:

> This week we have our very own *Brady Neal* giving a talk this *Friday*
> at *10:30AM* in room *AA1360*.
>
> I may be a little bit biased, but coming to this talk will be at least one
> standard deviation better than your usual Friday!
> Michael
>
> *TITLE *Towards Understanding Generalization in Deep Learning by
> Revisiting the Bias-Variance Decomposition
>
> *KEYWORDS *DL Theory, ML Theory
>
>
> *ABSTRACT*Generalization is at the very core of machine learning. The
> bias-variance decomposition in machine learning is an underused lens to
> view generalization through. While it’s more common to derive upper bounds
> on the generalization gap via more complicated measures of complexity such
> as the VC dimension and Rademacher complexity, the bias-variance
> decomposition is an *equality* that is noticeably simpler. Looking
> through this lens, we can quickly get to partial explanations for why
> larger neural networks seem to generalize better than their smaller
> counterparts, despite the fact that bounds based on VC dimension and
> Rademacher complexity suggest the opposite. We appeal to some of the
> blessings of high dimensionality to do this.
>
> While Zhang et al. (2017) were quite surprised by the results of their
> experiments in “Understanding deep learning requires rethinking
> generalization,” they would have been much less surprised if they were
> looking at their results through the lens of the bias-variance
> decomposition.
>
>
> *BIO*Brady was an intern at MILA and is now a Masters student at MILA
> with Ioannis Mitliagkas. He organizes the DL Theory Reading Group and is
> currently focused on generalization and optimization in deep learning
>
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