[Lisa_seminaires] [Visitor Talk] Gael Varoquaux, Friday 2pm

xavier bouthillier xavier.bouthillier at gmail.com
Jeu 23 Aou 11:16:10 EDT 2018


Hi all,

The talk will be streamed and recorded:
https://mila.bluejeans.com/809027115/webrtc

You can reserve a time-slot to meet him at the following link:
https://calendar.google.com/calendar/selfsched?sstoken=UUNKME5Da1BIQVRHfGRlZmF1bHR8N2Y2NzFjY2E0NGQyZjY0ZjdjY2M1MGM5YjdlMTdkODc

Thank you!
Xavier Bouthillier

On Wed, Aug 22, 2018 at 3:32 PM Pascal Vincent <pascal20100 at gmail.com>
wrote:

> Hello all, this Friday, we will have the visit of *Gaël Varoquaux* from
> INRIA, who will be giving a talk at 2 p.m. in AA-3195.
>
> Gaël is famous for creating the highly successful scikit-learn software
> and for his past work on brain imaging and neuroscience. But he will be
> talking primarily about his current research interests, which are machine
> learning approaches applicable to more general types of data.
>
> Xavier Bouthillier is coordinating his Friday visit, please get in touch
> with him (xavier.bouthillier at gmail.com) if you want to set aside time to
> meet the speaker.
>
> *When:* this Friday, august 24th, at 2 p.m.
>
> *Where:* Room 3195, Pavillon André Aisenstadt
>
> *Title:* Simple representations for learning: factorizations and
> similarities
>
> *Abstract*
>
> Real-life data seldom comes in the ideal form for statistical learning.
> This talk will focus on high-dimensional problems for signals and
> discrete entities: when dealing with many, correlated, signals or
> entities, it is useful to extract representations that capture these
> correlations.
>
> Matrix factorization models provide simple but powerful representations.
> They are used for recommender systems across discrete entities such as
> users and products, or to learn good dictionaries to represent images.
> However they entail large computing costs on very high-dimensional data,
> databases with many products or high-resolution images. I will present an
> algorithm to factorize huge matrices based on stochastic subsampling that
> gives up to 10-fold speed-ups [1].
>
> With discrete entities, the explosion of dimensionality may be due to
> variations in how a smaller number of categories are represented. Such a
> problem of "dirty categories" is typical of uncurated data sources. I
> will discuss how encoding this data based on similarities recovers a
> useful category structure with no preprocessing. I will show how it
> interpolates between one-hot encoding and techniques used in
> character-level natural language processing.
>
>
> [1] Stochastic subsampling for factorizing huge matrices
> A Mensch, J Mairal, B Thirion, G Varoquaux
> IEEE Transactions on Signal Processing 66 (1), 113-128
>
> [2] Similarity encoding for learning with dirty categorical variables. P
> Cerda, G Varoquaux, B Kégl Machine Learning (2018): 1-18
>
>
>
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