[Lisa_seminaires] [DIRO Talk] Marc Law (U of T) Tue Feb 20 11:00AM PCM Z240

Simon Lacoste-Julien slacoste at iro.umontreal.ca
Mar 20 Fév 10:47:18 EST 2018


Reminder: talk in 15 minutes in *Z-240* Claire-McNicoll (building just
the other side of the street from AA).

-S

On Thu, Feb 15, 2018 at 2:05 PM, Michael Noukhovitch <mnoukhov at gmail.com> wrote:
> We will have three special DIRO talks next week and kicking it off is Marc
> Law, from University of Toronto giving a talk on Tuesday Feb 20 at 11AM in
> room PCM Z240.
> *Note that this is on Tuesday, and in a different room*
>
> Checks all the boxes for a fantastic talk, but you should come see for
> yourself!
> Michael
>
> TITLE Shallow and Deep Metrics for Machine Learning and Computer Vision
>
> KEYWORDS computer vision, few/zero-shot learning, metric learning
>
> ABSTRACT
> Similarity functions and distance metrics are used in many machine learning
> and computer vision contexts such as clustering, k-nearest neighbors
> classification, support vector machine, information/image retrieval,
> visualization etc. Traditionally, machine learning methods fixed sample
> representations and the used metric before learning a model optimized for
> the target task. Metric learning approaches, which learn the employed metric
> in a supervised way, have been proposed to increase performance on tasks
> such as clustering. In particular, they have shown great generalization
> performance to compare objects from categories that were not seen during
> training (for instance in face verification or few-shot learning).
> In this talk, I will talk about different shallow and deep metric learning
> approaches optimized for clustering and reducing model complexity. In the
> clustering task, I will present efficient approaches to learn a metric in a
> supervised or weakly supervised way. In the model complexity context, I will
> present approaches to limit the rank of shallow approaches, or reduce the
> dimensionality of a pretrained deep neural network to perform visualization
> or increase zero-shot learning performance.
>
> BIO
> Marc Law received a PhD in Computer Science from Université Pierre et Marie
> Curie (Paris, France) in 2015. He was a visiting research scholar in the
> team of Professor Eric Xing at the School of Computer Science, Carnegie
> Mellon University in 2015~2016. Since June 2016, he is a postdoctoral fellow
> in the Department of Computer Science (Machine Learning group) at the
> University of Toronto under the supervision of Professor Raquel Urtasun and
> Professor Richard Zemel. During his PhD, he worked mostly on distance metric
> learning applied to different contexts of computer vision and web archiving.
> He is currently working on deep learning. His main focus is to propose
> scalable machine learning methods. He received an award for best PhD from
> the French Association for Artificial Intelligence in 2016.
>
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>



-- 
Simon Lacoste-Julien
Assistant Professor
Department of Computer Science and Operations Research
Université de Montréal
http://www.di.ens.fr/~slacoste/


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