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

Michael Noukhovitch mnoukhov at gmail.com
Jeu 15 Fév 14:05:50 EST 2018


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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