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.