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Eugene Belilovsky

Membre académique associé

Professeur adjoint, Concordia University, Département d'informatique et de génie logiciel

Professeur associé, Université de Montréal, Département d'informatique et de recherche opérationnelle

Biographie

Eugene Belilovsky est professeur adjoint au Département d'informatique et de génie logiciel de l'Université Concordia. Il est également membre associé de Mila – Institut québécois d’intelligence artificielle et professeur adjoint à l'Université de Montréal. Ses travaux se concentrent sur la vision par ordinateur et l'apprentissage profond. Ses intérêts de recherche actuels comprennent l'apprentissage continu, l'apprentissage à partir de peu de données (few-shot learning) et leurs applications au carrefour de la vision par ordinateur et du traitement du langage.

Étudiants actuels

Abhinav Moudgil

Doctorat - Concordia University

abhinav.moudgil@mila.quebec

Doctorat - Concordia University

adel.nabli@mila.quebec

Albert Orozco Camacho

Doctorat - Concordia University

Co-superviseur⋅e :

orozcoca@mila.quebec

Alex Fulleringer

Maîtrise recherche - Concordia University

alexander.fulleringer@mila.quebec

AmirHossein Zamani

Doctorat - Concordia University

amirhossein.zamani@mila.quebec

Benjamin Therien

Doctorat - Université de Montréal

Superviseur⋅e principal⋅e :

benjamin.therien@mila.quebec

Charles-Etienne Joseph

Maîtrise recherche - Université de Montréal

Co-superviseur⋅e :

charles-etienne.joseph@mila.quebec

Maîtrise recherche - Concordia University

congshu.zou@mila.quebec

Collaborateur·rice de recherche - Concordia University

Co-superviseur⋅e :

donald.shenaj@mila.quebec

Geraldin Nanfack

Postdoctorat - Concordia University

Co-superviseur⋅e :

geraldin.nanfack@mila.quebec

Doctorat - Concordia University

Co-superviseur⋅e :

gwendolyne.legate@mila.quebec

Humza Wajid Hameed

Maîtrise recherche - Concordia University

humza.wajid@mila.quebec

Stagiaire de recherche - Concordia University

louis.fournier@mila.quebec

Maîtrise recherche - Concordia University

Co-superviseur⋅e :

paria.mehrbod@mila.quebec

Doctorat - Concordia University

mohammadreza.davari@mila.quebec

Collaborateur·rice alumni

Co-superviseur⋅e :

nader.asadi@mila.quebec

Nicolas Bernier

Maîtrise recherche - Concordia University

nicolas.bernier@mila.quebec

Maîtrise recherche - Concordia University

paul.janson@mila.quebec

Collaborateur·rice de recherche - Université de Montréal

Superviseur⋅e principal⋅e :

pedro.vianna@mila.quebec

Doctorat - Concordia University

Co-superviseur⋅e :

vaibhav.singh@mila.quebec

Maîtrise recherche - Concordia University

zafir.khaled@mila.quebec

Publications

Online Continual Learning with Maximally Interfered Retrieval

Rahaf Aljundi

Lucas Caccia

Eugene Belilovsky

Massimo Caccia

Min Lin

Laurent Charlin

Tinne Tuytelaars

Continual learning, the setting where a learning agent is faced with a never ending stream of data, continues to be a great challenge for mo… (voir plus)dern machine learning systems. In particular the online or "single-pass through the data" setting has gained attention recently as a natural setting that is difficult to tackle. Methods based on replay, either generative or from a stored memory, have been shown to be effective approaches for continual learning, matching or exceeding the state of the art in a number of standard benchmarks. These approaches typically rely on randomly selecting samples from the replay memory or from a generative model, which is suboptimal. In this work, we consider a controlled sampling of memories for replay. We retrieve the samples which are most interfered, i.e. whose prediction will be most negatively impacted by the foreseen parameters update. We show a formulation for this sampling criterion in both the generative replay and the experience replay setting, producing consistent gains in performance and greatly reduced forgetting. We release an implementation of our method at this https URL.

2019-08-11

ArXiv (prépublication)

Learning Optimizers for Local SGD

Charles-Étienne Joseph

Benjamin Thérien

Abhinav Moudgil

Boris Knyazev

Eugene Belilovsky

Communication-efficient variants of SGD, specifically local SGD, have received a great deal of interest in recent years. These approaches co… (voir plus)mpute multiple gradient steps locally, that is on each worker, before averaging model parameters, helping relieve the critical communication bottleneck in distributed deep learning training. Although many variants of these approaches have been proposed, they can sometimes lag behind state-of-the-art optimizers for deep learning. In this work, we incorporate local optimizers that compute multiple updates into a learned optimization framework, allowing to meta-learn potentially more efficient local SGD algorithms. Our results demonstrate that local learned optimizers can substantially outperform local SGD and its sophisticated variants while maintaining their communication efficiency. We show that the learned optimizers can generalize to new datasets and architectures, demonstrating the potential of learned optimizers for improving communication-efficient distributed learning.

2000-01-01

(publié)

www.semanticscholar.org