Grant: Canada Research Chairs
Université de Montréal
$200,000.00
Learning representations that generalize systematically
EF 2022-23
Résumé
Deep learning algorithms are the driving force behind virtually all recent excitement around AI. Despite this, there are clear signs that the performance of these methods deteriorates outside of laboratory conditions. I set out a research program to develop learning representations that generalize learned associations beyond the training distribution, which we refer to as systematic generalization. To this end, we explore two broad categories of learning algorithms: (1) self-supervised learning methods and (2) iterated learning. We also propose to mathematically formalize the notion of systematic generalization and theoretically analyze promising avenues to achieve it.
Programme
Canada Research Chairs
Bénéficiaire
Emplacement
Quebec, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
CRC-2021-00162
Name nom
Courville, Aaron
Department département
Department of Computer Science and Operations Research
Organization id
63
Institution établissement
Université de Montréal
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2022
Competition year année de concours
2021
Award amount
200000
Program id
CRC
Program name en
Canada Research Chairs
Program name fr
Chaires de recherche du Canada
Group en
DISCOVERY RESEARCH
Group fr
RECHERCHE AXÉE SUR LA DÉCOUVERTE
Committee code
9998
Committee name en
Canada Research Chair Selection Committee
Committee name fr
Comité de sélection des Chaires de recherche du Canada
Area of application code
9999
Area of application group en
Not available
Area of application group fr
Non disponible
Area of application en
Not available
Area of application fr
Non disponible
Research subject code
2805
Research subject group en
Artificial intelligence
Research subject group fr
Intelligence artificielle
Research subject en
Learning and inference theories
Research subject fr
Théories de l'apprentissage et l'inférence
Installment
inst:1;4
Partie
4
Nb partie
4
Application title
Learning representations that generalize systematically
Application summary
Deep learning algorithms are the driving force behind virtually all recent excitement around AI. Despite this, there are clear signs that the performance of these methods deteriorates outside of laboratory conditions. I set out a research program to develop learning representations that generalize learned associations beyond the training distribution, which we refer to as systematic generalization. To this end, we explore two broad categories of learning algorithms: (1) self-supervised learning methods and (2) iterated learning. We also propose to mathematically formalize the notion of systematic generalization and theoretically analyze promising avenues to achieve it.
