Grant: Canada Research Chairs
Université de Montréal
$200,000.00
Learning representations that generalize systematically
FY 2023-24
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.
Program
Canada Research Chairs
Recipient
Location
Quebec, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
CRC-2021-00162
Name nom
Courville, Aaron
Department département
Computer Science and Operations Research - Université de Montréal
Organization id
63
Institution établissement
Université de Montréal
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2023
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
10000
Area of application group en
The socioeconomic objective available
Area of application group fr
L'objectif socioéconomique disponible
Area of application en
The socioeconomic objective available
Area of application fr
L'objectif socioéconomique disponible
Research subject code
10000
Research subject group en
The field of research available
Research subject group fr
The field of research available
Research subject en
Le domaine de recherche disponible
Research subject fr
Le domaine de recherche disponible
Field of research list codes
RDF1020104
Field of research list names en
Machine learning
Field of research list names fr
Apprentissage machine
Socioeconomic objective list codes
RDS10610
Socioeconomic objective list names en
Information systems, technologies and services
Socioeconomic objective list names fr
Systèmes, technologies et services d'information
Application title
Learning representations that generalize systematically
Keyword
Deep Learning; Neural Networks; Out-of-distribution Generalization; Systematic Generalization; Iterated Learning
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.
