Grant: Canada Research Chairs
Dalhousie University
$200,000.00
Interpretability for Machine Learning
EF 2020-21
Résumé
Recent important breakthroughs of modern Machine Learning (ML) and Deep Learning methods are accompanied by the concerns about transparency and fairness of ML systems. In this context interpretability, understood as the ability to explain and present ML systems to humans, becomes an important requirement for modern ML systems. This research program will tackle several important aspects of interpretability, including novel use of relational knowledge representation techniques in which I have significant accomplishments. The programs principal axes are the involvement of graduate students, as well as collaborative applications of Machine Learning in Dalhousie Universitys priority research areas of Health and Oceans.
Programme
Canada Research Chairs
Bénéficiaire
Emplacement
Nova Scotia, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
1000232586
Name nom
Matwin, Stan
Department département
Faculty of Computer Science
Organization id
79
Institution établissement
Dalhousie University
Province en
Nova Scotia
Province fr
Nouvelle-Écosse
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2020
Competition year année de concours
2019
Award amount
200000
Program id
CRC
Program name en
Canada Research Chairs
Program name fr
Chaires de recherche du Canada
Group en
Discovery grants programs
Group fr
Programmes de subventions à 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
2800
Research subject group en
Artificial intelligence
Research subject group fr
Intelligence artificielle
Research subject en
Artificial intelligence (computer vision, use2603)
Research subject fr
Intelligence artificielle (vision artificielle, utiliser 2603)
Partie
0
Nb partie
0
Application title
Interpretability for Machine Learning
Keyword
NSERC
Application summary
Recent important breakthroughs of modern Machine Learning (ML) and Deep Learning methods are accompanied by the concerns about transparency and fairness of ML systems. In this context interpretability, understood as the ability to explain and present ML systems to humans, becomes an important requirement for modern ML systems. This research program will tackle several important aspects of interpretability, including novel use of relational knowledge representation techniques in which I have significant accomplishments. The programs principal axes are the involvement of graduate students, as well as collaborative applications of Machine Learning in Dalhousie Universitys priority research areas of Health and Oceans.
