Experts in: Statistical models
BENGIO, Yoshua
Professeur titulaire
- Machine learning
- Representation learning
- Deep learning
- Temporal database
- Artificial intelligence
- Probabilistic models
- Statistical models
- Neural Networks
- Computer vision
- Data science
- Natural-language processing (NLP)
- Model Building
- COVID19
My long-term goal is to understand intelligence; understanding its underlying principles would give us access to artificial intelligence (AI), and I believe that learning algorithms are essential in this quest. Learning algorithms could give computers the ability to capture operational knowledge (not necessarily in symbolic / verbal form) from examples.
CHARLIN, Laurent
Professeur associé
COURVILLE, Aaron
Professeur titulaire
LACOSTE-JULIEN, Simon
Professeur agrégé
LE ROUX, Nicolas
Professeur associé
MITLIAGKAS, Ioannis
Professeur adjoint
SRIDHAR, Dhanya
Professeure adjointe
VINCENT, Pascal
Professeur associé
- Machine learning
- Representation learning
- Deep learning
- Artificial intelligence
- Big data
- Statistical models
- Pattern recognition
- Neural Networks
- Algorithmics
My research interests are centered around discovering fundamental computational principles that underlie the extraordinary capabilities to learn from the environment, understand it and adapt to it that characterize intelligence. The development of novel machine learning algorithms based on such principles, and trained on very large data sets, is at the heart of the latest technological breakthroughs in artificial intelligence.
More specifically, I research how higher level representations that carry meaning can be constructed autonomously, starting from streams of raw sensory input (such as images and sounds). Similarly to what our brain's neural networks naturally know how to do, this amounts to intelligently modeling the structure of the observed reality, by discovering and exploiting hidden and complex statistical regularities that it follows.