Université de Montréal

Faculté des arts et des sciences -Secteur des sciences
Département d'informatique et de recherche opérationnelle


Département d'informatique et de recherche opérationnelle
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Machine Learning Laboratory

Open positions

Canada Research Chair in Statistical Learning Algorithms

Machine learning in adaptive computer systems is an area of research at the intersection of artificial intelligence, statistical inference and optimization. Such systems must learn to perform a task from a set of examples and a priori knowledge about the task. These systems are often also adaptive, i.e. can adapt to changes in their environment. Several of the algorithms used in our laboratory are from research in artificial neural networks, inspired from the way in which the brain processes information. Many algorithms are expressed in the framework of probabilistic modeling, where uncertainty is expressed explicitly, and relations between random variables are captured by formulas called factors, that can be visualized graphically.

Machine learning algorithms are important when one does not have enough explicit knowledge about the problem to directly write a program or a set of rules that will solve the task, or when we don’t really know the form of the true distribution from which the observed data are coming from. In some cases one can obtain more information about the problem in the form of examples of the task to be performed, usually a set of (input, output) pairs (e.g. of patterns, or sequences). These examples tell the system what it should produce in output in a certain situation represented by its input, or what joint values are plausible for the observed variables. These examples must then be combined with explicit knowledge about the problem to design the system architecture and representation, and choose an appropriate learning algorithm.

The LISA (machine learning lab) aims towards improving our understanding of the principles that give rise to powerful learning and to intelligence, which will be important to make significant progress on learning algorithms and artificial intelligence (AI). Acquiring the kind of complex knowledge necessary for AI requires some form of learning, with the ability to discover hidden relationships and statistical structure that may be highly complex, with many interacting factors of variations explaining the observed high-dimensional data that sensors can provide. According to us this is the main challenge for machine learning and AI.

Like the brain, deep learning algorithms are based on several levels of representation and processing, creating several levels of levels of abstraction. Compared to learning algorithms based on shallower architectures, deep learners have the potential to efficiently represent highly complex functions and distributions. We explore various learning algorithms for deep learning, based in particular on unsupervised pre-training (e.g., various kinds of Boltzmann machines and auto-encoders).

Unsupervised pre-training allows to exploit very large quantities of mostly unlabeled examples (such as documents, images, and videos from the web). The learned representations capture the salient factors of variation (and invariances) implicitly present in the data, and can be exploited in the context of several supervised learning tasks (multi-task learning, self-taught learning, semi-supervised learning).

See Yoshua Bengio’s research page , and these few papers, to learn a bit more: