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Experts in: Neural Networks

Bengio, Yoshua

BENGIO, Yoshua

Professeur titulaire

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.

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ECK, Douglas

Professeur associé

Douglas Eck, research scientist and lead for Google Brain's Magenta project, is teaching computers how to generate their own music, video, images, and text using neural networks and other types of machine learning.

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Memisevic, Roland

MEMISEVIC, Roland

Professeur associé

My research interests are in machine learning and computer vision. I develop algorithms that extract information from large amounts of data, with a particular focus on the extraction of spatial and spatio-temporal features from images and videos. I am also interested in the development of deep learning architectures and bio-inspired models and their applications in automatic data analysis.

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RISH, Irina

Professeure agrégée

Her current research interests include continual lifelong learning, optimization algorithms for deep neural networks, sparse modeling and probabilistic inference, dialog generation, biologically plausible reinforcement learning, and dynamical systems approaches to brain imaging analysis. Before joining UdeM and MILA in 2019, Irina was a research scientist at the IBM T.J. Watson Research Center, where she worked on various projects at the intersection of neuroscience and AI, and led the Neuro-AI challenge. She received multiple IBM awards, including IBM Eminence & Excellence Award and IBM Outstanding Innovation Award in 2018, IBM Outstanding Technical Achievement Award in 2017, and IBM Research Accomplishment Award in 2009.

Dr. Rish holds 64 patents, has published over 80 research papers, several book chapters, three edited books, and a monograph on Sparse Modeling. She is IEEE TPAMI Associate Editor (since 2019), a member of the AI Journal (AIJ) editorial board (since 2016), served as a Senior Area Chair for NIPS-2017, NIPS-2018, ICML-2018, an Area Chair for ICLR-2019, ICLR-2018, JCAI-2015, ICML-2015, ICML-2016, NIPS-2010, tutorials chair for UAI-2012 and workshop chair for UAI-2015 and ICML-2012; she gave several tutorials (AAAI-1998, AAAI-2000, ICML-2010, ECML-2006) and co-organized multiple workshops at core AI conferences, including 11 workshops at NIPS (from 2003 to 2016), ICML-2008 and ECML-2006.

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Vincent, Pascal

VINCENT, Pascal

Professeur agrégé

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.
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