Portrait of Chris Pal

Chris Pal

Core Academic Member
Canada CIFAR AI Chair
Full Professor, Polytechnique Montréal, Department of Computer Engineering and Software Engineering
Assistant Professor, Université de Montréal, Department of Computer Science and Operations Research
Research Topics
Deep Learning

Biography

Christopher Pal is a Canada CIFAR AI Chair, full professor at Polytechnique Montréal and adjunct professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal. He is also a Distinguished Scientist at ServiceNow Research.

Pal has been involved in AI and machine learning research for over twenty-five years and has published extensively on large-scale language modelling methods and generative modelling techniques. He has a PhD in computer science from the University of Waterloo.

Current Students

Research Intern - McGill University
Postdoctorate - HEC Montréal
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Collaborating researcher - McGill University
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Master's Research - Université de Montréal
PhD - Polytechnique Montréal
PhD - McGill University
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PhD - Université de Montréal
Principal supervisor :
PhD - Polytechnique Montréal
Master's Research - Université de Montréal
Co-supervisor :
Collaborating Alumni - Polytechnique Montréal
PhD - Polytechnique Montréal
Postdoctorate - McGill University
Co-supervisor :
Master's Research - Polytechnique Montréal
PhD - Université de Montréal
Co-supervisor :
Master's Research - Concordia University
Co-supervisor :
Collaborating researcher - Université de Montréal
Master's Research - Université de Montréal
PhD - Université de Montréal
PhD - Polytechnique Montréal
PhD - Polytechnique Montréal
PhD - École de technologie suprérieure
PhD - Université de Montréal
Principal supervisor :
Postdoctorate - HEC Montréal
Principal supervisor :
PhD - Polytechnique Montréal
Principal supervisor :
PhD - McGill University
Principal supervisor :
PhD - Polytechnique Montréal

Publications

Deep Complex Networks
Chiheb Trabelsi
Olexa Bilaniuk
Dmitriy Serdyuk
Sandeep Subramanian
Joao Felipe Santos
Soroush Mehri
At present, the vast majority of building blocks, techniques, and architectures for deep learning are based on real-valued operations and re… (see more)presentations. However, recent work on recurrent neural networks and older fundamental theoretical analysis suggests that complex numbers could have a richer representational capacity and could also facilitate noise-robust memory retrieval mechanisms. Despite their attractive properties and potential for opening up entirely new neural architectures, complex-valued deep neural networks have been marginalized due to the absence of the building blocks required to design such models. In this work, we provide the key atomic components for complex-valued deep neural networks and apply them to convolutional feed-forward networks and convolutional LSTMs. More precisely, we rely on complex convolutions and present algorithms for complex batch-normalization, complex weight initialization strategies for complex-valued neural nets and we use them in experiments with end-to-end training schemes. We demonstrate that such complex-valued models are competitive with their real-valued counterparts. We test deep complex models on several computer vision tasks, on music transcription using the MusicNet dataset and on Speech Spectrum Prediction using the TIMIT dataset. We achieve state-of-the-art performance on these audio-related tasks.
Deep Complex Networks
Chiheb Trabelsi
Olexa Bilaniuk
Dmitriy Serdyuk
Sandeep Subramanian
Joao Felipe Santos
Soroush Mehri
Deep Complex Networks
Chiheb Trabelsi
Olexa Bilaniuk
Dmitriy Serdyuk
Sandeep Subramanian
Joao Felipe Santos
Soroush Mehri
Deep Complex Networks
Chiheb Trabelsi
Olexa Bilaniuk
Dmitriy Serdyuk
Sandeep Subramanian
Joao Felipe Santos
Soroush Mehri
Deep Complex Networks
Chiheb Trabelsi
Olexa Bilaniuk
Dmitriy Serdyuk
Sandeep Subramanian
Joao Felipe Santos
Soroush Mehri
Deep Complex Networks
Chiheb Trabelsi
Olexa Bilaniuk
Dmitriy Serdyuk
Sandeep Subramanian
Joao Felipe Santos
Soroush Mehri
Deep Complex Networks
Chiheb Trabelsi
Olexa Bilaniuk
Dmitriy Serdyuk
Sandeep Subramanian
Joao Felipe Santos
Soroush Mehri
Deep Complex Networks
Chiheb Trabelsi
Olexa Bilaniuk
Dmitriy Serdyuk
Sandeep Subramanian
Joao Felipe Santos
Soroush Mehri
Movie Description
Anna Rohrbach
Atousa Torabi
Marcus Rohrbach
Niket Tandon
Bernt Schiele
Brain tumor segmentation with Deep Neural Networks
Mohammad Havaei
Axel Davy
David Warde-Farley
Antoine Biard
Pierre-Marc Jodoin
Movie Description
Anna Rohrbach
Atousa Torabi
Marcus Rohrbach
Niket Tandon
Bernt Schiele
Movie Description
Anna Rohrbach
Atousa Torabi
Marcus Rohrbach
Niket Tandon
Bernt Schiele