Chris Pal

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

Mai Ababneh

Collaborating researcher - Formerly McGill University (but ending)

Shubham Agarwal

Postdoctorate - HEC Montréal

Principal supervisor :

Paul Barde

Collaborating researcher - McGill University

Principal supervisor :

Master's Research - Université de Montréal

Chris Beckham

PhD - Polytechnique Montréal

Can (Sam) Chen

Collaborating Alumni - McGill University

Principal supervisor :

Xue (Steve) Liu

Léa Demeule

PhD - Université de Montréal

Principal supervisor :

PhD - Polytechnique Montréal

Chris Emezue

Master's Research - Université de Montréal

Co-supervisor :

Collaborating Alumni - Polytechnique Montréal

Roger Girgis

PhD - Polytechnique Montréal

Florian Golemo

Postdoctorate - McGill University

Master's Research - Polytechnique Montréal

PhD - Université de Montréal

Co-supervisor :

Yousef Kotp

Master's Research - Concordia University

Co-supervisor :

Master's Research - Université de Montréal

Olga Luo

PhD - Université de Montréal

Aristides Milios

PhD - Université de Montréal

Joel Moniz

PhD - Polytechnique Montréal

Jonathan Pilault

PhD - Polytechnique Montréal

Juan Rodriguez

PhD - École de technologie suprérieure

Luke Rowe

PhD - Université de Montréal

Principal supervisor :

Gaurav Sahu

Postdoctorate - HEC Montréal

Principal supervisor :

PhD - Polytechnique Montréal

Principal supervisor :

PhD - McGill University

Principal supervisor :

PhD - Polytechnique Montréal

PhD - Université de Montréal

Direct Behavior Specification via Constrained Reinforcement Learning

Blog Posts

August 31, 2022

Julien Roy

Roger Girgis

Joshua Romoff

Pierre-Luc Bacon

Chris Pal

Read the article

Publications

Deep Complex Networks

Chiheb Trabelsi

Dmitriy Serdyuk

Sandeep Subramanian

Joao Felipe Santos

Soroush Mehri

2017-05-27

ArXiv (preprint)

Deep Complex Networks

Chiheb Trabelsi

Dmitriy Serdyuk

Sandeep Subramanian

Joao Felipe Santos

Soroush Mehri

2017-05-27

ArXiv (preprint)

Deep Complex Networks

Chiheb Trabelsi

Dmitriy Serdyuk

Sandeep Subramanian

Joao Felipe Santos

Soroush Mehri

2017-05-27

ArXiv (preprint)

Deep Complex Networks

Chiheb Trabelsi

Dmitriy Serdyuk

Sandeep Subramanian

Joao Felipe Santos

Soroush Mehri

2017-05-27

ArXiv (preprint)

Deep Complex Networks

Chiheb Trabelsi

Dmitriy Serdyuk

Sandeep Subramanian

Joao Felipe Santos

Soroush Mehri

2017-05-27

ArXiv (preprint)

Deep Complex Networks

Chiheb Trabelsi

Dmitriy Serdyuk

Sandeep Subramanian

Joao Felipe Santos

Soroush Mehri

2017-05-27

ArXiv (preprint)

Deep Complex Networks

Chiheb Trabelsi

Dmitriy Serdyuk

Sandeep Subramanian

Joao Felipe Santos

Soroush Mehri

2017-05-27

ArXiv (preprint)

Deep Complex Networks

Chiheb Trabelsi

Dmitriy Serdyuk

Sandeep Subramanian

Joao Felipe Santos

Soroush Mehri

2017-05-27

ArXiv (preprint)

Deep Complex Networks

Chiheb Trabelsi

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.

2017-05-27

ArXiv (preprint)

Movie Description

Anna Rohrbach

Atousa Torabi

Marcus Rohrbach

Niket Tandon

Hugo Larochelle

Aaron Courville

Bernt Schiele

2017-01-25

International Journal of Computer Vision (published)

doi.org

Brain tumor segmentation with Deep Neural Networks

Mohammad Havaei

Axel Davy

David Warde-Farley

Antoine Biard

Pierre-Marc Jodoin

2017-01-01

Medical Image Analysis (published)

doi.org

Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations

David Scott Krueger

Tegan Maharaj

J'anos Kram'ar

Mohammad Pezeshki

Nicolas Ballas

Nan Rosemary Ke

Anirudh Goyal

We propose zoneout, a novel method for regularizing RNNs. At each timestep, zoneout stochastically forces some hidden units to maintain thei… (see more)r previous values. Like dropout, zoneout uses random noise to train a pseudo-ensemble, improving generalization. But by preserving instead of dropping hidden units, gradient information and state information are more readily propagated through time, as in feedforward stochastic depth networks. We perform an empirical investigation of various RNN regularizers, and find that zoneout gives significant performance improvements across tasks. We achieve competitive results with relatively simple models in character- and word-level language modelling on the Penn Treebank and Text8 datasets, and combining with recurrent batch normalization yields state-of-the-art results on permuted sequential MNIST.

2016-06-03

ArXiv (preprint)