Portrait de Chris Pal

Chris Pal

Membre académique principal
christopher.pal@mila.quebec
Chaire en IA Canada-CIFAR
Professeur titulaire, Polytechnique Montréal, Département de génie informatique et de génie logiciel
Professeur associé, Université de Montréal, Département d'informatique et de recherche opérationnelle
Sujets de recherche
Apprentissage profond
Site web
Google Scholar
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Biographie

Christopher Pal est titulaire d'une chaire en IA Canada-CIFAR, professeur titulaire à Polytechnique Montréal et professeur adjoint au Département d'informatique et de recherche opérationnelle (DIRO) de l'Université de Montréal. Il est également chercheur émérite à ServiceNow Research. Il est engagé dans la recherche sur l'intelligence artificielle et l'apprentissage automatique depuis plus de 25 ans, publiant souvent des travaux sur les méthodes de modélisation du langage à grande échelle et les techniques de modélisation générative. Il a obtenu un doctorat en informatique à l'Université de Waterloo.

Étudiants actuels

Mai Ababneh
Collaborateur·rice de recherche - Formerly McGill (but ending)
ababneh.mai@gmail.com
Shubham Agarwal
Postdoctorat - HEC
Superviseur⋅e principal⋅e :
Laurent Charlin
Site web
Github
Google Scholar
Paul Barde
Collaborateur·rice de recherche - McGill
Superviseur⋅e principal⋅e :
Derek Nowrouzezahrai
paul.b.barde@gmail.com
Site web
Github
Google Scholar
Hugo Baudchon
Maîtrise recherche - UdeM
Github
Chris Beckham
Doctorat - Polytechnique
Can (Sam) Chen
Collaborateur·rice alumni - McGill
Superviseur⋅e principal⋅e :
Xue (Steve) Liu
chencan421@gmail.com
Site web
Léa Demeule
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Glen Berseth
Site web
Github
Amine El-hattami
Doctorat - Polytechnique
Chris Emezue
Maîtrise recherche - UdeM
Co-superviseur⋅e :
Derek Nowrouzezahrai
Site web
Github
Google Scholar
Matthew Fortier
Collaborateur·rice alumni - Polytechnique
Github
Google Scholar
Roger Girgis
Doctorat - Polytechnique
Florian Golemo
Postdoctorat - McGill
fgolemo@gmail.com
Site web
Github
Google Scholar
Anthony Gosselin
Maîtrise recherche - Polytechnique
Site web
Github
Google Scholar
Simon Guiroy
Doctorat - UdeM
Co-superviseur⋅e :
Sarath Chandar
Site web
Github
Google Scholar
Yousef Kotp
Maîtrise recherche - Concordia
Co-superviseur⋅e :
Mahdi Hosseini
Site web
Github
Google Scholar
Jae Hyun Lim
Collaborateur·rice de recherche - UdeM
Site web
Github
Google Scholar
Michelle Lin
Maîtrise recherche - UdeM
Site web
Github
Google Scholar
Olga Luo
Doctorat - UdeM
Aristides Milios
Doctorat - UdeM
Site web
Github
Google Scholar
Joel Moniz
Doctorat - Polytechnique
Jonathan Pilault
Doctorat - Polytechnique
Github
Google Scholar
Juan Rodriguez
Doctorat - École de technologie suprérieure
Site web
Github
Google Scholar
Luke Rowe
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Liam Paull
Site web
Github
Google Scholar
Gaurav Sahu
Postdoctorat - HEC
Superviseur⋅e principal⋅e :
Laurent Charlin
Site web
Github
Google Scholar
Aditya Sharma
Doctorat - Polytechnique
Superviseur⋅e principal⋅e :
Amal Zouaq
Site web
Github
Google Scholar
Mattie Tesfaldet
Doctorat - McGill
Superviseur⋅e principal⋅e :
Derek Nowrouzezahrai
Site web
Github
Google Scholar
Martin Weiss
Doctorat - Polytechnique
Site web
Github
Google Scholar
Matthew Wiens
Doctorat - UdeM
Site web

Publications

Movie Description
Anna Rohrbach
Atousa Torabi
Marcus Rohrbach
Niket Tandon
Chris Pal
Hugo Larochelle
Aaron Courville
Bernt Schiele
2017-01-25
International Journal of Computer Vision (publié)
doi.org
arxiv.org
Brain tumor segmentation with Deep Neural Networks
Mohammad Havaei
Axel Davy
David Warde-Farley
Antoine Biard
Aaron Courville
Yoshua Bengio
Chris Pal
Pierre-Marc Jodoin
Hugo Larochelle
2017-01-01
Medical Image Analysis (publié)
doi.org
arxiv.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
Yoshua Bengio
Hugo Larochelle
Aaron Courville
Chris Pal
We propose zoneout, a novel method for regularizing RNNs. At each timestep, zoneout stochastically forces some hidden units to maintain thei… (voir plus)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 (prépublication)
arxiv.org
arxiv.org
Movie Description
Anna Rohrbach
Atousa Torabi
Marcus Rohrbach
Niket Tandon
Chris Pal
Hugo Larochelle
Aaron Courville
Bernt Schiele
2016-05-12
ArXiv (prépublication)
doi.org
arxiv.org
Movie Description
Anna Rohrbach
Atousa Torabi
Marcus Rohrbach
Niket Tandon
Chris Pal
Hugo Larochelle
Aaron Courville
Bernt Schiele
Audio description (AD) provides linguistic descriptions of movies and allows visually impaired people to follow a movie along with their pee… (voir plus)rs. Such descriptions are by design mainly visual and thus naturally form an interesting data source for computer vision and computational linguistics. In this work we propose a novel dataset which contains transcribed ADs, which are temporally aligned to full length movies. In addition we also collected and aligned movie scripts used in prior work and compare the two sources of descriptions. We introduce the Large Scale Movie Description Challenge (LSMDC) which contains a parallel corpus of 128,118 sentences aligned to video clips from 200 movies (around 150 h of video in total). The goal of the challenge is to automatically generate descriptions for the movie clips. First we characterize the dataset by benchmarking different approaches for generating video descriptions. Comparing ADs to scripts, we find that ADs are more visual and describe precisely what is shown rather than what should happen according to the scripts created prior to movie production. Furthermore, we present and compare the results of several teams who participated in the challenges organized in the context of two workshops at ICCV 2015 and ECCV 2016.
2016-05-12
ArXiv (prépublication)
doi.org
arxiv.org
Movie Description
Anna Rohrbach
Atousa Torabi
Marcus Rohrbach
Niket Tandon
Chris Pal
Hugo Larochelle
Aaron Courville
Bernt Schiele
Audio description (AD) provides linguistic descriptions of movies and allows visually impaired people to follow a movie along with their pee… (voir plus)rs. Such descriptions are by design mainly visual and thus naturally form an interesting data source for computer vision and computational linguistics. In this work we propose a novel dataset which contains transcribed ADs, which are temporally aligned to full length movies. In addition we also collected and aligned movie scripts used in prior work and compare the two sources of descriptions. We introduce the Large Scale Movie Description Challenge (LSMDC) which contains a parallel corpus of 128,118 sentences aligned to video clips from 200 movies (around 150 h of video in total). The goal of the challenge is to automatically generate descriptions for the movie clips. First we characterize the dataset by benchmarking different approaches for generating video descriptions. Comparing ADs to scripts, we find that ADs are more visual and describe precisely what is shown rather than what should happen according to the scripts created prior to movie production. Furthermore, we present and compare the results of several teams who participated in the challenges organized in the context of two workshops at ICCV 2015 and ECCV 2016.
2016-05-12
ArXiv (prépublication)
doi.org
arxiv.org
Movie Description
Anna Rohrbach
Atousa Torabi
Marcus Rohrbach
Niket Tandon
Chris Pal
Hugo Larochelle
Aaron Courville
Bernt Schiele
2016-05-12
ArXiv (prépublication)
doi.org
arxiv.org
Movie Description
Anna Rohrbach
Atousa Torabi
Marcus Rohrbach
Niket Tandon
Chris Pal
Hugo Larochelle
Aaron Courville
Bernt Schiele
2016-05-12
ArXiv (prépublication)
doi.org
arxiv.org
Movie Description
Anna Rohrbach
Atousa Torabi
Marcus Rohrbach
Niket Tandon
Chris Pal
Hugo Larochelle
Aaron Courville
Bernt Schiele
Audio description (AD) provides linguistic descriptions of movies and allows visually impaired people to follow a movie along with their pee… (voir plus)rs. Such descriptions are by design mainly visual and thus naturally form an interesting data source for computer vision and computational linguistics. In this work we propose a novel dataset which contains transcribed ADs, which are temporally aligned to full length movies. In addition we also collected and aligned movie scripts used in prior work and compare the two sources of descriptions. We introduce the Large Scale Movie Description Challenge (LSMDC) which contains a parallel corpus of 128,118 sentences aligned to video clips from 200 movies (around 150 h of video in total). The goal of the challenge is to automatically generate descriptions for the movie clips. First we characterize the dataset by benchmarking different approaches for generating video descriptions. Comparing ADs to scripts, we find that ADs are more visual and describe precisely what is shown rather than what should happen according to the scripts created prior to movie production. Furthermore, we present and compare the results of several teams who participated in the challenges organized in the context of two workshops at ICCV 2015 and ECCV 2016.
2016-05-12
ArXiv (prépublication)
doi.org
arxiv.org
Movie Description
Anna Rohrbach
Atousa Torabi
Marcus Rohrbach
Niket Tandon
Chris Pal
Hugo Larochelle
Aaron Courville
Bernt Schiele
2016-05-12
ArXiv (prépublication)
doi.org
arxiv.org
Movie Description
Anna Rohrbach
Atousa Torabi
Marcus Rohrbach
Niket Tandon
Chris Pal
Hugo Larochelle
Aaron Courville
Bernt Schiele
Audio description (AD) provides linguistic descriptions of movies and allows visually impaired people to follow a movie along with their pee… (voir plus)rs. Such descriptions are by design mainly visual and thus naturally form an interesting data source for computer vision and computational linguistics. In this work we propose a novel dataset which contains transcribed ADs, which are temporally aligned to full length movies. In addition we also collected and aligned movie scripts used in prior work and compare the two sources of descriptions. We introduce the Large Scale Movie Description Challenge (LSMDC) which contains a parallel corpus of 128,118 sentences aligned to video clips from 200 movies (around 150 h of video in total). The goal of the challenge is to automatically generate descriptions for the movie clips. First we characterize the dataset by benchmarking different approaches for generating video descriptions. Comparing ADs to scripts, we find that ADs are more visual and describe precisely what is shown rather than what should happen according to the scripts created prior to movie production. Furthermore, we present and compare the results of several teams who participated in the challenges organized in the context of two workshops at ICCV 2015 and ECCV 2016.
2016-05-12
ArXiv (prépublication)
doi.org
arxiv.org
Movie Description
Anna Rohrbach
Atousa Torabi
Marcus Rohrbach
Niket Tandon
Chris Pal
Hugo Larochelle
Aaron Courville
Bernt Schiele
2016-05-12
ArXiv (prépublication)
doi.org
arxiv.org