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Chris Pal
Researcher at École Polytechnique de Montréal
Publications - 262
Citations - 20801
Chris Pal is an academic researcher from École Polytechnique de Montréal. The author has contributed to research in topics: Computer science & Recurrent neural network. The author has an hindex of 57, co-authored 235 publications receiving 16589 citations. Previous affiliations of Chris Pal include University of Guelph & Association for Computing Machinery.
Papers
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Journal ArticleDOI
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 +8 more
TL;DR: A fast and accurate fully automatic method for brain tumor segmentation which is competitive both in terms of accuracy and speed compared to the state of the art, and introduces a novel cascaded architecture that allows the system to more accurately model local label dependencies.
Posted Content
Theano: A Python framework for fast computation of mathematical expressions
Rami Al-Rfou,Guillaume Alain,Amjad Almahairi,Christof Angermueller,Dzmitry Bahdanau,Nicolas Ballas,Frédéric Bastien,Justin Bayer,Anatoly Belikov,Alexander Belopolsky,Yoshua Bengio,Arnaud Bergeron,James Bergstra,Valentin Bisson,Josh Bleecher Snyder,Nicolas Bouchard,Nicolas Boulanger-Lewandowski,Xavier Bouthillier,Alexandre de Brébisson,Olivier Breuleux,Pierre Luc Carrier,Kyunghyun Cho,Jan Chorowski,Paul F. Christiano,Tim Cooijmans,Marc-Alexandre Côté,Myriam Côté,Aaron Courville,Yann N. Dauphin,Olivier Delalleau,Julien Demouth,Guillaume Desjardins,Sander Dieleman,Laurent Dinh,Mélanie Ducoffe,Vincent Dumoulin,Samira Ebrahimi Kahou,Dumitru Erhan,Ziye Fan,Orhan Firat,Mathieu Germain,Xavier Glorot,Ian Goodfellow,Matthew M. Graham,Caglar Gulcehre,Philippe Hamel,Iban Harlouchet,Jean-Philippe Heng,Balázs Hidasi,Sina Honari,Arjun Jain,Sébastien Jean,Kai Jia,Mikhail Korobov,Vivek Kulkarni,Alex Lamb,Pascal Lamblin,Eric Larsen,César Laurent,Sean Lee,Simon Lefrancois,Simon Lemieux,Nicholas Léonard,Zhouhan Lin,Jesse A. Livezey,Cory Lorenz,Jeremiah Lowin,Qianli Ma,Pierre-Antoine Manzagol,Olivier Mastropietro,Robert T. McGibbon,Roland Memisevic,Bart van Merriënboer,Vincent Michalski,Mehdi Mirza,Alberto Orlandi,Chris Pal,Razvan Pascanu,Mohammad Pezeshki,Colin Raffel,Daniel Renshaw,Matthew Rocklin,Adriana Romero,Markus Roth,Peter Sadowski,John Salvatier,François Savard,Jan Schlüter,John Schulman,Gabriel Schwartz,Iulian Vlad Serban,Dmitriy Serdyuk,Samira Shabanian,Étienne Simon,Sigurd Spieckermann,S. Ramana Subramanyam,Jakub Sygnowski,Jérémie Tanguay,Gijs van Tulder,Joseph Turian,Sebastian Urban,Pascal Vincent,Francesco Visin,Harm de Vries,David Warde-Farley,Dustin J. Webb,Matthew Willson,Kelvin Xu,Lijun Xue,Li Yao,Saizheng Zhang,Ying Zhang +111 more
TL;DR: The performance of Theano is compared against Torch7 and TensorFlow on several machine learning models and recently-introduced functionalities and improvements are discussed.
Proceedings ArticleDOI
Describing Videos by Exploiting Temporal Structure
TL;DR: In this paper, a spatial temporal 3-D convolutional neural network (3-D CNN) representation of the short temporal dynamics is used for video description, which is trained on video action recognition tasks, so as to produce a representation that is tuned to human motion and behavior.
Proceedings ArticleDOI
Learning Conditional Random Fields for Stereo
Daniel Scharstein,Chris Pal +1 more
TL;DR: This paper has constructed a large number of stereo datasets with ground-truth disparities, and a subset of these datasets are used to learn the parameters of conditional random fields (CRFs) and presents experimental results illustrating the potential of this approach for automatically learning the Parameters of models with richer structure than standard hand-tuned MRF models.
Journal ArticleDOI
Deep Learning: A Primer for Radiologists
Gabriel Chartrand,Phillip M. Cheng,Eugene Vorontsov,Michal Drozdzal,Simon Turcotte,Chris Pal,Samuel Kadoury,An Tang +7 more
TL;DR: The key concepts of deep learning for clinical radiologists are reviewed, technical requirements are discussed, emerging applications in clinical radiology are described, and limitations and future directions in this field are outlined.