Publications

HeMIS: Hetero-Modal Image Segmentation
Mohammad Havaei
Nicolas Guizard
Nicolas Chapados
Yoshua Bengio
2016-07-18
ArXiv (preprint)
doi.org
arxiv.org
Theano: A Python framework for fast computation of mathematical expressions
Rami Al-rfou'
Guillaume Alain
Amjad Almahairi
Christof Angermüller
Dzmitry Bahdanau
Nicolas Ballas
Frédéric Bastien
Justin S. Bayer
A. Belikov
A. Belopolsky
Yoshua Bengio
Arnaud Bergeron
J. Bergstra
Valentin Bisson
Josh Bleecher Snyder
Nicolas Bouchard
Nicolas Boulanger-Lewandowski
Xavier Bouthillier
Alexandre De Brébisson
Olivier Breuleux … (see 92 more)
pierre luc carrier
Kyunghyun Cho
Jan Chorowski
Paul F. Christiano
Tim Cooijmans
Marc-Alexandre Côté
Myriam Côté
Aaron Courville
Yann Dauphin
Olivier Delalleau
Julien Demouth
Guillaume Desjardins
Sander Dieleman
Laurent Dinh
M'elanie Ducoffe
Vincent Dumoulin
Samira Ebrahimi Kahou
Dumitru Erhan
Ziye Fan
Orhan Firat
Mathieu Germain
Xavier Glorot
Ian J. Goodfellow
Matthew Graham
Caglar Gulcehre
Philippe Hamel
Iban Harlouchet
Jean-philippe Heng
Balázs Hidasi
Sina Honari
Arjun Jain
S'ebastien Jean
Kai Jia
Mikhail V. Korobov
Vivek Kulkarni
Alex Lamb
Pascal Lamblin
Eric P. Larsen
César Laurent
S. Lee
Simon-mark Lefrancois
Simon Lemieux
Nicholas Léonard
Zhouhan Lin
J. Livezey
Cory R. Lorenz
Jeremiah L. Lowin
Qianli M. Ma
Pierre-Antoine Manzagol
Olivier Mastropietro
R. McGibbon
Roland Memisevic
Bart van Merriënboer
Vincent Michalski
Mehdi Mirza
Alberto Orlandi
Chris Pal
Razvan Pascanu
Mohammad Pezeshki
Colin Raffel
Daniel Renshaw
Matthew David Rocklin
Adriana Romero Soriano
Markus Dr. Roth
Peter Sadowski
John Salvatier
Francois Savard
Jan Schlüter
John D. Schulman
Gabriel Schwartz
Iulian V. Serban
Dmitriy Serdyuk
Samira Shabanian
Etienne Simon
Sigurd Spieckermann
S. Subramanyam
Jakub Sygnowski
Jérémie Tanguay
Gijs van Tulder
Joseph P. Turian
Sebastian Urban
Pascal Vincent
Francesco Visin
Harm de Vries
David Warde-Farley
Dustin J. Webb
M. Willson
Kelvin Xu
Lijun Xue
Li Yao
Saizheng Zhang
Ying Zhang
Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficie… (see more)ntly. Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements. Theano is being actively and continuously developed since 2008, multiple frameworks have been built on top of it and it has been used to produce many state-of-the-art machine learning models. The present article is structured as follows. Section I provides an overview of the Theano software and its community. Section II presents the principal features of Theano and how to use them, and compares them with other similar projects. Section III focuses on recently-introduced functionalities and improvements. Section IV compares the performance of Theano against Torch7 and TensorFlow on several machine learning models. Section V discusses current limitations of Theano and potential ways of improving it.
2016-05-09
ArXiv (preprint)
arxiv.org
arxiv.org
Theano: A Python framework for fast computation of mathematical expressions
Rami Al-rfou'
Guillaume Alain
Amjad Almahairi
Christof Angermüller
Dzmitry Bahdanau
Nicolas Ballas
Frédéric Bastien
Justin S. Bayer
A. Belikov
A. Belopolsky
Yoshua Bengio
Arnaud Bergeron
J. Bergstra
Valentin Bisson
Josh Bleecher Snyder
Nicolas Bouchard
Nicolas Boulanger-Lewandowski
Xavier Bouthillier
Alexandre De Brébisson
Olivier Breuleux … (see 92 more)
pierre luc carrier
Kyunghyun Cho
Jan Chorowski
Paul F. Christiano
Tim Cooijmans
Marc-Alexandre Côté
Myriam Côté
Aaron Courville
Yann Dauphin
Olivier Delalleau
Julien Demouth
Guillaume Desjardins
Sander Dieleman
Laurent Dinh
M'elanie Ducoffe
Vincent Dumoulin
Samira Ebrahimi Kahou
Dumitru Erhan
Ziye Fan
Orhan Firat
Mathieu Germain
Xavier Glorot
Ian G Goodfellow
Matthew Graham
Caglar Gulcehre
Philippe Hamel
Iban Harlouchet
Jean-philippe Heng
Balázs Hidasi
Sina Honari
Arjun Jain
S'ebastien Jean
Kai Jia
Mikhail V. Korobov
Vivek Kulkarni
Alex Lamb
Pascal Lamblin
Eric Larsen
César Laurent
S. Lee
Simon-mark Lefrancois
Simon Lemieux
Nicholas Léonard
Zhouhan Lin
J. Livezey
Cory R. Lorenz
Jeremiah L. Lowin
Qianli M. Ma
Pierre-Antoine Manzagol
Olivier Mastropietro
R. McGibbon
Roland Memisevic
Bart van Merriënboer
Vincent Michalski
Mehdi Mirza
Alberto Orlandi
Chris Pal
Razvan Pascanu
Mohammad Pezeshki
Colin Raffel
Daniel Renshaw
Matthew David Rocklin
Adriana Romero Soriano
Markus Dr. Roth
Peter Sadowski
John Salvatier
Francois Savard
Jan Schlüter
John D. Schulman
Gabriel Schwartz
Iulian V. Serban
Dmitriy Serdyuk
Samira Shabanian
Etienne Simon
Sigurd Spieckermann
S. Subramanyam
Jakub Sygnowski
Jérémie Tanguay
Gijs van Tulder
Joseph P. Turian
Sebastian Urban
Pascal Vincent
Francesco Visin
Harm de Vries
David Warde-Farley
Dustin J. Webb
M. Willson
Kelvin Xu
Lijun Xue
Li Yao
Saizheng Zhang
Ying Zhang
Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficie… (see more)ntly. Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements. Theano is being actively and continuously developed since 2008, multiple frameworks have been built on top of it and it has been used to produce many state-of-the-art machine learning models. The present article is structured as follows. Section I provides an overview of the Theano software and its community. Section II presents the principal features of Theano and how to use them, and compares them with other similar projects. Section III focuses on recently-introduced functionalities and improvements. Section IV compares the performance of Theano against Torch7 and TensorFlow on several machine learning models. Section V discusses current limitations of Theano and potential ways of improving it.
2016-05-09
ArXiv (preprint)
arxiv.org
arxiv.org
A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues
Iulian V. Serban
Alessandro Sordoni
Ryan Lowe
Laurent Charlin
Joelle Pineau
Aaron Courville
Yoshua Bengio
Sequential data often possesses hierarchical structures with complex dependencies between sub-sequences, such as found between the utterance… (see more)s in a dialogue. To model these dependencies in a generative framework, we propose a neural network-based generative architecture, with stochastic latent variables that span a variable number of time steps. We apply the proposed model to the task of dialogue response generation and compare it with other recent neural-network architectures. We evaluate the model performance through a human evaluation study. The experiments demonstrate that our model improves upon recently proposed models and that the latent variables facilitate both the generation of meaningful, long and diverse responses and maintaining dialogue state.
2016-05-01
ArXiv (preprint)
doi.org
arxiv.org
Nash equilibria in the two-player kidney exchange game
Margarida Carvalho
Andrea Lodi
João Pedro Pedroso
Ana Luiza D'ávila Viana
2016-04-21
Mathematical Programming (published)
doi.org
Fault-Tolerant Associative Memories Based on $c$-Partite Graphs
François Leduc-Primeau
Vincent Gripon
Michael Rabbat
Warren Gross
Associative memories allow the retrieval of previously stored messages given a part of their content. In this paper, we are interested in as… (see more)sociative memories based on c-partite graphs that were recently introduced. These memories are almost optimal in terms of the amount of storage they require (efficiency) and allow retrieving messages with low complexity. We propose a generic implementation model for the retrieval algorithm that can be readily mapped to an integrated circuit and study the retrieval performance when hardware components are affected by faults. We show using analytical and simulation results that these associative memories can be made resilient to circuit faults with a minor modification of the retrieval algorithm. In one example, the memory retains 88% of its efficiency when 1% of the storage cells are faulty, or 98% when 0.1% of the binary outputs of the retrieval algorithm are faulty. When considering storage faults, the fault tolerance exhibited by the proposed associative memory can be comparable to using a capacity-achieving error correction code for protecting the stored information.
2016-02-01
IEEE Transactions on Signal Processing (published)
doi.org
Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging
Henning Müller
B. Kelm
Tal Arbel
Weidong (Tom) Cai
M. Cardoso
Georg Langs
Bjoern Menze
Dimitris N. Metaxas
Albert A. Montillo
W. Wells
Shaoting Zhang
Albert C. S. Chung
M. Jenkinson
Annemie Ribbens
2016-01-01
Lecture Notes in Computer Science (published)
doi.org
Clinical Image-Based Procedures. Translational Research in Medical Imaging
Ian J. Gerard
Marta Kersten-Oertel
Simon Drouin
Jeffery Alan Hall
Kevin Petrecca
Dante De Nigris
Tal Arbel
D. Collins
2015-01-01
Lecture Notes in Computer Science (published)
doi.org
Poisson Group Testing: A Probabilistic Model for Boolean Compressed Sensing
Amin Emad
Olgica Milenkovic
We introduce a novel probabilistic group testing framework, termed Poisson group testing, in which the number of defectives follows a right-… (see more)truncated Poisson distribution. The Poisson model has a number of new applications, including dynamic testing with diminishing relative rates of defectives. We consider both nonadaptive and semi-adaptive identification methods. For nonadaptive methods, we derive a lower bound on the number of tests required to identify the defectives with a probability of error that asymptotically converges to zero; in addition, we propose test matrix constructions for which the number of tests closely matches the lower bound. For semiadaptive methods, we describe a lower bound on the expected number of tests required to identify the defectives with zero error probability. In addition, we propose a stage-wise reconstruction algorithm for which the expected number of tests is only a constant factor away from the lower bound. The methods rely only on an estimate of the average number of defectives, rather than on the individual probabilities of subjects being defective.
2014-10-20
ArXiv (preprint)
doi.org
arxiv.org
High-Throughput Energy-Efficient LDPC Decoders Using Differential Binary Message Passing
Kevin Cushon
Saied Hemati
Camille Leroux
Shie Mannor
Warren Gross
In this paper, we present energy-efficient architectures for decoders of low-density parity check (LDPC) codes using the differential decodi… (see more)ng with binary message passing (DD-BMP) algorithm and its modified variant (MDD-BMP). We also propose an improved differential binary (IDB) decoding algorithm. These algorithms offer significant intrinsic advantages in the energy domain: simple computations, low interconnect complexity, and very high throughput, while achieving error correction performance up to within 0.25 dB of the offset min-sum algorithm. We report on fully parallel decoder implementations of (273, 191), (1023, 781), and (4095, 3367) finite geometry-based LDPC codes in 65 nm CMOS. Using the MDD-BMP algorithm, these decoders achieve respective areas of 0.28 mm2, 1.38 mm2, and 15.37 mm2, average throughputs of 37 Gbps, 75 Gbps, and 141 Gbps, and energy efficiencies of 4.9 pJ/bit, 13.2 pJ/bit, and 37.9 pJ/bit with a 1.0 V supply voltage in post-layout simulations. At a reduced supply voltage of 0.8 V, these decoders achieve respective throughputs of 26 Gbps, 54 Gbps, and 94 Gbps, and energy efficiencies of 3.1 pJ/bit, 8.2 pJ/bit, and 23.5 pJ/bit. We also report on a fully parallel implementation of IDB for the (2048, 1723) LDPC code specified in the IEEE 802.3an (10GBASE-T) standard. This decoder achieves an area of 1.44 mm2, average throughput of 172 Gbps, and an energy efficiency of 2.8 pJ/bit with a 1.0 V supply voltage; at 0.8 V, it achieves throughput of 116 Gbps and energy efficiency of 1.7 pJ/bit.
2014-02-01
IEEE Transactions on Signal Processing (published)
doi.org
Bayesian and grAphical Models for Biomedical Imaging
M. Cardoso
Ivor J. A. Simpson
Tal Arbel
Doina Precup
Annemie Ribbens
2014-01-01
Lecture Notes in Computer Science (published)
doi.org
Machine Learning and Interpretation in Neuroimaging
Irina Rish
Georg Langs
Leila Wehbe
Guillermo Cecchi
Kai-min Kevin Chang
Brian G Murphy
2014-01-01
Lecture Notes in Computer Science (published)
doi.org