Portrait de Yoshua Bengio

Yoshua Bengio

Membre académique principal
Chaire en IA Canada-CIFAR
Professeur titulaire, Université de Montréal, Département d'informatique et de recherche opérationnelle
Directeur scientifique, Équipe de direction
Observateur, Conseil d'administration, Mila
Sujets de recherche
Apprentissage automatique médical
Apprentissage de représentations
Apprentissage par renforcement
Apprentissage profond
Causalité
Modèles génératifs
Modèles probabilistes
Modélisation moléculaire
Neurosciences computationnelles
Raisonnement
Réseaux de neurones en graphes
Réseaux de neurones récurrents
Théorie de l'apprentissage automatique
Traitement du langage naturel
Site web
Google Scholar

Biographie

*Pour toute demande média, veuillez écrire à medias@mila.quebec.

Pour plus d’information, contactez Julie Mongeau, adjointe de direction à julie.mongeau@mila.quebec.

Reconnu comme une sommité mondiale en intelligence artificielle, Yoshua Bengio s’est surtout distingué par son rôle de pionnier en apprentissage profond, ce qui lui a valu le prix A. M. Turing 2018, le « prix Nobel de l’informatique », avec Geoffrey Hinton et Yann LeCun. Il est professeur titulaire à l’Université de Montréal, fondateur et directeur scientifique de Mila – Institut québécois d’intelligence artificielle, et codirige en tant que senior fellow le programme Apprentissage automatique, apprentissage biologique de l'Institut canadien de recherches avancées (CIFAR). Il occupe également la fonction de directeur scientifique d’IVADO.

En 2018, il a été l’informaticien qui a recueilli le plus grand nombre de nouvelles citations au monde. En 2019, il s’est vu décerner le prestigieux prix Killam. Depuis 2022, il détient le plus grand facteur d’impact (h-index) en informatique à l’échelle mondiale. Il est fellow de la Royal Society de Londres et de la Société royale du Canada, et officier de l’Ordre du Canada.

Soucieux des répercussions sociales de l’IA et de l’objectif que l’IA bénéficie à tous, il a contribué activement à la Déclaration de Montréal pour un développement responsable de l’intelligence artificielle.

Étudiants actuels

Jamal Abou Haibeh
Stagiaire de recherche - McGill
Github
Google Scholar
Mohammed Abukalam
Stagiaire de recherche - UdeM
Github
Rim Assouel
Doctorat - UdeM
Dan Assouline
Collaborateur·rice alumni
Github
Google Scholar
Ayoub Atanane
Stagiaire de recherche - Université du Québec à Rimouski
Github
Stefan Bauer
Visiteur de recherche indépendant
Co-superviseur⋅e :
Guillaume Lajoie
Google Scholar
Paul Bertin
Doctorat - UdeM
Ghait Boukachab
Stagiaire de recherche - UQAR
Github
Oussama Boussif
Doctorat - UdeM
Site web
Github
Google Scholar
Andrés Campero
Visiteur de recherche indépendant - MIT
Site web
Shahana Chatterjee
Collaborateur·rice de recherche - N/A
Superviseur⋅e principal⋅e :
David Rolnick
Xiaoyin Chen
Doctorat - UdeM
Chen Chen
Postdoctorat - UdeM
Co-superviseur⋅e :
Blake Richards
Sanghyeok Choi
Collaborateur·rice de recherche - KAIST
Site web
Github
Google Scholar
Pierre-Paul De Breuck
Collaborateur·rice alumni - UdeM
Site web
Github
Google Scholar
Tristan Deleu
Doctorat - UdeM
Site web
Github
Google Scholar
Aniket Didolkar
Doctorat - UdeM
Site web
Github
Google Scholar
Alexandre Duval
Collaborateur·rice de recherche - Université Paris-Saclay
Superviseur⋅e principal⋅e :
David Rolnick
Site web
Eric Elmoznino
Doctorat - UdeM
Co-superviseur⋅e :
Guillaume Lajoie
Site web
Github
Google Scholar
Akram Erraqabi
Doctorat - UdeM
Katie Everett
Doctorat - Massachusetts Institute of Technology
Léna Nehale Ezzine
Doctorat - UdeM
Jean-Pierre Falet
Doctorat - UdeM
Co-superviseur⋅e :
Guillaume Lajoie
Site web
Github
Google Scholar
Leo Feng
Doctorat - UdeM
leo.feng@mila.quebec
Site web
Google Scholar
Damiano Fornasiere
Stagiaire de recherche - Barcelona University
Piotr Gainski
Stagiaire de recherche - UdeM
Github
Ivan Grega
Collaborateur·rice de recherche - UdeM
Pietro Greiner
Stagiaire de recherche
Mohsin Hasan
Doctorat - UdeM
mohsin.hasan@mila.quebec
Site web
Github
Google Scholar
Alex Hernandez-Garcia
Postdoctorat - UdeM
Co-superviseur⋅e :
David Rolnick
Site web
Github
Google Scholar
Leon Hetzel
Visiteur de recherche indépendant - Technical University Munich (TUM)
Site web
Github
Google Scholar
Edward Hu
Doctorat - UdeM
Moksh Jain
Doctorat - UdeM
moksh.jain@mila.quebec
Site web
Github
Google Scholar
George Jiangyan Ma
Stagiaire de recherche - UdeM
Site web
Github
Google Scholar
Thomas Jiralerspong
Maîtrise recherche - UdeM
Co-superviseur⋅e :
Doina Precup
Site web
Github
Google Scholar
Younesse Kaddar
Stagiaire de recherche - UdeM
Site web
Github
Minsu Kim
Collaborateur·rice de recherche - UdeM
Site web
Github
Google Scholar
Maksym Korablyov
Doctorat - UdeM
Michał Koziarski
Postdoctorat - UdeM
Salem Lahlou
Doctorat - UdeM
Hae-Beom Lee
Collaborateur·rice alumni
Seanie Lee
Collaborateur·rice alumni - UdeM
Site web
Github
Google Scholar
Zhen Liu
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Liam Paull
Chenghao Liu
Collaborateur·rice alumni
Site web
Github
Google Scholar
Matt MacDermott
Stagiaire de recherche - Imperial College London
Site web
Github
Google Scholar
Kanika Madan
Doctorat - UdeM
Mohammed Mahfoud
Stagiaire de recherche - UdeM
Nikolay Malkin
Collaborateur·rice alumni - UdeM
Site web
Github
Google Scholar
Cristian Dragos Manta
Doctorat - UdeM
Co-superviseur⋅e :
Dhanya Sridhar
Github
Stefano Massaroli
Postdoctorat - UdeM
Cristian Meo
Collaborateur·rice alumni
Github
Google Scholar
Sören Mindermann
Collaborateur·rice de recherche - UdeM
Site web
Google Scholar
Sarthak Mittal
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Guillaume Lajoie
Site web
Github
Google Scholar
Jama Mohamud
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Mirco Ravanelli
Site web
Github
Google Scholar
Cyrus Neary Neary
Postdoctorat - UdeM
Superviseur⋅e principal⋅e :
Glen Berseth
Site web
Github
Google Scholar
Phong Nguyen
Visiteur de recherche indépendant - UdeM
Ling Pan
Visiteur de recherche indépendant - Hong Kong University of Science and Technology (HKUST)
Github
Ali Parviz
Collaborateur·rice de recherche - Ying Wu Coll of Computing
Google Scholar
Lena Podina
Doctorat - University of Waterloo
Superviseur⋅e principal⋅e :
David Rolnick
Google Scholar
Nassim Rahaman
Doctorat - Max-Planck-Institute for Intelligent Systems
Jarrid Rector-Brooks
Doctorat - UdeM
Co-superviseur⋅e :
Sarath Chandar
Danyal REHMAN
Postdoctorat - UdeM
James Requeima
Visiteur de recherche indépendant - UdeM
Oli RICHARDSON
Postdoctorat - UdeM
Site web
Github
Google Scholar
Jessie Richter-Powell
Visiteur de recherche indépendant - UdeM
Google Scholar
Camille Rochefort-Boulanger
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Julie Hussin
Github
Salwane Salwane
Stagiaire de recherche - UdeM
agassoussisalwane2@gmail.com
Theo Saulus
Collaborateur·rice de recherche
Superviseur⋅e principal⋅e :
David Rolnick
Victor Schmidt
Doctorat - UdeM
Site web
Github
Google Scholar
Luca Scimeca
Postdoctorat - UdeM
Site web
Github
Google Scholar
Dragos Secrieru
Maîtrise recherche - UdeM
Marcin Sendera
Stagiaire de recherche - UdeM
Github
Google Scholar
Dounia Shaaban Kabakibo
Stagiaire de recherche - UdeM
Github
Google Scholar
Vedant Shah
Maîtrise recherche - UdeM
Site web
Github
Google Scholar
Divya Sharma
Collaborateur·rice alumni
Site web
Github
Marco Stock
Visiteur de recherche indépendant - Technical University of Munich
marco.stock@tum.de
Github
Anja Surina
Doctorat - École Polytechnique Fédérale de Lausanne
Vincent Taboga
Postdoctorat - Polytechnique
Co-superviseur⋅e :
Pierre-Luc Bacon
Github
Google Scholar
Mélisande Astrid Crystal Teng
Doctorat - UdeM
Co-superviseur⋅e :
Hugo Larochelle
Site web
Github
Basile Terver
Collaborateur·rice de recherche
Superviseur⋅e principal⋅e :
David Rolnick
Alex Tong
Postdoctorat - UdeM
alexander.tong@mila.quebec
Site web
Github
Google Scholar
Prudencio Tossou
Collaborateur·rice de recherche - Valence
Superviseur⋅e principal⋅e :
Dominique Beaini
Donna Vakalis
Postdoctorat - UdeM
Co-superviseur⋅e :
David Rolnick
Github
Google Scholar
Viktor Viktor Todosijevic
Collaborateur·rice de recherche - RWTH Aachen University (Rheinisch-Westfälische Technische Hochschule Aachen)
Superviseur⋅e principal⋅e :
David Rolnick
Github
Sasha Volokhova
Doctorat - UdeM
Zichao Yan
Collaborateur·rice alumni - UdeM
Taeyoung YUN
Collaborateur·rice de recherche - KAIST
Github
Elmimouni Zakaria
Stagiaire de recherche - UdeM
Github
Tianyu Zhang
Doctorat - UdeM
Site web
Github
Google Scholar
Nicole Zhang
Doctorat - McGill
Superviseur⋅e principal⋅e :
Mathieu Blanchette
Google Scholar
Dinghuai Zhang
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Aaron Courville
Site web
Ruixiang Zhang
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Liam Paull
Site web
Harry Zhao
Doctorat - McGill
Superviseur⋅e principal⋅e :
Doina Precup
Site web
Github
Google Scholar

Publications

Commonsense mining as knowledge base completion? A study on the impact of novelty
Stanisław Jastrzębski
Dzmitry Bahdanau
Seyedarian Hosseini
Michael Noukhovitch
Yoshua Bengio
Jackie Cheung
Commonsense knowledge bases such as ConceptNet represent knowledge in the form of relational triples. Inspired by recent work by Li et al., … (voir plus)we analyse if knowledge base completion models can be used to mine commonsense knowledge from raw text. We propose novelty of predicted triples with respect to the training set as an important factor in interpreting results. We critically analyse the difficulty of mining novel commonsense knowledge, and show that a simple baseline method that outperforms the previous state of the art on predicting more novel triples.
2018-06-01
Proceedings of the Workshop on Generalization in the Age of Deep Learning (publié)
doi.org
arxiv.org
Learning Anonymized Representations with Adversarial Neural Networks
Clément Feutry
Pablo Piantanida
Yoshua Bengio
P. Duhamel
Statistical methods protecting sensitive information or the identity of the data owner have become critical to ensure privacy of individuals… (voir plus) as well as of organizations. This paper investigates anonymization methods based on representation learning and deep neural networks, and motivated by novel information theoretical bounds. We introduce a novel training objective for simultaneously training a predictor over target variables of interest (the regular labels) while preventing an intermediate representation to be predictive of the private labels. The architecture is based on three sub-networks: one going from input to representation, one from representation to predicted regular labels, and one from representation to predicted private labels. The training procedure aims at learning representations that preserve the relevant part of the information (about regular labels) while dismissing information about the private labels which correspond to the identity of a person. We demonstrate the success of this approach for two distinct classification versus anonymization tasks (handwritten digits and sentiment analysis).
2018-02-26
ArXiv (prépublication)
arxiv.org
arxiv.org
Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent Networks
Nan Rosemary Ke
Anirudh Goyal
Olexa Bilaniuk
Jonathan Binas
Laurent Charlin
Chris Pal
Yoshua Bengio
A major drawback of backpropagation through time (BPTT) is the difficulty of learning long-term dependencies, coming from having to propagat… (voir plus)e credit information backwards through every single step of the forward computation. This makes BPTT both computationally impractical and biologically implausible. For this reason, full backpropagation through time is rarely used on long sequences, and truncated backpropagation through time is used as a heuristic. However, this usually leads to biased estimates of the gradient in which longer term dependencies are ignored. Addressing this issue, we propose an alternative algorithm, Sparse Attentive Backtracking, which might also be related to principles used by brains to learn long-term dependencies. Sparse Attentive Backtracking learns an attention mechanism over the hidden states of the past and selectively backpropagates through paths with high attention weights. This allows the model to learn long term dependencies while only backtracking for a small number of time steps, not just from the recent past but also from attended relevant past states.
2017-11-07
ArXiv (prépublication)
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… (voir plus)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.
2017-02-12
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org
Diet Networks: Thin Parameters for Fat Genomics
Adriana Romero Soriano
pierre luc carrier
Akram Erraqabi
Tristan Sylvain
Alex Auvolat
Etienne Dejoie
Marc-André Legault
Marie-Pierre Dubé
Julie Hussin
Yoshua Bengio
Learning tasks such as those involving genomic data often poses a serious challenge: the number of input features can be orders of magnitude… (voir plus) larger than the number of training examples, making it difficult to avoid overfitting, even when using the known regularization techniques. We focus here on tasks in which the input is a description of the genetic variation specific to a patient, the single nucleotide polymorphisms (SNPs), yielding millions of ternary inputs. Improving the ability of deep learning to handle such datasets could have an important impact in medical research, more specifically in precision medicine, where high-dimensional data regarding a particular patient is used to make predictions of interest. Even though the amount of data for such tasks is increasing, this mismatch between the number of examples and the number of inputs remains a concern. Naive implementations of classifier neural networks involve a huge number of free parameters in their first layer (number of input features times number of hidden units): each input feature is associated with as many parameters as there are hidden units. We propose a novel neural network parametrization which considerably reduces the number of free parameters. It is based on the idea that we can first learn or provide a distributed representation for each input feature (e.g. for each position in the genome where variations are observed in data), and then learn (with another neural network called the parameter prediction network) how to map a feature's distributed representation (based on the feature's identity not its value) to the vector of parameters specific to that feature in the classifier neural network (the weights which link the value of the feature to each of the hidden units). This approach views the problem of producing the parameters associated with each feature as a multi-task learning problem. We show experimentally on a population stratification task of interest to medical studies that the proposed approach can significantly reduce both the number of parameters and the error rate of the classifier.
2017-01-01
ICLR.cc/2017/conference (poster)
openreview.net
openreview.net
Diet Networks: Thin Parameters for Fat Genomics
Adriana Romero Soriano
pierre luc carrier
Akram Erraqabi
Tristan Sylvain
Alex Auvolat
Etienne Dejoie
Marc-André Legault
Marie-Pierre Dubé
Julie Hussin
Yoshua Bengio
Learning tasks such as those involving genomic data often poses a serious challenge: the number of input features can be orders of magnitude… (voir plus) larger than the number of training examples, making it difficult to avoid overfitting, even when using the known regularization techniques. We focus here on tasks in which the input is a description of the genetic variation specific to a patient, the single nucleotide polymorphisms (SNPs), yielding millions of ternary inputs. Improving the ability of deep learning to handle such datasets could have an important impact in medical research, more specifically in precision medicine, where high-dimensional data regarding a particular patient is used to make predictions of interest. Even though the amount of data for such tasks is increasing, this mismatch between the number of examples and the number of inputs remains a concern. Naive implementations of classifier neural networks involve a huge number of free parameters in their first layer (number of input features times number of hidden units): each input feature is associated with as many parameters as there are hidden units. We propose a novel neural network parametrization which considerably reduces the number of free parameters. It is based on the idea that we can first learn or provide a distributed representation for each input feature (e.g. for each position in the genome where variations are observed in data), and then learn (with another neural network called the parameter prediction network) how to map a feature's distributed representation (based on the feature's identity not its value) to the vector of parameters specific to that feature in the classifier neural network (the weights which link the value of the feature to each of the hidden units). This approach views the problem of producing the parameters associated with each feature as a multi-task learning problem. We show experimentally on a population stratification task of interest to medical studies that the proposed approach can significantly reduce both the number of parameters and the error rate of the classifier.
2017-01-01
ICLR.cc/2017/conference (poster)
openreview.net
openreview.net
Diet Networks: Thin Parameters for Fat Genomic
Adriana Romero Soriano
pierre luc carrier
Akram Erraqabi
Tristan Sylvain
Alex Auvolat
Etienne Dejoie
Marc-andr'e Legault
M. Dubé
Julie Hussin
Yoshua Bengio
Learning tasks such as those involving genomic data often poses a serious challenge: the number of input features can be orders of magnitude… (voir plus) larger than the number of training examples, making it difficult to avoid overfitting, even when using the known regularization techniques. We focus here on tasks in which the input is a description of the genetic variation specific to a patient, the single nucleotide polymorphisms (SNPs), yielding millions of ternary inputs. Improving the ability of deep learning to handle such datasets could have an important impact in precision medicine, where high-dimensional data regarding a particular patient is used to make predictions of interest. Even though the amount of data for such tasks is increasing, this mismatch between the number of examples and the number of inputs remains a concern. Naive implementations of classifier neural networks involve a huge number of free parameters in their first layer: each input feature is associated with as many parameters as there are hidden units. We propose a novel neural network parametrization which considerably reduces the number of free parameters. It is based on the idea that we can first learn or provide a distributed representation for each input feature (e.g. for each position in the genome where variations are observed), and then learn (with another neural network called the parameter prediction network) how to map a feature's distributed representation to the vector of parameters specific to that feature in the classifier neural network (the weights which link the value of the feature to each of the hidden units). We show experimentally on a population stratification task of interest to medical studies that the proposed approach can significantly reduce both the number of parameters and the error rate of the classifier.
2016-11-04
ArXiv (prépublication)
arxiv.org
arxiv.org
HeMIS: Hetero-Modal Image Segmentation
Mohammad Havaei
Nicolas Guizard
Nicolas Chapados
Yoshua Bengio
2016-10-02
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016 (publié)
doi.org
arxiv.org
HeMIS: Hetero-Modal Image Segmentation
Mohammad Havaei
Nicolas Guizard
Nicolas Chapados
Yoshua Bengio
2016-07-18
ArXiv (prépublication)
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
James Bergstra
Valentin Bisson
Josh Bleecher Snyder
Nicolas Bouchard
Nicolas Boulanger-Lewandowski
Xavier Bouthillier
Alexandre De Brébisson
Olivier Breuleux … (voir 92 de plus)
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
Sébastien 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 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… (voir plus)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 (prépublication)
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 … (voir 92 de plus)
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… (voir plus)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
Statistical Language and Speech Processing
Yoshua Bengio
Fethi Bougares
Horia Cucu
Corneliu Burileanu
Myung-Jae Kim
Il-ho Yang
Jordan Rodu
Dean Phillips Foster
Weichen Wu
Stefan Bott
Felix Stahlberg
Luis A. Trindade
Hui Wang
2013-01-01
Lecture Notes in Computer Science (publié)
doi.org