Portrait de Arnaud Bergeron

Arnaud Bergeron

arnaud.bergeron@mila.quebec
Architecte logiciel, Innovation, développement et technologies

Publications

Automatic differentiation in ML: Where we are and where we should be going
Bart van Merriënboer
Olivier Breuleux
Arnaud Bergeron
Pascal Lamblin
We review the current state of automatic differentiation (AD) for array programming in machine learning (ML), including the different approa… (voir plus)ches such as operator overloading (OO) and source transformation (ST) used for AD, graph-based intermediate representations for programs, and source languages. Based on these insights, we introduce a new graph-based intermediate representation (IR) which specifically aims to efficiently support fully-general AD for array programming. Unlike existing dataflow programming representations in ML frameworks, our IR naturally supports function calls, higher-order functions and recursion, making ML models easier to implement. The ability to represent closures allows us to perform AD using ST without a tape, making the resulting derivative (adjoint) program amenable to ahead-of-time optimization using tools from functional language compilers, and enabling higher-order derivatives. Lastly, we introduce a proof of concept compiler toolchain called Myia which uses a subset of Python as a front end.
2018-10-01
ArXiv (prépublication)
arxiv.org
arxiv.org
Automatic differentiation in ML: Where we are and where we should be going
Bart van Merriënboer
Olivier Breuleux
Arnaud Bergeron
Pascal Lamblin
We review the current state of automatic differentiation (AD) for array programming in machine learning (ML), including the different approa… (voir plus)ches such as operator overloading (OO) and source transformation (ST) used for AD, graph-based intermediate representations for programs, and source languages. Based on these insights, we introduce a new graph-based intermediate representation (IR) which specifically aims to efficiently support fully-general AD for array programming. Unlike existing dataflow programming representations in ML frameworks, our IR naturally supports function calls, higher-order functions and recursion, making ML models easier to implement. The ability to represent closures allows us to perform AD using ST without a tape, making the resulting derivative (adjoint) program amenable to ahead-of-time optimization using tools from functional language compilers, and enabling higher-order derivatives. Lastly, we introduce a proof of concept compiler toolchain called Myia which uses a subset of Python as a front end.
2018-01-01
NeurIPS (publié)
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
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
Theano: Deep Learning on GPUs with Python
James Bergstra
Frédéric Bastien
Olivier Breuleux
Pascal Lamblin
Razvan Pascanu
Olivier Delalleau
Guillaume Desjardins
David Warde-Farley
Ian G Goodfellow
Arnaud Bergeron
Yoshua Bengio
In this paper, we present Theano 1 , a framework in the Python programming language for defining, optimizing and evaluating expressions invo… (voir plus)lving high-level operations on tensors. Theano offers most of NumPy’s functionality, but adds automatic symbolic differentiation, GPU support, and faster expression evaluation. Theano is a general mathematical tool, but it was developed with the goal of facilitating research in deep learning. The Deep Learning Tutorials 2 introduce recent advances in deep learning, and showcase how Theano
2012-01-01
(publié)
www.semanticscholar.org