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Nicolas Bouchard

Alumni

Publications

Theano: A Python framework for fast computation of mathematical expressions

Rami Al-rfou'

Guillaume Alain

Amjad Almahairi

Christof Angermüller

Dzmitry Bahdanau

Frédéric Bastien

Justin S. Bayer

A. Belikov

A. Belopolsky

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

Paul F. Christiano

Marc-Alexandre Côté

Myriam Côté

Aaron Courville

Olivier Delalleau

Julien Demouth

Guillaume Desjardins

Sander Dieleman

M'elanie Ducoffe

Vincent Dumoulin

Samira Ebrahimi Kahou

Ziye Fan

Mathieu Germain

Ian J. Goodfellow

Matthew Graham

Caglar Gulçehre

Iban Harlouchet

Jean-philippe Heng

Balázs Hidasi

Arjun Jain

S'ebastien Jean

Kai Jia

Mikhail V. Korobov

Vivek Kulkarni

Pascal Lamblin

Eric P. Larsen

S. Lee

Simon-mark Lefrancois

Nicholas Léonard

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

Mohammad Pezeshki

Colin Raffel

Daniel Renshaw

Matthew David Rocklin

Adriana Romero Soriano

Markus Dr. Roth

Peter Sadowski

John Salvatier

François Savard

Jan Schlüter

John D. Schulman

Gabriel Schwartz

Iulian V. Serban

Dmitriy Serdyuk

Samira Shabanian

Sigurd Spieckermann

S. Subramanyam

Jakub Sygnowski

Jeremie Tanguay

Gijs van Tulder

Joseph P. Turian

Sebastian Urban

Francesco Visin

David Warde-Farley

Dustin J. Webb

M. Willson

Lijun Xue

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)

Theano: A Python framework for fast computation of mathematical expressions

Rami Al-rfou'

Guillaume Alain

Amjad Almahairi

Christof Angermüller

Dzmitry Bahdanau

Frédéric Bastien

Justin S. Bayer

A. Belikov

A. Belopolsky

Arnaud Bergeron

Valentin Bisson

Josh Bleecher Snyder

Nicolas Bouchard

Nicolas Boulanger-Lewandowski

Xavier Bouthillier

Alexandre De Brébisson

Olivier Breuleux … (voir 92 de plus)

Pierre Luc Carrier

Paul F. Christiano

Marc-Alexandre Côté

Myriam Côté

Aaron Courville

Olivier Delalleau

Julien Demouth

Guillaume Desjardins

Sander Dieleman

M'elanie Ducoffe

Vincent Dumoulin

Samira Ebrahimi Kahou

Ziye Fan

Mathieu Germain

Ian G Goodfellow

Matthew Graham

Caglar Gulçehre

Iban Harlouchet

Jean-philippe Heng

Balázs Hidasi

Arjun Jain

Sébastien Jean

Kai Jia

Mikhail V. Korobov

Vivek Kulkarni

Pascal Lamblin

Eric Larsen

S. Lee

Simon-mark Lefrancois

Nicholas Léonard

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

Mohammad Pezeshki

Colin Raffel

Daniel Renshaw

Matthew David Rocklin

Adriana Romero Soriano

Markus Dr. Roth

Peter Sadowski

John Salvatier

François Savard

Jan Schlüter

John D. Schulman

Gabriel Schwartz

Iulian V. Serban

Dmitriy Serdyuk

Samira Shabanian

Sigurd Spieckermann

S. Subramanyam

Jakub Sygnowski

Jeremie Tanguay

Gijs van Tulder

Sebastian Urban

Francesco Visin

David Warde-Farley

Dustin J. Webb

M. Willson

Lijun Xue

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)