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Jan Chorowski

Alumni

Publications

Theano: A Python framework for fast computation of mathematical expressions
Rami Al-rfou'
Amjad Almahairi
Christof Angermüller
Frédéric Bastien
Justin S. Bayer
A. Belikov
A. Belopolsky
J. Bergstra
Josh Bleecher Snyder
Paul F. Christiano
Marc-Alexandre Côté
Myriam Côté
Julien Demouth
Sander Dieleman
M'elanie Ducoffe
Ziye Fan
Mathieu Germain
Ian J. Goodfellow
Matthew Graham
Balázs Hidasi
Arjun Jain
S'ebastien Jean
Kai Jia
Mikhail V. Korobov
Vivek Kulkarni
Pascal Lamblin
Eric P. Larsen
S. Lee
Simon-mark Lefrancois
J. Livezey
Cory R. Lorenz
Jeremiah L. Lowin
Qianli M. Ma
R. McGibbon
Mehdi Mirza
Alberto Orlandi
Colin Raffel
Daniel Renshaw
Matthew David Rocklin
Markus Dr. Roth
Peter Sadowski
John Salvatier
Jan Schlüter
John D. Schulman
Gabriel Schwartz
Iulian V. Serban
Samira Shabanian
Sigurd Spieckermann
S. Subramanyam
Gijs van Tulder
Joseph P. Turian
Sebastian Urban
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.
Theano: A Python framework for fast computation of mathematical expressions
Rami Al-rfou'
Amjad Almahairi
Christof Angermüller
Frédéric Bastien
Justin S. Bayer
A. Belikov
A. Belopolsky
Josh Bleecher Snyder
Paul F. Christiano
Marc-Alexandre Côté
Myriam Côté
Julien Demouth
Sander Dieleman
M'elanie Ducoffe
Ziye Fan
Mathieu Germain
Ian G Goodfellow
Matthew Graham
Balázs Hidasi
Arjun Jain
Kai Jia
Mikhail V. Korobov
Vivek Kulkarni
Pascal Lamblin
Eric Larsen
S. Lee
Simon-mark Lefrancois
J. Livezey
Cory R. Lorenz
Jeremiah L. Lowin
Qianli M. Ma
R. McGibbon
Mehdi Mirza
Alberto Orlandi
Colin Raffel
Daniel Renshaw
Matthew David Rocklin
Markus Dr. Roth
Peter Sadowski
John Salvatier
Jan Schlüter
John D. Schulman
Gabriel Schwartz
Iulian V. Serban
Samira Shabanian
Sigurd Spieckermann
S. Subramanyam
Gijs van Tulder
Sebastian Urban
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.
Task Loss Estimation for Structured Prediction