Publications

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
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
Algorithms in Bioinformatics
P. Agarwal
Tatsuya Akutsu
Amir Amihood
Alberto Apostolico
C. Benham
Gary Gustaf Benson
Mathieu Blanchette
Nadia El-Mabrouk
Olivier Gascuel
Raffaele Giancarlo
R. Guigó
Michael Hallet
D. Huson
G. Kucherov
Michelle R. Lacey
Jens Lagergren
Giuseppe Lancia
Gad M. Landau
Thierry. Lecroq
B. Moret … (voir 21 de plus)
S. Morishita
Elchanan Mossel
Vincent Moulton
Lior S. Pachter
Knut Reinert
I. Rigoutsos
David Sankoff
Sophie Schbath
Eran Segal
Charles Semple
J. Setubal
Roded Sharan
S. Skiena
Jens Stoye
Esko Ukkonen
Lisa Allen Vawter
Alfonso Valencia
Tandy J. Warnow
Lusheng Wang
Rita Casadio
Gene Myers
2012-10-01
Lecture Notes in Computer Science (publié)
doi.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
Delayed Stochastic Decoding of LDPC Codes
Ali Naderi
Shie Mannor
Mohamad Sawan
Warren Gross
A new stochastic decoding algorithm, called Delayed Stochastic (DS) decoding, is introduced to implement low-density-parity-check (LDPC) dec… (voir plus)oders. The delayed stochastic decoding uses an alternative method to track probability values, which results in reduction of hardware complexity and memory requirement of the stochastic decoders. It is therefore suitable for fully-parallel implementation of long LDPC codes with applications in optical communications. Two decoders are implemented using the DS algorithm for medium (2048, 1723) and long (32768, 26624) LDPC codes. The decoders occupy 3.93- mm2 and 56.5- mm2 silicon area using 90-nm CMOS technology and provide maximum core throughputs of 172.4 and 477.7 Gb/s at [(Eb)/(No)]=5.5 and 4.8 dB, respectively.
2011-11-01
IEEE Transactions on Signal Processing (publié)
doi.org
Delayed Stochastic Decoding of LDPC Codes
Ali Naderi
Shie Mannor
M. Sawan
Warren Gross
A new stochastic decoding algorithm, called Delayed Stochastic (DS) decoding, is introduced to implement low-density-parity-check (LDPC) dec… (voir plus)oders. The delayed stochastic decoding uses an alternative method to track probability values, which results in reduction of hardware complexity and memory requirement of the stochastic decoders. It is therefore suitable for fully-parallel implementation of long LDPC codes with applications in optical communications. Two decoders are implemented using the DS algorithm for medium (2048, 1723) and long (32768, 26624) LDPC codes. The decoders occupy 3.93- mm2 and 56.5- mm2 silicon area using 90-nm CMOS technology and provide maximum core throughputs of 172.4 and 477.7 Gb/s at [(Eb)/(No)]=5.5 and 4.8 dB, respectively.
2011-11-01
IEEE Transactions on Signal Processing (publié)
doi.org
Session details: Digital entertainment technologies and arts track papers
Mike Preuss
Christian Gagné
2011-07-12
Annual Conference on Genetic and Evolutionary Computation (publié)
doi.org
Session details: Digital entertainment technologies and arts track papers
Mike Preuss
Christian Gagné
2011-07-12
Proceedings of the 13th annual conference on Genetic and evolutionary computation (publié)
doi.org
Session details: Digital entertainment technologies and arts track posters
Mike Preuss
Christian Gagné
2011-07-12
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation (publié)
doi.org
Session details: Digital entertainment technologies and arts track posters
Mike Preuss
Christian Gagné
2011-07-12
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation (publié)
doi.org
Stochastic Multiple Stream Decoding of Cortex Codes
Matthieu Arzel
Cyril Lahuec
Christophe Jego
Warren Gross
Yvain Bruned
Being one of the most efficient solutions to implement forward error correction (FEC) decoders based on belief propagation, stochastic proce… (voir plus)ssing is thus a method worthy of consideration when addressing the decoding of emerging codes such as Cortex codes. This code family offers short block codes with large Hamming distances. Unfortunately, their construction introduces many hidden variables making them difficult to be efficiently decoded with digital circuits implementing the Sum-Product algorithm. With the introduction of multiple stochastic streams, the proposed solution alleviates the hidden variables problem thus yielding decoding performances close to optimal. Morevover, this new stochastic architecture is more efficient in terms of complexity-throughput ratio compared to recently published stochastic decoders using either edge or tracking forecast memories.
2011-07-01
IEEE Transactions on Signal Processing (publié)
doi.org
Stochastic Multiple Stream Decoding of Cortex Codes
Matthieu Arzel
Cyril Lahuec
Christophe Jego
Warren Gross
Yvain Bruned
Being one of the most efficient solutions to implement forward error correction (FEC) decoders based on belief propagation, stochastic proce… (voir plus)ssing is thus a method worthy of consideration when addressing the decoding of emerging codes such as Cortex codes. This code family offers short block codes with large Hamming distances. Unfortunately, their construction introduces many hidden variables making them difficult to be efficiently decoded with digital circuits implementing the Sum-Product algorithm. With the introduction of multiple stochastic streams, the proposed solution alleviates the hidden variables problem thus yielding decoding performances close to optimal. Morevover, this new stochastic architecture is more efficient in terms of complexity-throughput ratio compared to recently published stochastic decoders using either edge or tracking forecast memories.
2011-07-01
IEEE Transactions on Signal Processing (publié)
doi.org