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Dumitru Erhan

Alumni

Publications

Object Detection using Deep Learning
Chamarty Anusha
P. Avadhani
P. S.
Mohannad Elhamod
Martin D. Levine
Ajeet Ram Pathak
Manjusha Pandey
Siddharth S. Rautaray
Christian Szegedy
Alexander T Toshev
Dumitru Erhan
Xiaofeng Ning
Wen Zhu
Shifeng Chen
Zhong-Qiu Zhao
Peng Zheng
Shou-tao Xu
Xindong Wu
Sakshi Indolia
Anil Kumar Goswani … (voir 12 de plus)
S. P. Mishra
Pooja Asopa
Yann Lecun
Yoshua Bengio
Joseph Redmon
Santosh Kumar Divvala
Ross Girshick
Ali Farhadi
M. Kruithof
Henri Bouma
Noelle M. Fischer
Klamer Schutte
Autonomous vehicles, surveillance systems, face detection systems lead to the development of accurate object detection system [1]. These sys… (voir plus)tems recognize, classify and localize every object in an image by drawing bounding boxes around the object [2]. These systems use existing classification models as backbone for Object Detection purpose. Object detection is the process of finding instances of real-world objects such as human faces, animals and vehicles etc., in pictures, images or in videos. An Object detection algorithm uses extracted features and learning techniques to recognize the objects in an image. In this paper, various Object Detection techniques have been studied and some of them are implemented. As a part of this paper, three algorithms for object detection in an image were implemented and their results were compared. The algorithms are “Object Detection using Deep Learning Framework by OpenCV”, “Object Detection using Tensorflow” and “Object Detection using Keras models”.
2018-12-17
International Journal of Computer Applications (publié)
doi.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 Gulçehre
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
François Savard
Jan Schlüter
John D. Schulman
Gabriel Schwartz
Iulian V. Serban
Dmitriy Serdyuk
Samira Shabanian
Etienne Simon
Sigurd Spieckermann
S. Subramanyam
Jakub Sygnowski
Jeremie 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 Gulçehre
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
François Savard
Jan Schlüter
John D. Schulman
Gabriel Schwartz
Iulian V. Serban
Dmitriy Serdyuk
Samira Shabanian
Etienne Simon
Sigurd Spieckermann
S. Subramanyam
Jakub Sygnowski
Jeremie 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