Autoencoder

Results: 93



#Item
41Understanding the difficulty of training deep feedforward neural networks  Xavier Glorot Yoshua Bengio DIRO, Universit´e de Montr´eal, Montr´eal, Qu´ebec, Canada

Understanding the difficulty of training deep feedforward neural networks Xavier Glorot Yoshua Bengio DIRO, Universit´e de Montr´eal, Montr´eal, Qu´ebec, Canada

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Source URL: jmlr.org

Language: English - Date: 2010-03-31 18:50:33
42On the importance of initialization and momentum in deep learning  Ilya Sutskever1 James Martens George Dahl Geoffrey Hinton

On the importance of initialization and momentum in deep learning Ilya Sutskever1 James Martens George Dahl Geoffrey Hinton

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Source URL: www.cs.toronto.edu

Language: English - Date: 2013-05-14 14:30:22
43Department of Computer Science  On the use of Cortical Principles in Deep Learning Benjamin Pryke  __________________________________________

Department of Computer Science On the use of Cortical Principles in Deep Learning Benjamin Pryke __________________________________________

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Source URL: dl.dropboxusercontent.com

Language: English
44Adaptive dropout for training deep neural networks  Lei Jimmy Ba Brendan Frey Department of Electrical and Computer Engineering University of Toronto jimmy,

Adaptive dropout for training deep neural networks Lei Jimmy Ba Brendan Frey Department of Electrical and Computer Engineering University of Toronto jimmy,

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Source URL: papers.nips.cc

Language: English - Date: 2014-11-25 16:08:01
45Pedestrian Parsing via Deep Decompositional Network Ping Luo1,3 Xiaogang Wang2 Xiaoou Tang1,3∗ 1 Department of Information Engineering, The Chinese University of Hong Kong

Pedestrian Parsing via Deep Decompositional Network Ping Luo1,3 Xiaogang Wang2 Xiaoou Tang1,3∗ 1 Department of Information Engineering, The Chinese University of Hong Kong

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Source URL: mmlab.ie.cuhk.edu.hk

Language: English - Date: 2013-12-19 01:46:14
46Do Deep Nets Really Need to be Deep?  Rich Caruana Microsoft Research

Do Deep Nets Really Need to be Deep? Rich Caruana Microsoft Research

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Source URL: papers.nips.cc

Language: English - Date: 2015-01-20 14:19:55
47Deep Boltzmann Machines as Feed-Forward Hierarchies  Gr´ egoire Montavon Machine Learning Group TU Berlin

Deep Boltzmann Machines as Feed-Forward Hierarchies Gr´ egoire Montavon Machine Learning Group TU Berlin

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Source URL: gregoire.montavon.name

Language: English - Date: 2012-02-14 06:18:21
48T RAINING R ECURRENT N EURAL N ETWORKS  by Ilya Sutskever

T RAINING R ECURRENT N EURAL N ETWORKS by Ilya Sutskever

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Source URL: www.cs.utoronto.ca

Language: English - Date: 2013-01-10 22:14:39
49

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Source URL: www.cs.toronto.edu

Language: English - Date: 2013-03-19 23:10:51