Autoencoder

Results: 93



#Item
71Capturing, Analyzing, and Transmitting Intangible Cultural Heritage with the i-Treasures Project A. Jaumard-Hakoun1,2, S. K. Al Kork¹,2, M. Adda-Decker3, A. Amelot3, L. Buchman³, G. Dreyfus2 , T. Fux3, P. Roussel2, C.

Capturing, Analyzing, and Transmitting Intangible Cultural Heritage with the i-Treasures Project A. Jaumard-Hakoun1,2, S. K. Al Kork¹,2, M. Adda-Decker3, A. Amelot3, L. Buchman³, G. Dreyfus2 , T. Fux3, P. Roussel2, C.

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Source URL: www.qmu.ac.uk

Language: English - Date: 2013-11-11 17:54:38
72INTERSPEECH[removed]Deep Convex Net: A Scalable Architecture for Speech Pattern Classification Li Deng and Dong Yu Microsoft Research, Redmond, WA, USA {deng, dongyu}@microsoft.com

INTERSPEECH[removed]Deep Convex Net: A Scalable Architecture for Speech Pattern Classification Li Deng and Dong Yu Microsoft Research, Redmond, WA, USA {deng, dongyu}@microsoft.com

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Source URL: research.microsoft.com

Language: English - Date: 2011-09-03 16:09:44
73TRENDS & CONTROVERSIES Editor: erik cambria, MIT Media Laboratory, [removed] Extreme Learning Machines Erik Cambria, MIT Media Laboratory Guang-Bin Huang, Nanyang Technological University, Singapore

TRENDS & CONTROVERSIES Editor: erik cambria, MIT Media Laboratory, [removed] Extreme Learning Machines Erik Cambria, MIT Media Laboratory Guang-Bin Huang, Nanyang Technological University, Singapore

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Source URL: www3.ntu.edu.sg

Language: English - Date: 2014-02-08 17:56:55
74ON RECTIFIED LINEAR UNITS FOR SPEECH PROCESSING M.D. Zeiler1∗ , M. Ranzato2 , R. Monga2 , M. Mao2 , K. Yang2 , Q.V. Le2 , P. Nguyen2 , A. Senior2 , V. Vanhoucke2 , J. Dean2 , G.E. Hinton3 1  New York University, USA

ON RECTIFIED LINEAR UNITS FOR SPEECH PROCESSING M.D. Zeiler1∗ , M. Ranzato2 , R. Monga2 , M. Mao2 , K. Yang2 , Q.V. Le2 , P. Nguyen2 , A. Senior2 , V. Vanhoucke2 , J. Dean2 , G.E. Hinton3 1 New York University, USA

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Source URL: www.cs.toronto.edu

Language: English - Date: 2013-08-04 21:20:17
75Transforming Auto-encoders G. E. Hinton, A. Krizhevsky & S. D. Wang Department of Computer Science, University of Toronto {geoffrey.hinton, akrizhevsky, sidawang88}@gmail.com  Abstract. The artificial neural networks tha

Transforming Auto-encoders G. E. Hinton, A. Krizhevsky & S. D. Wang Department of Computer Science, University of Toronto {geoffrey.hinton, akrizhevsky, sidawang88}@gmail.com Abstract. The artificial neural networks tha

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Source URL: www.cs.toronto.edu

Language: English - Date: 2011-04-26 20:45:30
76IMPROVING DEEP NEURAL NETWORKS FOR LVCSR USING RECTIFIED LINEAR UNITS AND DROPOUT George E. Dahl ? Tara N. Sainath†

IMPROVING DEEP NEURAL NETWORKS FOR LVCSR USING RECTIFIED LINEAR UNITS AND DROPOUT George E. Dahl ? Tara N. Sainath†

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Source URL: www.cs.toronto.edu

Language: English - Date: 2013-08-04 20:58:12
77Shallow vs. Deep Sum-Product Networks  Olivier Delalleau Department of Computer Science and Operation Research Universit´e de Montr´eal [removed]

Shallow vs. Deep Sum-Product Networks Olivier Delalleau Department of Computer Science and Operation Research Universit´e de Montr´eal [removed]

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Source URL: deeplearning.cs.cmu.edu

Language: English - Date: 2013-09-23 22:24:23
78REPORTS  Fig. 3. Theory, presented as the experiment (see Fig. 1). The SHG source is the magnetic component of the Lorentz force on metal electrons in the SRRs. The setup for measuring the SHG is described

REPORTS Fig. 3. Theory, presented as the experiment (see Fig. 1). The SHG source is the magnetic component of the Lorentz force on metal electrons in the SRRs. The setup for measuring the SHG is described

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Source URL: deeplearning.cs.cmu.edu

Language: English - Date: 2013-10-16 13:22:12
79ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky University of Toronto [removed]

ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky University of Toronto [removed]

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Source URL: deeplearning.cs.cmu.edu

Language: English - Date: 2013-10-24 15:05:41