摘要: … 2012) does this by randomly dropping out (zeroing) hidden units and input features during … objective function of dropout training, we show how to do fast dropout training by sampling …
princeton.edu
stanford.edu 
acm.org 参考文章(17)
Richard Socher, Andrew Y. Ng, Eric H. Huang, Christopher D. Manning, Jeffrey Pennington, Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions empirical methods in natural language processing. pp. 151- 161 ,(2011)
Patrice Y. Simard, Yann A. LeCun, John S. Denker, Bernard Victorri, Transformation Invariance in Pattern Recognition-Tangent Distance and Tangent Propagation neural information processing systems. ,vol. 1524, pp. 239- 274 ,(1998) , 10.1007/978-3-642-35289-8_17
Ilya Sutskever, Geoffrey E. Hinton, Alex Krizhevsky, Ruslan R. Salakhutdinov, Nitish Srivastava, Improving neural networks by preventing co-adaptation of feature detectors arXiv: Neural and Evolutionary Computing. ,(2012)
Andrew M. Ross, Computing Bounds on the Expected Maximum of Correlated Normal Variables Methodology and Computing in Applied Probability. ,vol. 12, pp. 111- 138 ,(2010) , 10.1007/S11009-008-9097-Z
K. Matsuoka, Noise injection into inputs in back-propagation learning systems man and cybernetics. ,vol. 22, pp. 436- 440 ,(1992) , 10.1109/21.155944
David J. C. MacKay, The evidence framework applied to classification networks Neural Computation. ,vol. 4, pp. 720- 736 ,(1992) , 10.1162/NECO.1992.4.5.720
Charles Sutton, Michael Sindelar, Andrew McCallum, Reducing Weight Undertraining in Structured Discriminative Learning language and technology conference. pp. 89- 95 ,(2006) , 10.3115/1220835.1220847
Chris M. Bishop, Training with noise is equivalent to Tikhonov regularization Neural Computation. ,vol. 7, pp. 108- 116 ,(1995) , 10.1162/NECO.1995.7.1.108
Andrew Y. Ng, Christopher Potts, Andrew L. Maas, Dan Huang, Peter T. Pham, Raymond E. Daly, Learning Word Vectors for Sentiment Analysis meeting of the association for computational linguistics. pp. 142- 150 ,(2011)
Sadao Kurohashi, Kentaro Inui, Tetsuji Nakagawa, Dependency Tree-based Sentiment Classification using CRFs with Hidden Variables north american chapter of the association for computational linguistics. pp. 786- 794 ,(2010)