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Dropout: A simple way to prevent neural networks from overfitting
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Imagenet classification with deep convolutional neural networks
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Improving neural networks by preventing co-adaptation of feature detectors
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Building high-level features using large scale unsupervised learning
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DeCAF: A deep convolutional activation feature for generic visual recognition
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J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell. "DeCAF: a deep convolutional activation feature for generic visual recognition, " Proc. of International Conference on Machine Learning, pp. 647-655, 2013
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Speech recognition with deep recurrent neural networks
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Alex Graves, Abdel-rahman Mohamed and Geoffrey Hinton."Speech recognition with deep recurrent neural networks, " Proc. of International Conference on Acoustics, Speech and Signal Processing, pp.6645-6649, 2013.
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
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G. E. Hinton, et al. "Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups, " Signal Processing Magazine, vol.29, pp.82-97, 2012.
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