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There shows the paper list in the book of Deep learning in NLP(Li Deng&Yang Liu Editors)这里主要列出了《Deep Learning in Natural Language Processing》(Li Deng&Yang Liu Editors)一书中各个章节后的参考论文,便于学习!

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Paper-list-for-Deep-learning-in-NLP

There shows the paper list in the book of Deep learning in NLP(Li Deng&Yang Liu Editors)

这里主要列出了《Deep Learning in Natural Language Processing》(Li Deng&Yang Liu Editors)一书中各个章节后的参考论文,便于学习!

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2019-03-10:整理第一章

chapter 1 A Joint Introduction to Natural Language Processing and to Deep Learning

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Deng, L., Hinton, G., & Kingsbury, B. (2013). New types of deep neural network learning for speech recognition and related applications: An overview. In Proceedings of ICASSP.

Deng, L., Seltzer, M., Yu, D., Acero, A., Mohamed, A., & Hinton, G. (2010). Binary coding of speech spectrograms using a deep autoencoder. In Proceedings of Interspeech.

Deng, L., Yu, D., & Platt, J. (2012). Scalable stacking and learning for building deep architectures. In Proceedings of ICASSP.

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Dhingra, B., Li, L., Li, X., Gao, J., Chen, Y., Ahmed, F., & Deng, L. (2017). Towards end-to-end reinforcement learning of dialogue agents for information access. In Proceedings of ACL. Fang, H., et al. (2015). From captions to visual concepts and back. In Proceedings of CVPR.

Fei-Fei, L., & Perona, P. (2005). A Bayesian hierarchical model for learning natural scene categories. In Proceedings of CVPR.

Fei-Fei, L., & Perona, P. (2016). Stacked attention networks for image question answering. In Proceedings of CVPR.

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Gasic, M., Mrk, N., Rojas-Barahona, L., Su, P., Ultes, S., Vandyke, D., Wen, T., & Young, S. (2017).Dialogue manager domain adaptation using gaussian process reinforcement learning. Computer Speech and Language, 45.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. Cambridge: MIT Press.

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He, X., & Deng, L. (2012). Maximum expected BLEU training of phrase and lexicon translation models. In Proceedings of ACL.

He, X., & Deng, L. (2013). Speech-centric information processing: An optimization-oriented approach. Proceedings of the IEEE, 101.

He, X., Deng, L., & Chou, W. (2008). Discriminative learning in sequential pattern recognition. IEEE Signal Processing Magazine, 25(5).

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of CVPR.

Hinton, G., & Salakhutdinov, R. (2012). A better way to pre-train deep Boltzmann machines. In Proceedings of NIPS.

Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-r., Jaitly, N., Senior, A., Vanhoucke, V.,Nguyen, P., Kingsbury, B., & Sainath, T. (2012). Deep neural networks for acoustic modeling in speech recognition. IEEE Signal Processing Magazine, 29.

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Huang, P., et al. (2013b). Learning deep structured semantic models for web search using click-through data. Proceedings of CIKM.

Huang, J. -T., Li, J., Yu, D., Deng, L., & Gong, Y. (2013a). Cross-lingual knowledge transfer using multilingual deep neural networks with shared hidden layers. In Proceedings of ICASSP. Jackson, P. (1998). Introduction to Expert Systems. Boston: Addison-Wesley.

Jelinek, F. (1998). Statistical Models for Speech Recognition. Cambridge: MIT Press.

Juang, F. (2016). Deep neural networks a developmental perspective. APSIPA Transactions on Signal and Information Processing, 5.

Kaiser, L., Nachum, O., Roy, A., & Bengio, S. (2017). Learning to remember rare events. In Proceedings of ICLR.

Karpathy, A., & Fei-Fei, L. (2015). Deep visual-semantic alignments for generating image descriptions. In Proceedings of CVPR.

Koh, P., & Liang, P. (2017). Understanding black-box predictions via influence functions. In Proceedings of ICML.

Krizhevsky, A., Sutskever, I., & Hinton, G. (2012). Imagenet classification with deep convolutional neural networks. In Proceedings of NIPS.

Lafferty, J., McCallum, A., & Pereira, F. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of ICML.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521.

Lee, L., Attias, H., Deng, L., & Fieguth, P. (2004). A multimodal variational approach to learning and inference in switching state space models. In Proceedings of ICASSP.

Lee, M., et al. (2016). Reasoning in vector space: An exploratory study of question answering. In Proceedings of ICLR.

Lin, H., Deng, L., Droppo, J., Yu, D., & Acero, A. (2008). Learning methods in multilingual speech recognition. In NIPS Workshop.

Liu, Y., Chen, J., & Deng, L. (2017). An unsupervised learning method exploiting sequential output statistics. In arXiv:1702.07817.

Ma, J., & Deng, L. (2004). Target-directed mixture dynamic models for spontaneous speech recognition. IEEE Transaction on Speech and Audio Processing, 12(4).

Maclaurin, D., Duvenaud, D., & Adams, R. (2015). Gradient-based hyperparameter optimization through reversible learning. In Proceedings of ICML.

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Manning, C., & Socher, R. (2017). Lectures 17 and 18: Issues and Possible Architectures for NLP; Tackling the Limits of Deep Learning for NLP. CS224N Course: NLP with Deep Learning.

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Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Proceedings of NIPS.

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There shows the paper list in the book of Deep learning in NLP(Li Deng&Yang Liu Editors)这里主要列出了《Deep Learning in Natural Language Processing》(Li Deng&Yang Liu Editors)一书中各个章节后的参考论文,便于学习!

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