深度学习阅读书目Word文件下载.docx

1、o Graves, A. （2012）.Supervised sequence labelling with recurrent neural networks（Vol. 385）. Springer.o Schmidhuber, J. （2014）. Deep Learning in Neural Networks: An Overview. 75 pages, 850+ references,http:/arxiv.org/abs/1404.7828, PDF & LATEX source & complete public BIBTEX file under/www.idsia.ch/j

2、uergen/deep-learning-overview.html.Reinforcement Learningo Mnih, Volodymyr, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. “Playing Atari with deep reinforcement learning.”arXiv preprint arXiv:1312.5602（2013）.o Volodymyr Mnih, Nicolas Heess, A

3、lex Graves, Koray Kavukcuoglu. “Recurrent Models of Visual Attention” ArXiv e-print, 2014.Computer Visiono ImageNet Classification with Deep Convolutional Neural Networks, Alex Krizhevsky, Ilya Sutskever, Geoffrey E Hinton, NIPS 2012.o Going Deeper with Convolutions, Christian Szegedy, Wei Liu, Yang

4、qing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, 19-Sept-2014.o Learning Hierarchical Features for Scene Labeling, Clement Farabet, Camille Couprie, Laurent Najman and Yann LeCun, IEEE Transactions on Pattern Analysis and Machine Intellig

5、ence, 2013.Learning Convolutional Feature Hierachies for Visual Recognition,Koray Kavukcuoglu, Pierre Sermanet, Y-Lan Boureau, Karol Gregor, Michal Mathieu and Yann LeCun, Advances in Neural Information Processing Systems （NIPS 2010）, 23, 2010.o Graves, Alex, et al.“A novel connectionist system for

6、unconstrained handwriting recognition.”Pattern Analysis and Machine Intelligence, IEEE Transactions on31.5 （2009）: 855-868.o Cirean, D. C., Meier, U., Gambardella, L. M., & Schmidhuber, J. （2010）.Deep, big, simple neural nets for handwritten digit recognition.Neural computation,22（12）, 3207-3220.o C

7、iresan, Dan, Ueli Meier, and Jrgen Schmidhuber.“Multi-column deep neural networks for image classification.”Computer Vision and Pattern Recognition （CVPR）, 2012 IEEE Conference on. IEEE, 2012.o Ciresan, D., Meier, U., Masci, J., & Schmidhuber, J. （2011, July）.A committee of neural networks for traff

8、ic sign classification.InNeural Networks （IJCNN）, The 2011 International Joint Conference on（pp. 1918-1921）. IEEE.NLP and Speecho Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing, Antoine Bordes, Xavier Glorot, Jason Weston and Yoshua Bengio （2012）, in: Proceedings

9、of the 15th International Conference on Artificial Intelligence and Statistics （AISTATS）o Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. Socher, R., Huang, E. H., Pennington, J., Ng, A. Y., and Manning, C. D. （2011a）. In NIPS2011.o Semi-supervised recursive autoencode

10、rs for predicting sentiment distributions. Socher, R., Pennington, J., Huang, E. H., Ng, A. Y., and Manning, C. D. （2011b）. In EMNLP2011.o Mikolov Tom:Statistical Language Models based on Neural Networks. PhD thesis, Brno University of Technology, 2012.o Graves, Alex, and Jrgen Schmidhuber. “Framewi

11、se phoneme classification with bidirectional LSTM and other neural network architectures.”Neural Networks18.5 （2005）: 602-610.o Mikolov, Tomas, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean.“Distributed representations of words and phrases and their compositionality.” InAdvances in Neural

12、 Information Processing Systems, pp. 3111-3119. 2013.o K. Cho, B. van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, Y. Bengio.Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. EMNLP 2014.o Sutskever, Ilya, Oriol Vinyals, and Quoc VV Le.

13、“Sequence to sequence learning with neural networks.”Advances in Neural Information Processing Systems. 2014.Disentangling Factors and Variations with Deptho Goodfellow, Ian, et al. “Measuring invariances in deep networks.”Advances in neural information processing systems22 （2009）: 646-654.o Bengio,

14、 Yoshua, et al. “Better Mixing via Deep Representations.”1207.4404（2012）.o Xavier Glorot,Antoine BordesandYoshua Bengio,Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach, in: Proceedings of the Twenty-eight International Conference on Machine Learning （ICML11）, pag

15、es 97-110, 2011.Transfer Learning and domain adaptationo Raina, Rajat, et al. “Self-taught learning: transfer learning from unlabeled data.”Proceedings of the 24th international conference on Machine learning. ACM, 2007.o R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu and P. Kuksa.Nat

16、ural Language Processing （Almost） from Scratch.Journal of Machine Learning Research, 12:2493-2537, 2011.o Mesnil, Grgoire, et al. “Unsupervised and transfer learning challenge: a deep learning approach.”Unsupervised and Transfer Learning Workshop, in conjunction with ICML. 2011.o Ciresan, D. C., Mei

17、er, U., & Schmidhuber, J. （2012, June）.Transfer learning for Latin and Chinese characters with deep neural networks. InNeural Networks （IJCNN）, The 2012 International Joint Conference on（pp. 1-6）. IEEE.o Goodfellow, Ian, Aaron Courville, and Yoshua Bengio. “Large-Scale Feature Learning With Spike-an

18、d-Slab Sparse Coding.”ICML 2012.Practical Tricks and Guideso “Improving neural networks by preventing co-adaptation of feature detectors.”Hinton, Geoffrey E., et al. arXiv preprint arXiv:1207.0580 （2012）.o Practical recommendations for gradient-based training of deep architectures, Yoshua Bengio, U.

19、 Montreal, arXiv report:1206.5533, Lecture Notes in Computer Science Volume 7700, Neural Networks: Tricks of the Trade Second Edition, Editors: Grgoire Montavon, Genevive B. Orr, Klaus-Robert Mller, 2012.o A practicalguideto training Restricted Boltzmann Machines, by Geoffrey Hinton.Sparse Codingo E

20、mergence of simple-cell receptive field properties by learning a sparse code for natural images, Bruno Olhausen, Nature 1996.o Kavukcuoglu, Koray, MarcAurelio Ranzato, and Yann LeCun. “Fast inference in sparse coding algorithms with applications to object recognition.”1010.3467（2010）.o Efficient spa

21、rse coding algorithms. Honglak Lee, Alexis Battle, Raina Rajat and Andrew Y. Ng. InNIPS 19, 2007.pdfo “Sparse coding with an overcomplete basis set: A strategy employed by VI?.” . Olshausen, Bruno A., and David J. Field.Vision research37.23 （1997）: 3311-3326.Foundation Theory and Motivationo Hinton,

22、 Geoffrey E. “Deterministic Boltzmann learning performs steepest descent in weight-space.”Neural computation1.1 （1989）: 143-150.o Bengio, Yoshua, and Samy Bengio. “Modeling high-dimensional discrete data with multi-layer neural networks.”Advances in Neural Information Processing Systems12 （2000）: 40

23、0-406.o Bengio, Yoshua, et al. “Greedy layer-wise training of deep networks.”19 （2007）: 153.o Bengio, Yoshua, Martin Monperrus, and Hugo Larochelle. “Nonlocal estimation of manifold structure.”Neural Computation18.10 （2006）: 2509-2528.o Hinton, Geoffrey E., and Ruslan R. Salakhutdinov. “Reducing the

24、 dimensionality of data with neural networks.”Science313.5786 （2006）: 504-507.o MarcAurelio Ranzato, Y., Lan Boureau, and Yann LeCun. “Sparse feature learning for deep belief networks.”20 （2007）: 1185-1192.o Bengio, Yoshua, and Yann LeCun. “Scaling learning algorithms towards AI.”Large-Scale Kernel

25、Machines34 （2007）.o Le Roux, Nicolas, and Yoshua Bengio. “Representational power of restricted boltzmann machines and deep belief networks.”20.6 （2008）: 1631-1649.o Sutskever, Ilya, and Geoffrey Hinton. “Temporal-Kernel Recurrent Neural Networks.”23.2 （2010）: 239-243.o Le Roux, Nicolas, and Yoshua B

26、engio. “Deep belief networks are compact universal approximators.”22.8 （2010）: 2192-2207.o Bengio, Yoshua, and Olivier Delalleau. “On the expressive power of deep architectures.”Algorithmic Learning Theory. Springer Berlin/Heidelberg, 2011.o Montufar, Guido F., and Jason Morton. “When Does a Mixture

27、 of Products Contain a Product of Mixtures?.”1206.0387o Montfar, Guido, Razvan Pascanu, Kyunghyun Cho, and Yoshua Bengio. “On the Number of Linear Regions of Deep Neural Networks.” arXiv preprint arXiv:1402.1869 （2014）.Supervised Feedfoward Neural Networkso The Manifold Tangent Classifier, Salah Rif

28、ai, Yann Dauphin, Pascal Vincent, Yoshua Bengio and Xavier Muller, in: NIPS2011.o “Discriminative Learning of Sum-Product Networks.“, Gens, Robert, and Pedro Domingos, NIPS 2012 Best Student Paper.o Goodfellow, I., Warde-Farley, D., Mirza, M., Courville, A., and Bengio, Y. （2013）.Maxout networks. Technical Report, Universite de Montreal.o Hinton, Geoffrey E., et al. “Improving neural networks by preventing co-adaptation