目录
Generating Sequences With Recurrent Neural Networks
Abstract
1、Introduction
2 Prediction Network 预测网络
2.1 Long Short-Term Memory
3 Text Prediction 文本预测
3.1 Penn Treebank Experiments Penn Treebank实验
3.2 Wikipedia Experiments 维基百科的实验
4 Handwriting Prediction 笔迹的预测
4.1 Mixture Density Outputs 混合密度输出
4.2 Experiments
4.3 Samples 样品
5 Handwriting Synthesis 字合成
5.1 Synthesis Network 合成网络
5.2 Experiments 实验
5.3 Unbiased Sampling 公正的抽样
5.4 Biased Sampling 有偏见的抽样
5.5 Primed Sampling 启动采样
6 Conclusions and Future Work 结论与未来工作
Acknowledgements 致谢
References
Generating Sequences With Recurrent Neural Networks
利用递归神经网络生成序列
论文原文:Generating Sequences With Recurrent Neural Networks
作者:
Alex Graves Department of Computer Science
University of Toronto graves@cs.toronto.edu
Abstract
1、Introduction 介绍
2 Prediction Network 预测网络
2.1 Long Short-Term Memory
3 Text Prediction 文本预测
3.1 Penn Treebank Experiments Penn Treebank实验
3.2 Wikipedia Experiments 维基百科的实验
4 Handwriting Prediction 笔迹的预测
4.1 Mixture Density Outputs 混合密度输出
4.3 Samples 样品
5 Handwriting Synthesis 字合成
5.1 Synthesis Network 合成网络
5.2 Experiments 实验
5.3 Unbiased Sampling 公正的抽样
5.4 Biased Sampling 有偏见的抽样
5.5 Primed Sampling 启动采样
6 Conclusions and Future Work 结论与未来工作
Acknowledgements 致谢
Reference[1] Y. Bengio, P. Simard, and P. Frasconi. Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2):157–166, March 1994.
[2] C. Bishop. Mixture density networks. Technical report, 1994.
[3] C. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, Inc., 1995.
[4] N. Boulanger-Lewandowski, Y. Bengio, and P. Vincent. Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription. In Proceedings of the Twenty-nine International Conference on Machine Learning (ICML’12), 2012.
[5] J. G. Cleary, Ian, and I. H. Witten. Data compression using adaptive coding and partial string matching. IEEE Transactions on Communications, 32:396–402, 1984.
[6] D. Eck and J. Schmidhuber. A first look at music composition using lstm recurrent neural networks. Technical report, IDSIA USI-SUPSI Instituto Dalle Molle.
[7] F. Gers, N. Schraudolph, and J. Schmidhuber. Learning precise timing with LSTM recurrent networks. Journal of Machine Learning Research, 3:115–143, 2002.
[8] A. Graves. Practical variational inference for neural networks. In Advances in Neural Information Processing Systems, volume 24, pages 2348–2356. 2011.
[9] A. Graves. Sequence transduction with recurrent neural networks. In ICML Representation Learning Worksop, 2012.
[10] A. Graves, A. Mohamed, and G. Hinton. Speech recognition with deep recurrent neural networks. In Proc. ICASSP, 2013.
[11] A. Graves and J. Schmidhuber. Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks, 18:602–610, 2005.
[12] A. Graves and J. Schmidhuber. Offline handwriting recognition with multidimensional recurrent neural networks. In Advances in Neural Information Processing Systems, volume 21, 2008.
[13] P. D. Gr¨unwald. The Minimum Description Length Principle (Adaptive Computation and Machine Learning). The MIT Press, 2007.
[14] G. Hinton. A Practical Guide to Training Restricted Boltzmann Machines. Technical report, 2010.
[15] S. Hochreiter, Y. Bengio, P. Frasconi, and J. Schmidhuber. Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-term Dependencies. In S. C. Kremer and J. F. Kolen, editors, A Field Guide to Dynamical Recurrent Neural Networks. 2001.
[16] S. Hochreiter and J. Schmidhuber. Long Short-Term Memory. Neural Computation, 9(8):1735–1780, 1997.
[17] M. Hutter. The Human Knowledge Compression Contest, 2012. [18] K.-C. Jim, C. Giles, and B. Horne. An analysis of noise in recurrent neural networks: convergence and generalization. Neural Networks, IEEE Transactions on, 7(6):1424 –1438, 1996. [19] S. Johansson, R. Atwell, R. Garside, and G. Leech. The tagged LOB corpus user’s manual; Norwegian Computing Centre for the Humanities, 1986.
[20] B. Knoll and N. de Freitas. A machine learning perspective on predictive coding with paq. CoRR, abs/1108.3298, 2011.
[21] M. Liwicki and H. Bunke. IAM-OnDB - an on-line English sentence database acquired from handwritten text on a whiteboard. In Proc. 8th Int. Conf. on Document Analysis and Recognition, volume 2, pages 956– 961, 2005.
[22] M. P. Marcus, B. Santorini, and M. A. Marcinkiewicz. Building a large annotated corpus of english: The penn treebank. COMPUTATIONAL LINGUISTICS, 19(2):313–330, 1993.
[23] T. Mikolov. Statistical Language Models based on Neural Networks. PhD thesis, Brno University of Technology, 2012.
[24] T. Mikolov, I. Sutskever, A. Deoras, H. Le, S. Kombrink, and J. Cernocky. Subword language modeling with neural networks. Technical report, Unpublished Manuscript, 2012.
[25] A. Mnih and G. Hinton. A Scalable Hierarchical Distributed Language Model. In Advances in Neural Information Processing Systems, volume 21, 2008.
[26] A. Mnih and Y. W. Teh. A fast and simple algorithm for training neural probabilistic language models. In Proceedings of the 29th International Conference on Machine Learning, pages 1751–1758, 2012.
[27] T. N. Sainath, A. Mohamed, B. Kingsbury, and B. Ramabhadran. Lowrank matrix factorization for deep neural network training with highdimensional output targets. In Proc. ICASSP, 2013.
[28] M. Schuster. Better generative models for sequential data problems: Bidirectional recurrent mixture density networks. pages 589–595. The MIT Press, 1999.
[29] I. Sutskever, G. E. Hinton, and G. W. Taylor. The recurrent temporal restricted boltzmann machine. pages 1601–1608, 2008.
[30] I. Sutskever, J. Martens, and G. Hinton. Generating text with recurrent neural networks. In ICML, 2011.
[31] G. W. Taylor and G. E. Hinton. Factored conditional restricted boltzmann machines for modeling motion style. In Proc. 26th Annual International Conference on Machine Learning, pages 1025–1032, 2009.
[32] T. Tieleman and G. Hinton. Lecture 6.5 - rmsprop: Divide the gradient by a running average of its recent magnitude, 2012.
[33] R. Williams and D. Zipser. Gradient-based learning algorithms for recurrent networks and their computational complexity. In Back-propagation: Theory, Architectures and Applications, pages 433–486. 1995.