A Critical Review of Recurrent Neural Networks for Sequence Learning
作者： Charles Elkan , Zachary C. Lipton , John Berkowitz
DOI:
关键词:
摘要: … In this survey, we review and synthesize the research that over the past three decades first yielded and then made practical these powerful learning models. When appropriate, we …
arxiv-vanity.com
harvard.edu
arxiv.org参考文章(69)
Matthew D. Zeiler, ADADELTA: An Adaptive Learning Rate Method arXiv: Learning. ,(2012)
Zachary C. Lipton, Charles Elkan, Balakrishnan Naryanaswamy, Optimal thresholding of classifiers to maximize F1 measure european conference on machine learning. ,vol. 8725, pp. 225- 239 ,(2014) , 10.1007/978-3-662-44851-9_15
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)
Ilya Sutskever, Geoffrey Hinton, James Martens, George Dahl, On the importance of initialization and momentum in deep learning international conference on machine learning. pp. 1139- 1147 ,(2013)
Justin Bayer, Daan Wierstra, Julian Togelius, Jürgen Schmidhuber, Evolving Memory Cell Structures for Sequence Learning international conference on artificial neural networks. pp. 755- 764 ,(2009) , 10.1007/978-3-642-04277-5_76
Geoffrey E. Hinton, Learning distributed representations of concepts. Clarendon Press/Oxford University Press. ,(1989)
Ilya Sutskever, Geoffrey E. Hinton, James Martens, Generating Text with Recurrent Neural Networks international conference on machine learning. pp. 1017- 1024 ,(2011)
Ronan Collobert, Clément Farabet, Koray Kavukcuoglu, Torch7: A Matlab-like Environment for Machine Learning neural information processing systems. ,(2011)
Ilya Sutskever, James Martens, Learning Recurrent Neural Networks with Hessian-Free Optimization international conference on machine learning. pp. 1033- 1040 ,(2011)
Richard Socher, Andrew Y. Ng, Cliff C. Lin, Chris Manning, Parsing Natural Scenes and Natural Language with Recursive Neural Networks international conference on machine learning. pp. 129- 136 ,(2011)