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Recurrent neural networks (RNNs) are a widely used tool for modeling sequential data, yet they are often treated as inscrutable black boxes.
How the brain keeps the eyes still
H. S. Seung · 1996
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Nonlinear dimensionality reduction by locally linear embedding
Sam T. Roweis and Lawrence K. Saul · 2000
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Nonlinear Systems
Hassan K. Khalil · 2001
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A survey on the role of negation in sentiment analysis
Michael Wiegand, Alexandra Balahur, Benjamin Roth, Dietrich Klakow, and Andrés Montoyo · 2010
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts · 2011
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Baselines and bigrams: Simple, good sentiment and topic classification
Sida Wang and Christopher D. Manning · 2012
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Ng, and Christopher Potts · 2013
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Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks
David Sussillo and Omri Barak · 2013
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Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Visualizing and understanding recurrent networks
Andrej Karpathy, Justin Johnson, and Li Fei-Fei · 2015
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Sentiment analysis: Mining opinions, sentiments, and emotions
Bing Liu · 2015
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Visual analysis of hidden state dynamics in recurrent neural networks
Hendrik Strobelt, Sebastian Gehrmann, Bernd Huber, Hanspeter Pfister, Alexander M Rush, et al · 2016
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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FixedPointFinder: A tensorflow toolbox for identifying and characterizing fixed points in recurrent neural networks
Matthew Golub and David Sussillo · 2018
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On the importance of single directions for generalization
Ari S. Morcos, David G.T. Barrett, Neil C. Rabinowitz, and Matthew Botvinick · 2018
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Beyond word importance: Contextual decomposition to extract interactions from LSTMs
W. James Murdoch, Peter J. Liu, and Bin Yu · 2018
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun · 2015
Cited alongside, same era.
Capacity and trainability in recurrent neural networks
Jasmine Collins, Jascha Sohl-Dickstein, and David Sussillo · 2016
Cited alongside, same era.
Deep learning for sentiment analysis: A survey
Lei Zhang, Shuai Wang, and Bing Liu · 2018
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