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We present a novel algorithm that uses exact learning and abstraction to extract a deterministic finite automaton describing the state dynamics of a given trained RNN.
Dynamic construction of finite automata from examples using hill-climbing
Tomita, M · 1982
Earlier work this paper cites.
Learning regular sets from queries and counterexamples
Angluin, D · 1987
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Finding structure in time
Elman, J. L · 1990
Earlier work this paper cites.
Higher order recurrent networks and grammatical inference
Giles, C. L., Sun, G.-Z., Chen, H.-H., Lee, Y.-C., and Chen, D · 1990
Earlier work this paper cites.
A training algorithm for optimal margin classifiers
Boser, B. E., Guyon, I. M., and Vapnik, V. N · 1992
Earlier work this paper cites.
Learning finite state machines with self-clustering recurrent networks
Zeng, Z., Goodman, R. M., and Smyth, P · 1993
Earlier work this paper cites.
First-order versus second-order single-layer recurrent neural networks
Goudreau, M. W., Giles, C. L., Chakradhar, S. T., and Chen, D · 1994
Earlier work this paper cites.
On the complexity of teaching
Goldman, S. A. and Kearns, M. J · 1995
Earlier work this paper cites.
Extraction of rules from discrete-time recurrent neural networks
Omlin, C. W. and Giles, C. L · 1996
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Cechin, A. L., Simon, D. R. P., and Stertz, K · 2008
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On the properties of neural machine translation: Encoder-decoder approaches
Cho, K., van Merrienboer, B., Bahdanau, D., and Bengio, Y · 2014
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Chung, J., Gülçehre, Ç., Cho, K., and Bengio, Y · 2014
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Minimally supervised number normalization
Gorman, K. and Sproat, R · 2016
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Learning simpler language models with the delta recurrent neural network framework
Ororbia II, A. G., Mikolov, T., and Reitter, D · 2017
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Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
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Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E · 2011
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An empirical evaluation of recurrent neural network rule extraction
Wang, Q., Zhang, K., Ororbia II, A. G., Xing, X., Liu, X., and Giles, C. L · 2017
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Wang, Q., Zhang, K., Ororbia II, A. G., Xing, X., Liu, X., and Giles, C. L · 2018
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