HOME   ::   Back to the Paper   ::   References

Cernansky, M., Makula, M., and Benuskova, L. (2007) Organization of the state space of a simple recurrent network before and after training on recursive linguistic structures. Neural Networks, 20(2):236--244.

References (may not be complete)  [Original format]  [Sort by year]  [Sort by author]  [Sort by citations]

Chomsky, N. (1957). Syntactic structures. Mouton: The Hague.

Google

Christiansen, M. H., & Chater, N. (1999). Toward a connectionist model of recursion in human linguistic performance. Cognitive Science, 23, 417-437.

Google UIUC

Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14, 179-211.

Google UIUC

Feldkamp, L., Prokhorov, D., Eagen, C., & Yuan, F. (1998). Enhanced multistream Kalman filter training for recurrent networks. In J. Suykens, & J. Vandewalle (Eds.), Nonlinear modeling: Advanced black-box techniques (pp. 29-53). Kluwer Academic Publishers.

Google

Hanson, S. J., & Negishi, M. (2002). On the emergence of rules in neural networks. Neural Computation, 14(9), 2245-2268.

Google

Kolen, J. F. (1994a). The origin of clusters in recurrent neural network state space. In Proceedings of the 16th annual conference of the cognitive science society (pp. 508-513). Hillsdale, NJ: Lawrence Erlbaum Associates.

Google

Kolen, J. F. (1994b). Recurrent networks: state machines or iterated function systems? In M. C. Mozer, et al. (Eds.), Proceedings of the 1993 connectionist models summer school (pp. 203-210). Hillsdale, NJ: Lawrence Erlbaum Associates.

Google

Lawrence, S., Giles, C. L., & Fong, S. (2000). Natural language grammatical inference with recurrent neural networks. IEEE Transactions on Knowledge and Data Engineering, 12(1), 126-140.

Google

Parfitt, S. (1997). Aspects of anaphora resolution in artificial neural networks: Implications for nativism. Ph.D. thesis. London: Imperial College.

Google

P´erez-Ortiz, J. A., Gers, F. A., Eck, D., & Schmidh¨uber, J. (2003). Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets. Neural Networks, 16(2), 241-250.

Google

Ron, D., Singer, Y., & Tishby, N. (1996). The power of amnesia: learning probabilistic automata with variable memory length. Machine Learning, 25, 117-149.

Google

Servan-Schreiber, D., Cleeremans, A., & McClelland, J. L. (1989). Graded state machines: the representation of temporal contingencies in Simple Recurrent Networks. Machine Learning, 7, 161-193.

Google

Wan, E. A., & Nelson, A. T. (2000). Dual EKF methods. In S. Haykin (Ed.), Kalman filtering and neural networks (pp. 123-173). New York: Wiley.

Google

Welch, G., & Bishop, G. (1995). An introduction to the Kalman filter, TR95- 041. Department of Computer Science, University of North Carolina.

Google

Werbos, P. J. (1990). Backpropagation through time; what it does and how to do it. Proceedings of the IEEE, 78, 1550-1560.

Google

Williams, R. J., & Zipser, D. (1989). A learning algorithm for continually running fully recurrent neural networks. Neural Computation, 1, 270-280.

Google

Williams, R. J., & Zipser, D. (1995). Gradient-based learning algorithms for recurrent networks and their computational complexity. In Y. Chauvin, & D. E. Rumelhart (Eds.), Back-propagation: Theory, architectures and applications (pp. 433-486). Hillsdale, NJ: Lawrence Erlbaum Publishers.

Google

 HOME   ::   Back to the Paper   ::   References Comments to: junwang4 you-know-at gmail.com Last update: 2/3/09