Spatio-temporal connectionist networks

Handbook of Dynamic System Modeling

Volumn , Issue , 2007, Pages 22.1-22.10

  Kremer, Stefan C ^a  

^a UNIVERSITY OF GUELPH   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84869866548     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (27)

1. Bakker, B. and J. Schmidhuber (2004). Hierarchical reinforcement learning based on subgoal discovery and subpolicy specialization. In F. Groen, N. Amato, A. Bonarini, E. Yoshida, and B. Krse (Eds.), Proceedings of the 8-th Conference on Intelligent Autonomous Systems, IAS-8, Amsterdam, The Netherlands, pp. 438-445.
2. Barreto, G. A., A. F. R. Araújo, and S. C. Kremer (2003). A taxonomy for spatio-temporal connectionist networks revisited: The unsupervised case. Neural Computation 15(6), 1255-1320.
3. Eck, D. and J. Schmidhuber (2002). Learning the long-term structure of the blues. In J. Dorronsoro (Ed.), Artificial Neural Networks - ICANN 2002 (Proceedings), pp. 284-289. Berlin: Springer.
4. Elman, J. (1990). Finding structure in time. Cognitive Science 14, 179-211.
5. Elman, J. L. (1991). Distributed representations, simple recurrent networks and grammatical structure. Machine Learning 7(2/3), 195-226.
6. Giles, C., G. Sun, H. Chen, Y. Lee, and D. Chen (1990). Higher order recurrent networks & grammatical inference. In D. S. Touretzky (Ed.), Advances in Neural Information Processing Systems 2, San Mateo, CA, pp. 380-387. Morgan Kaufmann.
7. Giles, C. L. and T. Maxwell (1987). Learning, invariance, and generalization in high-order neural networks. Applied Optics 26(23), 4972-4978.
8. Giles, C. L., C. B. Miller, D. Chen, G. Z. Sun, H. H. Chen, and Y. C. Lee (1992). Extracting and learning an unknown grammar with recurrent neural networks. In J. E. Moody, S. J. Hanson, and R. P. Lippmann (Eds.), Advances in Neural Information Processing Systems 4, San Mateo, CA, pp. 317-324. Morgan Kaufmann.
9. Giles, C. L. and C. Omlin (1992). Inserting rules into recurrent neural networks. In S. Kung, F. Fallside, J. A. Sorenson, and C. Kamm (Eds.), Neural Networks for Signal Processing II, Proceedings of the 1992 IEEE Workshop, Piscataway, NJ, pp. 13-22. IEEE Press.
10. Hochreiter, S., Y. Bengio, P. Frasconi, and J. Schmidhuber (2001). Gradient flow in recurrent nets: The difficulty of learning long-term dependencies. In J. F. Kolen and S. C. Kremer (Eds.), A Field Guide to Dynamical Recurrent Networks, pp. 237-244. Piscataway, NJ: IEEE Press.
11. Hochreiter, S. and J. Schmidhuber (1997). Long short-term memory. Neural Computation 9(8), 1735-1780.
12. Hornik, K., M. Stinchcombe, and H. White (1989). Multilayer feedforward networks are universal approximators. Neural Networks 2(5), 359-366.
13. Kolen, J. F. (1994). Fool's gold: Extracting finite state machines from recurrent network dynamics. In J. D. Cowan, G. Tesauro, and J. Alspector (Eds.), Advances in Neuralinformation Processing Systems 6, Volume 6, pp. 501-508. San Mateo, CA: Morgan Kaufmann.
14. Kremer, S. C. (1995). On the computational power of elman-style recurrent networks. IEEE Transactions on Neural Networks 6(4), 1000-1004.
15. Kremer, S. C. (2001). Spatio-temporal connectionist networks: A taxonomy and review. Neural Computation 13(2), 249-306.
16. McCulloch, W. and W. Pitts (1943). A logical calculus of ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5,115-133.
17. Mozer, M. C. (1994). Neural net architectures for temporal sequence processing. In A. Weigend and N. Gershenfeld (Eds.), Time Series Prediction, pp. 243-264. Reading, MA: Addison-Wesley.
18. Palmer, J. and S. Kremer (2005). Learning long-term dependencies with reusable state modules and stochastic correlation. In Intelligent Engineering Systems Through Artificial Neural Networks, Volume 15, pp. 103-109. ASME.
19. Pearlmutter, B. (2001). Gradient calculations for dynamic recurrent networks. In J. F. Kolen and S. C. Kremer (Eds.), A Field Guide to Dynamical Recurrent Networks. pp. 179-206. Piscataway, NJ: IEEE Press.
20. Prokhorov, D., G. Puskorius, and L. Feldkamp (2001). Dynamical recurrent networks in control. In J. F. Kolen and S. C. Kremer (Eds.), A Field Guide to Dynamical Recurrent Networks. pp. 143-152. Piscataway, NJ: IEEE Press.
21. Rumelhart, D., G. Hinton, and R. Williams (1986). Learning internal representation by error propagation. In J. L. McClelland, D. Rumelhart, and the P. D. P. Group (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1: Foundations. Cambridge, MA: MIT Press.
22. 3) time complexity learning algorithm for fully recurrent continually running networks. Neural Computation 4(2), 243-248.
23. Siegelmann, H. (2001). Universal computation and super-turing capabilities. In J. F. Kolen and S. C.Kremer (Eds.), A Field Guide to Dynamical Recurrent Networks, pp. 143-152. Piscataway, NJ: IEEE Press.
24. Tabor, W. (2001). Sequence processing and linguistic structure. In J. F. Kolen and S. C. Kremer (Eds.), A Field Guide to Dynamical Recurrent Networks, pp. 291-310. Piscataway, NJ: IEEE Press.
25. Werbos, P. J. (1994). The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting. New York: Wiley.
26. Wiles, J., A. Blair, and M. Bodén (2001). Representation beyond finite states: Alternatives of push-down automata. In J. F. Kolen and S. C. Kremer (Eds.), A Field Guide to Dynamical Recurrent Networks, pp. 129-142. Piscataway, NJ: IEEE Press.
27. Williams, R. J. and D. Zipser (1989). A learning algorithm for continually running fully recurrent neural networks. Neural Computation 1 (2), 270-280.