Ronald J. Williams

• Jing Peng4
• David E. Rumelhart3
• David Zipser3
• Geoffrey E. Hinton3
• Leonel F. Murga2
• Mary Jo Ondrechen2
• Akochi Agunwamba1
• Andrew G. Barto1
• Arthur M. Nezu1
• Barbara J. Rolls1
• C. Lee Giles1
• Carla K. Miller1
• Cheryl Dellasega1
• Christopher Sciamanna1
• David E. Budil1
• Donna Kephart1
• Eric Loken1
• Erik Lehman1
• Gary M. Kuhn1
• Gordon Jensen1
• Heather L. Stuckey1
• Huyuan Yang1
• Jaeju Ko1
• Jane R. Schubart1

1. Learning Representations by Back-Propagating Errors

   著者: David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams - Nature 1986 被引用: 31,562

2. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning

   著者: Ronald J. Williams - Machine Learning, Mach. Learn. 1992 被引用: 7,462

3. A Learning Algorithm for Continually Running Fully Recurrent Neural Networks

   著者: Ronald J. Williams, David Zipser - Neural Computation, Neural Comput. 1989 被引用: 4,451

4. An Efficient Gradient-Based Algorithm for On-Line Training of Recurrent Network Trajectories

   著者: Ronald J. Williams, Jing Peng - Neural Computation, Neural Comput. 1990 被引用: 593

5. Dynamic recurrent neural networks: Theory and applications

   著者: C. Lee Giles, Gary M. Kuhn, Ronald J. Williams - IEEE Transactions on Neural Networks, IEEE Trans. Neural Networks Learn. Syst. 1994 被引用: 204

6. Function Optimization using Connectionist Reinforcement Learning Algorithms

   著者: Ronald J. Williams, Jing Peng - Connection Science 1991 被引用: 305

7. (1986) D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning internal representations by error propagation," Parallel Distributed Processing: Explorations in the Microstructures of Cognition, Vol. I, D. E. Rumelhart and J. L. McClelland (Eds.) Cambridge, MA: MIT Press, pp. 318-362

   著者: David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams - The MIT Press eBooks 1988 被引用: 103

8. Gradient-based learning algorithms for recurrent networks and their computational complexity

   著者: Ronald J. Williams, David Zipser - ftp://ftp.ccs.neu.edu/pub/people/rjw/bp-chap-95.ps 1995 被引用: 417

9. Experimental Analysis of the Real-time Recurrent Learning Algorithm

   著者: Ronald J. Williams, David Zipser - Connection Science 1989 被引用: 295

10. Incremental Multi-Step Q-Learning

    著者: Jing Peng, Ronald J. Williams - Elsevier eBooks, ICML 1994 被引用: 289

11. Efficient Learning and Planning Within the Dyna Framework

    著者: Jing Peng, Ronald J. Williams - Adaptive Behavior, Adapt. Behav. 1993 被引用: 205

12. On the use of backpropagation in associative reinforcement learning

    著者: Ronald J. Williams - IEEE International Conference on Neural Networks, ICNN 1988 被引用: 74

13. (1986) David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams," Learning representations by back-propagating errors," Nature 323: 533-536

    著者: David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams - The MIT Press eBooks 1988 被引用: 132

14. Reinforcement Learning is Direct Adaptive Optimal Control

    著者: Richard S. Sutton, Andrew G. Barto, Ronald J. Williams - American Control Conference 1991 被引用: 524

15. Practices Associated with Weight Loss Versus Weight-Loss Maintenance

    著者: Christopher Sciamanna, Michaela Kiernan, Barbara J. Rolls, Jarol Boan, Heather L. Stuckey, Donna Kephart, Carla K. Miller, Gordon Jensen, Terry J. Hartmann, Eric Loken, Kevin O. Hwang, Ronald J. Williams, Melissa A. Clark, Jane R. Schubart, Arthur M. Nezu, Erik Lehman, Cheryl Dellasega - American Journal of Preventive Medicine 2011 被引用: 99

16. Partial Order Optimum Likelihood (POOL): Maximum Likelihood Prediction of Protein Active Site Residues Using 3D Structure and Sequence Properties

    著者: Wenxu Tong, Ying Wei, Leonel F. Murga, Mary Jo Ondrechen, Ronald J. Williams - PLoS Computational Biology, PLoS Comput. Biol. 2009 被引用: 84

17. Statistical criteria for the identification of protein active sites using theoretical microscopic titration curves

    著者: Jaeju Ko, Leonel F. Murga, Pierrette André, Huyuan Yang, Mary Jo Ondrechen, Ronald J. Williams, Akochi Agunwamba, David E. Budil - Proteins Structure Function and Bioinformatics 2005 被引用: 70

18. Adaptive State Representation and Estimation Using Recurrent Connectionist Networks

    著者: Ronald J. Williams - The MIT Press eBooks 1991 被引用: 50

19. Hebrew Syntax: An Outline

    著者: Ronald J. Williams - 1968 被引用: 37

20. Robust, Efficient, Globally-Optimized Reinforcement Learning with the Parti-Game Algorithm

    著者: Mohammad A. Al-Ansari, Ronald J. Williams - ftp://ftp.ccs.neu.edu/pub/people/rjw/parti-game-nips-98.ps 1998 被引用: 10

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