Ronald J. Williams

11 papers A* 1Journal 7Unranked 3
YearRankTypeTitle / Venue / Authors
2009 J jnl
Partial Order Optimum Likelihood (POOL): Maximum Likelihood Prediction of Protein Active Site Residues Using 3D Structure and Sequence Properties.
PLoS Comput. Biol.
Wenxu Tong, Ying Wei, Leonel F. Murga, Mary Jo Ondrechen, Ronald J. Williams
1998 — conf
Robust, Efficient, Globally-Optimized Reinforcement Learning with the Parti-Game Algorithm.
NIPS
Mohammad A. Al-Ansari, Ronald J. Williams
1996 J jnl
Incremental Multi-Step Q-Learning.
Mach. Learn.
Jing Peng, Ronald J. Williams
1994 J jnl
Dynamic recurrent neural networks: Theory and applications.
IEEE Trans. Neural Networks Learn. Syst.
C. Lee Giles, Gary M. Kuhn, Ronald J. Williams
1994 A* conf
Incremental Multi-Step Q-Learning.
ICML
Jing Peng, Ronald J. Williams
1993 J jnl
Efficient Learning and Planning Within the Dyna Framework.
Adapt. Behav.
Jing Peng, Ronald J. Williams
1993 — conf
Efficient learning and planning within the Dyna framework.
ICNN
Jing Peng, Ronald J. Williams
1992 J jnl
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning.
Mach. Learn.
Ronald J. Williams
1990 J jnl
An Efficient Gradient-Based Algorithm for On-Line Training of Recurrent Network Trajectories.
Neural Comput.
Ronald J. Williams, Jing Peng
1989 J jnl
A Learning Algorithm for Continually Running Fully Recurrent Neural Networks.
Neural Comput.
Ronald J. Williams, David Zipser
1988 — conf
On the use of backpropagation in associative reinforcement learning.
ICNN
Ronald J. Williams