Ronald J. Williams

Ronald J. Williams's AcademicInfluence.com Rankings

Ronald J. Williams

Computer Science

#236

World Rank

#246

Historical Rank

#141

USA Rank

Machine Learning

#57

World Rank

#57

Historical Rank

#23

USA Rank

Artificial Intelligence

#138

World Rank

#141

Historical Rank

#62

USA Rank

Computational Linguistics

#737

World Rank

#750

Historical Rank

#37

USA Rank

computer-science Degrees

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Computer Science

Ronald J. Williams's Degrees

Bachelors Computer Science Stanford University
Masters Computer Science Stanford University
PhD Computer Science Stanford University

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Why Is Ronald J. Williams Influential?

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According to Wikipedia, Ronald J. Williams is professor of computer science at Northeastern University, and one of the pioneers of neural networks. He co-authored a paper on the backpropagation algorithm which triggered a boom in neural network research. He also made fundamental contributions to the fields of recurrent neural networks and reinforcement learning. Together with Wenxu Tong and Mary Jo Ondrechen he developed Partial Order Optimum Likelihood , a machine learning method used in the prediction of active amino acids in protein structures. POOL is a maximum likelihood method with a monotonicity constraint and is a general predictor of properties that depend monotonically on the input features.

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Ronald J. Williams's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

Learning representations by back-propagating errors (1986) (22496)
Learning internal representations by error propagation (1986) (20156)
Simple statistical gradient-following algorithms for connectionist reinforcement learning (1992) (6595)
A Learning Algorithm for Continually Running Fully Recurrent Neural Networks (1989) (4165)
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning (2004) (823)
An Efficient Gradient-Based Algorithm for On-Line Training of Recurrent Network Trajectories (1990) (676)
Gradient-based learning algorithms for recurrent networks and their computational complexity (1995) (597)
Learning representations of back-propagation errors (1986) (556)
Incremental multi-step Q-learning (1994) (409)
Experimental Analysis of the Real-time Recurrent Learning Algorithm (1989) (341)
Function Optimization using Connectionist Reinforcement Learning Algorithms (1991) (310)
Efficient Learning and Planning Within the Dyna Framework (1993) (287)
Reinforcement Learning is Direct Adaptive Optimal Control (1992) (274)
Tight Performance Bounds on Greedy Policies Based on Imperfect Value Functions (1993) (183)
Dynamic recurrent neural networks: Theory and applications (1994) (136)
Gradient-based learning algorithms for recurrent connectionist networks (1990) (85)
On the use of backpropagation in associative reinforcement learning (1988) (74)
Partial Order Optimum Likelihood (POOL): Maximum Likelihood Prediction of Protein Active Site Residues Using 3D Structure and Sequence Properties (2009) (68)
Analysis of Some Incremental Variants of Policy Iteration: First Steps Toward Understanding Actor-Cr (1993) (63)
Statistical criteria for the identification of protein active sites using theoretical microscopic titration curves (2005) (60)
Adaptive state representation and estimation using recurrent connectionist networks (1990) (60)
Gradient-Based Learning Algorithms for Recurrent Networks (1989) (54)
Enhanced performance in prediction of protein active sites with THEMATICS and support vector machines (2008) (38)
A MATHEMATICAL ANALYSIS OF ACTOR-CRITIC ARCHITECTURES FOR LEARNING OPTIMAL CONTROLS THROUGH INCREMENTAL DYNAMIC PROGRAMMING (1990) (30)
Robust, Efficient, Globally-Optimized Reinforcement Learning with the Parti-Game Algorithm (1998) (12)
Temporal Difference Learning: A Chemical Process Control Application - in Applications of Neural (1995) (5)
Incremental Multi-Step (1996) (5)
High Conservation of Amino Acids with Anomalous Protonation Behavior (2010) (3)
Modifying the Parti-game Algorithm for Increased Robustness, Higher Eeciency and Better Policies (1998) (2)
Incremental Multi-Step Q-Learning (1996) (1)
Some Observations on the Use of theExtended (2007) (0)

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Ronald J. Williams's Academic­Influence.com Rankings

Ronald J. Williams's Degrees

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Why Is Ronald J. Williams Influential?

Ronald J. Williams's Published Works

Published Works

Other Resources About Ronald J. Williams

What Schools Are Affiliated With Ronald J. Williams?

Ronald J. Williams's AcademicInfluence.com Rankings