Ronald J. Williams

65.1k citations

54 papers · 33.3k · 5 hit papers · h-index 26

Impact in

Artificial Intelligence top 0.02%
- Neural Networks and Applications
- Topic Modeling
- Reinforcement Learning in Robotics
- Natural Language Processing Techniques
- Anomaly Detection Techniques and Applications
Computer Vision and Pattern Recognition top 0.1%

Papers in

Artificial Intelligence 18
- Neural Networks and Applications 8
- Evolutionary Algorithms and Applications 5
- Reinforcement Learning in Robotics 4
Archeology 7
- Archaeology and Historical Studies 6
- Ancient Egypt and Archaeology 4

Co-authors: David E. Rumelhart (4 shared papers)Geoffrey E. Hinton (4 shared papers)David Zipser (3 shared papers)Jing Peng (6 shared papers)Gary M. Kuhn (1 shared paper)C. Lee Giles (1 shared paper)Mary Jo Ondrechen (4 shared papers)Leonel F. Murga (3 shared papers)
Journals: Machine Learning (4 papers)The Journal of Egyptian Archaeology (3 papers)Journal of Biblical Literature (2 papers)Neural Computation (2 papers)Connection Science (2 papers)
Partner nations: United States United Kingdom Vietnam

In The Last Decade

Ronald J. Williams

50 papers receiving 31.3k citations

Ronald J. Williams's Hit Papers

5 papers align trajectories log scale

Learning Representations by Back-Propagating Errors 2002 · 492 citations

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if any of the following hold:

it has ≥500 total citations;
it reaches ≥1.5× the top-1% citation threshold for papers in the same subfield and year (the threshold is the minimum needed to enter the top 1%, not the average within it);
it reaches the top citation threshold in at least one of its specific research topics.

2002 The MIT Press eBooks
1992 Machine Learning
1992 Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
1989 Neural Computation
1986 Nature

Peers

Replace Peter J. Rousseeuw with:

Peter J. Rousseeuw Belgium

Simon Haykin Canada

Chris Bishop United Kingdom

Kurt Hornik Austria

Joos Vandewalle Belgium

Alex Smola Australia

Johan A. K. Suykens Belgium

John Shawe‐Taylor United Kingdom

Christopher J. C. Burges United States

Teuvo Kohonen Finland

Ronald J. Williams relative to Peter J. Rousseeuw Belgium Peter J. Rousseeuw's profile →

Citations per field

00.5×1.6×

Peter J. Rousseeuw · 1×

×1.2 16k/13k

AI

×0.7 5k/7k

CVPR

×0.6 3k/4k

SP

×1.0 3k/3k

CSE

×1.6 2k/1k

CN

Citations per year

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Countries citing papers authored by Ronald J. Williams

Since Specialization

Citations

This map shows the geographic impact of Ronald J. Williams's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ronald J. Williams with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ronald J. Williams more than expected).

Fields of papers citing papers by Ronald J. Williams

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ronald J. Williams. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ronald J. Williams. The network helps show where Ronald J. Williams may publish in the future.

Co-authors

The 25 scholars most cited alongside Ronald J. Williams, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ronald J. Williams Line = papers co-authored together Ronald J. Williams links everyone, so they are left out of the graph.

All Works

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20 of 20 papers shown

Showing the 20 most-cited of 54 papers — load more, or switch the sort, to bring in the rest.

#	Work
1	Learning representations by back-propagating errors Nature ·David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams Hit paper breakdown →	1986	21669
2	Simple statistical gradient-following algorithms for connectionist reinforcement learning Machine Learning ·Ronald J. Williams Hit paper breakdown →	1992	4478
3	A Learning Algorithm for Continually Running Fully Recurrent Neural Networks Neural Computation ·Ronald J. Williams, David Zipser Hit paper breakdown →	1989	3195
4	Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning Ronald J. Williams Hit paper breakdown →	1992	651
5	Learning Representations by Back-Propagating Errors The MIT Press eBooks ·David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams Hit paper breakdown →	2002	492
6	An Efficient Gradient-Based Algorithm for On-Line Training of Recurrent Network Trajectories Neural Computation ·Ronald J. Williams, Jing Peng	1990	433
7	Gradient-based learning algorithms for recurrent networks and their computational complexity Ronald J. Williams, David Zipser	1995	305
8	Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning Machine Learning ·Ronald J. Williams	1992	260
9	Experimental Analysis of the Real-time Recurrent Learning Algorithm Connection Science ·Ronald J. Williams, David Zipser	1989	230
10	Incremental multi-step Q-learning Machine Learning ·Jing Peng, Ronald J. Williams	1996	205
11	Dynamic recurrent neural networks: Theory and applications IEEE Transactions on Neural Networks ·C. Lee Giles, Gary M. Kuhn, Ronald J. Williams	1994	163
12	Efficient Learning and Planning Within the Dyna Framework Adaptive Behavior ·Jing Peng, Ronald J. Williams	1993	149
13	Function Optimization using Connectionist Reinforcement Learning Algorithms Connection Science ·Ronald J. Williams, Jing Peng	1991	136
14	(1986) David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams," Learning representations by back-propagating errors," Nature 323: 533-536 The MIT Press eBooks ·David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams	1988	108
15	Incremental Multi-Step Q-Learning Elsevier eBooks ·Jing Peng, Ronald J. Williams	1994	83
16	Practices Associated with Weight Loss Versus Weight-Loss Maintenance American Journal of Preventive Medicine ·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	2011	78
17	(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 The MIT Press eBooks ·David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams	1988	62
18	Partial Order Optimum Likelihood (POOL): Maximum Likelihood Prediction of Protein Active Site Residues Using 3D Structure and Sequence Properties PLoS Computational Biology ·Wenxu Tong, Ying Wei, Leonel F. Murga, Mary Jo Ondrechen, Ronald J. Williams	2009	58
19	Reinforcement Learning is Direct Adaptive Optimal Control Richard S. Sutton, Andrew G. Barto, Ronald J. Williams	1991	50
20	Statistical criteria for the identification of protein active sites using theoretical microscopic titration curves Proteins Structure Function and Bioinformatics ·Jaeju Ko, Leonel F. Murga, Pierrette André, Huyuan Yang, Mary Jo Ondrechen, Ronald J. Williams, Akochi Agunwamba, David E. Budil	2005	48

About Ronald J. Williams

Ronald J. Williams is a scholar working on Artificial Intelligence, Archeology, Statistical and Nonlinear Physics, Computational Theory and Mathematics and Molecular Biology, having authored 54 papers that have together received 33.3k indexed citations. Recurring topics across this work include Neural Networks and Applications (8 papers), Archaeology and Historical Studies (6 papers), Evolutionary Algorithms and Applications (5 papers), Biblical Studies and Interpretation (4 papers), Protein Structure and Dynamics (4 papers), Ancient Egypt and Archaeology (4 papers), Reinforcement Learning in Robotics (4 papers) and Adaptive Dynamic Programming Control (3 papers). The work is most often cited by research in Artificial Intelligence (15.7k citations), Computer Vision and Pattern Recognition (5.4k citations), Signal Processing (2.5k citations), Control and Systems Engineering (3.4k citations) and Cognitive Neuroscience (2.4k citations). Ronald J. Williams has collaborated with scholars based in United States, United Kingdom and Vietnam. Frequent co-authors include David E. Rumelhart, Geoffrey E. Hinton, David Zipser, Jing Peng, Gary M. Kuhn, C. Lee Giles, Mary Jo Ondrechen, Leonel F. Murga, Andrew G. Barto and Richard S. Sutton. Their work appears in journals such as Machine Learning, The Journal of Egyptian Archaeology, Journal of Biblical Literature, Neural Computation and Connection Science.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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Ronald J. Williams

Ronald J. Williams's Hit Papers

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Fields of papers citing papers by Ronald J. Williams

All Works

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