Ronald J. Williams Explained

Ronald J. Williams (1945 in California - February 16, 2024 in Framingham Massachusetts)^[1] 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.^[2] He also made fundamental contributions to the fields of recurrent neural networks^{[3] [4]} and reinforcement learning.^[5] Together with Wenxu Tong and Mary Jo Ondrechen he developed Partial Order Optimum Likelihood (POOL), 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.^[6]

External links

• Home page of Ronald J. Williams

Notes and References

1. Web site: Donaghy . Roger . 2024-03-05 . A tribute to Ron Williams, Khoury professor and machine learning pioneer . 2024-06-25 . Khoury College of Computer Sciences . en-US.
2. David E. Rumelhart, Geoffrey E. Hinton und Ronald J. Williams. Learning representations by back-propagating errors., Nature (London) 323, S. 533-536
3. Williams, R. J. and Zipser, D. (1989). A learning algorithm for continually running fully recurrent neural networks. Neural Computation, 1, 270-280.
4. R. J. Williams and D. Zipser. Gradient-based learning algorithms for recurrent networks and their computational complexity. In Back-propagation: Theory, Architectures and Applications. Hillsdale, NJ: Erlbaum, 1994.
5. Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8, 229-256.
6. W. Tong, Y. Wei, L.F. Murga, M.J. Ondrechen, and R.J. Williams (2009). Partial Order Optimum Likelihood (POOL): Maximum Likelihood Prediction of Active Site Residues Using 3D Structure and Sequence Properties. PLoS Computational Biology, 5(1): e1000266.