Ronald J. Williams
Ronald J. Williams (1945 in California – February 16, 2024 in Framingham Massachusetts)[1] was 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]
References
[edit]- ^ Donaghy, Roger (2024-03-05). "A tribute to Ron Williams, Khoury professor and machine learning pioneer". Khoury College of Computer Sciences. Retrieved 2024-06-25.
- ^ David E. Rumelhart, Geoffrey E. Hinton und Ronald J. Williams. Learning representations by back-propagating errors., Nature (London) 323, S. 533-536
- ^ Williams, R. J. and Zipser, D. (1989). A learning algorithm for continually running fully recurrent neural networks. Neural Computation, 1, 270-280.
- ^ 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.
- ^ Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8, 229-256.
- ^ 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.
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