Paul_Werbos

Paul Werbos

Paul Werbos

American scientist and machine learning expert

Paul John Werbos (born 1947) is an American social scientist and machine learning pioneer. He is best known for his 1974 dissertation, which first described the process of training artificial neural networks through backpropagation of errors.[1] He also was a pioneer of recurrent neural networks.[2]

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Werbos was one of the original three two-year Presidents of the International Neural Network Society (INNS). In 1995, he was awarded the IEEE Neural Network Pioneer Award for the discovery of backpropagation and other basic neural network learning frameworks such as Adaptive Dynamic Programming.[3]

Werbos has also written on quantum mechanics and other areas of physics.[4][5] He also has interest in larger questions relating to consciousness, the foundations of physics, and human potential.

He served as program director in the National Science Foundation for several years until 2015.

References

  1. [1]
    The thesis, and some supplementary information, can be found in his book, Werbos, Paul J. (1994). The Roots of Backpropagation : From Ordered Derivatives to Neural Networks and Political Forecasting. New York: John Wiley & Sons. ISBN 0-471-59897-6.
  2. [2]
    Werbos, P. (1990). "Backpropagation Through Time: What It Does and How to Do It". Proceedings of the IEEE. 78 (10): 1550–1560. doi:10.1109/5.58337. S2CID 18470994.
  3. [3]
    "Award Recipients | IEEE Computational Intelligence Society". cis.ieee.org. Archived from the original on 2013-01-17. Retrieved 2017-09-12.
  4. [4]
    Werbos, Paul J. (2005). "A Conjecture About Fermi–Bose Equivalence". arXiv:hep-th/0505023. Bibcode:2005hep.th....5023W. {{cite journal}}: Cite journal requires |journal= (help)
  5. [5]
    "Discussion with Paul Werbos on the Nature of Quantum Nonlocality". December 7, 2012.
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