Terrence J. Sejnowski

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Terrence Sejnowski

Terrence J. (Terry) Sejnowski,
an American physicist, computer scientist, neurobiologist, investigator at the Howard Hughes Medical Institute, Francis Crick professor at the Salk Institute for Biological Studies, La Jolla, San Diego, California, and adjunct professor in the departments of neurosciences, psychology, cognitive science, computer science and engineering at the University of California, San Diego. He was further member of the advisory committee for President Obama’s BRAIN Initiative. Terrence Sejnowski received his M.Sc. in physics under John Wheeler in 1970 at Princeton University, and his Ph.D. in physics under John Hopfield in 1978 at Princeton as well. In 1982, he joined the department of biology at Johns Hopkins University before moving to San Diego in 1988. He pioneered in neural networks and computational neuroscience, and co-invented the Boltzmann machine, a type of recurrent neural network capable of learning internal representations , and researched on problems in speech concerning English pronunciation, resulting in NETtalk . In collaboration with Barbara Oakley, Terrence Sejnowski co-created and taught Learning How To Learn: Powerful mental tools to help you master tough subjects, available as MOOC on Coursera .

Learning in Games

Along with Gerald Tesauro, Terrence Sejnowski worked and published on neural networks applied to Backgammon , and along with Nicol N. Schraudolph and Peter Dayan on temporal difference learning to evaluate positions in Go .

Selected Publications

1985 ...

• David H. Ackley, Geoffrey E. Hinton, Terrence J. Sejnowski (1985). A Learning Algorithm for Boltzmann Machines. Cognitive Science, Vol. 9, No. 1, pdf
• Terrence J. Sejnowski, Charles R. Rosenberg (987). Parallel Networks That Learn to Pronounce English Text. Complex Systems, Vol. 1, pdf
• Gerald Tesauro, Terrence J. Sejnowski (1987). A 'Neural' Network that Learns to Play Backgammon. NIPS 1987, pdf
• Gerald Tesauro, Terrence J. Sejnowski (1989). A Parallel Network that Learns to Play Backgammon. Artificial Intelligence, Vol. 39, No. 3

1990 ...

• Yan Fang, Terrence J. Sejnowski (1990). Faster Learning for Dynamic Recurrent Backpropagation. Neural Computation, Vol. 2, No. 3, pdf
• Nicol N. Schraudolph, Terrence J. Sejnowski (1991). Competitive Anti-Hebbian Learning of Invariants. NIPS 1991
• Patricia Churchland, Terrence J. Sejnowski (1992). The Computational Brain. MIT Press
• Nicol N. Schraudolph, Terrence J. Sejnowski (1992). Unsupervised Discrimination of Clustered Data via Optimization of Binary Information Gain. NIPS 1992
• Nicol N. Schraudolph, Peter Dayan, Terrence J. Sejnowski (1993). Temporal Difference Learning of Position Evaluation in the Game of Go. NIPS 1993
• Peter Dayan, Terrence J. Sejnowski (1994). TD(λ) converges with Probability 1. Machine Learning, Vol. 14, No. 1, pdf
• Nicol N. Schraudolph, Terrence J. Sejnowski (1995). Tempering Backpropagation Networks: Not All Weights are Created Equal. NIPS 1995, pdf
• Peter Dayan, Terrence J. Sejnowski (1996). Exploration Bonuses and Dual Control. Machine Learning, Vol. 25, No. 1, pdf
• Laurence F. Abbott, Terrence J. Sejnowski (eds.) (1999). Neural Codes and Distributed Representations. MIT Press
• Geoffrey E. Hinton, Terrence J. Sejnowski (eds.) (1999). Unsupervised Learning: Foundations of Neural Computation. MIT Press

2000 ...

• Nicol N. Schraudolph, Peter Dayan, Terrence J. Sejnowski (2001). Learning to Evaluate Go Positions via Temporal Difference Methods. Computational Intelligence in Games, Studies in Fuzziness and Soft Computing. Physica-Verlag, pdf
• Michael I. Jordan, Terrence J. Sejnowski (eds.) (2002). Graphical Models: Foundations of Neural Computation. MIT Press

2010 ...

• Patricia Churchland, Terrence J. Sejnowski (2016). The Computational Brain, 25th Anniversary Edition. MIT Press

External Links

• Terrence Sejnowski - Salk Institute for Biological Studies
• The Computational Neurobiology Laboratory - The Sejnowski Lab: Bridging the Levels
• Terry Sejnowski from Wikipedia
• Terrence Sejnowski - Deep Learning: Artificial Intelligence Meets Human Intelligence, May 24, 2017, YouTube Video

References

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