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Peter L. Bartlett. Professor, Computer Science and Statistics. Berkeley AI Research Lab. Director, Foundations of Data Science Institute. Director, Collaboration on the Theoretical Foundations of Deep Learning. ML Research Director, Simons Institute for the Theory of Computing. UC Berkeley. Principal Scientist, Google DeepMind.
Neural network learning: Theoretical foundations. M Anthony, PL Bartlett, PL Bartlett. cambridge university press 9, 8. , 1999. 2512. 1999. For valid generalization the size of the weights is more important than the size of the network. P Bartlett. Advances in neural information processing systems 9.
Peter Bartlett is a professor in the Department of Electrical Engineering and Computer Sciences and the Department of Statistics and Head of Google Research Australia. Since 2020, he has been Director of the Foundations of Data Science Institute and Director of the Collaboration on the Theoretical Foundations of Deep Learning.
24 de jul. de 2022 · Department of Statistics. Berkeley AI Research Lab. University of California at Berkeley. Director. Collaboration on the Theoretical Foundations of Deep Learning. Director. Foundations of Data Science Institute. Head. Google Research Australia. Address: 387 Soda Hall #1776. Berkeley, CA 94720-1776. Phone: 510-642-7780. Fax: 510-642-5775.
Peter Bartlett is a professor in the Department of Electrical Engineering and Computer Sciences and the Department of Statistics at the University of California at Berkeley, Director of the Foundations of Data Science Institute, Director of the Collaboration on the Theoretical Foundations of Deep Learning, and Head of Google Research Australia.
Research Description. Peter Bartlett is a professor in the Computer Science Division and Department of Statistics at the University of California at Berkeley and Head of Google Research Australia. His research interests include machine learning and statistical learning theory.
Peter L. Bartlett, David P. Helmbold, and Philip M. Long. Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks. In Jennifer Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning (ICML-18) , volume 80 of Proceedings of Machine Learning Research , pages 521--530.
