20:12
Michael Mahoney: Approximate computation and implicit regularization in large-scale data analysis
Stanford Computer Science Theory
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Michael Mahoney: "Why Deep Learning Works: Implicit Self-Regularization in Deep Neural Networks"
Institute for Pure & Applied Mathematics (IPAM)
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1:13:16
Practical Theory and Neural Network Models - Prof. Michael W. Mahoney
IARAI Research
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1:10:52
1:06:34
NJIT Data Science Seminar: Michael Mahoney, UC Berkeley
NJIT Institute for Data Science
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1:18:57
Why Deep Learning Works: Implicit Self-Regularization in DNNs, Michael W. Mahoney 20190225
San Francisco Bay ACM
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46:43
38:13
Why Deep Learning Works: Implicit Self-Regularization in Deep Neural Networks
Simons Institute for the Theory of Computing
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1:06:05
1:01:19
Optimization's Untold Gift to Learning: Implicit Regularization
Simons Institute for the Theory of Computing
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1:16:51
28:46
Implicit Regularization in Nonconvex Statistical Estimation
Simons Institute for the Theory of Computing
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1:02:55
Sampling for Linear Algebra, Statistics, and Optimization I
Simons Institute for the Theory of Computing
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43:34
59:25
Implementing Randomized Matrix Algorithms in Parallel & Distributed Environments, Mahoney 20140602
San Francisco Bay ACM
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