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Theory III: Dynamics and Generalization in Deep Networks - a simple solution


Apr 11, 2019
Andrzej Banburski, Qianli Liao, Brando Miranda, Lorenzo Rosasco, Bob Liang, Jack Hidary, Tomaso Poggio

* 50 pages, 11 figures. This replaces previous versions of Theory III, that appeared on Arxiv [arXiv:1806.11379, arXiv:1801.00173] or on the CBMM site. v2: Some arguments in sections 3, 7 and the appendix have been strengthened, in particular an observation on intrinsic normalization of standard gradient descent 

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A Surprising Linear Relationship Predicts Test Performance in Deep Networks


Jul 25, 2018
Qianli Liao, Brando Miranda, Andrzej Banburski, Jack Hidary, Tomaso Poggio


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Theory IIIb: Generalization in Deep Networks


Jun 29, 2018
Tomaso Poggio, Qianli Liao, Brando Miranda, Andrzej Banburski, Xavier Boix, Jack Hidary

* 38 pages, 7 figures 

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Theory of Deep Learning III: explaining the non-overfitting puzzle


Jan 16, 2018
Tomaso Poggio, Kenji Kawaguchi, Qianli Liao, Brando Miranda, Lorenzo Rosasco, Xavier Boix, Jack Hidary, Hrushikesh Mhaskar


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Theory of Deep Learning IIb: Optimization Properties of SGD


Jan 07, 2018
Chiyuan Zhang, Qianli Liao, Alexander Rakhlin, Brando Miranda, Noah Golowich, Tomaso Poggio


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Why and When Can Deep -- but Not Shallow -- Networks Avoid the Curse of Dimensionality: a Review


Feb 04, 2017
Tomaso Poggio, Hrushikesh Mhaskar, Lorenzo Rosasco, Brando Miranda, Qianli Liao


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