Phil Long
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Learning Theory is a research field devoted to studying the design and analysis of machine learning algorithms. In particular, such algorithms aim at making accurate predictions or representations based on observations. Proceedings of COLT automatically accepted in the Conference and Proceedings Track of the Journal of Machine Learning Research.
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Philip Long
Philip Long
2003 Edgewood Drive
Palo Alto, California, 94303
US
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And P M. Long. Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks. Arxiv, 1802.06093. P M Long. New bounds on the price of bandit feedback for mistake-bounded online multiclass learning. And P M. Long. Surprising properties of dropout in deep networks. JMLR, to appear. Et al Visual product discovery. KDD Workshop Machine Learning Meets Fashion, 2016. And P M. Long. On the inductive bias of dropout. JMLR, 163403-3454, 2015.PARSED CONTENT
The website had the following on the web page, "Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks." We viewed that the web page said " New bounds on the price of bandit feedback for mistake-bounded online multiclass learning." It also said " Surprising properties of dropout in deep networks. Et al Visual product discovery. KDD Workshop Machine Learning Meets Fashion, 2016. On the inductive bias of dropout. JMLR, 163403-3454, 2015."ANALYZE SUBSEQUENT WEB PAGES
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