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• W2914331387 abstract "A complete understanding of the widely used over-parameterized deep networks is a key step for AI. In this work we try to give a geometric picture of over-parameterized deep networks using our geometrization scheme. We show that the Riemannian geometry of network complexity plays a key role in understanding the basic properties of over-parameterizaed deep networks, including the generalization, convergence and parameter sensitivity. We also point out deep networks share lots of similarities with quantum computation systems. This can be regarded as a strong support of our proposal that geometrization is not only the bible for physics, it is also the key idea to understand deep learning systems." @default.
• W2914331387 created "2019-02-21" @default.
• W2914331387 creator A5021875517 @default.
• W2914331387 creator A5082181196 @default.
• W2914331387 date "2019-02-11" @default.
• W2914331387 modified "2023-09-27" @default.
• W2914331387 title "Understanding over-parameterized deep networks by geometrization" @default.
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