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• W3034531839 abstract "Sobolev loss is used when training a network to approximate the values and derivatives of a target function at a prescribed set of input points. Recent works have demonstrated its successful applications in various tasks such as distillation or synthetic gradient prediction. In this work we prove that an overparameterized two-layer relu neural network trained on the Sobolev loss with gradient flow from random initialization can fit any given function values and any given directional derivatives, under a separation condition on the input data." @default.
• W3034531839 created "2020-06-19" @default.
• W3034531839 creator A5043156635 @default.
• W3034531839 creator A5091836791 @default.
• W3034531839 date "2020-06-14" @default.
• W3034531839 modified "2023-09-27" @default.
• W3034531839 title "Global Convergence of Sobolev Training for Overparameterized Neural Networks." @default.
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• W3034531839 hasPublicationYear "2020" @default.
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