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- W2895036831 abstract "Despite its remarkable empirical success as a highly competitive branch of artificial intelligence, deep learning is often blamed for its widely known low interpretation and lack of firm and rigorous mathematical foundation. However, most theoretical endeavor is devoted in discriminative deep learning case, whose complementary part is generative deep learning. To the best of our knowledge, we firstly highlight landscape of empirical error in generative case to complete the full picture through exquisite design of image super resolution under norm based capacity control. Our theoretical advance in interpretation of the training dynamic is achieved from both mathematical and biological sides." @default.
- W2895036831 created "2018-10-12" @default.
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- W2895036831 date "2018-10-03" @default.
- W2895036831 modified "2023-09-27" @default.
- W2895036831 title "Theory of Generative Deep Learning : Probe Landscape of Empirical Error via Norm Based Capacity Control" @default.
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