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- W4362598889 abstract "Depth separation -- why a deeper network is more powerful than a shallower one -- has been a major problem in deep learning theory. Previous results often focus on representation power. For example, arXiv:1904.06984 constructed a function that is easy to approximate using a 3-layer network but not approximable by any 2-layer network. In this paper, we show that this separation is in fact algorithmic: one can learn the function constructed by arXiv:1904.06984 using an overparameterized network with polynomially many neurons efficiently. Our result relies on a new way of extending the mean-field limit to multilayer networks, and a decomposition of loss that factors out the error introduced by the discretization of infinite-width mean-field networks." @default.
- W4362598889 created "2023-04-06" @default.
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- W4362598889 date "2023-04-03" @default.
- W4362598889 modified "2023-09-27" @default.
- W4362598889 title "Depth Separation with Multilayer Mean-Field Networks" @default.
- W4362598889 doi "https://doi.org/10.48550/arxiv.2304.01063" @default.
- W4362598889 hasPublicationYear "2023" @default.
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