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- W4381570275 abstract "In this paper, we present a novel characterization of the smoothness of a model based on basic principles of Large Deviation Theory. In contrast to prior work, where the smoothness of a model is normally characterized by a real value (e.g., the weights' norm), we show that smoothness can be described by a simple real-valued function. Based on this concept of smoothness, we propose an unifying theoretical explanation of why some interpolators generalize remarkably well and why a wide range of modern learning techniques (i.e., stochastic gradient descent, $ell_2$-norm regularization, data augmentation, invariant architectures, and overparameterization) are able to find them. The emergent conclusion is that all these methods provide complimentary procedures that bias the optimizer to smoother interpolators, which, according to this theoretical analysis, are the ones with better generalization error." @default.
- W4381570275 created "2023-06-22" @default.
- W4381570275 creator A5053881061 @default.
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- W4381570275 date "2023-06-19" @default.
- W4381570275 modified "2023-10-18" @default.
- W4381570275 title "Understanding Generalization in the Interpolation Regime using the Rate Function" @default.
- W4381570275 doi "https://doi.org/10.48550/arxiv.2306.10947" @default.
- W4381570275 hasPublicationYear "2023" @default.
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