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Data-Dependent Path Normalization in Neural Networks

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arxiv 1511.06747 v4 pith:IH27XCX2 submitted 2015-11-20 cs.LG

Behnam Neyshabur , Ryota Tomioka , Ruslan Salakhutdinov , Nathan Srebro This is my paper · ORCID
classification cs.LG
keywords frameworkneuralnormalizationoptimizationacrossbatch-normalizationconnectiondata
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We propose a unified framework for neural net normalization, regularization and optimization, which includes Path-SGD and Batch-Normalization and interpolates between them across two different dimensions. Through this framework we investigate issue of invariance of the optimization, data dependence and the connection with natural gradients.

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  1. Towards Better Generalization: BP-SVRG in Training Deep Neural Networks

    stat.ML 2019-08 conditional novelty 6.0 of 10

    A sign-flipped SVRG variant called BP-SVRG adds stochastic-gradient noise instead of cancelling it, and empirically generalizes better than standard SVRG and often better than SGD on CIFAR and SVHN image classifiers.

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