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arXiv · 1511.06747

Data-Dependent Path Normalization in Neural Networks

Behnam Neyshabur·Ryota Tomioka·Ruslan Salakhutdinov·Nathan Srebro
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Abstract

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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Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro. 2016-01-19. Data-Dependent Path Normalization in Neural Networks. https://arxiv.org/abs/1511.06747

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