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arXiv · 1412.6614

In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning

Behnam Neyshabur·Ryota Tomioka·Nathan Srebro
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Abstract

We present experiments demonstrating that some other form of capacity control, different from network size, plays a central role in learning multilayer feed-forward networks. We argue, partially through analogy to matrix factorization, that this is an inductive bias that can help shed light on deep learning.

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BibTeXRIS

Behnam Neyshabur, Ryota Tomioka, Nathan Srebro. 2014-12-20. In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning. https://arxiv.org/abs/1412.6614

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