Failures of Gradient-Based Deep Learning

Shai Shalev-Shwartz; Ohad Shamir; Shaked Shammah

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arXiv · 1703.07950

Failures of Gradient-Based Deep Learning

Shai Shalev-Shwartz·Ohad Shamir·Shaked Shammah

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Abstract

In recent years, Deep Learning has become the go-to solution for a broad range of applications, often outperforming state-of-the-art. However, it is important, for both theoreticians and practitioners, to gain a deeper understanding of the difficulties and limitations associated with common approaches and algorithms. We describe four types of simple problems, for which the gradient-based algorithms commonly used in deep learning either fail or suffer from significant difficulties. We illustrate the failures through practical experiments, and provide theoretical insights explaining their source, and how they might be remedied.

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cs.LG cs.NE stat.ML

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Shai Shalev-Shwartz, Ohad Shamir, Shaked Shammah. 2017-03-23. Failures of Gradient-Based Deep Learning. https://arxiv.org/abs/1703.07950

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