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Poster
Failures of Gradient-Based Deep Learning
Shaked Shammah · Shai Shalev-Shwartz · Ohad Shamir

Mon Aug 07 01:30 AM -- 05:00 AM (PDT) @ Gallery #56
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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.

Author Information

Shaked Shammah (Hebrew University, Jerusalem)
Shai Shalev-Shwartz
Ohad Shamir (Weizmann Institute of Science)

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