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Exploring Generalization in Deep Learning
Behnam Neyshabur · Srinadh Bhojanapalli · David Mcallester · Nati Srebro

Tue Dec 05 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #142

With a goal of understanding what drives generalization in deep networks, we consider several recently suggested explanations, including norm-based control, sharpness and robustness. We study how these measures can ensure generalization, highlighting the importance of scale normalization, and making a connection between sharpness and PAC-Bayes theory. We then investigate how well the measures explain different observed phenomena.

Author Information

Behnam Neyshabur (New York University)

I am a staff research scientist at Google. Before that, I was a postdoctoral researcher at New York University and a member of Theoretical Machine Learning program at Institute for Advanced Study (IAS) in Princeton. In summer 2017, I received a PhD in computer science at TTI-Chicago where I was fortunate to be advised by Nati Srebro.

Srinadh Bhojanapalli (Google Research)
David Mcallester (Toyota Tech Institute Chicago)
Nati Srebro (TTI-Chicago)

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