Thu, Oct 14, 2021 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Vitaly Feldman, Apple AI Research
Talk Title: Chasing the Long Tail: What Neural Networks Memorize and Why
Series: NL Seminar
Abstract: REMINDER: Meeting hosts only admit guests that they know to the Zoom meeting. Hence, you're highly encouraged to use your USC account to sign into Zoom. If you're an outside visitor, please inform nlg DASH seminar DASH host AT isi.edu beforehand so we'll be aware of your attendance and let you in.
Deep learning algorithms that achieve state of the art results on image and text recognition tasks tend to fit the entire training dataset nearly perfectly including mislabeled examples and outliers. This propensity to memorize seemingly useless data and the resulting large generalization gap have puzzled many practitioners and is not explained by existing theories of machine learning. We provide a simple conceptual explanation and a theoretical model demonstrating that memorization of outliers and mislabeled examples is necessary for achieving close to optimal generalization error when learning from long tailed data distributions. Image and text data are known to follow such distributions and therefore our results establish a formal link between these empirical phenomena. We then demonstrate the utility of memorization and support our explanation empirically. These results rely on a new technique for efficiently estimating memorization and influence of training data points.
Biography: Vitaly Feldman is a research scientist at Apple AI Research working on foundations of machine learning and privacy preserving data analysis. His recent research interests include tools for analysis of generalization, distributed privacy preserving learning, privacy preserving optimization, and adaptive data analysis.
Vitaly holds a Ph.D. from Harvard 2006, advised by Leslie Valiant and was previously a research scientist at Google Research Brain Team and IBM Research Almaden. His work was recognized by the COLT Best Student Paper Award in 2005 and 2013 student co authored and by the IBM Research Best Paper Award in 2014, 2015 and 2016. His recent research on foundations of adaptive data analysis has been featured in CACM Research Highlights, Science, and the research blogs of IBM, Google, and Microsoft. He served as a program co chair for COLT 2016 and ALT 2021 conferences and as a co organizer of the Simons Institute Program on Data Privacy in 2019.
Host: Jon May and Thamme Gowda
More Info: https://nlg.isi.edu/nl-seminar/
WebCast Link: https://youtu.be/_R8JFXvjnPc
Audiences: NL Seminar
Contact: Pete Zamar