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BIU AI and ML Learning Club, June 30 – What Makes Data Suitable for Deep Learning?
June 30 @ 12:00 pm - 1:00 pm IDT
On June 30, Dr. Nadav Cohen from the Tel Aviv University will give a talk titled: What Makes Data Suitable for Deep Learning?
Abstract:
Deep learning is delivering unprecedented performance when applied to various data modalities, yet there are data distributions over which it utterly fails. The question of what makes a data distribution suitable for deep learning is a fundamental open problem in the field. In this talk I will present a recent theory aiming to address the problem via tools from quantum physics. The theory establishes that certain neural networks are capable of accurate prediction over a data distribution if and only if the data distribution admits low quantum entanglement under certain partitions of features. This brings forth practical methods for adaptation of data to neural networks, and vice versa. Experiments with widespread models over various datasets will demonstrate the findings. An underlying theme of the talk will be the potential of physics to advance our understanding of the relation between deep learning and real-world data.
The talk is based on NeurIPS 2023 papers co-authored with my students Noam Razin, Yotam Alexander, Nimrod De La Vega and Tom Verbin.
BIO:
Nadav Cohen is an Asst. Professor of Computer Science at Tel Aviv University. His research focuses on the theoretical and algorithmic foundations of neural networks. In particular, he is interested in mathematically analyzing aspects of expressiveness, optimization and generalization, with the goal of deriving theoretically founded procedures and algorithms that improve practical performance. Nadav is also a Co-Founder and the Chief Scientist at Imubit, a company that applies neural networks for control and optimization of industrial manufacturing lines, thereby reducing CO2 emissions while improving yield.
Nadav earned a BSc in electrical engineering and a BSc in mathematics (both summa cum laude) at the Technion Excellence Program for Distinguished Undergraduates. He obtained his PhD (direct track) at the School of Computer Science and Engineering in the Hebrew University of Jerusalem, under the supervision of Prof. Amnon Shashua. Subsequently, he was a postdoctoral research scholar at the School of Mathematics in the Institute for Advanced Study of Princeton. For his contributions to the foundations of neural networks, Nadav received various honors and awards, including the Google Doctoral Fellowship in Machine Learning, the Rothschild Postdoctoral Fellowship, the Zuckerman Postdoctoral Fellowship, and the Google Research Scholar Award.