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IDEAS/STAT Optimization Seminar
February 6 at 12:00 PM - 1:15 PM
Zoom link: https://upenn.zoom.us/j/98843354016
Shirin Saeedi Bidokhti
Title: Learning-Based Data Compression: Fundamental limits and Algorithms
Abstract:
Data-driven methods have been the driving force of many scientific
disciplines in the past decade, relying on huge amounts of empirical,
experimental, and scientific data. Working with big data is impossible
without data compression techniques that reduce the dimension and size
of the data for storage and communication purposes and effectively
denoise for efficient and accurate processing. In the past decade,
learning-based compressors such as nonlinear transform coding (NTC)
have shown great success in the task of compression by learning to map
a high dimensional source onto its representative latent space of
lower dimension using neural networks and compressing in that latent
space. Despite this success, it is unknown how the rate-distortion
performance of such compressors compare with the optimal limits of
compression (known as the rate-distortion function) that information
theory characterizes. It is also unknown how advances in the field of
information theory translate to practice in the paradigm of deep
learning.
In the first part of the talk, we develop neural estimation methods to
compute the rate-distortion function of high dimensional real-world
datasets. Using our estimate, and through experiments, we show that
the rate-distortion achieved by NTC compressors are within several
bits of the rate-distortion function for real-world datasets such as
MNIST. We then ask if this gap can be closed using ideas in
information theory. In particular, incorporating lattice coding in the
latent domain, we propose lattice transform coding as a novel
framework for neural compression. LTC provides significant improvement
compared to the state of the art on synthetic and real-world sources.
Bio:
Shirin Saeedi Bidokhti is an assistant professor in the
Department of Electrical and Systems Engineering at the University of
Pennsylvania (UPenn). She received her M.Sc. and Ph.D. degrees in
Computer and Communication Sciences from the Swiss Federal Institute
of Technology (EPFL). Prior to joining UPenn, she was a postdoctoral
scholar at Stanford University and the Technical University of Munich.
She has also held short-term visiting positions at ETH Zurich,
University of California at Los Angeles, and the Pennsylvania State
University. Her research interests broadly include the design and
analysis of network strategies that are scalable, practical, and
efficient for use in Internet of Things (IoT) applications,
information transfer on networks, as well as data compression
techniques for big data. She is a recipient of the 2023 Communications
Society & Information Theory Society Joint Paper Award, 2022 IT
society Goldsmith lecturer award, 2021 NSF-CAREER award, 2019 NSF-CRII
Research Initiative award and the prospective researcher and advanced
postdoctoral fellowships from the Swiss National Science Foundation.