Learn to Compress

Workshop at the International Symposium on Information Theory (ISIT) 2024

This workshop is inspired by the belief that data compression, foundations of which are rooted in information theory, is on the brink of a significant transformation. The emergence of deep generative models, like variational autoencoders, generative adversarial networks (GANs), normalizing flows, and diffusion models, has opened up a fresh path for data compression, one that fully taps into the power of machine learning. These methods demonstrate impressive capabilities, particularly with image and video data, yet challenges remain for practical applications.

This workshop will act as a dynamic platform for fostering interdisciplinary collaborations, featuring distinguished experts in machine learning and computer science, who will contribute valuable practical insights to the real-world applications of data compression. For ISIT regulars, it will acquaint them with recent advances in data compression, enabling them to explore the interplay between classical and contemporary methods. For those new to the field and experts alike, it will provide a chance to learn from experienced researchers from the industry and academia, and connect with peers who share similar research interests. The workshop aims to leave attendees with a more comprehensive intellectual toolkit in the era of machine learning.

If you are interested in submitting a paper, please refer to the Call for Papers.

If you are interested in reviewing, please drop us a quick email at learn.to.compress.workshop@gmail.com.

The workshop will be held (in-person) on Sunday 7th July 2024 - we look forward to seeing you in Athens!

To stay tuned for more information on invited keynote speakers and submission guidelines and more, follow us on Twitter @learn_to_cmpress.

Keynote Speakers

Dr. Johannes Ballé

Google Research

Prof. José Miguel Hernández-Lobato

University of Cambridge

Prof. Shirin Jalali

Rutgers University

Dr. Lucas Theis

Google DeepMind

Organizers

Prof. Elza Erkip

New York University

Ezgi Ozyilkan

New York University

Prof. Aaron B. Wagner

Cornell University




Questions

Contact us at learn.to.compress.workshop@gmail.com.
Follow us on Twitter @learn_to_cmpress.


Special thanks to Ugur Y. Yavuz for technical help with the website.