TP7a.4

On the Optimality of Neural Compression for Structured Stationary Sources

Ali Zafari, Rutgers University, United States; Jinheng Zhang, Shirin Saeedi Bidokhti, University of Pennsylvania, United States; Shirin Jalali, Rutgers University, United States
Session:
TP7a: Learning Compact Representations: Theory and Applications Lecture
Track:
Adaptive Systems, Machine Learning, and Data Analytics
Location:
Chapel
Presentation Time:
Tue, 27 Oct, 14:45 - 15:10 PT (UTC -7)
Session Co-Chairs:
Shirin Saeedi Bidokhti, University of Pennsylvania, United States and Shirin Jalali, Rutgers University, United States
Presentation
Discussion
Resources
No resources available.
Session TP7a
TP7a.1: Unsupervised Audio Denoising via Neural Lossy Compression
Aayush Rajesh, Tsachy Weissman, Stanford University, United States
TP7a.2: Communications for Vision-Language Models: Task-Aware Visual Token Transmission
Harshithanjani Athi, Sravan Kumar Ankireddy, The University of Texas at Austin, United States; Jianzhong Charlie Zhang, Samsung Research America, Samsung, United States; Hyeji Kim, The University of Texas at Austin, United States
TP7a.3: Unconditional CNN denoisers contain sparse semantic representation of images
Zahra Kadkhodaie, Massachusetts Institute of Technology, United States; Stéphane Mallat, Collège de France, France; Eero Simoncelli, New York University, United States
TP7a.4: On the Optimality of Neural Compression for Structured Stationary Sources
Ali Zafari, Rutgers University, United States; Jinheng Zhang, Shirin Saeedi Bidokhti, University of Pennsylvania, United States; Shirin Jalali, Rutgers University, United States
Contacts