MA6a.4

Towards a Sampling Theory for Implicit Neural Representations

Mahrokh Najaf, Gregory Ongie, Marquette University, United States
Session:
MA6a: Machine Learning Methods for Inverse Problems in Biomedical Imaging Lecture
Track:
Biomedical Signal and Image Processing
Location:
Evergreen
Presentation Time:
Mon, 28 Oct, 09:30 - 09:55 PT (UTC -8)
Session Chair:
Gregory Ongie, Marquette University
Presentation
Discussion
Resources
No resources available.
Session MA6a
MA6a.1: Inverse-Problem Uncertainty Quantification via Conformal Bounds on Downstream Tasks
Jeffrey Wen, Rizwan Ahmad, Philip Schniter, The Ohio State University, United States
MA6a.2: Pruning Unrolled Networks (PUN) at Initialization for MRI Reconstruction Improves Generalization
Shijun Liang, Evan Bell, Avrajit Ghosh, Saiprasad Ravishankar, Michigan State University, United States
MA6a.3: Estimating Epistemic and Aleatoric Uncertainty with a Single Diffusion Model
Matthew Chan, Maria Molina, Christopher Metzler, University of Maryland, College Park, United States
MA6a.4: Towards a Sampling Theory for Implicit Neural Representations
Mahrokh Najaf, Gregory Ongie, Marquette University, United States
Contacts