Uncertainty Quantification in Computational Imaging
Abstract:
This session aims to cover recent advances in the theory and methods of uncertainty quantification for computational imaging, including but not limited to approaches that involve learning-based methodologies.
Organiser & chair: Mujdat Cetin
Session Schedule
- 17:30 - 17:50
- Invited talk - Does your computational imaging algorithm know what it doesn’t know?
- Mujdat Cetin
- 17:50 - 18:10
- Invited talk - Toward Formal Interpretable Machine Learning
- Jeremias Sulam
- 18:10 - 18:30
- Invited talk - Uncertainty Visualization via Posterior PCA and Posterior Hierarchical Trees
- Tomer Michaeli
- 18:30 - 18:50
- Invited talk - Hyper-DDPM: Estimating Epistemic and Aleatoric Uncertainty with a Single Model
- Chris Metzler
- 18:50 - 19:10
- Invited talk - Generalized Localized Imaging for Out-of-distribution Generalization
- Ivan Dokmanic
- 19:10 - 20:30
- Invited poster - Uncertainty Based Error Quantification
- Andreas Habring
- 19:10 - 20:30
- Invited poster - Distribution-free Uncertainty Quantification with Applications to Computational Imaging
- Amit Kohli
- 19:10 - 20:30
- Contributed poster - TBC
- TBC
- 19:10 - 20:30
- Contributed poster - TBC
- TBC
- 19:10 - 20:30
- Contributed poster - TBC
- TBC