Generative Inference and Calibration
Abstract:
Generative models are experiencing a second youth in imaging and scientific inference. New ideas include injective models for sampling high-dimensional posteriors, theoretical advances on statistical, approximation-theoretic, and topological questions, generating continuous functions which dovetail with the downstream PDE solvers, and creative uses of generative models to probe performance limits of inference systems. The session “Generative Inference and Calibration” brings together prominent researchers spearheading these exciting new directions.
Organisers: Ivan Dokmanić (Chair) & Philip Schniter (Committee member)
Session Schedule
- 08:00 - 08:20 Invited talk -
- Gen-Alpha Generative Models for Imaging
- Ivan Dokmanić
- 08:20 - 08:40 Invited talk -
- Continuous Generative Neural Networks
- Giovanni Alberti
- 08:40 - 09:00 Invited talk -
- Deep and Shallow Generative Models of Images
- Yair Weiss
- 09:00 - 09:20 Invited talk -
- Learning to Bound: A Generative Cram ́er-Rao Bound
- Yoram Bresler
- 09:20 - 09:40 Invited talk -
- Interferometric Phase Image Estimation Using Importance Sampling
- Mario Figueiredo
- 09:40 - 11:00 Invited poster -
- Deep Invertible Approximation of Topologically Rich Maps between Manifolds
- Maarten de Hoop
- 09:40 - 11:00 Contributed poster -
- Validation Diagnostics for SBI algorithms based on Normalizing Flows
- Julia Linhart*
- 09:40 - 11:00 Contributed poster -
- Stable deep MRI reconstruction using Generative Priors
- Martin Zach*
- 09:40 - 11:00 Committee member poster -
- A Regularized Conditional GAN for Posterior Sampling in Inverse Problems
- Phil Schniter