NeurIPS 2023 workshop on Deep Learning and Inverse Problems

Chris Metzler informed the CI TC about the NeurIPS 2023 workshop on Deep Learning and Iverse Problems. The former CI TC chair, Jong Chul Ye will present an invited talk in this workshop. Detail can be found at https://deep-inverse.org/. 

Workshop Description
Inverse problems are ubiquitous in science, medicine, and engineering, and research in this area has produced real-world impact in medical tomography, seismic imaging, computational photography, and other domains. The recent rapid progress in learning-based image generation raises exciting opportunities in inverse problems, and this workshop seeks to gather a diverse set of participants who apply machine learning to inverse problems, from mathematicians and computer scientists to physicists and biologists. This gathering will facilitate new collaborations and will help develop more effective, reliable, and trustworthy learning-based solutions to inverse problems.

Call for Papers and Submission Instructions
We invite researchers to submit anonymous papers of up to 4 pages (excluding references and appendices) which will be considered for contributed workshop papers. No specific formatting is required. Authors are encouraged to use the NeurIPS style file, but they may use any other style as long as it has standard font size (11pt) and margins (1in).

Submission at OpenReview is open now until the submission deadline on September 25, 2023.

We welcome all submissions in the intersection of inverse problems and deep learning, including but not limited to submissions on the following topics:

Fundamental approaches to address model uncertainty in learning-based solutions for inverse problems: Currently, the best DL-based solutions heavily rely on knowing the inverse system’s forward model and assume simple models of distortion (such as additive Gaussian noise). What algorithms and analysis techniques do we require for applications where we only have access to partial information about the system model?

Diffusion models: Diffusion models have recently gained attention as powerful learned priors for solving inverse problems, due to their ability to model complex high-dimensional data across diverse modalities such as MRI, acoustics, graphs, proteins, etc. What are their benefits and limitations, and what are the optimal algorithms?

Important Dates
Submission Deadline: September 25, 2023.
Notification of acceptance: October 20, 2023.
Workshop: (TBD) Either December 15 or 16, 2023.