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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.


Time (CDT) Event
9:00 Invited talk 1: Namrata Vaswani
9:30 Invited talk 2: Stella Yu
10:00 Break and Discussion 1
10:30 Contributed talk 1: “Phase Retrieval via Deep Expectation-Consistent Approximation”, Saurav K Shastri, Philip Schniter
10:45 Contributed talk 2: “Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models”, Gabriele Corso, Yilun Xu, Valentin De Bortoli, Regina Barzilay, Tommi Jaakkola
11:00 Invited talk 3: Jong Chul Ye
11:30 Invited talk 4: Ben Poole
12:00 Lunch break
1:30 Contributed talk 3: “Space-Time Implicit Neural Representations for Atomic Electron Tomography on Dynamic Samples”, Tiffany Chien, Colin Ophus, Laura Waller
1:45 Contributed talk 4: “AmbientFlow: Invertible generative models from incomplete, noisy imaging measurements”, Varun A. Kelkar, Rucha Deshpande, Arindam Banerjee, Mark Anastasio
2:00 Poster session and Discussion 2
3:00 Invited talk 5: Eric Price
3:30 Break
4:00 Invited talk 6: Ellen Zhong
4:30 Contributed talk 5: “Quantifying Generative Model Uncertainty in Posterior Sampling Methods for Computational Imaging”, Canberk Ekmekci · Mujdat Cetin
4:45 Contributed talk 6: “Model-adapted Fourier sampling for generative compressed sensing”, Aaron Berk, Simone Brugiapaglia, Yaniv Plan, Matthew Scott, Xia Sheng, Ozgur Yilmaz
5:00 End of official program


Namrata Vaswani Ellen Zhong Jong Chul Ye
Namrata Vaswani
Iowa State University
Ellen Zhong
Princeton University
Jong Chul Ye
Stella Yu Ben Poole Eric Price
Stella Yu
University of Michigan, Ann Arbor
Ben Poole
Google Brain
Eric Price
UT Austin


Christopher Metzler Shirin Jalali Ajil Jalal
Christopher Metzler
University of Maryland
Shirin Jalali
Rutgers University
Ajil Jalal
UC Berkeley
Paul Hand Reinhard Heckel Jon Tamir
Paul Hand
Northeastern University
Reinhard Heckel
Technical University of Munich
Jon Tamir
UT Austin
Arian Maleki Rich Baraniuk  
Arian Maleki
Columbia University
Richard Baraniuk
Rice University

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 October 6, 2023.

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

Important Dates and Logistics

Dual Submission Policy

This is a non-archival workshop. Hence, you are allowed to resubmit to other venues, as long as the other venue permits it.

If your main paper was accepted at NeurIPS and you wish to submit to this workshop, we expect: (a) the two manuscripts to have sufficient differences in experiments or theory, and (b) an author to physically present accepted posters on the day of the workshop.

Please email with any questions.