| Uncertainty quantification for deep unrolling-based computational imaging C Ekmekci, M Cetin IEEE Transactions on Computational Imaging 8, 1195-1209, 2022 | 5 | 2022 |
| What does your computational imaging algorithm not know?: A Plug-and-Play model quantifying model uncertainty C Ekmekci, M Cetin Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 5 | 2021 |
| Model-based Bayesian deep learning architecture for linear inverse problems in computational imaging C Ekmekci, M Cetin Electronic Imaging 33, 1-7, 2021 | 3 | 2021 |
| On the detection of upper mantle discontinuities with radon-transformed receiver functions (CRISP-RF) T Olugboji, Z Zhang, S Carr, C Ekmekci, M Cetin Geophysical Journal International 236 (2), 748-763, 2024 | 1 | 2024 |
| Quantifying Generative Model Uncertainty in Posterior Sampling Methods for Computational Imaging C Ekmekci, M Cetin NeurIPS 2023 Workshop on Deep Learning and Inverse Problems, 2023 | 1 | 2023 |
| Seeing into the Mantle by Sifting through Echoes, Reverbs & Noise TM Olugboji, Z Zhang, C Ekmekci, C Mujdat, S Carr AGU23, 2023 | | 2023 |
| Automatic Parameter Tuning for Plug-and-Play Algorithms Using Generalized Cross Validation and Stein’s Unbiased Risk Es-timation for Linear Inverse Problems in Computational … C Ekmekci, M Cetin | | 2023 |
Mujdat CetinUniversity of RochesterVerified email at rochester.edu
