受强制性开放获取政策约束的文章 - Canberk Ekmekci了解详情
可在其他位置公开访问的文章：6 篇
Uncertainty quantification for deep unrolling-based computational imaging
C Ekmekci, M Cetin
IEEE Transactions on Computational Imaging 8, 1195-1209, 2022
强制性开放获取政策： US National Science Foundation
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
强制性开放获取政策： US National Science Foundation
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
强制性开放获取政策： US National Science Foundation
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
强制性开放获取政策： US National Science Foundation
Model-based Bayesian deep learning architecture for linear inverse problems in computational imaging
C Ekmekci, M Cetin
Electronic Imaging 33, 1-7, 2021
强制性开放获取政策： US National Science Foundation
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
强制性开放获取政策： US National Science Foundation
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