Theoretical Perspectives on Deep Learning Methods in Inverse Problems-Reference-Cited by-同舟云学术

Theoretical Perspectives on Deep Learning Methods in Inverse Problems

Author:

Scarlett Jonathan¹^ORCID,Heckel Reinhard²^ORCID,Rodrigues Miguel R. D.³^ORCID,Hand Paul⁴,Eldar Yonina C.⁵^ORCID

Affiliation:

1. Department of Computer Science, the Department of Mathematics, Institute of Data Science, National University of Singapore, Queenstown, Singapore

2. Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany

3. Department of Electronic and Electrical Engineering, University College London, London, U.K

4. College of Science and the Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA

5. Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel

Funder

Singapore National Research Foundation

Institute of Advanced Studies at the Technical University of Munich and the Deutsche Forschungsgemeinschaft

The Weizmann-UK Making Connections Programme

Alan Turing Institute

National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Medicine

Link

Reference150 articles.

1. Deep decoder: Concise image representations from untrained non-convolutional networks;heckel;Proc Int Conf Learn Represent,2019

3. Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximation;heckel;Proc Int Conf Mach Learn,2020

5. Phase retrieval under a generative prior;hand;Proc Conf Neural Inf Process Syst,2018

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