Limitations of Deep Learning for Inverse Problems on Digital Hardware

Author:

Boche Holger1ORCID,Fono Adalbert2ORCID,Kutyniok Gitta2

Affiliation:

1. Institute of Theoretical Information Technology, Technical University of Munich, Munich, Germany

2. Department of Mathematics, Ludwig-Maximilians-Universität München, Munich, Germany

Funder

German Federal Ministry of Education and Research (BMBF) in the Project Hardware Platforms and Computing Models for Neuromorphic Computing

National Initiative on 6G Communication Systems through the Research Hub 6G-life

Konrad Zuse School of Excellence in Reliable AI [German Academic Exchange Service (DAAD)]

Munich Center for Machine Learning

German Research Foundation

LMUexcellent

BMBF, by the Free State of Bavaria under the Excellence Strategy of the Federal Government and the Länder and by the Hightech Agenda Bavaria

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Library and Information Sciences,Computer Science Applications,Information Systems

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Solving inverse problems in physics by optimizing a discrete loss: Fast and accurate learning without neural networks;PNAS Nexus;2023-12-21

2. Generative Modeling Based Manifold Learning for Adaptive Filtering Guidance;ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2023-06-04

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