The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
AMP-Inspired Deep Networks for Sparse Linear Inverse Problems
Open accessAbstract
Deep learning has gained great popularity due to its widespread success on many inference problems. We consider the application of deep learning to the sparse linear inverse problem, where one seeks to recover a sparse signal from a few noisy linear measurements. In this paper, we propose two novel neural-network architectures that decouple prediction errors across layers in the same way that the approximate message passing (AMP) algorithms decouple them across iterations: through Onsager correction. First, we propose a “learned AMP” network that significantly improves upon Gregor and LeCun's “learned ISTA.” Second, inspired by the recently proposed “vector AMP” (VAMP) algorithm, we propose a “learned VAMP” network that offers increased robustness to deviations in the measurement matrix from i.i.d. Gaussian. In both cases, we jointly learn the linear transforms and scalar nonlinearities of the network. Interestingly, with i.i.d. signals, the linear transforms and scalar nonlinearities prescribed by the VAMP algorithm coincide with the values learned through back-propagation, leading to an intuitive interpretation of learned VAMP. Finally, we apply our methods to two problems from 5G wireless communications: compressive random access and massive-MIMO channel estimation.
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Deep Learning Techniques for Inverse Problems in Imaging
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Model-Driven Deep Learning for MIMO Detection
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Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions
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Metadata
- Authors
- Borgerding, M., Schniter, P., & Rangan, S.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2017-08-15
- Volume
- 65
- Issue
- 16
- Pages
- 4293-4308
- ISSN
- 1053-587X (ISSN-L)
- 1941-0476
- OpenAlex
- W2619204584
- Cited by
- 455
- References
- 58
- FWCI
- 29.99
- Type
- journal article
- Language
- en
- Open access
- green
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