Name
Abstract | ||
|---|---|---|
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 ... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TSP.2017.2708040 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
Signal processing algorithms,Inverse problems,Approximation algorithms,Machine learning,Message passing,Transforms,Probability density function | Approximation algorithm,Matrix (mathematics),Computer science,Theoretical computer science,Robustness (computer science),Inverse problem,Artificial intelligence,Deep learning,Message passing,Compressed sensing,Random access | Journal |
Volume | Issue | ISSN |
65 | 16 | 1053-587X |
Citations | PageRank | References |
35 | 0.97 | 31 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mark Borgerding | 1 | 49 | 2.19 |
| Philip Schniter | 2 | 1620 | 93.74 |
| Sundeep Rangan | 3 | 3101 | 163.90 |