Published November 2019
| public
Book Section - Chapter
DSP-Inspired Deep Learning: A Case Study Using Ramanujan Subspaces
- Creators
- Tenneti, Srikanth V.
- Vaidyanathan, P. P.
Abstract
Can Deep Learning be used to augment DSP techniques? Algorithms in DSP are typically developed starting from a mathematical model of an application. In some cases however, simplicity of the model can result in deterioration of performance when there is a severe modeling mis-match. This paper explores the idea of implementing a DSP technique as a computational graph, so that hundreds of parameters can jointly be trained to adapt to any given dataset. Using the specific example of period estimation by Ramanujan Subspaces, significant improvement in estimation accuracies under high noise and very short datalengths is demonstrated.
Additional Information
© 2019 IEEE. This work was supported in parts by the NSF grant CCF-1712633, the ONR grant N00014-18-1-2390, and an Amazon AI fellowship facilitated through IST, California Institute of Technology.Additional details
- Eprint ID
- 102334
- DOI
- 10.1109/ieeeconf44664.2019.9048783
- Resolver ID
- CaltechAUTHORS:20200403-143552815
- CCF-1712633
- NSF
- N00014-18-1-2390
- Office of Naval Research (ONR)
- Amazon AI
- Created
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2020-04-03Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field