@inproceedings{82d821d2d5e243a0b27191f325ede703,
title = "Quantized wavelet scattering networks for signal classification",
abstract = "While convolutional neural networks (CNNs) are powerful tools in machine learning, their construction is far from a science. In addition, instantiations of CNNs are highly memory expensive and typically require large training sets. Wavelet scattering networks (WSNs) could provide a simple means of testing quantization schemes for CNNs, without the added complexity of adjustable parameters. Using the MSTAR database, the performance of a WSN in combination with several quantization schemes is examined.",
author = "Fox, {Maxine R.} and Raj, {Raghu G.} and Narayanan, {Ram M.}",
note = "Funding Information: This work was supported by the US Office of Naval Research Grant Number N00014-16-1-2354 Funding Information: This work was supported by the US Office of Naval Research Grant Number N00014-16-1-2354 (POC: Joong Kim). Publisher Copyright: {\textcopyright} 2109 SPIE.; Radar Sensor Technology XXIII 2019 ; Conference date: 15-04-2019 Through 17-04-2019",
year = "2019",
doi = "10.1117/12.2519659",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Ranney, {Kenneth I.} and Armin Doerry",
booktitle = "Radar Sensor Technology XXIII",
address = "United States",
}