@inproceedings{eec0c120090d4a99b8479190bfae0f1a,
title = "Symbolic Regression for Data Storage with Side Information",
abstract = "There are various ways to use machine learning to improve data storage techniques. In this paper, we introduce symbolic regression, a machine-learning method for recovering the symbolic form of a function from its samples. We present a new symbolic regression scheme that utilizes side information for higher accuracy and speed in function recovery. The scheme enhances latest results on symbolic regression that were based on recurrent neural networks and genetic programming. The scheme is tested on a new benchmark of functions for data storage.",
keywords = "data storage, deep learning, genetic programming, side information, symbolic regression",
author = "Xiangwu Zuo and Jiang, \{Anxiao Andrew\} and Netanel Raviv and Siegel, \{Paul H.\}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Information Theory Workshop, ITW 2022 ; Conference date: 01-11-2022 Through 09-11-2022",
year = "2022",
doi = "10.1109/ITW54588.2022.9965879",
language = "English",
series = "2022 IEEE Information Theory Workshop, ITW 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "208--213",
booktitle = "2022 IEEE Information Theory Workshop, ITW 2022",
}