Symbolic Regression for Data Storage with Side Information

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@InProceedings{Zuo:2022:ITW,

• author = "Xiangwu Zuo and Anxiao Andrew Jiang and Netanel Raviv and Paul H. Siegel",
• booktitle = "2022 IEEE Information Theory Workshop (ITW)",
• title = "Symbolic Regression for Data Storage with Side Information",
• year = "2022",
• pages = "208--213",
• month = nov,
• keywords = "genetic algorithms, genetic programming, symbolic regression, data storage, side information, deep learning",
• DOI = "doi:10.1109/ITW54588.2022.9965879",
• size = "6 pages",
• 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 uses 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.",
• notes = "Also known as \cite{9965879}",

}

Genetic Programming entries for Xiangwu Zuo Anxiao (Andrew) Jiang Netanel Raviv Paul H Siegel

Citations