@inproceedings{1176eddb889949ca9d6bf244ff15622d,
title = "Neuro-Symbolic Regression with Applications",
abstract = "Discovering symbolic models is growing in popularity with the increasing interest in interpretable machine learning. Symbolic regression is the task of learning an analytical form of underlying models in data. Two machine learning techniques have proven their effectiveness: reinforce trick and transformer neural network. This paper discusses in detail the two techniques and presents the application of symbolic regression on a simulated data set that describes a high-energy physics process.",
keywords = "Model discovery, Neural network, Physics data, Symbolic regression, Transformer network",
author = "Nour Makke and Sanjay Chawla",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 10th International Conference on Big Data Analytics, BDA 2022 ; Conference date: 05-12-2022 Through 07-12-2022",
year = "2023",
doi = "10.1007/978-3-031-28350-5\_4",
language = "English",
isbn = "9783031283499",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "38--50",
editor = "Shelly Sachdeva and Yutaka Watanobe and Subhash Bhalla",
booktitle = "Big Data Analytics in Astronomy, Science, and Engineering - 10th International Conference on Big Data Analytics, BDA 2022, Proceedings",
address = "Germany",
}