L. Biggio, T. Bendinelli, A. Neitz, A. Lucchi, и G. Parascandolo. Proceedings of the 38th International Conference on Machine Learning, том 139 из Proceedings of Machine Learning Research, стр. 936--945. PMLR, (18--24 Jul 2021)
Аннотация
Symbolic equations are at the core of scientific discovery. The task of discovering the underlying equation from a set of input-output pairs is called symbolic regression. Traditionally, symbolic regression methods use hand-designed strategies that do not improve with experience. In this paper, we introduce the first symbolic regression method that leverages large scale pre-training. We procedurally generate an unbounded set of equations, and simultaneously pre-train a Transformer to predict the symbolic equation from a corresponding set of input-output-pairs. At test time, we query the model on a new set of points and use its output to guide the search for the equation. We show empirically that this approach can re-discover a set of well-known physical equations, and that it improves over time with more data and compute.
%0 Conference Paper
%1 pmlr-v139-biggio21a
%A Biggio, Luca
%A Bendinelli, Tommaso
%A Neitz, Alexander
%A Lucchi, Aurelien
%A Parascandolo, Giambattista
%B Proceedings of the 38th International Conference on Machine Learning
%D 2021
%E Meila, Marina
%E Zhang, Tong
%I PMLR
%K ak-symbolic-numeric equations mathematicalrelation maths neural-network reading-done regression symbolic
%P 936--945
%T Neural Symbolic Regression that scales
%U https://proceedings.mlr.press/v139/biggio21a.html
%V 139
%X Symbolic equations are at the core of scientific discovery. The task of discovering the underlying equation from a set of input-output pairs is called symbolic regression. Traditionally, symbolic regression methods use hand-designed strategies that do not improve with experience. In this paper, we introduce the first symbolic regression method that leverages large scale pre-training. We procedurally generate an unbounded set of equations, and simultaneously pre-train a Transformer to predict the symbolic equation from a corresponding set of input-output-pairs. At test time, we query the model on a new set of points and use its output to guide the search for the equation. We show empirically that this approach can re-discover a set of well-known physical equations, and that it improves over time with more data and compute.
@inproceedings{pmlr-v139-biggio21a,
abstract = {Symbolic equations are at the core of scientific discovery. The task of discovering the underlying equation from a set of input-output pairs is called symbolic regression. Traditionally, symbolic regression methods use hand-designed strategies that do not improve with experience. In this paper, we introduce the first symbolic regression method that leverages large scale pre-training. We procedurally generate an unbounded set of equations, and simultaneously pre-train a Transformer to predict the symbolic equation from a corresponding set of input-output-pairs. At test time, we query the model on a new set of points and use its output to guide the search for the equation. We show empirically that this approach can re-discover a set of well-known physical equations, and that it improves over time with more data and compute.},
added-at = {2023-04-26T15:07:41.000+0200},
author = {Biggio, Luca and Bendinelli, Tommaso and Neitz, Alexander and Lucchi, Aurelien and Parascandolo, Giambattista},
biburl = {https://www.bibsonomy.org/bibtex/2b4764963c5a1dd1f3470c84ae47bcfa5/adulny},
booktitle = {Proceedings of the 38th International Conference on Machine Learning},
description = {Neural Symbolic Regression that scales},
editor = {Meila, Marina and Zhang, Tong},
interhash = {f3e2718fefb52ab3582c90f341464516},
intrahash = {b4764963c5a1dd1f3470c84ae47bcfa5},
keywords = {ak-symbolic-numeric equations mathematicalrelation maths neural-network reading-done regression symbolic},
month = {18--24 Jul},
pages = {936--945},
pdf = {http://proceedings.mlr.press/v139/biggio21a/biggio21a.pdf},
publisher = {PMLR},
series = {Proceedings of Machine Learning Research},
timestamp = {2023-10-23T13:07:19.000+0200},
title = {Neural Symbolic Regression that scales},
url = {https://proceedings.mlr.press/v139/biggio21a.html},
volume = 139,
year = 2021
}