End-to-end symbolic regression with transformers | Proceedings of the 36th International Conference on Neural Information Processing Systems (2024)

End-to-end symbolic regression with transformers | Proceedings of the 36th International Conference on Neural Information Processing Systems (2)

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  • Authors:
  • Pierre-Alexandre Kamienny Meta AI and ISIR MLIA, Sorbonne Université

    Meta AI and ISIR MLIA, Sorbonne Université

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  • Stéphane d'Ascoli Meta AI and Department of Physics, Ecole Normale Supérieure

    Meta AI and Department of Physics, Ecole Normale Supérieure

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  • Guillaume Lample Meta AI

    Meta AI

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  • François Charton Meta AI

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NIPS '22: Proceedings of the 36th International Conference on Neural Information Processing SystemsNovember 2022Article No.: 746Pages 10269–10281

Published:03 April 2024Publication History

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NIPS '22: Proceedings of the 36th International Conference on Neural Information Processing Systems

End-to-end symbolic regression with transformers

Pages 10269–10281

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End-to-end symbolic regression with transformers | Proceedings of the 36th International Conference on Neural Information Processing Systems (3)

ABSTRACT

Symbolic regression, the task of predicting the mathematical expression of a function from the observation of its values, is a difficult task which usually involves a two-step procedure: predicting the "skeleton" of the expression up to the choice of numerical constants, then fitting the constants by optimizing a non-convex loss function. The dominant approach is genetic programming, which evolves candidates by iterating this subroutine a large number of times. Neural networks have recently been tasked to predict the correct skeleton in a single try, but remain much less powerful.

In this paper, we challenge this two-step procedure, and task a Transformer to directly predict the full mathematical expression, constants included. One can subsequently refine the predicted constants by feeding them to the non-convex optimizer as an informed initialization. We present ablations to show that this end-to-end approach yields better results, sometimes even without the refinement step. We evaluate our model on problems from the SRBench benchmark and show that our model approaches the performance of state-of-the-art genetic programming with several orders of magnitude faster inference.

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          End-to-end symbolic regression with transformers | Proceedings of the 36th International Conference on Neural Information Processing Systems (56)

          NIPS '22: Proceedings of the 36th International Conference on Neural Information Processing Systems

          November 2022

          39114 pages

          ISBN:9781713871088

          • Editors:
          • S. Koyejo,
          • S. Mohamed,
          • A. Agarwal,
          • D. Belgrave,
          • K. Cho,
          • A. Oh

          Copyright © 2022 Neural Information Processing Systems Foundation, Inc.

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              • Published: 3 April 2024

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