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Interpretable ML for Science with PySR

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Paper summary

An open-source library for practical symbolic regression in the sciences.

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Key points

Distributed back-end: Built on a high-performance distributed back-end for scaling to larger scientific datasets.

DL integration: Interfaces with several deep learning packages so symbolic regression can be used alongside neural networks.

EmpiricalBench benchmark: Releases a new benchmark for quantifying the applicability of symbolic regression algorithms in science.

Science-AI tool: Became a widely-used tool for scientists seeking interpretable equations from data, complementing black-box DL.

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