Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl
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- @Misc{cranmer2023interpretablemachinelearningscience,
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title = "Interpretable Machine Learning for Science with PySR
and SymbolicRegression.jl",
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author = "Miles Cranmer",
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howpublished = "arXiv",
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year = "2023",
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edition = "v3",
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month = "5 " # may,
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keywords = "genetic algorithms, genetic programming, GPU, Julia",
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eprint = "2305.01582",
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archiveprefix = "arXiv",
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primaryclass = "astro-ph.IM",
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URL = "https://arxiv.org/abs/2305.01582",
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code_url = "https://github.com/MilesCranmer/PySR",
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size = "24 pages",
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abstract = "PySR is an open-source library for practical symbolic
regression, a type of machine learning which aims to
discover human-interpretable symbolic models. PySR was
developed to democratize and popularize symbolic
regression for the sciences, and is built on a
high-performance distributed back-end, a flexible
search algorithm, and interfaces with several deep
learning packages. PySR internal search algorithm is a
multi-population evolutionary algorithm, which consists
of a unique evolve-simplify-optimize loop, designed for
optimization of unknown scalar constants in
newly-discovered empirical expressions. PySR backend is
the extremely optimized Julia library
SymbolicRegression.jl, which can be used directly from
Julia. It is capable of fusing user-defined operators
into SIMD kernels at runtime, performing automatic
differentiation, and distributing populations of
expressions to thousands of cores across a cluster. In
describing this software, we also introduce a new
benchmark, EmpiricalBench, to quantify the
applicability of symbolic regression algorithms in
science. This benchmark measures recovery of historical
empirical equations from original and synthetic
datasets.",
- }
Genetic Programming entries for
Miles Cranmer
Citations