Explore tweets tagged as #symbolicregression
SymbolicRegression.jl → 1.0 🎉 . After several years of work, I'm thrilled to announce some major new features! Let me show you what's possible now:
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SymbolicRegression.jl v1.10.0 is out! It can now evolve expressions over arbitrary input types. The video below shows it reverse-engineering a string transformation from examples. Curious to see how people use this!
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Happy to announce differential operators for PySR + SymbolicRegression.jl!. This means you can literally just. evolve an integral:
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Preview of SymbolicRegression.jl v1:. Symbolic regression on arbitrary structs🔥
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Finally got Enzyme.jl working with SymbolicRegression.jl after a year of debugging. In the end, the solution was to simply. increase the stack size. 🙂. Anyways, now you can do crazy-fast reverse-mode autodiff on runtime-generated expressions for symbolic regression:
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TensorBoard integration in SymbolicRegression.jl!. Still a heavy work-in-progress but very excited about this –.
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⚠️New paper alert ⚠️. Improving Genetic Programming for Symbolic Regression with Equality Graphs exploits e-graphs to generate equivalent expressions proposes new operators that creates unvisited expressions during the search. #GECCO2025 #SymbolicRegression #GeneticProgramming
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Symbolic regression update—new versions of PySR & SymbolicRegression.jl just released with improvements to mini-batching. Should work much better for large datasets!
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Interested in feedback on this syntax idea for predefined "template expressions" in SymbolicRegression.jl and PySR. Basically: what is an intuitive way to prescribe a fixed skeleton for a symbolic search? Ideally the syntax should be flexible and extensible too.
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This was mixed with a sparse regression for automating the discovery of the missing reaction terms. @MilesCranmer's SymbolicRegression.jl outperformed the sparse regression techniques commonly used by SINDy.
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新しい科学的方程式を導くための機械学習ツール プリンストン大などの研究者らが発表. 論文： 実験データから正確かつシンプルな方程式を見つける回帰分析を行うためのオープンソースライブラリPySRとSymbolicRegression.jlが開��されました。
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This is how Julia finds out the underlying mathematical formula of XOR function using SymbolicRegression and MLJ. #JuliaLang
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Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl. PySR is an open-source library for practical symbolic regression. It aims to discover human-interpretable. 🧵 👇
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🧠 Olaris Journal Club highlight:.Symbolic Regression = interpretable AI for real-world science. 🔹 Better NMR insights .🔹 More trust in ML .🔹 Easier regulatory validation . 👏 Led by our Data Engineer,nJowin! .#AI #SymbolicRegression #DataScience #OlarisInsights
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"Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl". Link:
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Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl.
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Training and testing XOR data using the Julia package SymbolicRegression.jl, the function is estimated as . |x1 - x2|. which produces the exact same output as . x1 XOR x2. machine, fit!, report, and predict functions are members of the MLJ.jl package. #JuliaLang @JuliaLanguage
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So, the good news is that with DynamicQuantities.jl, now you can finally use dimensional constraints in both PySR and SymbolicRegression.jl 🚀.
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