Symbolic Regression
108 papers with code • 0 benchmarks • 3 datasets
producing a mathematical expression (symbolic expression) that fits a given tabular data.
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Libraries
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SRSD-Feynman (Easy set)
Most implemented papers
Exhaustive Symbolic Regression
To address these issues we introduce Exhaustive Symbolic Regression (ESR), which systematically and efficiently considers all possible equations -- made with a given basis set of operators and up to a specified maximum complexity -- and is therefore guaranteed to find the true optimum (if parameters are perfectly optimised) and a complete function ranking subject to these constraints.
OccamNet: A Fast Neural Model for Symbolic Regression at Scale
Neural networks' expressiveness comes at the cost of complex, black-box models that often extrapolate poorly beyond the domain of the training dataset, conflicting with the goal of finding compact analytic expressions to describe scientific data.
Contemporary Symbolic Regression Methods and their Relative Performance
We assess 14 symbolic regression methods and 7 machine learning methods on a set of 252 diverse regression problems.
Grammar Variational Autoencoder
mkusner/grammarVAE
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ICML 2017
Crucially, state-of-the-art methods often produce outputs that are not valid.
Learning a Formula of Interpretability to Learn Interpretable Formulas
We show that it is instead possible to take a meta-learning approach: an ML model of non-trivial Proxies of Human Interpretability (PHIs) can be learned from human feedback, then this model can be incorporated within an ML training process to directly optimize for interpretability.
Discovering Symbolic Models from Deep Learning with Inductive Biases
MilesCranmer/PySR
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NeurIPS 2020
The technique works as follows: we first encourage sparse latent representations when we train a GNN in a supervised setting, then we apply symbolic regression to components of the learned model to extract explicit physical relations.
On Neural Differential Equations
Topics include: neural ordinary differential equations (e. g. for hybrid neural/mechanistic modelling of physical systems); neural controlled differential equations (e. g. for learning functions of irregular time series); and neural stochastic differential equations (e. g. to produce generative models capable of representing complex stochastic dynamics, or sampling from complex high-dimensional distributions).
End-to-end symbolic regression with transformers
facebookresearch/symbolicregression
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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.
Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl
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.
AI Feynman: a Physics-Inspired Method for Symbolic Regression
SJ001/AI-Feynman
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A core challenge for both physics and artificial intellicence (AI) is symbolic regression: finding a symbolic expression that matches data from an unknown function.
