Symbolic Regression
67 papers with code • 0 benchmarks • 4 datasets
producing a mathematical expression (symbolic expression) that fits a given tabular data.
Benchmarks
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Libraries
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SRSD-Feynman (Easy set)
SESYD Dataset
Most implemented papers
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.
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.
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.
Fast Neural Models for Symbolic Regression at Scale
Deep learning owes much of its success to the astonishing expressiveness of neural networks.
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).
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.
SymbolicGPT: A Generative Transformer Model for Symbolic Regression
Symbolic regression is the task of identifying a mathematical expression that best fits a provided dataset of input and output values.
