Symbolic Regression

109 papers with code • 0 benchmarks • 3 datasets

producing a mathematical expression (symbolic expression) that fits a given tabular data.


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Most implemented papers

Exhaustive Symbolic Regression

deaglanbartlett/esr 21 Nov 2022

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

druidowm/OccamNet_Public 16 Jul 2020

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

EpistasisLab/srbench 29 Jul 2021

We assess 14 symbolic regression methods and 7 machine learning methods on a set of 252 diverse regression problems.

On Neural Differential Equations

rtqichen/torchdiffeq 4 Feb 2022

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).

Grammar Variational Autoencoder

mkusner/grammarVAE ICML 2017

Crucially, state-of-the-art methods often produce outputs that are not valid.

Learning a Formula of Interpretability to Learn Interpretable Formulas

marcovirgolin/pyNSGP 23 Apr 2020

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 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.

End-to-end symbolic regression with transformers

facebookresearch/symbolicregression 22 Apr 2022

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

MilesCranmer/PySR 2 May 2023

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 27 May 2019

A core challenge for both physics and artificial intellicence (AI) is symbolic regression: finding a symbolic expression that matches data from an unknown function.