Symbolic Regression
104 papers with code • 0 benchmarks • 4 datasets
producing a mathematical expression (symbolic expression) that fits a given tabular data.
Benchmarks
These leaderboards are used to track progress in Symbolic Regression
Libraries
Use these libraries to find Symbolic Regression models and implementations
SRSD-Feynman (Easy set)
SESYD Dataset
Most implemented papers
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
SJ001/AI-Feynman
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NeurIPS 2020
We present an improved method for symbolic regression that seeks to fit data to formulas that are Pareto-optimal, in the sense of having the best accuracy for a given complexity.
Neural Symbolic Regression that Scales
SymposiumOrganization/NeuralSymbolicRegressionThatScales
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We procedurally generate an unbounded set of equations, and simultaneously pre-train a Transformer to predict the symbolic equation from a corresponding set of input-output-pairs.
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.
Chaos as an interpretable benchmark for forecasting and data-driven modelling
Our dataset is annotated with known mathematical properties of each system, and we perform feature analysis to broadly categorize the diverse dynamics present across the collection.
Symbolic Regression via Neural-Guided Genetic Programming Population Seeding
Symbolic regression is the process of identifying mathematical expressions that fit observed output from a black-box process.
SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training
deep-symbolic-mathematics/Multimodal-Math-Pretraining
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To bridge the gap, we introduce SNIP, a Symbolic-Numeric Integrated Pre-training model, which employs contrastive learning between symbolic and numeric domains, enhancing their mutual similarities in the embeddings.
Deep Generative Symbolic Regression
samholt/deepgenerativesymbolicregression
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Symbolic regression (SR) aims to discover concise closed-form mathematical equations from data, a task fundamental to scientific discovery.
Automated discovery of symbolic laws governing skill acquisition from naturally occurring data
ccnu-mathits/adm
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Skill acquisition is a key area of research in cognitive psychology as it encompasses multiple psychological processes.
Multi Expression Programming
Multi Expression Programming (MEP) is a new evolutionary paradigm intended for solving computationally difficult problems.
P-Tree Programming
From this prototype tree we form program instances which we evaluate on a given problem.
