Symbolic regression
PulseAugur coverage of Symbolic regression — every cluster mentioning Symbolic regression across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
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LLM-powered agent discovers biological ODEs with symbolic regression
Researchers have developed MEDA, a new system that combines large language models (LLMs) with symbolic regression to automatically discover Ordinary Differential Equations (ODEs) for biological systems. This agentic fra…
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New probabilistic framework VaSST enhances symbolic regression with soft symbolic trees
Researchers have introduced VaSST, a novel probabilistic framework for symbolic regression designed to address limitations in current AI-driven scientific discovery methods. VaSST employs soft symbolic trees, a continuo…
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New research explores genetic programming for symbolic regression · 2 sources tracked
Two recent arXiv papers explore genetic programming (GP) for symbolic regression (SR). One study, "Evaluation of Population Initialization Methods for Genetic Programming-based Symbolic Regression," found that different…
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LLMs' rise prompts debate on Symbolic Regression's future
The discussion on Reddit's r/MachineLearning explores the current relevance of Symbolic Regression (SR) in light of advancements in Large Language Models (LLMs). Users are questioning whether LLMs' capabilities in code …
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New benchmark ERBench evaluates equation discovery algorithms
Researchers have introduced ERBench, a new benchmark and test suite specifically designed to evaluate algorithms for equation discovery. This framework focuses on assessing how well these algorithms can recover known gr…
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Survey paper highlights need for uncertainty quantification in symbolic regression
A new survey paper addresses the critical gap in uncertainty quantification (UQ) for symbolic regression (SR) methods. The paper aims to introduce UQ concepts and review existing literature, categorizing current researc…
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Symbolic regression method introduces partial parameter sharing
Researchers have developed a new method for symbolic regression that allows for partial parameter sharing across multiple categorical variables. This approach enables the discovery of single expressions that can describ…
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New GP-GOMEA method optimizes expression structure and constants
Researchers have developed a new approach to symbolic regression using genetic programming, a method for constructing symbolic expressions that fit data. Their novel technique simultaneously optimizes both the structure…
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New SAGE-Fit framework enhances symbolic regression accuracy
Researchers have developed SAGE-Fit, a new framework designed to improve symbolic regression (SR) by addressing the issue of poor parameter optimization. Existing SR methods often struggle with non-convex inner loops, l…
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New methods advance symbolic regression for data analysis
Researchers have developed two new approaches to symbolic regression, a technique for finding mathematical expressions that fit data. One method, Latent Equation Embedding (LEE), uses iterative refinement in a latent sp…