Symbolic regression
PulseAugur coverage of Symbolic regression — every cluster mentioning Symbolic regression across labs, papers, and developer communities, ranked by signal.
7 day(s) with sentiment data
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New pipeline estimates galactic potential using symbolic regression
Researchers have developed a new pipeline to estimate the distribution function of stars in the galaxy, which is crucial for understanding dark matter density. This method linearizes the collisionless Boltzmann equation…
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New SMILE framework bridges continuous optimization and discrete symbolic recovery
Researchers have developed SMILE, a novel framework for symbolic regression that combines continuous optimization with discrete symbolic recovery. This hybrid approach first analyzes data to understand the compositional…
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New open corpus and ML models for soil compaction tests released
Researchers have released a large, open corpus of soil compaction tests, containing 2,854 laboratory compaction tests from six public sources across various energy levels and fines content. This dataset aims to overcome…
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New Neural Symbolic Regression framework combines deep learning with sparse modeling
Researchers have developed a new Neural Symbolic Regression (NSR) framework that combines neural networks with sparse modeling techniques to discover succinct mathematical expressions from data. This approach first uses…
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New method combines symbolic regression and LLMs for automated feature engineering
Researchers have developed SymboLLM-FE, a novel approach that combines symbolic regression with large language models (LLMs) for automated feature engineering on tabular data. This method aims to overcome the limitation…
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Agentic AI automates genetic programming configuration
Researchers have developed an agentic AI framework to automate the configuration of parent selection algorithms in genetic programming. This framework utilizes large language model reasoning and retrieval-augmented gene…
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New method enhances evolutionary feature construction in symbolic regression
Researchers have developed an adaptive protection mechanism to improve evolutionary feature construction in symbolic regression. This new method uses feature importance metrics to selectively preserve crucial constructe…
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LLMs accelerate kinetic model discovery in chemical engineering
Researchers have developed DASyR-LLM, a novel framework that integrates Large Language Models (LLMs) with symbolic regression to accelerate kinetic model discovery in chemical engineering. This LLM-guided approach injec…
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LLM-guided symbolic regression accelerates kinetic model discovery
A new framework called DASyR-LLM integrates Large Language Models (LLMs) with symbolic regression to accelerate the discovery of kinetic models in chemical engineering. The LLM component provides domain-specific critiqu…
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New DDRSR method enhances symbolic regression with broader applicability
Researchers have introduced Deep Divide and Reduce in Symbolic Regression (DDRSR), a novel method designed to improve the discovery of mathematical expressions from data. Unlike previous approaches that rely on brute-fo…
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New LLM framework enhances scientific equation discovery
Researchers have developed MOT-SR, a novel framework for scientific equation discovery using large language models. This approach addresses limitations in existing methods by integrating external analytical tools to unc…
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Symbolic regression discovers novel neural network optimizers
Researchers have explored the use of symbolic regression to discover novel weight-update rules for feed-forward neural networks. In experiments across 30 benchmark and neural network combinations, the symbolic regressio…
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LLMs show promise in generating genetic programming operators
Researchers have evaluated the ability of eight large language models (LLMs) to generate effective parent-selection operators for genetic programming (GP) in symbolic regression tasks. The study found that models like C…
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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…