Results 1 to 10 of about 150,984 (329)
SRBench++: Principled Benchmarking of Symbolic Regression With Domain-Expert Interpretation [PDF]
IEEE Trans Evol ComputFabrício Olivetti de França +23 more
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Symbolic Regression Methods for Reinforcement Learning
IEEE Access, 2021 Reinforcement learning algorithms can solve dynamic decision-making and optimal control problems. With continuous-valued state and input variables, reinforcement learning algorithms must rely on function approximators to represent the value function and ...
Jiri Kubalik +3 more
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Controllable Neural Symbolic Regression
, 2023 In symbolic regression, the goal is to find an analytical expression that accurately fits experimental data with the minimal use of mathematical symbols such as operators, variables, and constants. However, the combinatorial space of possible expressions can make it challenging for traditional evolutionary algorithms to find the correct expression in a
Bendinelli, Tommaso +2 more
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Matching Large Biomedical Ontologies Using Symbolic Regression Using Symbolic Regression
Journal of Data Intelligence, 2022 The problem of ontology matching consists of finding the semantic correspondences between two ontologies that, although belonging to the same domain, have been developed separately. Ontology matching methods are of great importance today since they allow us to find the pivot points from which an automatic data integration process can be established ...
Martinez-Gil, Jorge +3 more
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Sequential Symbolic Regression with Genetic Programming [PDF]
, 2015 This chapter describes the Sequential Symbolic Regression (SSR) method, a new strategy for function approximation in symbolic regression. The SSR method is inspired by the sequential covering strategy from machine learning, but instead of sequentially ...
D White +4 more
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, 2023 Automating scientific discovery has been a grand goal of Artificial Intelligence (AI) and will bring tremendous societal impact. Learning symbolic expressions from experimental data is a vital step in AI-driven scientific discovery. Despite exciting progress, most endeavors have focused on the horizontal discovery paths, i.e., they directly search for ...
Jiang, Nan, Nasim, Md, Xue, Yexiang
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Symbolic regression in materials science [PDF]
MRS Communications, 2019 We showcase the potential of symbolic regression as an analytic method for use in materials research. First, we briefly describe the current state-of-the-art method, genetic programming-based symbolic regression (GPSR), and recent advances in symbolic regression techniques.
Wang, Yiqun +2 more
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Neural Symbolic Regression that Scales
, 2021 Accepted at the 38th International Conference on Machine Learning (ICML ...
Biggio, Luca +4 more
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Assessment of the effect of the financial crisis on agents’ expectations through symbolic regression [PDF]
, 2016 Agents’ perceptions on the state of the economy can be affected during economic crises. Tendency surveys are the main source of agents’ expectations.
Claveria, Oscar +2 more
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Symbolic regression for the interpretation of quantitative structure-property relationships
Artificial Intelligence in the Life Sciences, 2022 The interpretation of quantitative structure–activity or structure–property relationships is important in the field of chemoinformatics. Although multivariate linear regression models are typically interpretable, they do not generally have high ...
Katsushi Takaki, Tomoyuki Miyao
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