Results 21 to 30 of about 13,019 (261)
CoRR, 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 ...
Nan Jiang 0012, Md. Nasim, Yexiang Xue
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IET Collaborative Intelligent Manufacturing, 2021 Predictive models are increasingly deployed within smart manufacturing for the control of industrial plants. With this arises, the need for long‐term monitoring of model performance and adaptation of models if surrounding conditions change and the ...
Florian Bachinger +2 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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Neural Symbolic Regression that Scales
CoRR, 2021 Accepted at the 38th International Conference on Machine Learning (ICML ...
Biggio, Luca +4 more
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Regression Models for Symbolic Interval-Valued Variables
Entropy, 2021 This paper presents new approaches to fit regression models for symbolic internal-valued variables, which are shown to improve and extend the center method suggested by Billard and Diday and the center and range method proposed by Lima-Neto, E.A.and De ...
Jose Emmanuel Chacón +1 more
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Controllable Neural Symbolic Regression
CoRR, 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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Symbolic Regression is NP-hard
Trans. Mach. Learn. Res., 2022 corrected citation Abbass 2002 -> Cramer ...
M. Virgolin (Marco), S. Pissis (Solon)
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Smooth Symbolic Regression: Transformation of Symbolic Regression into a Real-Valued Optimization Problem [PDF]
, 2015 The typical methods for symbolic regression produce rather abrupt changes in solution candidates. In this work, we have tried to transform symbolic regression from an optimization problem, with a landscape that is so rugged that typical analysis methods do not produce meaningful results, to one that can be compared to typical and very smooth real ...
Erik Pitzer, Gabriel Kronberger
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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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The Lookup Table Regression Model for Histogram-Valued Symbolic Data
Stats, 2022 This paper presents the Lookup Table Regression Model (LTRM) for histogram-valued symbolic data. We first transform the given symbolic data to a numerical data table by the quantile method.
Manabu Ichino
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