1 citations · 1 across the 7 of their papers we have counts for
8 papers
Exhaustive Canonical Skeletons Generation: A Formal Dataset for Decomposable Symbolic Regression
Giorgio Morales, Anikó Ekárt
Neural symbolic regression (SR) models require diverse datasets of mathematical expressions for robust training. However, existing datasets rely predominantly on stochastic samplin…
Learning Parametric Nitrogen Fertilizer Response Curves Using Neuro Symbolic Regression
Giorgio Morales, John Sheppard
Accurately modeling crop response to Nitrogen (N) fertilization is a fundamental challenge in precision agriculture, as it impacts both economic returns and environmental sustainab…
Neutrino Oscillation Parameter Estimation Using Structured Hierarchical Transformers
Giorgio Morales, Gregory Lehaut, Antonin Vacheret +2
Neutrino oscillations encode fundamental information about neutrino masses and mixing parameters, offering a unique window into physics beyond the Standard Model. Estimating these…
Towards Uncertainty Quantification in Generative Model Learning
Giorgio Morales, Frederic Jurie, Jalal Fadili
While generative models have become increasingly prevalent across various domains, fundamental concerns regarding their reliability persist. A crucial yet understudied aspect of th…
Decomposable Neural Symbolic Regression
Giorgio Morales, John W. Sheppard
Symbolic regression (SR) models complex systems by discovering mathematical expressions that capture underlying relationships in observed data. However, most SR methods prioritize…
Adaptive Sampling to Reduce Epistemic Uncertainty Using Prediction Interval-Generation Neural Networks
Giorgio Morales, John Sheppard
Obtaining high certainty in predictive models is crucial for making informed and trustworthy decisions in many scientific and engineering domains. However, extensive experimentatio…
Univariate Skeleton Prediction in Multivariate Systems Using Transformers
Giorgio Morales, John W. Sheppard
Symbolic regression (SR) methods attempt to learn mathematical expressions that approximate the behavior of an observed system. However, when dealing with multivariate systems, the…
Counterfactual Analysis of Neural Networks Used to Create Fertilizer Management Zones
Giorgio Morales, John Sheppard
In Precision Agriculture, the utilization of management zones (MZs) that take into account within-field variability facilitates effective fertilizer management. This approach enabl…