26 citations · 54 across the 7 of their papers we have counts for
3 papers · 1 filter
Deep Learning and Symbolic Regression for Discovering Parametric Equations
Michael Zhang, Samuel Kim, Peter Y. Lu +1
Symbolic regression is a machine learning technique that can learn the governing formulas of data and thus has the potential to transform scientific discovery. However, symbolic re…
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure
Samuel Kim, Peter Y. Lu, Charlotte Loh +3
Bayesian optimization (BO) is a popular paradigm for global optimization of expensive black-box functions, but there are many domains where the function is not completely a black-b…
OccamNet: A Fast Neural Model for Symbolic Regression at Scale
Owen Dugan, Rumen Dangovski, Allan Costa +4
Neural networks' expressiveness comes at the cost of complex, black-box models that often extrapolate poorly beyond the domain of the training dataset, conflicting with the goal of…