7 citations · 7 across the 4 of their papers we have counts for
3 papers · 1 filter
Graph-Eq: Discovering Mathematical Equations using Graph Generative Models
Nisal Ranasinghe, Damith Senanayake, Saman Halgamuge
The ability to discover meaningful, accurate, and concise mathematical equations that describe datasets is valuable across various domains. Equations offer explicit relationships b…
GINN-KAN: Interpretability pipelining with applications in Physics Informed Neural Networks
Nisal Ranasinghe, Yu Xia, Sachith Seneviratne +1
Neural networks are powerful function approximators, yet their ``black-box" nature often renders them opaque and difficult to interpret. While many post-hoc explanation methods exi…
GINN-LP: A Growing Interpretable Neural Network for Discovering Multivariate Laurent Polynomial Equations
Nisal Ranasinghe, Damith Senanayake, Sachith Seneviratne +2
Traditional machine learning is generally treated as a black-box optimization problem and does not typically produce interpretable functions that connect inputs and outputs. Howeve…