1 code implementation • 17 Sep 2022 • Daniel M. DiPietro, Vivek Hazari
Data is a key component of modern machine learning, but statistics for assessing data label quality remain sparse in literature.
no code implementations • 4 Sep 2022 • Daniel M. DiPietro, Bo Zhu
Here we present Symplectically Integrated Symbolic Regression (SISR), a novel technique for learning physical governing equations from data.
no code implementations • 4 Sep 2022 • Daniel M. DiPietro
Empirically, stopword lists generated via this approach drastically reduce dataset size while negligibly impacting model performance, in one such example shrinking the corpus by 28. 4% while improving the accuracy of a trained logistic regression model by 0. 25%.
1 code implementation • NeurIPS 2020 • Daniel M. DiPietro, Shiying Xiong, Bo Zhu
We introduce Sparse Symplectically Integrated Neural Networks (SSINNs), a novel model for learning Hamiltonian dynamical systems from data.