4 citations · 4 across the 3 of their papers we have counts for
3 papers
Tunable Complexity Benchmarks for Evaluating Physics-Informed Neural Networks on Coupled Ordinary Differential Equations
Alexander New, Benjamin Eng, Andrea C. Timm +1
In this work, we assess the ability of physics-informed neural networks (PINNs) to solve increasingly-complex coupled ordinary differential equations (ODEs). We focus on a pair of…
Scatterbrained: A flexible and expandable pattern for decentralized machine learning
Miller Wilt, Jordan K. Matelsky, Andrew S. Gearhart
Federated machine learning is a technique for training a model across multiple devices without exchanging data between them. Because data remains local to each compute node, federa…
Geometry and Generalization: Eigenvalues as predictors of where a network will fail to generalize
Susama Agarwala, Benjamin Dees, Andrew Gearhart +1
We study the deformation of the input space by a trained autoencoder via the Jacobians of the trained weight matrices. In doing so, we prove bounds for the mean squared errors for…