RepNN reparameterizes the first hidden layer of DNNs to enable adaptive frequency scaling, improving accuracy on oscillatory and multiscale functions with minimal extra cost.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A scoping review of physics-informed machine learning for seismic wave propagation finds applications in forward and inverse problems with often comparable accuracy at lower cost, while identifying gaps in benchmarking, training cost, and 3D/experimental validation.
citing papers explorer
-
RepNN: Tackling spectral bias in deep neural networks via parameter reparameterization
RepNN reparameterizes the first hidden layer of DNNs to enable adaptive frequency scaling, improving accuracy on oscillatory and multiscale functions with minimal extra cost.
-
A Scoping Review of Physics Informed Machine Learning for Wave Propagation Modeling in Seismology
A scoping review of physics-informed machine learning for seismic wave propagation finds applications in forward and inverse problems with often comparable accuracy at lower cost, while identifying gaps in benchmarking, training cost, and 3D/experimental validation.