48 citations · 81 across the 23 of their papers we have counts for
6 papers · 1 filter
Manifold Filter-Combine Networks
David R. Johnson, Joyce A. Chew, Edward De Brouwer +3
In order to better understand manifold neural networks (MNNs), we introduce Manifold Filter-Combine Networks (MFCNs). Our filter-combine framework parallels the popular aggregate-c…
Geometric Scattering on Measure Spaces
Joyce Chew, Matthew Hirn, Smita Krishnaswamy +5
The scattering transform is a multilayered, wavelet-based transform initially introduced as a model of convolutional neural networks (CNNs) that has played a foundational role in o…
Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networks
Frederik Wenkel, Yimeng Min, Matthew Hirn +2
Geometric deep learning has made great strides towards generalizing the design of structure-aware neural networks from traditional domains to non-Euclidean ones, giving rise to gra…
Understanding Graph Neural Networks with Generalized Geometric Scattering Transforms
Michael Perlmutter, Alexander Tong, Feng Gao +2
The scattering transform is a multilayered wavelet-based deep learning architecture that acts as a model of convolutional neural networks. Recently, several works have introduced g…
Geometric Wavelet Scattering Networks on Compact Riemannian Manifolds
Michael Perlmutter, Feng Gao, Guy Wolf +1
The Euclidean scattering transform was introduced nearly a decade ago to improve the mathematical understanding of convolutional neural networks. Inspired by recent interest in geo…
Geometric Scattering on Manifolds
Michael Perlmutter, Guy Wolf, Matthew Hirn
The Euclidean scattering transform was introduced nearly a decade ago to improve the mathematical understanding of the success of convolutional neural networks (ConvNets) in image…