3 citations · 6 across the 4 of their papers we have counts for
4 papers · 1 filter
Multiscale Hodge Scattering Networks for Data Analysis
Naoki Saito, Stefan C. Schonsheck, Eugene Shvarts
We propose new scattering networks for signals measured on simplicial complexes, which we call \emph{Multiscale Hodge Scattering Networks} (MHSNs). Our construction builds on multi…
Semi-Supervised Manifold Learning with Complexity Decoupled Chart Autoencoders
Stefan C. Schonsheck, Scott Mahan, Timo Klock +2
Autoencoding is a popular method in representation learning. Conventional autoencoders employ symmetric encoding-decoding procedures and a simple Euclidean latent space to detect h…
Chart Auto-Encoders for Manifold Structured Data
Stefan Schonsheck, Jie Chen, Rongjie Lai
Deep generative models have made tremendous advances in image and signal representation learning and generation. These models employ the full Euclidean space or a bounded subset as…
Parallel Transport Convolution: A New Tool for Convolutional Neural Networks on Manifolds
Stefan C. Schonsheck, Bin Dong, Rongjie Lai
Convolution has been playing a prominent role in various applications in science and engineering for many years. It is the most important operation in convolutional neural networks…