9 citations · 10 across the 11 of their papers we have counts for
3 papers · 2 filters
A Convergence Rate for Manifold Neural Networks
Joyce Chew, Deanna Needell, Michael Perlmutter
High-dimensional data arises in numerous applications, and the rapidly developing field of geometric deep learning seeks to develop neural network architectures to analyze such dat…
The Manifold Scattering Transform for High-Dimensional Point Cloud Data
Joyce Chew, Holly R. Steach, Siddharth Viswanath +5
The manifold scattering transform is a deep feature extractor for data defined on a Riemannian manifold. It is one of the first examples of extending convolutional neural network-l…
Guided Semi-Supervised Non-negative Matrix Factorization on Legal Documents
Pengyu Li, Christine Tseng, Yaxuan Zheng +4
Classification and topic modeling are popular techniques in machine learning that extract information from large-scale datasets. By incorporating a priori information such as label…