48 citations · 81 across the 23 of their papers we have counts for
7 papers · 1 filter
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…
Learnable Filters for Geometric Scattering Modules
Alexander Tong, Frederik Wenkel, Dhananjay Bhaskar +5
We propose a new graph neural network (GNN) module, based on relaxations of recently proposed geometric scattering transforms, which consist of a cascade of graph wavelet filters.…
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…
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…
Taxonomy of Benchmarks in Graph Representation Learning
Renming Liu, Semih Cantürk, Frederik Wenkel +9
Graph Neural Networks (GNNs) extend the success of neural networks to graph-structured data by accounting for their intrinsic geometry. While extensive research has been done on de…
Can Hybrid Geometric Scattering Networks Help Solve the Maximum Clique Problem?
Yimeng Min, Frederik Wenkel, Michael Perlmutter +1
We propose a geometric scattering-based graph neural network (GNN) for approximating solutions of the NP-hard maximum clique (MC) problem. We construct a loss function with two ter…
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…