48 citations · 81 across the 23 of their papers we have counts for
25 papers
Transversal Pooling Neural Networks
Emily J. King, Dustin G. Mixon, Michael Perlmutter +1
Many learning tasks require stability to small transformations while retaining sensitivity to larger ones. We introduce \emph{transversal pooling neural networks}, which generalize…
Towards a General Recipe for Combinatorial Optimization with Multi-Filter GNNs
Frederik Wenkel, Semih Cantürk, Stefan Horoi +2
Graph neural networks (GNNs) have achieved great success for a variety of tasks such as node classification, graph classification, and link prediction. However, the use of GNNs (an…
A Flow Artist for High-Dimensional Cellular Data
Kincaid MacDonald, Dhananjay Bhaskar, Guy Thampakkul +5
We consider the problem of embedding point cloud data sampled from an underlying manifold with an associated flow or velocity. Such data arises in many contexts where static snapsh…
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…
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.…