5 papers
Towards a Taxonomy of Graph Learning Datasets
Renming Liu, Semih Cantürk, Frederik Wenkel +10
Graph neural networks (GNNs) have attracted much attention due to their ability to leverage the intrinsic geometries of the underlying data. Although many different types of GNN mo…
Molecular Graph Generation via Geometric Scattering
Dhananjay Bhaskar, Jackson D. Grady, Michael A. Perlmutter +1
Graph neural networks (GNNs) have been used extensively for addressing problems in drug design and discovery. Both ligand and target molecules are represented as graphs with node a…
A Hybrid Scattering Transform for Signals with Isolated Singularities
Michael Perlmutter, Jieqian He, Mark Iwen +1
The scattering transform is a wavelet-based model of Convolutional Neural Networks originally introduced by S. Mallat. Mallat's analysis shows that this network has desirable stabi…
On audio enhancement via online non-negative matrix factorization
Andrew Sack, Wenzhao Jiang, Michael Perlmutter +2
We propose a method for noise reduction, the task of producing a clean audio signal from a recording corrupted by additive noise. Many common approaches to this problem are based u…
Modewise Operators, the Tensor Restricted Isometry Property, and Low-Rank Tensor Recovery
Mark A. Iwen, Deanna Needell, Michael Perlmutter +1
Recovery of sparse vectors and low-rank matrices from a small number of linear measurements is well-known to be possible under various model assumptions on the measurements. The ke…