87 citations · 450 across the 34 of their papers we have counts for
6 papers · 1 filter
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…
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…
Accurately Modeling Biased Random Walks on Weighted Graphs Using
Renming Liu, Matthew Hirn, Arjun Krishnan
Node embedding is a powerful approach for representing the structural role of each node in a graph. is a widely used method for node embedding that works by exp…
Unbiasing Procedures for Scale-invariant Multi-reference Alignment
Matthew Hirn, Anna Little
This article discusses a generalization of the 1-dimensional multi-reference alignment problem. The goal is to recover a hidden signal from many noisy observations, where each nois…
Texture synthesis via projection onto multiscale, multilayer statistics
Jieqian He, Matthew Hirn
We provide a new model for texture synthesis based on a multiscale, multilayer feature extractor. Within the model, textures are represented by a set of statistics computed from Re…
MagNet: A Neural Network for Directed Graphs
Xitong Zhang, Yixuan He, Nathan Brugnone +2
The prevalence of graph-based data has spurred the rapid development of graph neural networks (GNNs) and related machine learning algorithms. Yet, despite the many datasets natural…