12 citations · 13 across the 5 of their papers we have counts for
5 papers · 1 filter
An Empirical Study of Graph-Based Approaches for Semi-Supervised Time Series Classification
Dominik Alfke, Miriam Gondos, Lucile Peroche +1
Time series data play an important role in many applications and their analysis reveals crucial information for understanding the underlying processes. Among the many time series l…
Pseudoinverse Graph Convolutional Networks: Fast Filters Tailored for Large Eigengaps of Dense Graphs and Hypergraphs
Dominik Alfke, Martin Stoll
Graph Convolutional Networks (GCNs) have proven to be successful tools for semi-supervised classification on graph-based datasets. We propose a new GCN variant whose three-part fil…
Semi-Supervised Classification on Non-Sparse Graphs Using Low-Rank Graph Convolutional Networks
Dominik Alfke, Martin Stoll
Graph Convolutional Networks (GCNs) have proven to be successful tools for semi-supervised learning on graph-based datasets. For sparse graphs, linear and polynomial filter functio…
The Oracle of DLphi
Dominik Alfke, Weston Baines, Jan Blechschmidt +24
We present a novel technique based on deep learning and set theory which yields exceptional classification and prediction results. Having access to a sufficiently large amount of l…
NFFT meets Krylov methods: Fast matrix-vector products for the graph Laplacian of fully connected networks
Dominik Alfke, Daniel Potts, Martin Stoll +1
The graph Laplacian is a standard tool in data science, machine learning, and image processing. The corresponding matrix inherits the complex structure of the underlying network an…