3 papers
cs.LG2021
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…
cs.LG2020
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…
cs.LG2019
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…