1 code implementation • 16 Apr 2021 • Dominik Alfke, Miriam Gondos, Lucile Peroche, Martin Stoll
Among the many time series learning tasks of great importance, we here focus on semi-supervised learning based on a graph representation of the data.
1 code implementation • 3 Aug 2020 • Dominik Alfke, Martin Stoll
Our method overcomes these issues by utilizing the pseudoinverse of the Laplacian.
2 code implementations • 24 May 2019 • Dominik Alfke, Martin Stoll
Graph Convolutional Networks (GCNs) have proven to be successful tools for semi-supervised learning on graph-based datasets.
no code implementations • 17 Jan 2019 • Dominik Alfke, Weston Baines, Jan Blechschmidt, Mauricio J. del Razo Sarmina, Amnon Drory, Dennis Elbrächter, Nando Farchmin, Matteo Gambara, Silke Glas, Philipp Grohs, Peter Hinz, Danijel Kivaranovic, Christian Kümmerle, Gitta Kutyniok, Sebastian Lunz, Jan Macdonald, Ryan Malthaner, Gregory Naisat, Ariel Neufeld, Philipp Christian Petersen, Rafael Reisenhofer, Jun-Da Sheng, Laura Thesing, Philipp Trunschke, Johannes von Lindheim, David Weber, Melanie Weber
We present a novel technique based on deep learning and set theory which yields exceptional classification and prediction results.
no code implementations • 14 Aug 2018 • Dominik Alfke, Daniel Potts, Martin Stoll, Toni Volkmer
The graph Laplacian is a standard tool in data science, machine learning, and image processing.