Rieck Lab
2021
Brüningk S.C. ; Hensel F. ; Lukas L. ; Kuijs M. ; Jutzeler C.R. ; Rieck B.
Back to the basics with inclusion of clinical domain knowledge — A simple, scalable, and effective model of Alzheimer’s Disease classification.
Proceedings of Machine Learning Research 149: 1-24 (2021)
O’Bray L. ; Rieck B. ; Borgwardt K.
Filtration Curves for Graph Representation Filtration Curves for Graph Representation
Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), DOI: 10.1145/3447548.3467442 (2021)
Moor M. ; Rieck B. ; Horn M. ; Jutzeler C.R. ; Borgwardt K.
Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review
Frontiers in Medicine 8: 607952, DOI: 10.3389/fmed.2021.607952 (2021)
Hensel F. ; Moor M. ; Rieck B.
A Survey of Topological Machine Learning Methods
Frontiers in Artificial Intelligence 4: 681108, DOI: 10.3389/frai.2021.681108 (2021)
Vandaele R. ; Rieck B. ; Saeys Y. ; De Bie T.
Stable Topological Signatures for Metric Trees through Graph Approximations
Pattern Recognition Letters 147, pp. 85–92, DOI: 10.1016/j.patrec.2021.03.035 (2021)
2020
Rieck B. ; Yates T. ; Bock C. ; Borgwardt K. ; Wolf G. ; Turk-Browne N. ; Krishnaswamy S.
Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence
Advances in Neural Information Processing Systems (NeurIPS), Volume 33, pp. 6900–6912, arXiv: 2006.07882 (2020)
Borgwardt K. ; Ghisu E. ; Llinares-López F. ; O’Bray L. ; Rieck B.
Graph Kernels: State-of-the-Art and Future Challenges
Foundations and Trends® in Machine Learning 13:5–6, pp. 531–712, DOI: 10.1561/2200000076 (2020)
