250 citations · 937 across the 31 of their papers we have counts for
6 papers · 1 filter
Set Functions for Time Series
Max Horn, Michael Moor, Christian Bock +2
Despite the eminent successes of deep neural networks, many architectures are often hard to transfer to irregularly-sampled and asynchronous time series that commonly occur in real…
Wasserstein Weisfeiler-Lehman Graph Kernels
Matteo Togninalli, Elisabetta Ghisu, Felipe Llinares-López +2
Most graph kernels are an instance of the class of -Convolution kernels, which measure the similarity of objects by comparing their substructures. Despite their empiri…
Topological Autoencoders
Michael Moor, Max Horn, Bastian Rieck +1
We propose a novel approach for preserving topological structures of the input space in latent representations of autoencoders. Using persistent homology, a technique from topologi…
Representation Learning for Dynamic Graphs: A Survey
Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain +4
Graphs arise naturally in many real-world applications including social networks, recommender systems, ontologies, biology, and computational finance. Traditionally, machine learni…
Machine learning for early prediction of circulatory failure in the intensive care unit
Stephanie L. Hyland, Martin Faltys, Matthias Hüser +12
Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems. The limited ability of humans to proce…
Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping
Michael Moor, Max Horn, Bastian Rieck +2
Sepsis is a life-threatening host response to infection associated with high mortality, morbidity, and health costs. Its management is highly time-sensitive since each hour of dela…