165 citations · 558 across the 40 of their papers we have counts for
10 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…
Topological Machine Learning with Persistence Indicator Functions
Bastian Rieck, Filip Sadlo, Heike Leitte
Techniques from computational topology, in particular persistent homology, are becoming increasingly relevant for data analysis. Their stable metrics permit the use of many distanc…
Hierarchies and Ranks for Persistence Pairs
Bastian Rieck, Filip Sadlo, Heike Leitte
We develop a novel hierarchy for zero-dimensional persistence pairs, i.e., connected components, which is capable of capturing more fine-grained spatial relations between persisten…
Persistence Concepts for 2D Skeleton Evolution Analysis
Bastian Rieck, Filip Sadlo, Heike Leitte
In this work, we present concepts for the analysis of the evolution of two-dimensional skeletons. By introducing novel persistence concepts, we are able to reduce typical temporal…
Persistent Intersection Homology for the Analysis of Discrete Data
Bastian Rieck, Markus Banagl, Filip Sadlo +1
Topological data analysis is becoming increasingly relevant to support the analysis of unstructured data sets. A common assumption in data analysis is that the data set is a sample…
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…
Graph Filtration Learning
Christoph D. Hofer, Florian Graf, Bastian Rieck +2
We propose an approach to learning with graph-structured data in the problem domain of graph classification. In particular, we present a novel type of readout operation to aggregat…
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…