250 citations · 937 across the 31 of their papers we have counts for
5 papers · 1 filter
Weisfeiler and Leman go Machine Learning: The Story so far
Christopher Morris, Yaron Lipman, Haggai Maron +5
In recent years, algorithms and neural architectures based on the Weisfeiler--Leman algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a powerful…
The magnitude vector of images
Michael F. Adamer, Edward De Brouwer, Leslie O'Bray +1
The magnitude of a finite metric space has recently emerged as a novel invariant quantity, allowing to measure the effective size of a metric space. Despite encouraging first resul…
Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning
Michael Moor, Nicolas Bennet, Drago Plecko +5
Despite decades of clinical research, sepsis remains a global public health crisis with high mortality, and morbidity. Currently, when sepsis is detected and the underlying pathoge…
Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions
Leslie O'Bray, Max Horn, Bastian Rieck +1
Graph generative models are a highly active branch of machine learning. Given the steady development of new models of ever-increasing complexity, it is necessary to provide a princ…
Topological Graph Neural Networks
Max Horn, Edward De Brouwer, Michael Moor +3
Graph neural networks (GNNs) are a powerful architecture for tackling graph learning tasks, yet have been shown to be oblivious to eminent substructures such as cycles. We present…