8 citations · 15 across the 7 of their papers we have counts for
7 papers
Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research
Tobias Hille, Maximilian Stubbemann, Tom Hanika
Difficulties in replication and reproducibility of empirical evidences in machine learning research have become a prominent topic in recent years. Ensuring that machine learning re…
Selecting Features by their Resilience to the Curse of Dimensionality
Maximilian Stubbemann, Tobias Hille, Tom Hanika
Real-world datasets are often of high dimension and effected by the curse of dimensionality. This hinders their comprehensibility and interpretability. To reduce the complexity fea…
Intrinsic Dimension for Large-Scale Geometric Learning
Maximilian Stubbemann, Tom Hanika, Friedrich Martin Schneider
The concept of dimension is essential to grasp the complexity of data. A naive approach to determine the dimension of a dataset is based on the number of attributes. More sophistic…
The Mont Blanc of Twitter: Identifying Hierarchies of Outstanding Peaks in Social Networks
Maximilian Stubbemann, Gerd Stumme
The investigation of social networks is often hindered by their size as such networks often consist of at least thousands of vertices and edges. Hence, it is of major interest to d…
LG4AV: Combining Language Models and Graph Neural Networks for Author Verification
Maximilian Stubbemann, Gerd Stumme
The automatic verification of document authorships is important in various settings. Researchers are for example judged and compared by the amount and impact of their publications…
FCA2VEC: Embedding Techniques for Formal Concept Analysis
Dominik Dürrschnabel, Tom Hanika, Maximilian Stubbemann
Embedding large and high dimensional data into low dimensional vector spaces is a necessary task to computationally cope with contemporary data sets. Superseding latent semantic an…
Orometric Methods in Bounded Metric Data
Maximilian Stubbemann, Tom Hanika, Gerd Stumme
A large amount of data accommodated in knowledge graphs (KG) is actually metric. For example, the Wikidata KG contains a plenitude of metric facts about geographic entities like ci…