6 citations · 11 across the 9 of their papers we have counts for
6 papers · 1 filter
Internal Evaluation of Density-Based Clusterings with Noise
Anna Beer, Lena Krieger, Pascal Weber +3
Being able to evaluate the quality of a clustering result even in the absence of ground truth cluster labels is fundamental for research in data mining. However, most cluster valid…
MNIST-Nd: a set of naturalistic datasets to benchmark clustering across dimensions
Polina Turishcheva, Laura Hansel, Martin Ritzert +2
Driven by advances in recording technology, large-scale high-dimensional datasets have emerged across many scientific disciplines. Especially in biology, clustering is often used t…
Distinguished In Uniform: Self Attention Vs. Virtual Nodes
Eran Rosenbluth, Jan Tönshoff, Martin Ritzert +2
Graph Transformers (GTs) such as SAN and GPS are graph processing models that combine Message-Passing GNNs (MPGNNs) with global Self-Attention. They were shown to be universal func…
Boosting, Voting Classifiers and Randomized Sample Compression Schemes
Arthur da Cunha, Kasper Green Larsen, Martin Ritzert
In boosting, we aim to leverage multiple weak learners to produce a strong learner. At the center of this paradigm lies the concept of building the strong learner as a voting class…
Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark
Jan Tönshoff, Martin Ritzert, Eran Rosenbluth +1
The recent Long-Range Graph Benchmark (LRGB, Dwivedi et al. 2022) introduced a set of graph learning tasks strongly dependent on long-range interaction between vertices. Empirical…
Learning MSO-definable hypotheses on string
Martin Grohe, Christof Löding, Martin Ritzert
We study the classification problems over string data for hypotheses specified by formulas of monadic second-order logic MSO. The goal is to design learning algorithms that run in…