11 citations · 17 across the 5 of their papers we have counts for
5 papers
Hierarchical clustering with maximum density paths and mixture models
Martin Ritzert, Polina Turishcheva, Laura Hansel +3
Hierarchical clustering is an effective, interpretable method for analyzing structure in data. It reveals insights at multiple scales without requiring a predefined number of clust…
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…
Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos
Polina Turishcheva, Paul G. Fahey, Michaela Vystrčilová +22
Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. Artificial neural…
The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos
Polina Turishcheva, Paul G. Fahey, Laura Hansel +10
Understanding how biological visual systems process information is challenging due to the complex nonlinear relationship between neuronal responses and high-dimensional visual inpu…
Self-Supervised Graph Representation Learning for Neuronal Morphologies
Marissa A. Weis, Laura Hansel, Timo Lüddecke +1
Unsupervised graph representation learning has recently gained interest in several application domains such as neuroscience, where modeling the diverse morphology of cell types in…