1 code implementation • 6 Nov 2023 • Sebastian Damrich, Philipp Berens, Dmitry Kobak
As a remedy, we find that spectral distances on the $k$-nearest-neighbor graph of the data, such as diffusion distance and effective resistance, allow to detect the correct topology even in the presence of high-dimensional noise.
no code implementations • 30 Jun 2023 • Philipp Nazari, Sebastian Damrich, Fred A. Hamprecht
Visualization is a crucial step in exploratory data analysis.
2 code implementations • 3 Jun 2022 • Sebastian Damrich, Jan Niklas Böhm, Fred A. Hamprecht, Dmitry Kobak
We exploit this new conceptual connection to propose and implement a generalization of negative sampling, allowing us to interpolate between (and even extrapolate beyond) $t$-SNE and UMAP and their respective embeddings.
1 code implementation • NeurIPS 2021 • Enrique Fita Sanmartin, Sebastian Damrich, Fred A. Hamprecht
We propose the "Directed Probabilistic Watershed", an extension of the Probabilistic Watershed algorithm to directed graphs.
1 code implementation • NeurIPS 2021 • Sebastian Damrich, Fred A. Hamprecht
As a consequence, we show that UMAP does not aim to reproduce its theoretically motivated high-dimensional UMAP similarities.
1 code implementation • 11 Feb 2021 • Quentin Garrido, Sebastian Damrich, Alexander Jäger, Dario Cerletti, Manfred Claassen, Laurent Najman, Fred Hamprecht
Motivation: Single cell RNA sequencing (scRNA-seq) data makes studying the development of cells possible at unparalleled resolution.
1 code implementation • 26 Nov 2020 • Florin C. Walter, Sebastian Damrich, Fred A. Hamprecht
Instance segmentation of overlapping objects in biomedical images remains a largely unsolved problem.
1 code implementation • NeurIPS 2019 • Enrique Fita Sanmartin, Sebastian Damrich, Fred A. Hamprecht
The seeded Watershed algorithm / minimax semi-supervised learning on a graph computes a minimum spanning forest which connects every pixel / unlabeled node to a seed / labeled node.