Sebastian Damrich

Publication Activity (10 Years)

Years Active: 2019-2023
Publications (10 Years): 14

Top Topics

Supervised Learning

Spectral Methods

Top Venues

Publications

Sebastian Damrich, Jan Niklas Böhm, Fred A. Hamprecht, Dmitry Kobak
From $t$-SNE to UMAP with contrastive learning. ICLR (2023)
Philipp Nazari, Sebastian Damrich, Fred A. Hamprecht
Geometric Autoencoders - What You See is What You Decode. CoRR (2023)
Sebastian Damrich, Philipp Berens, Dmitry Kobak
Persistent homology for high-dimensional data based on spectral methods. CoRR (2023)
Philipp Nazari, Sebastian Damrich, Fred A. Hamprecht
Geometric Autoencoders - What You See is What You Decode. ICML (2023)
Sebastian Damrich, Jan Niklas Böhm, Fred A. Hamprecht, Dmitry Kobak
Contrastive learning unifies t-SNE and UMAP. CoRR (2022)
Enrique Fita Sanmartín, Sebastian Damrich, Fred A. Hamprecht
The Algebraic Path Problem for Graph Metrics. ICML (2022)
Enrique Fita Sanmartin, Sebastian Damrich, Fred A. Hamprecht
Directed Probabilistic Watershed. NeurIPS (2021)
Sebastian Damrich, Fred A. Hamprecht
UMAP does not reproduce high-dimensional similarities due to negative sampling. CoRR (2021)
Quentin Garrido, Sebastian Damrich, Alexander Jäger, Dario Cerletti, Manfred Claassen, Laurent Najman, Fred A. Hamprecht
Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder. CoRR (2021)
Sebastian Damrich, Fred A. Hamprecht
On UMAP's True Loss Function. NeurIPS (2021)
Florin C. Walter, Sebastian Damrich, Fred A. Hamprecht
Multistar: Instance Segmentation Of Overlapping Objects With Star-Convex Polygons. ISBI (2021)
Florin C. Walter, Sebastian Damrich, Fred A. Hamprecht
MultiStar: Instance Segmentation of Overlapping Objects with Star-Convex Polygons. CoRR (2020)
Enrique Fita Sanmartin, Sebastian Damrich, Fred A. Hamprecht
Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning. NeurIPS (2019)
Enrique Fita Sanmartin, Sebastian Damrich, Fred A. Hamprecht
Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning. CoRR (2019)