Sebastian Damrich

25 papers A* 8Journal 15Unranked 1
YearRankTypeTitle / Venue / Authors
2026 J jnl
DREAMS: Preserving both Local and Global Structure in Dimensionality Reduction.
Trans. Mach. Learn. Res.
Noël Kury, Dmitry Kobak, Sebastian Damrich
2025 J jnl
DREAMS: Preserving both Local and Global Structure in Dimensionality Reduction.
CoRR
Noël Kury, Dmitry Kobak, Sebastian Damrich
2025 J jnl
Low-dimensional embeddings of high-dimensional data.
CoRR
Cyril de Bodt, Alex Diaz-Papkovich, Michael Bleher, Kerstin Bunte, Corinna Coupette, Sebastian Damrich, Enrique Fita Sanmartin, Fred A. Hamprecht, Emoke-Ágnes Horvát, Dhruv Kohli, Smita Krishnaswamy, John A. Lee, Boudewijn P. F. Lelieveldt, Leland McInnes, Ian T. Nabney, Maximilian Noichl, Pavlin G. Policar, Bastian Rieck, Guy Wolf, Gal Mishne, Dmitry Kobak
2025 J jnl
Node Embeddings via Neighbor Embeddings.
CoRR
Jan Niklas Böhm, Marius Keute, Alica Guzmán, Sebastian Damrich, Andrew Draganov, Dmitry Kobak
2025 J jnl
Node Embeddings via Neighbor Embeddings.
Trans. Mach. Learn. Res.
Jan Niklas Böhm, Marius Keute, Alica Guzmán, Sebastian Damrich, Andrew Draganov, Dmitry Kobak
2025 A* conf
On the Importance of Embedding Norms in Self-Supervised Learning.
ICML
Andrew Draganov, Sharvaree Vadgama, Sebastian Damrich, Jan Niklas Böhm, Lucas Maes, Dmitry Kobak, Erik J. Bekkers
2025 J jnl
On the Importance of Embedding Norms in Self-Supervised Learning.
CoRR
Andrew Draganov, Sharvaree P. Vadgama, Sebastian Damrich, Jan Niklas Böhm, Lucas Maes, Dmitry Kobak, Erik J. Bekkers
2024 J jnl
Low-Dimensional Embeddings of High-Dimensional Data: Algorithms and Applications (Dagstuhl Seminar 24122).
Dagstuhl Reports
Dmitry Kobak, Fred A. Hamprecht, Smita Krishnaswamy, Gal Mishne, Sebastian Damrich
2024 A* conf
Persistent Homology for High-dimensional Data Based on Spectral Methods.
NeurIPS
Sebastian Damrich, Philipp Berens, Dmitry Kobak
2023 — —
Discovering structure without labels.
Sebastian Damrich
2023 A* conf
From $t$-SNE to UMAP with contrastive learning.
ICLR
Sebastian Damrich, Jan Niklas Böhm, Fred A. Hamprecht, Dmitry Kobak
2023 A* conf
Geometric Autoencoders - What You See is What You Decode.
ICML
Philipp Nazari, Sebastian Damrich, Fred A. Hamprecht
2023 J jnl
Geometric Autoencoders - What You See is What You Decode.
CoRR
Philipp Nazari, Sebastian Damrich, Fred A. Hamprecht
2023 J jnl
Persistent homology for high-dimensional data based on spectral methods.
CoRR
Sebastian Damrich, Philipp Berens, Dmitry Kobak
2022 J jnl
Contrastive learning unifies t-SNE and UMAP.
CoRR
Sebastian Damrich, Jan Niklas Böhm, Fred A. Hamprecht, Dmitry Kobak
2022 A* conf
The Algebraic Path Problem for Graph Metrics.
ICML
Enrique Fita Sanmartín, Sebastian Damrich, Fred A. Hamprecht
2022 J jnl
Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder.
Bioinform.
Quentin Garrido, Sebastian Damrich, Alexander Jäger, Dario Cerletti, Manfred Claassen, Laurent Najman, Fred A. Hamprecht
2021 A* conf
Directed Probabilistic Watershed.
NeurIPS
Enrique Fita Sanmartin, Sebastian Damrich, Fred A. Hamprecht
2021 — conf
Multistar: Instance Segmentation Of Overlapping Objects With Star-Convex Polygons.
ISBI
Florin C. Walter, Sebastian Damrich, Fred A. Hamprecht
2021 A* conf
On UMAP's True Loss Function.
NeurIPS
Sebastian Damrich, Fred A. Hamprecht
2021 J jnl
UMAP does not reproduce high-dimensional similarities due to negative sampling.
CoRR
Sebastian Damrich, Fred A. Hamprecht
2021 J jnl
Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder.
CoRR
Quentin Garrido, Sebastian Damrich, Alexander Jäger, Dario Cerletti, Manfred Claassen, Laurent Najman, Fred A. Hamprecht
2020 J jnl
MultiStar: Instance Segmentation of Overlapping Objects with Star-Convex Polygons.
CoRR
Florin C. Walter, Sebastian Damrich, Fred A. Hamprecht
2019 A* conf
Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning.
NeurIPS
Enrique Fita Sanmartin, Sebastian Damrich, Fred A. Hamprecht
2019 J jnl
Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning.
CoRR
Enrique Fita Sanmartin, Sebastian Damrich, Fred A. Hamprecht