Dmitry Kobak

36 papers A* 5Journal 26Unranked 5
YearRankTypeTitle / Venue / Authors
2026 J jnl
Cropping outperforms dropout as an augmentation strategy for self-supervised training of text embeddings.
Trans. Mach. Learn. Res.
Rita González-Márquez, Philipp Berens, Dmitry Kobak
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
Combined statistical-biophysical modeling links ion channel genes to physiology of cortical neuron types.
Patterns
Yves Bernaerts, Michael Deistler, Pedro J. Gonçalves, Jonas Beck, Marcel Stimberg, Federico Scala, Andreas S. Tolias, Jakob H. Macke, Dmitry Kobak, Philipp Berens
2025 J jnl
Cropping outperforms dropout as an augmentation strategy for training self-supervised text embeddings.
CoRR
Rita González-Márquez, Philipp Berens, Dmitry Kobak
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
Delving into ChatGPT usage in academic writing through excess vocabulary.
CoRR
Dmitry Kobak, Rita González-Márquez, Emoke-Ágnes Horvát, Jan Lause
2024 J jnl
Learning representations of learning representations.
CoRR
Rita González-Márquez, Dmitry Kobak
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 J jnl
Meet the authors: Rita González-Márquez, Philipp Berens, and Dmitry Kobak.
Patterns
Rita González-Márquez, Philipp Berens, Dmitry Kobak
2024 A* conf
Persistent Homology for High-dimensional Data Based on Spectral Methods.
NeurIPS
Sebastian Damrich, Philipp Berens, Dmitry Kobak
2024 A* conf
Scaling Down Deep Learning with MNIST-1D.
ICML
Samuel Greydanus, Dmitry Kobak
2024 J jnl
Self-supervised Visualisation of Medical Image Datasets.
CoRR
Ifeoma Veronica Nwabufo, Jan Niklas Böhm, Philipp Berens, Dmitry Kobak
2024 J jnl
The art of seeing the elephant in the room: 2D embeddings of single-cell data do make sense.
PLoS Comput. Biol.
Jan Lause, Philipp Berens, Dmitry Kobak
2024 J jnl
The landscape of biomedical research.
Patterns
Rita González-Márquez, Luca Schmidt, Benjamin M. Schmidt, Philipp Berens, Dmitry Kobak
2023 A* conf
From $t$-SNE to UMAP with contrastive learning.
ICLR
Sebastian Damrich, Jan Niklas Böhm, Fred A. Hamprecht, Dmitry Kobak
2023 J jnl
Persistent homology for high-dimensional data based on spectral methods.
CoRR
Sebastian Damrich, Philipp Berens, Dmitry Kobak
2023 A* conf
Unsupervised visualization of image datasets using contrastive learning.
ICLR
Jan Niklas Böhm, Philipp Berens, Dmitry Kobak
2022 J jnl
Attraction-Repulsion Spectrum in Neighbor Embeddings.
J. Mach. Learn. Res.
Jan Niklas Böhm, 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 — conf
Two-dimensional visualization of large document libraries using t-SNE.
TAG-ML
Rita González-Márquez, Philipp Berens, Dmitry Kobak
2022 J jnl
Unsupervised visualization of image datasets using contrastive learning.
CoRR
Jan Niklas Böhm, Philipp Berens, Dmitry Kobak
2022 — conf
Wasserstein t-SNE.
ECML/PKDD (1)
Fynn Bachmann, Philipp Hennig, Dmitry Kobak
2022 J jnl
Wasserstein t-SNE.
CoRR
Fynn Bachmann, Philipp Hennig, Dmitry Kobak
2022 — conf
t-SNE Highlights Phylogenetic and Temporal Patterns of SARS-CoV-2 Spike and Nucleocapsid Protein Evolution.
ISBRA
Gaik Tamazian, Andrey B. Komissarov, Dmitry Kobak, Dmitry Polyakov, Evgeny Andronov, Sergei Nechaev, Sergey Kryzhevich, Yuri Porozov, Eugene Stepanov
2021 — conf
Interpretable Gender Classification from Retinal Fundus Images Using BagNets.
MICCAI (3)
Indu Ilanchezian, Dmitry Kobak, Hanna Faber, Focke Ziemssen, Philipp Berens, Murat Seçkin Ayhan
2020 J jnl
A Systematic Evaluation of Interneuron Morphology Representations for Cell Type Discrimination.
Neuroinformatics
Sophie Laturnus, Dmitry Kobak, Philipp Berens
2020 J jnl
A Unifying Perspective on Neighbor Embeddings along the Attraction-Repulsion Spectrum.
CoRR
Jan Niklas Böhm, Philipp Berens, Dmitry Kobak
2020 J jnl
Sparse Bottleneck Networks for Exploratory Analysis and Visualization of Neural Patch-seq Data.
CoRR
Yves Bernaerts, Philipp Berens, Dmitry Kobak
2020 J jnl
The Optimal Ridge Penalty for Real-world High-dimensional Data Can Be Zero or Negative due to the Implicit Ridge Regularization.
J. Mach. Learn. Res.
Dmitry Kobak, Jonathan Lomond, Benoit Sanchez
2019 — conf
Heavy-Tailed Kernels Reveal a Finer Cluster Structure in t-SNE Visualisations.
ECML/PKDD (1)
Dmitry Kobak, George C. Linderman, Stefan Steinerberger, Yuval Kluger, Philipp Berens
2019 J jnl
Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations.
CoRR
Dmitry Kobak, George C. Linderman, Stefan Steinerberger, Yuval Kluger, Philipp Berens