Heavy-Tailed Kernels Reveal a Finer Cluster Structure in t-SNE Visualisations. - Dmitry Kobak; George C. Linderman; Stefan Steinerberger; Yuval Kluger; Philipp Berens (ECML/PKDD (1), 2019)

Sparse Bottleneck Networks for Exploratory Analysis and Visualization of Neural Patch-seq Data. - Yves Bernaerts; Philipp Berens; Dmitry Kobak (CoRR, 2020)

A Unifying Perspective on Neighbor Embeddings along the Attraction-Repulsion Spectrum. - Jan Niklas Böhm; Philipp Berens; Dmitry Kobak (CoRR, 2020)

The Optimal Ridge Penalty for Real-world High-dimensional Data Can Be Zero or Negative due to the Implicit Ridge Regularization. - Dmitry Kobak; Jonathan Lomond; Benoit Sanchez (J. Mach. Learn. Res., 2020)

A Systematic Evaluation of Interneuron Morphology Representations for Cell Type Discrimination. - Sophie Laturnus; Dmitry Kobak; Philipp Berens (Neuroinformatics, 2020)

Interpretable Gender Classification from Retinal Fundus Images Using BagNets. - Indu Ilanchezian; Dmitry Kobak; Hanna Faber; Focke Ziemssen; Philipp Berens; Murat Seçkin Ayhan (MICCAI (3), 2021)

t-SNE Highlights Phylogenetic and Temporal Patterns of SARS-CoV-2 Spike and Nucleocapsid Protein Evolution. - Gaik Tamazian; Andrey B. Komissarov; Dmitry Kobak; Dmitry Polyakov; Evgeny Andronov; Sergei Nechaev; Sergey Kryzhevich; Yuri Porozov; Eugene Stepanov (ISBRA, 2022)

Wasserstein t-SNE. - Fynn Bachmann; Philipp Hennig; Dmitry Kobak (ECML/PKDD (1), 2022)

Two-dimensional visualization of large document libraries using t-SNE. - Rita González-Márquez; Philipp Berens; Dmitry Kobak (TAG-ML, 2022)

Contrastive learning unifies t-SNE and UMAP. - Sebastian Damrich; Jan Niklas Böhm; Fred A. Hamprecht; Dmitry Kobak (CoRR, 2022)

Attraction-Repulsion Spectrum in Neighbor Embeddings. - Jan Niklas Böhm; Philipp Berens; Dmitry Kobak (J. Mach. Learn. Res., 2022)

Unsupervised visualization of image datasets using contrastive learning. - Jan Niklas Böhm; Philipp Berens; Dmitry Kobak (ICLR, 2023)

From $t$-SNE to UMAP with contrastive learning. - Sebastian Damrich; Jan Niklas Böhm; Fred A. Hamprecht; Dmitry Kobak (ICLR, 2023)

Scaling Down Deep Learning with MNIST-1D. - Samuel Greydanus; Dmitry Kobak (ICML, 2024)

Persistent Homology for High-dimensional Data Based on Spectral Methods. - Sebastian Damrich; Philipp Berens; Dmitry Kobak (NeurIPS, 2024)

Self-supervised Visualisation of Medical Image Datasets. - Ifeoma Veronica Nwabufo; Jan Niklas Böhm; Philipp Berens; Dmitry Kobak (CoRR, 2024)

Learning representations of learning representations. - Rita González-Márquez; Dmitry Kobak (CoRR, 2024)

Delving into ChatGPT usage in academic writing through excess vocabulary. - Dmitry Kobak; Rita González-Márquez; Emoke-Ágnes Horvát; Jan Lause (CoRR, 2024)

Low-Dimensional Embeddings of High-Dimensional Data: Algorithms and Applications (Dagstuhl Seminar 24122). - Dmitry Kobak; Fred A. Hamprecht; Smita Krishnaswamy; Gal Mishne; Sebastian Damrich (Dagstuhl Reports, 2024)

Meet the authors: Rita González-Márquez, Philipp Berens, and Dmitry Kobak. - Rita González-Márquez; Philipp Berens; Dmitry Kobak (Patterns, 2024)

The landscape of biomedical research. - Rita González-Márquez; Luca Schmidt; Benjamin M. Schmidt; Philipp Berens; Dmitry Kobak (Patterns, 2024)

The art of seeing the elephant in the room: 2D embeddings of single-cell data do make sense. - Jan Lause; Philipp Berens; Dmitry Kobak (PLoS Comput. Biol., 2024)

On the Importance of Embedding Norms in Self-Supervised Learning. - Andrew Draganov; Sharvaree P. Vadgama; Sebastian Damrich; Jan Niklas Böhm; Lucas Maes; Dmitry Kobak; Erik J. Bekkers (CoRR, 2025)

Node Embeddings via Neighbor Embeddings. - Jan Niklas Böhm; Marius Keute; Alica Guzmán; Sebastian Damrich; Andrew Draganov; Dmitry Kobak (CoRR, 2025)

Cropping outperforms dropout as an augmentation strategy for training self-supervised text embeddings. - Rita González-Márquez; Philipp Berens; Dmitry Kobak (CoRR, 2025)