2 code implementations • 12 Apr 2024 • Rita González-Márquez, Dmitry Kobak
The ICLR conference is unique among the top machine learning conferences in that all submitted papers are openly available.
1 code implementation • 22 Feb 2024 • Ifeoma Veronica Nwabufo, Jan Niklas Böhm, Philipp Berens, Dmitry Kobak
Self-supervised learning methods based on data augmentations, such as SimCLR, BYOL, or DINO, allow obtaining semantically meaningful representations of image datasets and are widely used prior to supervised fine-tuning.
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.
1 code implementation • 18 Oct 2022 • Jan Niklas Böhm, Philipp Berens, Dmitry Kobak
This problem can be circumvented by self-supervised approaches based on contrastive learning, such as SimCLR, relying on data augmentation to generate implicit neighbors, but these methods do not produce two-dimensional embeddings suitable for visualization.
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.
2 code implementations • 16 May 2022 • Fynn Bachmann, Philipp Hennig, Dmitry Kobak
We use t-SNE to construct 2D embeddings of the units, based on the matrix of pairwise Wasserstein distances between them.
1 code implementation • eLife 2021 • Ariel Karlinsky, Dmitry Kobak
Comparing the impact of the COVID-19 pandemic between countries or across time is difficult because the reported numbers of cases and deaths can be strongly affected by testing capacity and reporting policy.
1 code implementation • 17 Jul 2020 • Jan Niklas Böhm, Philipp Berens, Dmitry Kobak
Neighbor embeddings are a family of methods for visualizing complex high-dimensional datasets using $k$NN graphs.
2 code implementations • 18 Jun 2020 • Yves Bernaerts, Philipp Berens, Dmitry Kobak
Patch-seq, a recently developed experimental technique, allows neuroscientists to obtain transcriptomic and electrophysiological information from the same neurons.
2 code implementations • 15 Feb 2019 • Dmitry Kobak, George Linderman, Stefan Steinerberger, Yuval Kluger, Philipp Berens
T-distributed stochastic neighbour embedding (t-SNE) is a widely used data visualisation technique.
1 code implementation • 28 May 2018 • Dmitry Kobak, Jonathan Lomond, Benoit Sanchez
We use a spiked covariance model as an analytically tractable example and prove that the optimal ridge penalty in this case is negative when $n\ll p$.
2 code implementations • 22 Oct 2014 • Dmitry Kobak, Wieland Brendel, Christos Constantinidis, Claudia E. Feierstein, Adam Kepecs, Zachary F. Mainen, Ranulfo Romo, Xue-Lian Qi, Naoshige Uchida, Christian K. Machens
Neurons in higher cortical areas, such as the prefrontal cortex, are known to be tuned to a variety of sensory and motor variables.