717 citations · 901 across the 13 of their papers we have counts for
13 papers
Self-supervised Visualisation of Medical Image Datasets
Ifeoma Veronica Nwabufo, Jan Niklas Böhm, Philipp Berens +1
Self-supervised learning methods based on data augmentations, such as SimCLR, BYOL, or DINO, allow obtaining semantically meaningful representations of image datasets and are widel…
Persistent Homology for High-dimensional Data Based on Spectral Methods
Sebastian Damrich, Philipp Berens, Dmitry Kobak
Persistent homology is a popular computational tool for analyzing the topology of point clouds, such as the presence of loops or voids. However, many real-world datasets with low i…
Unsupervised visualization of image datasets using contrastive learning
Jan Niklas Böhm, Philipp Berens, Dmitry Kobak
Visualization methods based on the nearest neighbor graph, such as t-SNE or UMAP, are widely used for visualizing high-dimensional data. Yet, these approaches only produce meaningf…
From -SNE to UMAP with contrastive learning
Sebastian Damrich, Jan Niklas Böhm, Fred A. Hamprecht +1
Neighbor embedding methods -SNE and UMAP are the de facto standard for visualizing high-dimensional datasets. Motivated from entirely different viewpoints, their loss functions…
Attraction-Repulsion Spectrum in Neighbor Embeddings
Jan Niklas Böhm, Philipp Berens, Dmitry Kobak
Neighbor embeddings are a family of methods for visualizing complex high-dimensional datasets using NN graphs. To find the low-dimensional embedding, these algorithms combine an…
Sparse bottleneck neural networks for exploratory non-linear visualization of Patch-seq data
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. Efficiently anal…
Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations
Dmitry Kobak, George Linderman, Stefan Steinerberger +2
T-distributed stochastic neighbour embedding (t-SNE) is a widely used data visualisation technique. It differs from its predecessor SNE by the low-dimensional similarity kernel: th…
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
A conventional wisdom in statistical learning is that large models require strong regularization to prevent overfitting. Here we show that this rule can be violated by linear regre…
Putin's peaks: Russian election data revisited
Dmitry Kobak, Sergey Shpilkin, Maxim S. Pshenichnikov
We study the anomalous prevalence of integer percentages in the last parliamentary (2016) and presidential (2018) Russian elections. We show how this anomaly in Russian federal ele…
Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parameters
Dmitry Kobak, Wieland Brendel, Christos Constantinidis +7
Neurons in higher cortical areas, such as the prefrontal cortex, are known to be tuned to a variety of sensory and motor variables. The resulting diversity of neural tuning often o…
Integer percentages as electoral falsification fingerprints
Dmitry Kobak, Sergey Shpilkin, Maxim S. Pshenichnikov
We hypothesize that if election results are manipulated or forged, then, due to the well-known human attraction to round numbers, the frequency of reported round percentages can be…
Motor skill learning by increasing the movement planning horizon
Luke Bashford, Dmitry Kobak, Carsten Mehring
We investigated motor skill learning using a path tracking task, where human subjects had to track various curved paths as fast as possible, in the absence of any external perturba…
Statistical anomalies in 2011-2012 Russian elections revealed by 2D correlation analysis
Dmitry Kobak, Sergey Shpilkin, Maxim S. Pshenichnikov
Here we perform a statistical analysis of the official data from recent Russian parliamentary and presidential elections (held on December 4th, 2011 and March 4th, 2012, respective…