1 citations · 1 across the 3 of their papers we have counts for
4 papers
Cropping outperforms dropout as an augmentation strategy for self-supervised training of text embeddings
Rita González-Márquez, Philipp Berens, Dmitry Kobak
Text embeddings, i.e. vector representations of entire texts, play an important role in many NLP applications, such as retrieval-augmented generation, clustering, or visualizing co…
TRACE: Contrastive learning for multi-trial time-series data in neuroscience
Lisa Schmors, Dominic Gonschorek, Jan Niklas Böhm +9
Modern neural recording techniques such as two-photon imaging or Neuropixel probes allow to acquire vast time-series datasets with responses of hundreds or thousands of neurons. Co…
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…