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cs.CL2025
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…
q-bio.NC2025
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…