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Jan Niklas Böhm

2 accepted papers

2025

On the Importance of Embedding Norms in Self-Supervised Learning

ICML 2025poster

Self-supervised learning (SSL) allows training data representations without a supervised signal and has become an important paradigm in machine learning. Most SSL methods employ the cosine similarity between embedding vectors and hence effectively embed data on a hypersphere. While this seemingly im…

2025

TRACE: Contrastive learning for multi-trial time series data in neuroscience

NeurIPS 2025poster

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. Contrastive learning is a powerful self-supervised framework for learning representations of complex datasets. Existing ap…

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