3 papers
cs.LG2026
Device Passport: Enabling Spatio-Temporal Pretrained Models to Generalize Across Input Layouts
Geeling Chau, Ran Liu, Juri Minxha +5
New device layouts pose a challenging modeling problem due to the lack of large datasets for each specific layout. Biosignal foundation models offer a plausible solution if they ar…
cs.LG2025
Learning Time-Scale Invariant Population-Level Neural Representations
Eshani Patel, Yisong Yue, Geeling Chau
General-purpose foundation models for neural time series can help accelerate neuroscientific discoveries and enable applications such as brain computer interfaces (BCIs). A key com…
cs.LG2025
Learning the relative composition of EEG signals using pairwise relative shift pretraining
Christopher Sandino, Sayeri Lala, Geeling Chau +6
Self-supervised learning (SSL) offers a promising approach for learning electroencephalography (EEG) representations from unlabeled data, reducing the need for expensive annotation…