Generalizability Under Sensor Failure: Tokenization + Transformers Enable More Robust Latent Spaces

arXiv:2402.18546v1 Announce Type: new
Abstract: A major goal in neuroscience is to discover neural data representations that generalize. This goal is challenged by variability along recording sessions (e.g. environment), subjects (e.g. varying neural structures), and sensors (e.g. sensor noise), among others. Recent work has begun to address generalization across sessions and subjects, but few study robustness to sensor failure which is highly prevalent in neuroscience experiments. In order to address these generalizability dimensions we first collect our own electroencephalography dataset …

abstract arxiv begun cs.lg data environment failure major neuroscience noise recording robust sensor sensors spaces tokenization transformers type work