{"ID":2835768,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.22096","arxiv_id":"2511.22096","title":"Density-based Neural Temporal Point Processes for Heartbeat Dynamics","abstract":"Temporal point processes (TPPs) provide a natural mathematical framework for modeling heartbeats due to capturing underlying physiological inductive biases. In this work, we apply density-based neural TPPs to model heartbeat dynamics from 18 subjects. We adapt a goodness-of-fit framework from classical point process literature to Neural TPPs and use it to optimize hyperparameters, identify appropriate training sequence lengths to capture temporal dependencies, and demonstrate zero-shot predictive capability on heartbeat data.","short_abstract":"Temporal point processes (TPPs) provide a natural mathematical framework for modeling heartbeats due to capturing underlying physiological inductive biases. In this work, we apply density-based neural TPPs to model heartbeat dynamics from 18 subjects. We adapt a goodness-of-fit framework from classical point process li...","url_abs":"https://arxiv.org/abs/2511.22096","url_pdf":"https://arxiv.org/pdf/2511.22096v1","authors":"[\"Sandya Subramanian\",\"Bharath Ramsundar\"]","published":"2025-11-27T04:34:38Z","proceeding":"q-bio.TO","tasks":"[\"q-bio.TO\",\"eess.SP\",\"stat.AP\"]","methods":"[]","has_code":false}