Public articles linked to the same research event.
arXiv The authors propose HyperNSDE, a continuous-time generative model that encodes heterogeneous static covariates with an HI-VAE, maps the static latent representation through a hypernetwork to subject-specific drift parameters driving a latent Neural SDE, and jointly models observation times with a latent-state-dependent intensity process, trained via a deterministic–stochastic path decomposition with a signature-kernel objective; on simulated data and the VELOUR and PPMI cohorts it achieves better observation-time fidelity and several distributional and privacy metrics, while forecasting and correlation performance is mixed and affected by observation-grid regularity and trajectory smoothness.
The authors propose HyperNSDE, a continuous-time generative model that encodes heterogeneous static covariates with an HI-VAE, maps the static latent representation through a hypernetwork to subject-specific drift parameters driving a latent Neural SDE, and jointly models observation times with a latent-state-dependent intensity process, trained via a deterministic–stochastic path decomposition with a signature-kernel objective; on simulated data and the VELOUR and PPMI cohorts it achieves better observation-time fidelity and several distributional and privacy metrics, while forecasting and correlation performance is mixed and affected by observation-grid regularity and trajectory smoothness.
The authors propose HyperNSDE, a continuous-time generative model that encodes heterogeneous static covariates with an HI-VAE, maps the static latent representation through a hypernetwork to subject-specific drift parameters driving a latent Neural SDE, and jointly models observation times with a latent-state-dependent intensity process, trained via a deterministic–stochastic path decomposition with a signature-kernel objective; on simulated data and the VELOUR and PPMI cohorts it achieves better observation-time fidelity and several distributional and privacy metrics, while forecasting and correlation performance is mixed and affected by observation-grid regularity and trajectory smoothness.
The authors propose HyperNSDE, a continuous-time generative model that encodes heterogeneous static covariates with an HI-VAE, maps the static latent representation through a hypernetwork to subject-specific drift parameters driving a latent Neural SDE, and jointly models observation times with a latent-state-dependent intensity process, trained via a deterministic–stochastic path decomposition with a signature-kernel objective; on simulated data and the VELOUR and PPMI cohorts it achieves better observation-time fidelity and several distributional and privacy metrics, while forecasting and correlation performance is mixed and affected by observation-grid regularity and trajectory smoothness.