FrailNet
Deep survival modeling for rare conflict onset
Survival Analysis
Deep Learning
Conflict Forecasting
Frailnet is a deep survival modeling framework that extends Cox regression to better handle non-proportional temporal dynamics, missingness, and unobserved heterogeneity (frailty).
FrailNet is a deep survival modeling framework designed for time-to-event prediction in rare, time-dependent social processes. The model retains a Cox partial-likelihood objective while extending hazard specification to better accommodate non-proportional temporal dynamics, missingness, and unobserved heterogeneity.
Key components include recurrent layers for temporal dependence, an incomplete-aware reconstruction module for denoising and imputation, and multiplicative frailty terms to capture subject-level variation not explained by observed covariates.
Materials
Draft note: This PDF reflects the current draft of an active research manuscript and is being finalized.