  Survival analysis aims at modeling the relationship between covariates and
event occurrence with some untracked (censored) samples. In implementation,
existing methods model the survival distribution with strong assumptions or in
a discrete time space for likelihood estimation with censorship, which leads to
weak generalization. In this paper, we propose Implicit Survival Function (ISF)
based on Implicit Neural Representation for survival distribution estimation
without strong assumptions,and employ numerical integration to approximate the
cumulative distribution function for prediction and optimization. Experimental
results show that ISF outperforms the state-of-the-art methods in three public
datasets and has robustness to the hyperparameter controlling estimation
precision.
