  Semi-parametric survival analysis methods like the Cox Proportional Hazards
(CPH) regression (Cox, 1972) are a popular approach for survival analysis.
These methods involve fitting of the log-proportional hazard as a function of
the covariates and are convenient as they do not require estimation of the
baseline hazard rate. Recent approaches have involved learning non-linear
representations of the input covariates and demonstrate improved performance.
In this paper we argue against such deep parameterizations for survival
analysis and experimentally demonstrate that more interpretable semi-parametric
models inspired from mixtures of experts perform equally well or in some cases
better than such overly parameterized deep models.
