  We propose a novel method, termed SuMo-net, that uses partially monotonic
neural networks to learn a time-to-event distribution from a sample of
covariates and right-censored times. SuMo-net models the survival function and
the density jointly, and optimizes the likelihood for right-censored data
instead of the often used partial likelihood. The method does not make
assumptions about the true survival distribution and avoids computationally
expensive integration of the hazard function. We evaluate the performance of
the method on a range of datasets and find competitive performance across
different metrics and improved computational time of making new predictions.
