5 papers with code • 0 benchmarks • 2 datasets
New methods for time-to-event prediction are proposed by extending the Cox proportional hazards model with neural networks.
We describe a new approach to estimating relative risks in time-to-event prediction problems with censored data in a fully parametric manner.
The derived uncertainty-based ranking loss is found to significantly boost model performance by improving the quality of relational features.
Ranked #1 on Accident Anticipation on CCD