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Found 6 papers, 3 papers with code
X-CAL: Explicit Calibration for Survival Analysis
A survival model's calibration can be measured using, for instance, distributional calibration (D-CALIBRATION) [Haider et al., 2020] which computes the squared difference between the observed and predicted number of events within different time intervals.
Survival Mixture Density Networks
Survival MDN applies an invertible positive function to the output of Mixture Density Networks (MDNs).
Inverse-Weighted Survival Games
When the loss is proper, we show that the games always have the true failure and censoring distributions as a stationary point.
Kernelized Complete Conditional Stein Discrepancy
We introduce kernelized complete conditional Stein discrepancies (KCC-SDs).
Adversarial Examples for Electrocardiograms
For this model, we utilized a new technique to generate smoothed examples to produce signals that are 1) indistinguishable to cardiologists from the original examples and 2) incorrectly classified by the neural network.
Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach
We examine counterfactual explanations for explaining the decisions made by model-based AI systems.
