4 citations · 5 across the 4 of their papers we have counts for
3 papers · 1 filter
Neural Diffusion Processes for Physically Interpretable Survival Prediction
Alessio Cristofoletto, Cesare Rollo, Giovanni Birolo +1
We introduce DeepFHT, a survival-analysis framework that couples deep neural networks with first hitting time (FHT) distributions from stochastic process theory. Time to event is r…
Beyond Cox Models: Assessing the Performance of Machine-Learning Methods in Non-Proportional Hazards and Non-Linear Survival Analysis
Ivan Rossi, Flavio Sartori, Cesare Rollo +3
Survival analysis often relies on Cox models, assuming both linearity and proportional hazards (PH). This study evaluates machine and deep learning methods that relax these constra…
How good Neural Networks interpretation methods really are? A quantitative benchmark
Antoine Passemiers, Pietro Folco, Daniele Raimondi +3
Saliency Maps (SMs) have been extensively used to interpret deep learning models decision by highlighting the features deemed relevant by the model. They are used on highly nonline…