Davide Wiest

Kernelized Parameter Sensitivity Atlases for Amortized Conditional Interpretability

Gradient-based attribution provides input-specific information about model parameters, but computing it via backpropagation for many samples is expensive, while static importance scores discard input dependence. We derive KPSA (Kernelized Parameter Sensitivity Atlas) from a block decomposition of the empirical neural t...