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Communication Dans Un Congrès Année : 2023

Gradient-Informed Neural Network Statistical Robustness Estimation


Deep neural networks are robust against random corruptions of the inputs to some extent. This global sense of safety is not sufficient in critical applications where probabilities of failure must be assessed with accuracy. Some previous works applied known statistical methods from the field of rare event analysis to classification. Yet, they use classifiers as black-box models without taking into account gradient information, readily available for deep learning models via autodifferentiation. We propose a new and highly efficient estimator of probabilities of failure dedicated to neural networks as it leverages the fast computation of gradients of the model through back-propagation.
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hal-03987284 , version 1 (14-02-2023)


  • HAL Id : hal-03987284 , version 1


Karim Tit, Teddy Furon, Mathias Rousset. Gradient-Informed Neural Network Statistical Robustness Estimation. AISTATS 2023 - 26th International Conference on Artificial Intelligence and Statistics, Apr 2023, Valencia, Spain. ⟨hal-03987284⟩
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