What is robustness in neural networks?
Because verifying the correctness of neural networks is extremely challenging, it is common to focus on the verification of other properties of these systems. One important property, in particular, is robustness. Most existing definitions of robustness, however, focus on the worst-case scenario where the inputs are adversarial.
What is a neural network?
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini and David Wagner University of California, Berkeley Background ??A neural network is a function with trainable parameters that learns a given mapping ??Given an image, classify it as a cat or dog ??Given a review, classify it as good or bad
How to check whether a neural network is probabilistically robust?
We also present an algorithm, based on abstract interpretation and importance sampling, for checking whether a neural network is probabilistically robust. Our algorithm uses abstract interpretation to approximate the behavior of a neural network and compute an overapproximation of the input regions that violate robustness.
Who are the best authors on robustness of neural networks?
Dan Hendrycks and Thomas Dietterich. Benchmarking neural network robustness to common cor- ruptions and perturbations. Proceedings of the International Conference on Learning Represen- tations, 2019.2,5 Iasonas Kokkinos. Ubernet: Training a universal convolutional neural network for low-, mid-, and
A Causal View on Robustness of Neural Networks