[PDF] Explainable AI: A Review of Machine Learning Interpretability Methods

A Causal View on Robustness of Neural Networks

On the other hand human perception is robust to such perturbations thanks to the capability of causal reasoning [36

Robust Natural Language Processing: Recent Advances

3 janv. 2022 attacks as a means to evaluate robustness of NLP systems to ... neural network-based models consistently misclassify AEs.

Towards Better Understanding of Training Certifiably Robust Models

15 nov. 2021 To build a model that is robust to adversarial attacks ... “Towards evaluating the robustness of neural networks”.

CS 886: Robustness of Machine Learning

Carlini and Wagner (2017) Towards Evaluating the Robustness of Neural Networks. • Advantages of CW attack: • Achieve low amount of perturbation to fool a 

Explainable AI: A Review of Machine Learning Interpretability Methods

25 déc. 2020 user and secondly the end goal is not to evaluate a produced ... of max-pooling in convolutional neural networks for small images is ...

Adversarial examples in Deep Neural Networks

"Synthesizing robust adversarial examples." arXiv preprint arXiv:1707.07397 (2017). N. Carlini D. Wagner

Are Self-Driving Cars Secure? Evasion Attacks against Deep Neural

15 avr. 2019 against Deep Neural Networks for Steering Angle ... metric used to evaluate regression. ... “Towards evaluating the robustness of neural.

Deep Neural Network Perception Models and Robust Autonomous

4 mars 2020 those routes evaluating compliance with passenger prefer- ... niques to improve robustness of deep neural networks against.

Robust Physical-World Attacks on Deep Learning Visual Classification

Deep Neural Networks (DNNs) have achieved state-of- pipeline to generate and evaluate robust physical adversarial perturbations.

Adversarial Robustness of Stabilized Neural ODE Might be from

white-box attacks to improve robustness of neural networks

Robustness of Neural Networks: A Probabilistic and Practical Approach

Towards Evaluating the Robustness of Neural Networks Nicholas Carlini David Wagner University of California Berkeley ABSTRACT Neural networks provide state-of-the-art results for most machine learning tasks Unfortunately neural networks are vulnerable to adversarial examples: given an input xand any

Towards Evaluating the Robustness of Neural Networks

robustnessof a neural network de?ned as a measure of how easy it is to ?nd adversarial examples that are close to their original input In this paper we study one of thesedistillation as a defense [39] that hopes to secure an arbitrary neural network

Towards Evaluating the Robustness of Neural Networks

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

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