Add to ORKG© 2018 Curran Associates Inc..All rights reserved. Finding minimum distortion of adversarial examples and thus certifying robustness in neural network classifiers for given data points is known to be a challenging problem. Nevertheless, recently it has been shown to be possible to give a nontrivial certified lower bound of minimum adversarial distortion, and some recent progress has been made towards this direction by exploiting the piece-wise linear nature of ReLU activations. However, a generic robustness certification for general activation functions still remains largely unexplored. To address this issue, in this paper we introduce CROWN, a general framework to certify robustness of neural networks with general activation functions for ...
MEng (Computer and Electronic Engineering), North-West University, Potchefstroom CampusThe ability o...
Recent work has shown that state-of-the-art classifiers are quite brittle, in the sense that a small...
Although machine learning has achieved great success in numerous complicated tasks, many machine lea...
This paper focuses on the enhancement of the generalization ability and training stability of deep n...
Neural networks(NNs) have been widely used over the past decade at the core of intelligentsystems fr...
It has been shown that neural network classifiers are not robust. This raises concerns about their u...
The rapid growth of deep learning applications in real life is accompanied by severe safety concerns...
© 35th International Conference on Machine Learning, ICML 2018.All Rights Reserved. Verifying the r...
The robustness of deep neural networks has received significant interest recently, especially when b...
The desire to provide robust guarantees on neural networks has never been more important, as their p...
Despite having high accuracy, neural nets have been shown to be susceptible to adversarial examples,...
The robustness of neural networks can be quantitatively indicated by a lower bound within which any ...
Despite having high accuracy, neural nets have been shown to be susceptible to adversarial examples,...
The Jacobian matrix (or the gradient for single-output networks) is directly related to many importa...
In this article we present new results on neural networks with linear threshold activation functions...
MEng (Computer and Electronic Engineering), North-West University, Potchefstroom CampusThe ability o...
Recent work has shown that state-of-the-art classifiers are quite brittle, in the sense that a small...
Although machine learning has achieved great success in numerous complicated tasks, many machine lea...
This paper focuses on the enhancement of the generalization ability and training stability of deep n...
Neural networks(NNs) have been widely used over the past decade at the core of intelligentsystems fr...
It has been shown that neural network classifiers are not robust. This raises concerns about their u...
The rapid growth of deep learning applications in real life is accompanied by severe safety concerns...
© 35th International Conference on Machine Learning, ICML 2018.All Rights Reserved. Verifying the r...
The robustness of deep neural networks has received significant interest recently, especially when b...
The desire to provide robust guarantees on neural networks has never been more important, as their p...
Despite having high accuracy, neural nets have been shown to be susceptible to adversarial examples,...
The robustness of neural networks can be quantitatively indicated by a lower bound within which any ...
Despite having high accuracy, neural nets have been shown to be susceptible to adversarial examples,...
The Jacobian matrix (or the gradient for single-output networks) is directly related to many importa...
In this article we present new results on neural networks with linear threshold activation functions...
MEng (Computer and Electronic Engineering), North-West University, Potchefstroom CampusThe ability o...
Recent work has shown that state-of-the-art classifiers are quite brittle, in the sense that a small...
Although machine learning has achieved great success in numerous complicated tasks, many machine lea...