Add to ORKGNeural networks are currently used in a large number of applications, that is why the following questions arise when using them. How secure are the networks against disturbances in the input data? How does the selection of hyperparameters affect the results? When is it more convenient to use one network architecture against another? Therefore, analyze and verify the behavior of Neural networks is one of the most important challenges nowadays, overall in the most critical systems, such as autonomous driving vehicles, drones or robots
Recent and rapid advances in Artificial Intelligence (AI), particularly in the form of deep neural n...
Prevalent use of Neural Networks for Classification Tasks has brought to attention the security and ...
The complexity of state-of-the-art Deep Neural Network (DNN) architectures exacerbates the search fo...
Neural networks have shown immense promise in solving a variety of challenging problems including co...
International audienceThis paper presents a quantitative approach to demonstrate the robustness of n...
IEEE Neural networks (NNs) are now routinely implemented on systems that must operate in uncertain e...
Neural networks(NNs) have been widely used over the past decade at the core of intelligentsystems fr...
International audienceThe stability of neural networks with respect to adversarial perturbations has...
Problem statement. Despite their success-story, neural networks have one major disadvantage compared...
Despite having high accuracy, neural nets have been shown to be susceptible to adversarial examples,...
Neural Networks (NNs) are popular machine learning models which have found successful application in...
We present a new approach to assessing the robustness of neural networks based on estimating the pro...
Under the umbrella of Technology, there has been a rising interest in the following topics, Artifici...
Despite having high accuracy, neural nets have been shown to be susceptible to adversarial examples,...
Deep Neural Networks (DNNs) have proven excellent performance and are very successful in image class...
Recent and rapid advances in Artificial Intelligence (AI), particularly in the form of deep neural n...
Prevalent use of Neural Networks for Classification Tasks has brought to attention the security and ...
The complexity of state-of-the-art Deep Neural Network (DNN) architectures exacerbates the search fo...
Neural networks have shown immense promise in solving a variety of challenging problems including co...
International audienceThis paper presents a quantitative approach to demonstrate the robustness of n...
IEEE Neural networks (NNs) are now routinely implemented on systems that must operate in uncertain e...
Neural networks(NNs) have been widely used over the past decade at the core of intelligentsystems fr...
International audienceThe stability of neural networks with respect to adversarial perturbations has...
Problem statement. Despite their success-story, neural networks have one major disadvantage compared...
Despite having high accuracy, neural nets have been shown to be susceptible to adversarial examples,...
Neural Networks (NNs) are popular machine learning models which have found successful application in...
We present a new approach to assessing the robustness of neural networks based on estimating the pro...
Under the umbrella of Technology, there has been a rising interest in the following topics, Artifici...
Despite having high accuracy, neural nets have been shown to be susceptible to adversarial examples,...
Deep Neural Networks (DNNs) have proven excellent performance and are very successful in image class...
Recent and rapid advances in Artificial Intelligence (AI), particularly in the form of deep neural n...
Prevalent use of Neural Networks for Classification Tasks has brought to attention the security and ...
The complexity of state-of-the-art Deep Neural Network (DNN) architectures exacerbates the search fo...