Full experimental data for the paper "A Robust Optimisation Perspective on Counterexample-Guided Repair of Neural Networks". We also include a virtual machine for reproducing our experiments
We propose a method for evolving neural network controllers robust with respect to variations of the...
A method is proposed to estimate the fault tolerance of feedforward Artificial Neural Nets (ANNs) an...
Artificial neural networks (ANN) are extensively utilized in structural health monitoring. Neverthel...
Machine learning models have many applications, being used for example in pattern analysis, image cl...
The project for the paper "ε-weakened Robustness of Deep Neural Networks" published on ISSTA2022
With their supreme performance in dealing with a large amount of data, neural networks have signific...
We present a new approach to assessing the robustness of neural networks based on estimating the pro...
We present a novel methodology for repairing neural networks that use ReLU activation functions. Unl...
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...
With the development of neural networks based machine learning and their usage in mission critical a...
IEEE Neural networks (NNs) are now routinely implemented on systems that must operate in uncertain e...
Recent public calls for the development of explainable and verifiable AI led to a growing interest i...
In the wake of the explosive growth in smartphones and cyber-physical systems, there has been an acc...
Durability describes the ability of a device to operate properly in imperfect conditions. We have re...
We propose a method for evolving neural network controllers robust with respect to variations of the...
A method is proposed to estimate the fault tolerance of feedforward Artificial Neural Nets (ANNs) an...
Artificial neural networks (ANN) are extensively utilized in structural health monitoring. Neverthel...
Machine learning models have many applications, being used for example in pattern analysis, image cl...
The project for the paper "ε-weakened Robustness of Deep Neural Networks" published on ISSTA2022
With their supreme performance in dealing with a large amount of data, neural networks have signific...
We present a new approach to assessing the robustness of neural networks based on estimating the pro...
We present a novel methodology for repairing neural networks that use ReLU activation functions. Unl...
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...
With the development of neural networks based machine learning and their usage in mission critical a...
IEEE Neural networks (NNs) are now routinely implemented on systems that must operate in uncertain e...
Recent public calls for the development of explainable and verifiable AI led to a growing interest i...
In the wake of the explosive growth in smartphones and cyber-physical systems, there has been an acc...
Durability describes the ability of a device to operate properly in imperfect conditions. We have re...
We propose a method for evolving neural network controllers robust with respect to variations of the...
A method is proposed to estimate the fault tolerance of feedforward Artificial Neural Nets (ANNs) an...
Artificial neural networks (ANN) are extensively utilized in structural health monitoring. Neverthel...
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