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Found 7 papers, 3 papers with code
Explainable Artificial Intelligence for Improved Modeling of Processes
On the other hand, Machine Learning (ML) benefits from the vast amount of data available and can deal with high-dimensional sources, yet it has rarely been applied to being used in processes.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Intuitiveness in Active Teaching
After developing a theoretical framework of intuitiveness as a property of algorithms, we introduce an active teaching paradigm involving a prototypical two-dimensional spatial learning task as a method to judge the efficacy of human-machine interactions.
Interpretable Locally Adaptive Nearest Neighbors
When training automated systems, it has been shown to be beneficial to adapt the representation of data by learning a problem-specific metric.
How to compare adversarial robustness of classifiers from a global perspective
Adversarial robustness of machine learning models has attracted considerable attention over recent years.
Recovering Localized Adversarial Attacks
In this contribution, we focus on the capabilities of explainers for convolutional deep neural networks in an extreme situation: a setting in which humans and networks fundamentally disagree.
Adversarial Robustness Curves
The existence of adversarial examples has led to considerable uncertainty regarding the trust one can justifiably put in predictions produced by automated systems.
Adversarial attacks hidden in plain sight
Convolutional neural networks have been used to achieve a string of successes during recent years, but their lack of interpretability remains a serious issue.
