Towards Understanding Human Functional Brain Development With Explainable Art…
The last decades have seen significant advancements in non-invasive neuroimaging technologies that have been increasingly adopted to examine human brain development. However, these improvements hav…
RecoXplainer : A Library for Development and Offline Evaluation of Explainabl…
Since recommender systems play an important role in our online experience today and are involved in a wide range of decisions, multiple stakeholders are requesting explanations for the correspondin…
PIP : Pictorial Interpretable Prototype Learning for Time Series Classification
Time series classifiers are not only challenging to design, but they are also notoriously difficult to deploy for critical applications because end users may not understand or trust black-box model…
Collective eXplainable AI : Explaining Cooperative Strategies and Agent Contr…
While Explainable Artificial Intelligence (XAI) is increasingly expanding more areas of application, little has been applied to make deep Reinforcement Learning (RL) more comprehensible. As RL beco…
Bridging the Gap Between AI and Explainability in the GDPR : Towards Trustwor…
Can satisfactory explanations for complex machine learning models be achieved in high-risk automated decision-making? How can such explanations be integrated into a data protection framework safegu…
A Multivariate Time Series Streaming Classifier for Predicting Hard Drive Fai…
Digital data storage systems such as hard drives can suffer breakdowns that cause the loss of stored data. Due to the cost of data and the damage that its loss entails, hard drive failure predictio…
Difficulties in Fair Performance Comparison of Multi-Objective Evolutionary A…
The performance of a newly designed evolutionary algorithm is usually evaluated by computational experiments in comparison with existing algorithms. However, comparison results depend on experiment…
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