As the societal impact of Deep Neural Networks (DNNs) grows, the goals for advancing
DNNs become more complex and diverse, ranging from improving a conventional model …

Explanations have gained an increasing level of interest in the AI and Machine Learning
(ML) communities in order to improve model transparency and allow users to form a mental …

Debugging a machine learning model is hard since the bug usually involves the training
data and the learning process. This becomes even harder for an opaque deep learning …

Part-prototype Networks (ProtoPNets) are concept-based classifiers designed to achieve the
same performance as black-box models without compromising transparency. ProtoPNets …

We tackle sequential learning under label noise in applications where a human supervisor
can be queried to relabel suspicious examples. Existing approaches are flawed, in that they …

As machine learning models become larger, and are increasingly trained on large and
uncurated datasets in weakly supervised mode, it becomes important to establish …

Abstract Explanatory Interactive Learning (XIL) collects user feedback on visual model
explanations to implement a Human-in-the-Loop (HITL) based interactive learning scenario …

eXplanation Based Learning (XBL) is a form of Interactive Machine Learning (IML) that
provides a model refining approach via user feedback collected on model explanations …

Future zero-touch artificial intelligence (AI)-driven 6G network automation requires building
trust in the AI black boxes via explainable artificial intelligence (XAI), where it is expected …

Data subset selection is a crucial task in deploying machine learning algorithms under strict
constraints regarding memory and computation resources. Despite extensive research in …