Abstract Decision Trees (DTs) are predictive models in supervised learning, known not only
for their unquestionable utility in a wide range of applications but also for their interpretability …

Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

Prototype-based methods use interpretable representations to address the black-box nature
of deep learning models, in contrast to post-hoc explanation methods that only approximate …

Floods are natural hazards that lead to devastating financial losses and large displacements
of people. Flood susceptibility maps can improve mitigation measures according to the …

Landslides are among the most devastating natural hazards, severely impacting human
lives and damaging property and infrastructure. Landslide susceptibility maps, which help to …

In the meantime, a wide variety of terminologies, motivations, approaches, and evaluation
criteria have been developed within the research field of explainable artificial intelligence …

Vision transformers (ViTs), which have demonstrated a state-of-the-art performance in image
classification, can also visualize global interpretations through attention-based contributions …

Droughts are the most spatially complex geohazard, which often lasts for years, thereby
severely impacting socio-economic sectors. One of the critical aspects of drought studies is …

Scene graph generation (SGG) is designed to extract (subject, predicate, object) triplets in
images. Recent works have made a steady progress on SGG, and provide useful tools for …

We show how fitting sparse linear models over learned deep feature representations can
lead to more debuggable neural networks. These networks remain highly accurate while …