Explainable artificial intelligence (XAI) methods are portrayed as a remedy for debugging
and trusting statistical and deep learning models, as well as interpreting their predictions …

Open-vocabulary models are a promising new paradigm for image classification. Unlike
traditional classification models, open-vocabulary models classify among any arbitrary set of …

Large language models (LLMs) have demonstrated impressive capabilities in natural
language processing. However, their internal mechanisms are still unclear and this lack of …

Lithium (Li) metal batteries (LMBs) are regarded as one of the most promising energy
storage systems due to their ultrahigh theoretical energy density. However, the high …

Interpretable machine learning has exploded as an area of interest over the last decade,
sparked by the rise of increasingly large datasets and deep neural networks …

Quantum Federated Learning (QFL) recently becomes a promising approach with the
potential to revolutionize Machine Learning (ML). It merges the established strengths of …

Transformers have become a default architecture in computer vision, but understanding
what drives their predictions remains a challenging problem. Current explanation …

Feature importance evaluation is one of the prevalent approaches to interpreting Machine
Learning (ML) models. A drawback of using these methods for high-dimensional datasets is …

Predominately in explainable artificial intelligence (XAI) research, the Shapley value (SV) is
applied to determine feature attributions for any black box model. Shapley interaction …

To explain predictions made by complex machine learning models, many feature attribution
methods have been developed that assign importance scores to input features. Some recent …