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 …

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

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

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

In the complex and multidimensional field of medicine, multimodal data are prevalent and
crucial for informed clinical decisions. Multimodal data span a broad spectrum of data types …

The denitrifying sulfur (S) conversion-associated enhanced biological phosphorus removal
(DS-EBPR) process for treating saline wastewater is characterized by its unique microbial …

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

Originally rooted in game theory, the Shapley Value (SV) has recently become an important
tool in machine learning research. Perhaps most notably, it is used for feature attribution and …