The rising popularity of explainable artificial intelligence (XAI) to understand high-performing
black boxes raised the question of how to evaluate explanations of machine learning (ML) …

Despite their success, deep networks are used as black-box models with outputs that are not
easily explainable during the learning and the prediction phases. This lack of interpretability …

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

Attention is an important mechanism that can be employed for a variety of deep learning
models across many different domains and tasks. This survey provides an overview of the …

Interpretable graph learning is in need as many scientific applications depend on learning
models to collect insights from graph-structured data. Previous works mostly focused on …

Attention Model has now become an important concept in neural networks that has been
researched within diverse application domains. This survey provides a structured and …

The performance of deep learning models in NLP and other fields of machine learning has
led to a rise in their popularity, and so the need for explanations of these models becomes …

With the constantly growing number of reviews and other sentiment-bearing texts on the
Web, the demand for automatic sentiment analysis algorithms continues to expand. Aspect …

Deep learning has demonstrated remarkable performance in the medical domain, with
accuracy that rivals or even exceeds that of human experts. However, it has a significant …

There is a recent surge of interest in using attention as explanation of model predictions,
with mixed evidence on whether attention can be used as such. While attention conveniently …