Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler
of its great success is the availability of abundant and high-quality data for building machine …

Modern deep learning methods have long been considered black boxes due to the lack of
insights into their decision-making process. However, recent advances in explainable …

Explainable AI (XAI) has found numerous applications in computer vision. While image
classification-based explainability techniques have garnered significant attention, their …

Recent advancements in explainable machine learning provide effective and faithful
solutions for interpreting model behaviors. However, many explanation methods encounter …

Densely structured pruning methods utilizing simple pruning heuristics can deliver
immediate compression and acceleration benefits with acceptable benign performances …

Even though Shapley value provides an effective explanation for a DNN model prediction,
the computation relies on the enumeration of all possible input feature coalitions, which …

Explainable artificial intelligence (XAI) techniques are used to understand the rationale
behind the decision-making of machine learning models. In addition to the need for model …

The exponential growth in scholarly publications necessitates advanced tools for efficient
article retrieval, especially in interdisciplinary fields where diverse terminologies are used to …

As Graph Neural Networks (GNNs) have been widely used in real-world applications, model
explanations are required not only by users but also by legal regulations. However …

Existing work on fairness modeling commonly assumes that sensitive attributes for all
instances are fully available, which may not be true in many real-world applications due to …