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) …

Deep neural networks have been well-known for their superb handling of various machine
learning and artificial intelligence tasks. However, due to their over-parameterized black-box …

Abstract Machine learning (ML) techniques are often employed for the accurate prediction of
the compressive strength of concrete. Despite higher accuracy, previous ML models failed to …

The last decade of machine learning has seen drastic increases in scale and capabilities.
Deep neural networks (DNNs) are increasingly being deployed in the real world. However …

In the past few decades, artificial intelligence (AI) technology has experienced swift
developments, changing everyone's daily life and profoundly altering the course of human …

Explainable machine learning offers the potential to provide stakeholders with insights into
model behavior by using various methods such as feature importance scores, counterfactual …

Abstract Visual Counterfactual Explanations (VCEs) are an important tool to understand the
decisions of an image classifier. They are “small” but “realistic” semantic changes of the …

Adversarial attacks and defenses in machine learning and deep neural network (DNN) have
been gaining significant attention due to the rapidly growing applications of deep learning in …

We introduce a feature scattering-based adversarial training approach for improving model
robustness against adversarial attacks. Conventional adversarial training approaches …

Recently, a significant amount of research has been investigated on interpretation of deep
neural networks (DNNs) which are normally processed as black box models. Among the …