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 …

Interpretability and explainability are crucial for machine learning (ML) and statistical
applications in medicine, economics, law, and natural sciences and form an essential …

Self-attention techniques, and specifically Transformers, are dominating the field of text
processing and are becoming increasingly popular in computer vision classification tasks. In …

Backdoor learning is an emerging and vital topic for studying deep neural networks'
vulnerability (DNNs). Many pioneering backdoor attack and defense methods are being …

Transformers are increasingly dominating multi-modal reasoning tasks, such as visual
question answering, achieving state-of-the-art results thanks to their ability to contextualize …

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 …

A critical problem in the field of post hoc explainability is the lack of a common foundational
goal among methods. For example, some methods are motivated by function approximation …

Transformers have become an important workhorse of machine learning, with numerous
applications. This necessitates the development of reliable methods for increasing their …

With the rapid development of Deepfake synthesis technology, our information security and
personal privacy have been severely threatened in recent years. To achieve a robust …

Despite a sea of interpretability methods that can produce plausible explanations, the field
has also empirically seen many failure cases of such methods. In light of these results, it …