Along with the great success of deep neural networks, there is also growing concern about
their black-box nature. The interpretability issue affects people's trust on deep learning …

As the societal impact of Deep Neural Networks (DNNs) grows, the goals for advancing
DNNs become more complex and diverse, ranging from improving a conventional model …

Abstract Machine learning may aid the choice of optimal combinations of anticancer drugs
by explaining the molecular basis of their synergy. By combining accurate models with …

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 …

The close coupling of artificial intelligence (AI) and electroencephalography (EEG) has
substantially advanced human-computer interaction (HCI) technologies in the AI era …

As machine learning (ML) pipelines affect an increasing array of stakeholders, there is a
growing need for documenting how input from stakeholders is recorded and incorporated …

Interpretable time series prediction is crucial for safety-critical areas such as healthcare and
autonomous driving. Most existing methods focus on interpreting predictions by assigning …

As neural networks increasingly make critical decisions in high-stakes settings, monitoring
and explaining their behavior in an understandable and trustworthy manner is a necessity …

This paper investigates a 2D to 3D image translation method with a straightforward
technique enabling correlated 2D X-ray to 3D CT-like reconstruction. We observe that …

Synchronization is a ubiquitous phenomenon in nature that enables the orderly presentation
of information. In the human brain, for instance, functional modules such as the visual, motor …