Deep learning bears promise for drug discovery, including advanced image analysis,
prediction of molecular structure and function, and automated generation of innovative …

With the broader and highly successful usage of machine learning (ML) in industry and the
sciences, there has been a growing demand for explainable artificial intelligence (XAI) …

We present the Language Interpretability Tool (LIT), an open-source platform for
visualization and understanding of NLP models. We focus on core questions about model …

Despite impressive performance on standard benchmarks, deep neural networks are often
brittle when deployed in real-world systems. Consequently, recent research has focused on …

An automated system that could assist a judge in predicting the outcome of a case would
help expedite the judicial process. For such a system to be practically useful, predictions by …

Explanation methods promise to make black-box classifiers more transparent. As a result, it
is hoped that they can act as proof for a sensible, fair and trustworthy decision-making …

Psychiatric research is often confronted with complex abstractions and dynamics that are not
readily accessible or well-defined to our perception and measurements, making data-driven …

Abstract Artificial Intelligence (AI) and Machine Learning (ML) are currently hot topics in
industry and business practice, while management-oriented research disciplines seem …

Deep neural networks have become increasingly popular for analyzing ECG data because
of their ability to accurately identify cardiac conditions and hidden clinical factors. However …

Capsule Networks, as alternatives to Convolutional Neural Networks, have been proposed
to recognize objects from images. The current literature demonstrates many advantages of …