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) …

For a machine learning model to generalize well, one needs to ensure that its decisions are
supported by meaningful patterns in the input data. A prerequisite is however for the model …

Current learning machines have successfully solved hard application problems, reaching
high accuracy and displaying seemingly intelligent behavior. Here we apply recent …

Most of state of the art methods applied on time series consist of deep learning methods that
are too complex to be interpreted. This lack of interpretability is a major drawback, as several …

Translating machine learning (ML) models effectively to clinical practice requires
establishing clinicians' trust. Explainability, or the ability of an ML model to justify its …

With the advent of machine learning (ML) and deep learning (DL) empowered applications
for critical applications like healthcare, the questions about liability, trust, and interpretability …

Purpose We present image classifiers based on Dense Convolutional Networks and transfer
learning to classify chest X-ray images according to three labels: COVID-19, pneumonia …

Within the last decade, neural network based predictors have demonstrated impressive-and
at times superhuman-capabilities. This performance is often paid for with an intransparent …

At present, data-driven methods have achieved satisfactory results in building energy
consumption prediction, especially deep learning models such as long short-term memory …

Attribution methods aim to explain a neural network's prediction by highlighting the most
relevant image areas. A popular approach is to backpropagate (BP) a custom relevance …