Abstract Machine learning (ML) and its subset, deep learning (DL), are branches of artificial
intelligence (AI) showing promising findings in the medical field, especially when applied to …

Deep neural networks (DNNs) are an indispensable machine learning tool despite the
difficulty of diagnosing what aspects of a model's input drive its decisions. In countless real …

Deep neural networks have achieved near-human accuracy levels in various types of
classification and prediction tasks including images, text, speech, and video data. However …

We propose an approach for explaining Bayesian network classifiers, which is based on
compiling such classifiers into decision functions that have a tractable and symbolic form …

Highlights•Review of papers focused on machine learning methods to predict molecular
biomarkers utilizing MRI imaging.•Deep learning is emerging as tool for molecular …

Understanding and interpreting the decisions made by deep learning models is valuable in
many domains. In computer vision, computing heatmaps from a deep network is a popular …

Understanding the predictions of a machine learning model can be as crucial as the model's
accuracy in many application domains. However, the black-box nature of most highly …

It is generally believed that submodular functions–and the more general class of $\gamma $-
weakly submodular functions–may only be optimized under the non-negativity assumption …

Streaming algorithms are generally judged by the quality of their solution, memory footprint,
and computational complexity. In this paper, we study the problem of maximizing a …

We connect high-dimensional subset selection and submodular maximization. Our results
extend the work of Das and Kempe [In ICML (2011) 1057–1064] from the setting of linear …