Abstract Machine learning (ML) models are nowadays used in complex applications in
various domains, such as medicine, bioinformatics, and other sciences. Due to their black …

t-Distributed Stochastic Neighbor Embedding (t-SNE) for the visualization of
multidimensional data has proven to be a popular approach, with successful applications in …

There is fast‐growing literature on provenance‐related research, covering aspects such as
its theoretical framework, use cases, and techniques for capturing, visualizing, and …

Automated Machine Learning (AutoML) systems are emerging that automatically search for
possible solutions from a large space of possible kinds of models. Although fully automated …

Nonlinear kernel-based subspace clustering methods that can reveal the multi-cluster
nonlinear structure of samples are an emerging research topic. However, the existing kernel …

ABSTRACT Recent advances in Artificial Intelligence raise interest in its participation in
design activity, which is commonly considered to be complex and human-dominated. In this …

In this paper, we define the concept of exploratory labeling: the use of computational and
interactive methods to help analysts categorize groups of documents into a set of unknown …

Deep learning, an area of machine learning, is set to revolutionize patient care. But it is not
yet part of standard of care, especially when it comes to individual patient care. In fact, it is …

Community organizations face challenges in harnessing the power of qualitative data
analysis, or sensemaking, to understand the diverse perspectives and needs brought up by …

Interactive machine learning techniques have a great potential to personalize media
recognition models for each individual user by letting them browse and annotate a large …