With the surge of data-driven analysis techniques, there is a rising demand for enhancing
the exploration of large high-dimensional data by enabling interactions for the joint analysis …

The introduction of machine learning to small molecule research–an inherently
multidisciplinary field in which chemists and data scientists combine their expertise and …

In this work, we propose an interactive visual approach for the exploration and formation of
structural relationships in embeddings of high-dimensional data. These structural …

In data science and visualization, dimensionality reduction techniques have been
extensively employed for exploring large datasets. These techniques involve the …

Diffusion models generate high-quality samples by corrupting data with Gaussian noise and
iteratively reconstructing it with deep learning, slowly transforming noisy images into refined …

We present an approach for visual analysis of high‐dimensional measurement data with
varying sampling rates as routinely recorded in intensive care units. In intensive care, most …

Dimensionality reduction is often used to project time series data from multidimensional to
two-dimensional space to generate visual representations of the temporal evolution. In this …

Chemical reaction optimization (RO) is an iterative process that results in large, high-
dimensional datasets. Current tools allow for only limited analysis and understanding of …

Understanding the behavior of deep reinforcement learning agents is a crucial requirement
throughout their development. Existing work has addressed the identification of observable …

In this work we propose Marjorie, a visual analytics approach to address the challenge of
analyzing patients' diabetes data during brief regular appointments with their diabetologists …