Since 2016, we have witnessed the tremendous growth of artificial intelligence+
visualization (AI+ VIS) research. However, existing survey articles on AI+ VIS focus on visual …

We present TSR-TVD, a novel deep learning framework that generates temporal super-
resolution (TSR) of time-varying data (TVD) using adversarial learning. TSR-TVD is the first …

In this paper, we examine whether or not information theory can be one of the theoretic
frameworks for visualization. We formulate concepts and measurements for qualifying visual …

We present STNet, an end-to-end generative framework that synthesizes spatiotemporal
super-resolution volumes with high fidelity for time-varying data. STNet includes two …

We propose a new approach for analyzing the temporal growth of the uncertainty in
ensembles of weather forecasts which are started from perturbed but similar initial …

We present SSR-TVD, a novel deep learning framework that produces coherent spatial
super-resolution (SSR) of time-varying data (TVD) using adversarial learning. In scientific …

We present a technique to synthesize and analyze volume-rendered images using
generative models. We use the Generative Adversarial Network (GAN) framework to …

In recent years, there is an emerging direction that leverages information theory to solve
many challenging problems in scientific data analysis and visualization. In this article, we …

Segmentation of medical images is a prerequisite in clinical practice. Many segmentation
algorithms use statistical shape models. Due to the lack of tools providing prior information …

In this paper, we present an abstract model of visualization and inference processes, and
describe an information-theoretic measure for optimizing such processes. In order to obtain …