16 citations · 28 across the 5 of their papers we have counts for
7 papers
Not As Easy As You Think -- Experiences and Lessons Learnt from Trying to Create a Bottom-Up Visualization Image Typology
Jian Chen, Petra Isenberg, Robert S. Laramee +4
We present and discuss the results of a two-year qualitative analysis of images published in IEEE Visualization (VIS) papers. Specifically, we derive a typology of 13 visualization…
GNN-Surrogate: A Hierarchical and Adaptive Graph Neural Network for Parameter Space Exploration of Unstructured-Mesh Ocean Simulations
Neng Shi, Jiayi Xu, Skylar W. Wurster +4
We propose GNN-Surrogate, a graph neural network-based surrogate model to explore the parameter space of ocean climate simulations. Parameter space exploration is important for dom…
DeepGD: A Deep Learning Framework for Graph Drawing Using GNN
Xiaoqi Wang, Kevin Yen, Yifan Hu +1
In the past decades, many graph drawing techniques have been proposed for generating aesthetically pleasing graph layouts. However, it remains a challenging task since different la…
FTK: A Simplicial Spacetime Meshing Framework for Robust and Scalable Feature Tracking
Hanqi Guo, David Lenz, Jiayi Xu +7
We present the Feature Tracking Kit (FTK), a framework that simplifies, scales, and delivers various feature-tracking algorithms for scientific data. The key of FTK is our high-dim…
CNNPruner: Pruning Convolutional Neural Networks with Visual Analytics
Guan Li, Junpeng Wang, Han-Wei Shen +3
Convolutional neural networks (CNNs) have demonstrated extraordinarily good performance in many computer vision tasks. The increasing size of CNN models, however, prevents them fro…
InSituNet: Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations
Wenbin He, Junpeng Wang, Hanqi Guo +5
We propose InSituNet, a deep learning based surrogate model to support parameter space exploration for ensemble simulations that are visualized in situ. In situ visualization, gene…