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CECAV-DNN: Collective Ensemble Comparison and Visualization using Deep Neural Networks

Wenbin He, Junpeng Wang, Hanqi Guo, Han-Wei Shen, Tom Peterka
2020 Visual Informatics  
To this end, we choose to train a deep discriminative neural network to measure the dissimilarity between two given ensembles, and to identify when and where the two ensembles are different.  ...  We also design and develop a visualization system to help users understand the collective comparison results based on the discriminative network.  ...  Acknowledgments This work was supported in part by US Department of Energy Los Alamos National Laboratory contract 47145 and UT-Battelle LLC contract 4000159447 program manager Laura Biven.  ... 
doi:10.1016/j.visinf.2020.04.004 fatcat:fzo6yn5ymnbwpmebirgw6c4fwa

DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization [article]

Chaoli Wang, Jun Han
2022 arXiv   pre-print
We classify and discuss these works along six dimensions: domain setting, research task, learning type, network architecture, loss function, and evaluation metric.  ...  However, existing survey papers on AI+VIS focus on visual analytics and information visualization, not scientific visualization (SciVis).  ...  National Science Foundation through grants IIS-1455886, CNS-1629914, DUE-1833129, IIS-1955395, IIS-2101696, and OAC-2104158.  ... 
arXiv:2204.06504v1 fatcat:33fc2smtuffwll6pghdffbebi4