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Inspired by the great success of machine learning (ML), researchers have applied ML techniques to visualizations to achieve a better design, development, and evaluation of visualizations.
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2012
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K. P. Murphy,
2012
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B. Steichen, G. Carenini, and C. Conati, “User-adaptive information visualization: using eye gaze data to infer visualization tasks and user cognitive abilities,” in
2013
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M. A. Borkin, A. A. Vo, Z. Bylinskii, P. Isola, S. Sunkavalli, A. Oliva, and H. Pfister, “What makes a visualization memorable?”
2013
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2014
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2014
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2015
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M. Sedlmair and M. Aupetit, “Data-driven evaluation of visual quality measures,”
2015
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2015
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2015
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2015
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V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski
2015
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B. Mutlu, E. Veas, and C. Trattner, “Vizrec: Recommending personalized visualizations,”
2016
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M. Aupetit and M. Sedlmair, “Sepme: 2002 new visual separation measures,” in
2016
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N. Siegel, Z. Horvitz, R. Levin, S. Divvala, and A. Farhadi, “Figureseer: Parsing result-figures in research papers,” in
2016
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A. Kembhavi, M. Salvato, E. Kolve, M. J. Seo, H. Hajishirzi, and A. Farhadi, “A diagram is worth a dozen images,” in
2016
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R. Al-Zaidy, S. Choudhury, and C. Giles, “Automatic summary generation for scientific data charts,” in
2016
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N. Pezzotti, B. P. Lelieveldt, L. Van Der Maaten, T. Höllt, E. Eisemann, and A. Vilanova, “Approximated and user steerable tsne for progressive visual analytics,”
2016
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D. Sacha, L. Zhang, M. Sedlmair, J. A. Lee, J. Peltonen, D. Weiskopf, S. C. North, and D. A. Keim, “Visual interaction with dimensionality reduction: A structured literature analysis,”
2016
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2016
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A. Grotov and M. de Rijke, “Online learning to rank for information retrieval: Sigir 2016 tutorial,” in
2016
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J. Poco, A. Mayhua, and J. Heer, “Extracting and retargeting color mappings from bitmap images of visualizations,”
2017
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O.-H. Kwon, T. Crnovrsanin, and K.-L. Ma, “What would a graph look like in this layout? a machine learning approach to large graph visualization,”
2017
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Z. Bylinskii, N. W. Kim, P. O’Donovan, S. Alsheikh, S. Madan, H. Pfister, F. Durand, B. Russell, and A. Hertzmann, “Learning visual importance for graphic designs and data visualizations,” in
2017
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2017
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2017
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J. Poco and J. Heer, “Reverse-engineering visualizations: Recovering visual encodings from chart images,”
2017
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2017
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2017
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R. A. Al-Zaidy and C. L. Giles, “A machine learning approach for semantic structuring of scientific charts in scholarly documents,” in
2017
Cited alongside, same era.
C. C. Gramazio, J. Huang, and D. H. Laidlaw, “An analysis of automated visual analysis classification: Interactive visualization task inference of cancer genomics domain experts,”
2017
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Y. Wang, K. Feng, X. Chu, J. Zhang, C.-W. Fu, M. Sedlmair, X. Yu, and B. Chen, “A perception-driven approach to supervised dimensionality reduction for visualization,”
2017
Cited alongside, same era.
A. Endert, W. Ribarsky, C. Turkay, B. W. Wong, I. Nabney, I. D. Blanco, and F. Rossi, “The state of the art in integrating machine learning into visual analytics,”
2017
Cited alongside, same era.
M. Vartak, S. Huang, T. Siddiqui, S. Madden, and A. Parameswaran, “Towards visualization recommendation systems,”
Y. Wang, Z. Zhong, and J. Hua, “Deeporgannet: On-the-fly reconstruction and visualization of 3d/4d lung models from single-view projections by deep deformation network,”
2019
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Z. Huang, Y. Zhao, W. Chen, S. Gao, K. Yu, W. Xu, M. Tang, M. Zhu, and M. Xu, “A natural-language-based visual query approach of uncertain human trajectories,”
2019
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F. Hong, C. Liu, and X. Yuan, “Dnn-volvis: Interactive volume visualization supported by deep neural network,” in
2019
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C. Fan and H. Hauser, “On KDE-based Brushing in Scatterplots and how it Compares to CNN-based Brushing,” in
2019
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A. Ottley, R. Garnett, and R. Wan, “Follow the clicks: Learning and anticipating mouse interactions during exploratory data analysis,”
2019
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2017
Cited alongside, same era.
K. Wongsuphasawat, Z. Qu, D. Moritz, R. Chang, F. Ouk, A. Anand, J. Mackinlay, B. Howe, and J. Heer, “Voyager 2: Augmenting visual analysis with partial view specifications,” in
2017
Cited alongside, same era.
M. Correll and J. Heer, “Black hat visualization,” in
2017
Cited alongside, same era.
Z. Zhao, E. Zgraggen, L. De Stefani, C. Binnig, E. Upfal, and T. Kraska, “Safe visual data exploration,” in
2017
Cited alongside, same era.
——, “Converting basic d3 charts into reusable style templates,”
2017
Cited alongside, same era.
T. Siddiqui, P. Luh, Z. Wang, K. Karahalios, and A. Parameswaran, “Shapesearch: flexible pattern-based querying of trend line visualizations,”
2018
Cited alongside, same era.
Y. Luo, X. Qin, N. Tang, G. Li, and X. Wang, “Deepeye: Creating good data visualizations by keyword search,” in
2018
Cited alongside, same era.
T. Milo and A. Somech, “Next-step suggestions for modern interactive data analysis platforms,” in
2018
Cited alongside, same era.
M. M. Abbas, M. Aupetit, M. Sedlmair, and H. Bensmail, “Clustme: A visual quality measure for ranking monochrome scatterplots based on cluster patterns,”
2019
Later among the works it cites.
J. Kassel and M. Rohs, “Online learning of visualization preferences through dueling bandits for enhancing visualization recommendations,” in
2019
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K. Hu, M. A. Bakker, S. Li, T. Kraska, and C. Hidalgo, “VizML: A machine learning approach to visualization recommendation,” in
2019
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C. Fan and H. Hauser, “Personalized sketch-based brushing in scatterplots,”
2019
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S. Amershi, D. S. Weld, M. Vorvoreanu, A. Fourney, B. Nushi, P. Collisson, J. Suh, S. T. Iqbal, P. N. Bennett, K. Inkpen, J. Teevan, R. KikinGil, and E. Horvitz, “Guidelines for human-ai interaction,” in
2019
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D. Sacha, M. Kraus, D. A. Keim, and M. Chen, “VIS4ML: an ontology for visual analytics assisted machine learning,”
2019
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T. Spinner, U. Schlegel, H. Schäfer, and M. El-Assady, “explainer: A visual analytics framework for interactive and explainable machine learning,”
2019
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S. Liu, X. Wang, C. Collins, W. Dou, F. Ou-Yang, M. El-Assady, L. Jiang, and D. A. Keim, “Bridging text visualization and mining: A task-driven survey,”
2019
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S. Hazarika, H. Li, K.-C. Wang, H.-W. Shen, and C.-S. Chou, “NNVA: Neural network assisted visual analysis of yeast cell polarization simulation,”
2019
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N. W. Kim, “From exploration to explanation: Designing for visual data storytelling,” Ph.D. dissertation, 2019
2019
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K. Hu, S. Gaikwad, M. Hulsebos, M. A. Bakker, E. Zgraggen, C. Hidalgo, T. Kraska, G. Li, A. Satyanarayan, and Ç. Demiralp, “Viznet: Towards a large-scale visualization learning and benchmarking repository,” in
2019
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Y. Ming, P. Xu, H. Qu, and L. Ren, “Interpretable and steerable sequence learning via prototypes,” in
2019
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R. Savvides, A. Henelius, E. Oikarinen, and K. Puolamäki, “Significance of patterns in data visualisations,” in
2019
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M. Hearst, M. Tory, and V. Setlur, “Toward interface defaults for vague modifiers in natural language interfaces for visual analysis,” in
2019
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J. Han, J. Tao, and C. Wang, “Flownet: A deep learning framework for clustering and selection of streamlines and stream surfaces,”
2020
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S. Smart, K. Wu, and D. A. Szafir, “Color crafting: Automating the construction of designer quality color ramps,”
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K. Kafle, R. Shrestha, S. Cohen, B. Price, and C. Kanan, “Answering questions about data visualizations using efficient bimodal fusion,” in
2020
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H. Mohammed, “Continuous prefetch for interactive data applications,” in
2020
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P. Zhang, C. Li, and C. Wang, “Viscode: Embedding information in visualization images using encoder-decoder network,”
2020
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A. Wu, W. Tong, T. Dwyer, B. Lee, P. Isenberg, and H. Qu, “MobileVisFixer: Tailoring web visualizations for mobile phones leveraging an explainable reinforcement learning framework,”
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T. Tang, R. Li, X. Wu, S. Liu, J. Knittel, S. Koch, T. Ertl, L. Yu, P. Ren, and Y. Wu, “PlotThread: Creating expressive storyline visualizations using reinforcement learning,”
2020
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C. Qian, S. Sun, W. Cui, J.-G. Lou, H. Zhang, and D. Zhang, “Retrieve-then-adapt: Example-based automatic generation for proportion-related infographics,”
2020
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Y. Wang, G. Yan, H. Zhu, S. Buch, Y. Wang, E. M. Haacke, J. Hua, and Z. Zhong, “Vc-net: Deep volume-composition networks for segmentation and visualization of highly sparse and noisy image data,”
2020
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M. Oppermann, R. Kincaid, and T. Munzner, “Vizcommender: Computing text-based similarity in visualization repositories for content-based recommendations,”
2020
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C. Fosco, V. Casser, A. K. Bedi, P. O’Donovan, A. Hertzmann, and Z. Bylinskii, “Predicting visual importance across graphic design types,” in
2020
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L. Giovannangeli, R. Bourqui, R. Giot, and D. Auber, “Toward automatic comparison of visualization techniques: Application to graph visualization,”
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Y. Luo, C. Chai, X. Qin, N. Tang, and G. Li, “Interactive cleaning for progressive visualization through composite questions,” in
2020
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C. Lai, Z. Lin, R. Jiang, Y. Han, C. Liu, and X. Yuan, “Automatic annotation synchronizing with textual description for visualization,” in
2020
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D. H. Kim, E. Hoque, and M. Agrawala, “Answering questions about charts and generating visual explanations,” in
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M. Lu, C. Wang, J. Lanir, N. Zhao, H. Pfister, D. Cohen-Or, and H. Huang, “Exploring visual information flows in infographics,” in
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2020
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S. Zhu, G. Sun, Q. Jiang, M. Zha, and R. Liang, “A survey on automatic infographics and visualization recommendations,”
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E. Dimara and C. Perin, “What is interaction for data visualization?”
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