IEEE Best Paper: Automatic Generation of Data Visualizations Using Sequence-to-Sequence Recurrent Neural Networks

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It presents the first effort that automatically generates visualizations by applying deep neural translation to a set of visualization examples. The problem formulation and the Data2Vis model in the paper hold great potential to facilitate the future development of learning-based approaches for visualization generation. By the time of award nominations, this article already had 18 citations (now 31), more than any other paper nominated for the award, attesting to its potential large impact in the field.

is a Machine Learning Research Engineer at Cloudera Fast Forward Labs and Cagatay Demiralpis a Senior Research Scientist at MegaFon Labs. The awarded paper was part of the IEEE CG&A Special Issue on Visual Data Science in 2019.

He and co-author

won the IEEE CG&A Best Paper Award: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9242416

Read the paper at : https://www.computer.org/csdl/magazine/cg/2019/05/08744242/1cFV5domibu

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