G-Rap: interactive text synthesis using recurrent neural network suggestions

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2018
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ESANN 2018 proceedings. 2018
Zusammenfassung

Finding the best neural network configuration for a given goal can be challenging, especially when it is not possible to assess the output quality of a network automatically. We present G-Rap, an interactive interface based on Visual Analytics principles for comparing outputs of multiple RNNs for the same training data. G-Rap enables an iterative result generation process that allows a user to evaluate the outputs with contextual statistics.

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Fachgebiet (DDC)
004 Informatik
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Deep Learning, Recurrent Neural Networks, Interactive Machine Learning
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European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning : ESANN 2018, 25. Apr. 2018 - 27. Apr. 2018, Brügge, Belgien
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Zitieren
ISO 690SCHLEGEL, Udo, Eren CAKMAK, Juri F. BUCHMÜLLER, Daniel A. KEIM, 2018. G-Rap: interactive text synthesis using recurrent neural network suggestions. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning : ESANN 2018. Brügge, Belgien, 25. Apr. 2018 - 27. Apr. 2018. In: ESANN 2018 proceedings. 2018
BibTex
@inproceedings{Schlegel2018inter-42292,
  year={2018},
  title={G-Rap: interactive text synthesis using recurrent neural network suggestions},
  booktitle={ESANN 2018 proceedings},
  author={Schlegel, Udo and Cakmak, Eren and Buchmüller, Juri F. and Keim, Daniel A.}
}
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