G-Rap: interactive text synthesis using recurrent neural network suggestions
Udo Schlegel, Eren Cakmak, Juri Buchmüller, Daniel A. Keim · KOPS (University of Konstanz) · 2018
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.