Visual Ensemble Analysis to Study the Influence of Hyper-parameters on Training Deep Neural Networks (Unknown language)

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A good deep neural network design allows for efficient training and high accuracy. The training step requires a suitable choice of several hyper-parameters. Limited knowledge exists on how the hyper-parameters impact the training process, what is the interplay of multiple hyper-parameters, and what is the interrelation of hyper-parameters and network topology. In this paper, we present a structured analysis towards these goals by investigating an ensemble of training runs.We propose a visual ensemble analysis based on hyper-parameter space visualizations, performance visualizations, and visualizing correlations of topological structures. As a proof of concept, we apply our approach to deep convolutional neural networks.

Table of contents conference proceedings

The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.

1
On KDE-based Brushing in Scatterplots and how it Compares to CNN-based Brushing
Fan, Chaoran / Hauser, Helwig | 2019
7
Interpreting Black-Box Semantic Segmentation Models in Remote Sensing Applications
Janik, Adrianna / Sankaran, Kris / Ortiz, Anthony | 2019
13
Visual Analysis of Multivariate Urban Traffic Data Resorting to Local Principal Curves
Silva, Carla / d'Orey, Pedro M. / Aguiar, Ana | 2019
19
Visual Ensemble Analysis to Study the Influence of Hyper-parameters on Training Deep Neural Networks
Hamid, Sagad / Derstroff, Adrian / Klemm, Sören / Ngo, Quynh Quang / Jiang, Xiaoyi / Linsen, Lars | 2019
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