Machine Learning Methods in Visualisation for Big Data 2020

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/2632905

Browse

Browsing Machine Learning Methods in Visualisation for Big Data 2020 by Subject "Human"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    ModelSpeX: Model Specification Using Explainable Artificial Intelligence Methods
    (The Eurographics Association, 2020) Schlegel, Udo; Cakmak, Eren; Keim, Daniel A.; Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko
    Explainable artificial intelligence (XAI) methods aim to reveal the non-transparent decision-making mechanisms of black-box models. The evaluation of insight generated by such XAI methods remains challenging as the applied techniques depend on many factors (e.g., parameters and human interpretation). We propose ModelSpeX, a visual analytics workflow to interactively extract human-centered rule-sets to generate model specifications from black-box models (e.g., neural networks). The workflow enables to reason about the underlying problem, to extract decision rule sets, and to evaluate the suitability of the model for a particular task. An exemplary usage scenario walks an analyst trough the steps of the workflow to show the applicability.
  • Thumbnail Image
    Item
    Visual Analysis of the Impact of Neural Network Hyper-Parameters
    (The Eurographics Association, 2020) Jönsson, Daniel; Eilertsen, Gabriel; Shi, Hezi; Zheng, Jianmin; Ynnerman, Anders; Unger, Jonas; Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko
    We present an analysis of the impact of hyper-parameters for an ensemble of neural networks using tailored visualization techniques to understand the complicated relationship between hyper-parameters and model performance. The high-dimensional error surface spanned by the wide range of hyper-parameters used to specify and optimize neural networks is difficult to characterize - it is non-convex and discontinuous, and there could be complex local dependencies between hyper-parameters. To explore these dependencies, we make use of a large number of sampled relations between hyper-parameters and end performance, retrieved from thousands of individually trained convolutional neural network classifiers. We use a structured selection of visualization techniques to analyze the impact of different combinations of hyper-parameters. The results reveal how complicated dependencies between hyper-parameters influence the end performance, demonstrating how the complete picture painted by considering a large number of trainings simultaneously can aid in understanding the impact of hyper-parameter combinations.