Machine Learning Methods in Visualisation for Big Data 2020

 

Norrköping, Sweden, May 25-29, 2020 (Virtual)

Table of Contents
Papers
Progressive Multidimensional Projections: A Process Model based on Vector Quantization
[full paper Image] [meta data Image]
Elio Alejandro Ventocilla, Rafael M. Martins, Fernando V. Paulovich, and Maria Riveiro
ModelSpeX: Model Specification Using Explainable Artificial Intelligence Methods
[full paper Image] [meta data Image]
Udo Schlegel, Eren Cakmak, and Daniel A. Keim
Visual Analysis of the Impact of Neural Network Hyper-Parameters
[full paper Image] [meta data Image]
Daniel Jönsson, Gabriel Eilertsen, Hezi Shi, Jianmin Zheng, Anders Ynnerman, and Jonas Unger
Improving the Sensitivity of Statistical Testing for Clusterability with Mirrored-Density Plots
[full paper Image] [meta data Image]
Michael C. Thrun
Visual Interpretation of DNN-based Acoustic Models using Deep Autoencoders
[full paper Image] [meta data Image]
Tamás Grósz and Mikko Kurimo

Recent Submissions

  • Visual Interpretation of DNN-based Acoustic Models using Deep Autoencoders 

    Grósz, Tamás; Kurimo, Mikko (The Eurographics Association, 2020)
    In the past few years, Deep Neural Networks (DNN) have become the state-of-the-art solution in several areas, including automatic speech recognition (ASR), unfortunately, they are generally viewed as black boxes. Recently, ...

  • Improving the Sensitivity of Statistical Testing for Clusterability with Mirrored-Density Plots 

    Thrun, Michael C. (The Eurographics Association, 2020)
    For many applications, it is crucial to decide if a dataset possesses cluster structures. This property is called clusterability and is usually investigated with the usage of statistical testing. Here, it is proposed to ...

  • Visual Analysis of the Impact of Neural Network Hyper-Parameters 

    Jönsson, Daniel; Eilertsen, Gabriel; Shi, Hezi; Zheng, Jianmin; Ynnerman, Anders; Unger, Jonas (The Eurographics Association, 2020)
    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. ...

  • ModelSpeX: Model Specification Using Explainable Artificial Intelligence Methods 

    Schlegel, Udo; Cakmak, Eren; Keim, Daniel A. (The Eurographics Association, 2020)
    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 ...

  • Progressive Multidimensional Projections: A Process Model based on Vector Quantization 

    Ventocilla, Elio Alejandro; Martins, Rafael M.; Paulovich, Fernando V.; Riveiro, Maria (The Eurographics Association, 2020)
    As large datasets become more common, so becomes the necessity for exploratory approaches that allow iterative, trial-anderror analysis. Without such solutions, hypothesis testing and exploratory data analysis may become ...

  • MLVis 2020: Frontmatter 

    Archambault, Daniel; Nabney, Ian; Peltonen, Jaakko (The Eurographics Association, 2020)