Machine Learning Methods in Visualisation for Big Data
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Machine Learning Methods in Visualisation for Big Data 2018
ISBN 978-3-03868-062-8 -
Machine Learning Methods in Visualisation for Big Data 2019
ISBN 978-3-03868-089-5 -
Machine Learning Methods in Visualisation for Big Data 2020
ISBN 978-3-03868-113-7
Recent Submissions
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Visual Interpretation of DNN-based Acoustic Models using Deep Autoencoders
(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, ... -
Visual Analysis of the Impact of Neural Network Hyper-Parameters
(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. ... -
Improving the Sensitivity of Statistical Testing for Clusterability with Mirrored-Density Plots
(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 ... -
ModelSpeX: Model Specification Using Explainable Artificial Intelligence Methods
(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
(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
(The Eurographics Association, 2020) -
Visual Ensemble Analysis to Study the Influence of Hyper-parameters on Training Deep Neural Networks
(The Eurographics Association, 2019)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 ... -
Visual Analysis of Multivariate Urban Traffic Data Resorting to Local Principal Curves
(The Eurographics Association, 2019)Traffic congestion causes major economic, environmental and social problems in modern cities. We present an interactive visualization tool to assist domain experts on the identification and analysis of traffic patterns at ... -
Interpreting Black-Box Semantic Segmentation Models in Remote Sensing Applications
(The Eurographics Association, 2019)In the interpretability literature, attention is focused on understanding black-box classifiers, but many problems ranging from medicine through agriculture and crisis response in humanitarian aid are tackled by semantic ... -
On KDE-based Brushing in Scatterplots and how it Compares to CNN-based Brushing
(The Eurographics Association, 2019)In this paper, we investigate to which degree the human should be involved into the model design and how good the empirical model can be with more careful design. To find out, we extended our previously published Mahalanobis ... -
MLVis 2019: Frontmatter
(The Eurographics Association, 2019) -
Panning for Insight: Amplifying Insight through Tight Integration of Machine Learning, Data Mining, and Visualization
(The Eurographics Association, 2018)With the rapid progress made in Data Mining, Visualization, and Machine Learning during the last years, combinations of these methods have gained increasing interest. This paper summarizes ideas behind ongoing work on ... -
Machine Learning Methods in Visualisation for Big Data 2018: Frontmatter
(The Eurographics Association, 2018)
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