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 -
Machine Learning Methods in Visualisation for Big Data 2021
ISBN 978-3-03868-146-5 -
Machine Learning Methods in Visualisation for Big Data 2022
ISBN 978-3-03868-182-3 -
Machine Learning Methods in Visualisation for Big Data 2023
ISBN 978-3-03868-224-0
Recent Submissions
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Interactive Dense Pixel Visualizations for Time Series and Model Attribution Explanations
(The Eurographics Association, 2023)The field of Explainable Artificial Intelligence (XAI) for Deep Neural Network models develops significantly, offering numerous techniques to extract explanations from models. However, evaluating explanations is often not ... -
MLVis 2023: Frontmatter
(The Eurographics Association, 2023) -
Saliency Clouds: Visual Analysis of Point Cloud-oriented Deep Neural Networks in DeepRL for Particle Physics
(The Eurographics Association, 2022)We develop and describe saliency clouds, that is, visualization methods employing explainable AI methods to analyze and interpret deep reinforcement learning (DeepRL) agents working on point cloud-based data. The agent in ... -
ViNNPruner: Visual Interactive Pruning for Deep Learning
(The Eurographics Association, 2022)Neural networks grow vastly in size to tackle more sophisticated tasks. In many cases, such large networks are not deployable on particular hardware and need to be reduced in size. Pruning techniques help to shrink deep ... -
MLVis 2022: Frontmatter
(The Eurographics Association, 2022) -
Visual Exploration of Neural Network Projection Stability
(The Eurographics Association, 2022)We present a method to visually assess the stability of deep learned projections. For this, we perturb the high-dimensional data by controlled sequences and visualize the resulting changes in the 2D projection. We apply ... -
Revealing Multimodality in Ensemble Weather Prediction
(The Eurographics Association, 2021)Ensemble methods are widely used to simulate complex non-linear systems and to estimate forecast uncertainty. However, visualizing and analyzing ensemble data is challenging, in particular when multimodality arises, i.e., ... -
MLVis 2021: Frontmatter
(The Eurographics Association, 2021) -
Controllably Sparse Perturbations of Robust Classifiers for Explaining Predictions and Probing Learned Concepts
(The Eurographics Association, 2021)Explaining the predictions of a deep neural network (DNN) in image classification is an active area of research. Many methods focus on localizing pixels, or groups of pixels, which maximize a relevance metric for the ... -
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, ... -
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 ... -
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. ... -
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 ... -
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 ... -
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 ... -
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 ... -
MLVis 2019: Frontmatter
(The Eurographics Association, 2019)
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