Browsing by Author "Schlegel, Udo"

Now showing 1 - 4 of 4
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    A Comprehensive Workflow for Effective Imitation and Reinforcement Learning with Visual Analytics
    (The Eurographics Association, 2022) Metz, Yannick; Schlegel, Udo; Seebacher, Daniel; El-Assady, Mennatallah; Keim, Daniel; Bernard, Jürgen; Angelini, Marco
    Multiple challenges hinder the application of reinforcement learning algorithms in experimental and real-world use cases even with recent successes in such areas. Such challenges occur at different stages of the development and deployment of such models. While reinforcement learning workflows share similarities with machine learning approaches, we argue that distinct challenges can be tackled and overcome using visual analytic concepts. Thus, we propose a comprehensive workflow for reinforcement learning and present an implementation of our workflow incorporating visual analytic concepts integrating tailored views and visualizations for different stages and tasks of the workflow.
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    Interactive Dense Pixel Visualizations for Time Series and Model Attribution Explanations
    (The Eurographics Association, 2023) Schlegel, Udo; Keim, Daniel; Archambault, Daniel; Nabney, Ian; Peltonen, Jaakko
    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 trivial, and differences in applied metrics can be subtle, especially with non-intelligible data. Thus, there is a need for visualizations tailored to explore explanations for domains with such data, e.g., time series. We propose DAVOTS, an interactive visual analytics approach to explore raw time series data, activations of neural networks, and attributions in a dense-pixel visualization to gain insights into the data, models' decisions, and explanations. To further support users in exploring large datasets, we apply clustering approaches to the visualized data domains to highlight groups and present ordering strategies for individual and combined data exploration to facilitate finding patterns. We visualize a CNN trained on the FordA dataset to demonstrate the approach.
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    SpatialRugs: Enhancing Spatial Awareness of Movement in Dense Pixel Visualizations
    (The Eurographics Association, 2020) Buchmüller, Juri F.; Schlegel, Udo; Cakmak, Eren; Keim, Daniel A.; Dimara, Evanthia; Turkay, Cagatay and Vrotsou, Katerina
    Compact visual summaries of spatio-temporal movement data often strive to express accurate positions of movers. We present SpatialRugs, a technique to enhance the spatial awareness of movements in dense pixel visualizations. SpatialRugs apply 2D colormaps to visualize location mapped to a juxtaposed display. We explore the effect of various colormaps discussing perceptual limitations and introduce a custom color-smoothing method to mitigate distorted patterns of collective movement behavior.
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    ViNNPruner: Visual Interactive Pruning for Deep Learning
    (The Eurographics Association, 2022) Schlegel, Udo; Schiegg, Samuel; Keim, Daniel A.; Archambault, Daniel; Nabney, Ian; Peltonen, Jaakko
    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 neural networks to smaller sizes by only decreasing their performance as little as possible. However, such pruning algorithms are often hard to understand by applying them and do not include domain knowledge which can potentially be bad for user goals. We propose ViNNPruner, a visual interactive pruning application that implements state-of-the-art pruning algorithms and the option for users to do manual pruning based on their knowledge. We show how the application facilitates gaining insights into automatic pruning algorithms and semi-automatically pruning oversized networks to make them more efficient using interactive visualizations.