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

Now showing items 1-5 of 5

    • Interactive Dense Pixel Visualizations for Time Series and Model Attribution Explanations 

      Schlegel, Udo; Keim, Daniel (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 ...

    • 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 ...

    • Saliency Clouds: Visual Analysis of Point Cloud-oriented Deep Neural Networks in DeepRL for Particle Physics 

      Mulawade, Raju Ningappa; Garth, Christoph; Wiebel, Alexander (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 

      Schlegel, Udo; Schiegg, Samuel; Keim, Daniel A. (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 ...

    • Visual Exploration of Neural Network Projection Stability 

      Bredius, Carlo; Tian, Zonglin; Telea, Alexandru (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 ...