Machine Learning Methods in Visualisation for Big Data

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

Now showing 1 - 7 of 7
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    Interpreting Black-Box Semantic Segmentation Models in Remote Sensing Applications
    (The Eurographics Association, 2019) Janik, Adrianna; Sankaran, Kris; Ortiz, Anthony; Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko
    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 segmentation models. The absence of interpretability for these canonical problems in computer vision motivates this study. In this study we present a usercentric approach that blends techniques from interpretability, representation learning, and interactive visualization. It allows to visualize and link latent representation to real data instances as well as qualitatively assess strength of predictions. We have applied our method to a deep learning model for semantic segmentation, U-Net, in a remote sensing application of building detection. This application is of high interest for humanitarian crisis response teams that rely on satellite images analysis. Preliminary results shows utility in understanding semantic segmentation models, demo presenting the idea is available online.
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    ModelSpeX: Model Specification Using Explainable Artificial Intelligence Methods
    (The Eurographics Association, 2020) Schlegel, Udo; Cakmak, Eren; Keim, Daniel A.; Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko
    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 techniques depend on many factors (e.g., parameters and human interpretation). We propose ModelSpeX, a visual analytics workflow to interactively extract human-centered rule-sets to generate model specifications from black-box models (e.g., neural networks). The workflow enables to reason about the underlying problem, to extract decision rule sets, and to evaluate the suitability of the model for a particular task. An exemplary usage scenario walks an analyst trough the steps of the workflow to show the applicability.
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    On KDE-based Brushing in Scatterplots and how it Compares to CNN-based Brushing
    (The Eurographics Association, 2019) Fan, Chaoran; Hauser, Helwig; Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko
    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 brush (the best current empirical model in terms of accuracy for brushing points in a scatterplot) by further incorporating the data distribution information that is captured by the kernel density estimation (KDE). Based on this work, we then include a short discussion between the empirical model, designed in detail by an expert and the deep learning-based model that is learned from user data directly.
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    Panning for Insight: Amplifying Insight through Tight Integration of Machine Learning, Data Mining, and Visualization
    (The Eurographics Association, 2018) Karer, Benjamin; Scheler, Inga; Hagen, Hans; Ian Nabney and Jaakko Peltonen and Daniel Archambault
    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 combining methods of these three domains into an insight-driven interactive data analysis workflow. Based on their interpretation of data visualizations, users generate metadata to be fed back into the analysis. The resulting resonance effect improves the performance of subsequent analysis. The paper outlines the ideas behind the workflow, indicates the benefits and discusses how to avoid potential pitfalls.
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    Revealing Multimodality in Ensemble Weather Prediction
    (The Eurographics Association, 2021) Galmiche, Natacha; Hauser, Helwig; Spengler, Thomas; Spensberger, Clemens; Brun, Morten; Blaser, Nello; Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko
    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., distinct likely outcomes. We propose a graph-based approach that explores multimodality in univariate ensemble data from weather prediction. Our solution utilizes clustering and a novel concept of life span associated with each cluster. We applied our method to historical predictions of extreme weather events and illustrate that our method aids the understanding of the respective ensemble forecasts.
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    Visual Analysis of Multivariate Urban Traffic Data Resorting to Local Principal Curves
    (The Eurographics Association, 2019) Silva, Carla; d'Orey, Pedro; Aguiar, Ana; Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko
    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 a city scale making use of multivariate empirical urban data and fundamental diagrams. The proposed method combines visualization techniques with an improved local principle curves method to model traffic dynamics and facilitate comparison of traffic patterns - resorting to the fitted curve with a confidence interval - between different road segments and for different external conditions. We demonstrate the proposed technique in an illustrative real-world case study in the city of Porto, Portugal.
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    Visual Analysis of the Impact of Neural Network Hyper-Parameters
    (The Eurographics Association, 2020) Jönsson, Daniel; Eilertsen, Gabriel; Shi, Hezi; Zheng, Jianmin; Ynnerman, Anders; Unger, Jonas; Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko
    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. The high-dimensional error surface spanned by the wide range of hyper-parameters used to specify and optimize neural networks is difficult to characterize - it is non-convex and discontinuous, and there could be complex local dependencies between hyper-parameters. To explore these dependencies, we make use of a large number of sampled relations between hyper-parameters and end performance, retrieved from thousands of individually trained convolutional neural network classifiers. We use a structured selection of visualization techniques to analyze the impact of different combinations of hyper-parameters. The results reveal how complicated dependencies between hyper-parameters influence the end performance, demonstrating how the complete picture painted by considering a large number of trainings simultaneously can aid in understanding the impact of hyper-parameter combinations.