Browsing Machine Learning Methods in Visualisation for Big Data 2020 by Title
Now showing items 1-6 of 6
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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 ... -
MLVis 2020: Frontmatter
(The Eurographics Association, 2020) -
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 ... -
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. ... -
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, ...