Machine Learning Methods in Visualisation for Big Data 2020
Norrköping, Sweden, May 25-29, 2020 (Virtual)
Progressive Multidimensional Projections: A Process Model based on Vector Quantization
ModelSpeX: Model Specification Using Explainable Artificial Intelligence Methods
Visual Analysis of the Impact of Neural Network Hyper-Parameters
Improving the Sensitivity of Statistical Testing for Clusterability with Mirrored-Density Plots
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, ...
(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. ...
(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 ...
(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 ...
(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 ...
(The Eurographics Association, 2020)