Machine Learning Methods in Visualisation for Big Data 2020

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https://diglib.eg.org/handle/10.2312/2632905

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Browsing Machine Learning Methods in Visualisation for Big Data 2020 by Subject "Neural networks"

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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.
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    Visual Interpretation of DNN-based Acoustic Models using Deep Autoencoders
    (The Eurographics Association, 2020) Grósz, Tamás; Kurimo, Mikko; Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko
    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, this started to change as researchers have dedicated much effort into interpreting their behavior. In this work, we concentrate on visual interpretation by depicting the hidden activation vectors of the DNN, and propose the usage of deep Autoencoders (DAE) to transform these hidden representations for inspection. We use multiple metrics to compare our approach with other, widely-used algorithms and the results show that our approach is quite competitive. The main advantage of using Autoencoders over the existing ones is that after the training phase, it applies a fixed transformation that can be used to visualize any hidden activation vector without any further optimization, which is not true for the other methods.