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Titlebook: Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery; Boris Kovalerchuk,Kawa Nazemi,Ebad Banissi Book 2022

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樓主: Filament
31#
發(fā)表于 2025-3-26 23:56:39 | 只看該作者
Visualizing and Explaining Language Modelswords and phrases, clustering or neuron activations can be used to quickly understand the underlying models. This paper showcases the techniques used in some of the most popular Deep Learning for NLP visualizations, with a special focus on interpretability and explainability.
32#
發(fā)表于 2025-3-27 03:08:43 | 只看該作者
33#
發(fā)表于 2025-3-27 06:06:49 | 只看該作者
Visual Analytics of Hierarchical and Network Timeseries Modelsnd navigation through sub-graphs; hierarchical clustering of nodes; and aggregation of links and nodes. These visual analytics allow expert users to compare the many aspects of the model to their real-world knowledge helping them gain an understanding of the model and ultimately build confidence.
34#
發(fā)表于 2025-3-27 12:34:44 | 只看該作者
Visual Discovery of Malware Patterns in?Android Appsidentify anomalous and malicious software on mobile devices. The visual findings are reached through text, tree and other techniques. An app inspection tool is also provided and its usability has been evaluated with an experimental study with ten participants.
35#
發(fā)表于 2025-3-27 14:51:48 | 只看該作者
36#
發(fā)表于 2025-3-27 21:01:06 | 只看該作者
Visual Analytics for Strategic Decision Making in Technology Managementa more sophisticated market positioning. The enhancements in machine learning and artificial intelligence allow more automatic detection of early trends to create future courses and make strategic decisions. Visual Analytics combines methods of automated data analysis through machine learning method
37#
發(fā)表于 2025-3-27 22:01:14 | 只看該作者
38#
發(fā)表于 2025-3-28 03:02:29 | 只看該作者
39#
發(fā)表于 2025-3-28 07:37:47 | 只看該作者
Non-linear Visual Knowledge Discovery with Elliptic Paired Coordinatesficiency of discovering predictive machine learning models interactively using new Elliptic Paired coordinates (EPC) visualizations. It is shown that EPC are capable to visualize multidimensional data and support visual machine learning with preservation of multidimensional information in 2-D. Relat
40#
發(fā)表于 2025-3-28 12:36:58 | 只看該作者
Convolutional Neural Networks Analysis Using Concentric-Rings Interactive Visualizationto represent the layers of a deep learning model, where each circular ring encodes the feature maps of that layer. The proposed technique allows to perform analysis of tasks over time regarding a single model or a comparison between two distinct models, thus contributing to a better understanding of
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