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Titlebook: Explainable and Interpretable Models in Computer Vision and Machine Learning; Hugo Jair Escalante,Sergio Escalera,Marcel‘van Ger Book 2018

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發(fā)表于 2025-3-25 04:28:49 | 只看該作者
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發(fā)表于 2025-3-25 14:11:20 | 只看該作者
Structuring Neural Networks for More Explainable Predictionsroach where the original neural network architecture is modified parsimoniously in order to reduce common biases observed in the explanations. Our approach leads to explanations that better separate classes in feed-forward networks, and that also better identify relevant time steps in recurrent neural networks.
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發(fā)表于 2025-3-25 17:54:30 | 只看該作者
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發(fā)表于 2025-3-26 05:12:54 | 只看該作者
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發(fā)表于 2025-3-26 11:44:38 | 只看該作者
Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges context in which explanation methods can be evaluated regarding their adequacy. The goal of this chapter is to bridge the gap between expert users and lay users. Different kinds of users are identified and their concerns revealed, relevant statements from the General Data Protection Regulation are
29#
發(fā)表于 2025-3-26 14:56:34 | 只看該作者
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發(fā)表于 2025-3-26 18:35:26 | 只看該作者
Learning Interpretable Rules for Multi-Label Classificationted with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analy
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