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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 2025-3-26 23:42:21 | 只看該作者
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發(fā)表于 2025-3-27 04:39:42 | 只看該作者
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發(fā)表于 2025-3-27 07:56:31 | 只看該作者
Ensembling Visual Explanations of multiple models. Also, the top-ranked systems in many data-mining and computer vision competitions use ensembles. Although ensembles are popular, they are opaque and hard to interpret. Explanations make AI systems more transparent and also justify their predictions. However, there has been littl
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發(fā)表于 2025-3-27 12:27:15 | 只看該作者
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發(fā)表于 2025-3-27 15:56:45 | 只看該作者
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發(fā)表于 2025-3-27 18:45:40 | 只看該作者
Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisionsticular, assessment of apparent personality gives insights into the first impressions evoked by a candidate. Such analysis tools can be used for training purposes, if they can be configured to provide appropriate and clear feedback. In this chapter, we describe a multimodal system that analyzes a sh
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發(fā)表于 2025-3-27 22:43:20 | 只看該作者
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發(fā)表于 2025-3-28 06:06:51 | 只看該作者
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發(fā)表于 2025-3-28 06:49:54 | 只看該作者
Cooling System Study and Simulationare missing and we speculate which criteria these methods/interfaces should satisfy. Finally it is noted that two important concerns are difficult to address with explanation methods: the concern about bias in datasets that leads to biased DNNs, as well as the suspicion about unfair outcomes.
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發(fā)表于 2025-3-28 10:52:36 | 只看該作者
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