无缘无故
发表于 2025-3-21 17:06:13
书目名称Explainable AI: Interpreting, Explaining and Visualizing Deep Learning影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0319285<br><br> <br><br>书目名称Explainable AI: Interpreting, Explaining and Visualizing Deep Learning影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0319285<br><br> <br><br>书目名称Explainable AI: Interpreting, Explaining and Visualizing Deep Learning网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0319285<br><br> <br><br>书目名称Explainable AI: Interpreting, Explaining and Visualizing Deep Learning网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0319285<br><br> <br><br>书目名称Explainable AI: Interpreting, Explaining and Visualizing Deep Learning被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0319285<br><br> <br><br>书目名称Explainable AI: Interpreting, Explaining and Visualizing Deep Learning被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0319285<br><br> <br><br>书目名称Explainable AI: Interpreting, Explaining and Visualizing Deep Learning年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0319285<br><br> <br><br>书目名称Explainable AI: Interpreting, Explaining and Visualizing Deep Learning年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0319285<br><br> <br><br>书目名称Explainable AI: Interpreting, Explaining and Visualizing Deep Learning读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0319285<br><br> <br><br>书目名称Explainable AI: Interpreting, Explaining and Visualizing Deep Learning读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0319285<br><br> <br><br>
metropolitan
发表于 2025-3-21 23:18:22
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无关紧要
发表于 2025-3-22 03:21:50
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Omniscient
发表于 2025-3-22 06:54:58
Understanding Neural Networks via Feature Visualization: A Surveys in machine learning enable a family of methods to synthesize preferred stimuli that cause a neuron in an artificial or biological brain to fire strongly. Those methods are known as Activation Maximization (AM) [.] or Feature Visualization via Optimization. In this chapter, we (1) review existing A
压碎
发表于 2025-3-22 10:20:28
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Indict
发表于 2025-3-22 12:54:50
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Indict
发表于 2025-3-22 18:43:36
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spinal-stenosis
发表于 2025-3-22 21:30:23
Explanations for Attributing Deep Neural Network Predictionsalthcare decision-making, there is a great need for . and . . of “why” an algorithm is making a certain prediction. In this chapter, we introduce 1. Meta-Predictors as Explanations, a principled framework for learning explanations for any black box algorithm, and 2. Meaningful Perturbations, an inst
Insensate
发表于 2025-3-23 05:21:49
Gradient-Based Attribution Methodsile several methods have been proposed to explain network predictions, the definition itself of explanation is still debated. Moreover, only a few attempts to compare explanation methods from a theoretical perspective has been done. In this chapter, we discuss the theoretical properties of several a
Console
发表于 2025-3-23 08:40:29
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