Enlightening
发表于 2025-3-21 17:23:00
书目名称Discovery Science影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0281055<br><br> <br><br>书目名称Discovery Science影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0281055<br><br> <br><br>书目名称Discovery Science网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0281055<br><br> <br><br>书目名称Discovery Science网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0281055<br><br> <br><br>书目名称Discovery Science被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0281055<br><br> <br><br>书目名称Discovery Science被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0281055<br><br> <br><br>书目名称Discovery Science年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0281055<br><br> <br><br>书目名称Discovery Science年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0281055<br><br> <br><br>书目名称Discovery Science读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0281055<br><br> <br><br>书目名称Discovery Science读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0281055<br><br> <br><br>
波动
发表于 2025-3-21 23:16:15
978-3-030-61526-0Springer Nature Switzerland AG 2020
Intercept
发表于 2025-3-22 04:24:45
Discovery Science978-3-030-61527-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
colony
发表于 2025-3-22 07:39:52
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发源
发表于 2025-3-22 12:34:13
Studienpionier:innen und Soziale Arbeitrtinent issue. This line of investigation is particularly lacking within clinical decision-making, for which the consequences can be life-altering. Certain real-world clinical ML decision tools are known to demonstrate significant levels of discrimination. There is currently indication that fairness
痛苦一下
发表于 2025-3-22 15:53:45
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痛苦一下
发表于 2025-3-22 18:58:53
https://doi.org/10.1007/978-3-7091-6825-7ting work is algorithm-specific, limited to simple together and apart constraints and does not attempt to satisfy all constraints. This limits applications including where satisfying all constraints is required such as fairness. In this work, we take the novel direction of post-processing the result
难听的声音
发表于 2025-3-22 22:56:59
Bernd-Christian Funk,Bernd Schilcherbe extremely hard. On the other hand, applying traditional clustering and feature reduction techniques to the highly dimensional raw pixel space can be ineffective. To overcome these problems, we propose to use a deep convolutional embedding clustering framework. The model simultaneously optimizes t
invade
发表于 2025-3-23 03:27:12
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材料等
发表于 2025-3-23 07:34:50
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