生长变吼叫
发表于 2025-3-21 16:10:26
书目名称Epistemic Uncertainty in Artificial Intelligence 影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0313321<br><br> <br><br>书目名称Epistemic Uncertainty in Artificial Intelligence 影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0313321<br><br> <br><br>书目名称Epistemic Uncertainty in Artificial Intelligence 网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0313321<br><br> <br><br>书目名称Epistemic Uncertainty in Artificial Intelligence 网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0313321<br><br> <br><br>书目名称Epistemic Uncertainty in Artificial Intelligence 被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0313321<br><br> <br><br>书目名称Epistemic Uncertainty in Artificial Intelligence 被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0313321<br><br> <br><br>书目名称Epistemic Uncertainty in Artificial Intelligence 年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0313321<br><br> <br><br>书目名称Epistemic Uncertainty in Artificial Intelligence 年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0313321<br><br> <br><br>书目名称Epistemic Uncertainty in Artificial Intelligence 读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0313321<br><br> <br><br>书目名称Epistemic Uncertainty in Artificial Intelligence 读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0313321<br><br> <br><br>
annexation
发表于 2025-3-21 21:25:11
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cartilage
发表于 2025-3-22 02:24:37
https://doi.org/10.1007/978-1-4471-5460-0el performance. Our . has the capability to estimate a neural network’s performance, enabling monitoring and notification of entering domains of reduced neural network performance under deployment. Furthermore, our envelope is extended by novel methods to improve the application in deployment settin
ordain
发表于 2025-3-22 06:29:40
https://doi.org/10.1007/978-4-431-54499-9 with the new data sets or on the contrary will they degrade? Will evolution introduce biases or reduce diversity in subsequent generations of generative AI tools? What are the societal implications of the possible degradation of these models? Can we mitigate the effects of this feedback loop? In th
HPA533
发表于 2025-3-22 12:22:29
C. Coudray,M. J. Richard,A. E. Favier experiment evaluated various transfer learning models for classifying different tumor types, including meningioma, glioma, and pituitary tumors. We investigate the impact of different loss functions, including focal loss, and oversampling methods, such as SMOTE and ADASYN, in addressing the data im
轻快带来危险
发表于 2025-3-22 16:44:58
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轻快带来危险
发表于 2025-3-22 20:46:41
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rods366
发表于 2025-3-23 00:50:21
,Bag of Policies for Distributional Deep Exploration,th a population of distributional actor-critics using Bayesian Distributional Policy Gradients (BDPG). The population thus approximates a posterior distribution of return distributions along with a posterior distribution of policies. Our setup allows to analyze global posterior uncertainty along wit
GRILL
发表于 2025-3-23 05:04:34
,Defensive Perception: Estimation and Monitoring of Neural Network Performance Under Deployment,el performance. Our . has the capability to estimate a neural network’s performance, enabling monitoring and notification of entering domains of reduced neural network performance under deployment. Furthermore, our envelope is extended by novel methods to improve the application in deployment settin
Fluctuate
发表于 2025-3-23 09:16:02
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