Titlebook: Epistemic Uncertainty in Artificial Intelligence ; First International Fabio Cuzzolin,Maryam Sultana Conference proceedings 2024 The Edito

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书目名称Epistemic Uncertainty in Artificial Intelligence 影响因子(影响力)




书目名称Epistemic Uncertainty in Artificial Intelligence 影响因子(影响力)学科排名




书目名称Epistemic Uncertainty in Artificial Intelligence 网络公开度




书目名称Epistemic Uncertainty in Artificial Intelligence 网络公开度学科排名




书目名称Epistemic Uncertainty in Artificial Intelligence 被引频次




书目名称Epistemic Uncertainty in Artificial Intelligence 被引频次学科排名




书目名称Epistemic Uncertainty in Artificial Intelligence 年度引用




书目名称Epistemic Uncertainty in Artificial Intelligence 年度引用学科排名




书目名称Epistemic Uncertainty in Artificial Intelligence 读者反馈




书目名称Epistemic Uncertainty in Artificial Intelligence 读者反馈学科排名

annexation

cartilage

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

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

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

轻快带来危险

rods366

,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

,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

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