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

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書目名稱Epistemic Uncertainty in Artificial Intelligence
副標(biāo)題First International
編輯Fabio Cuzzolin,Maryam Sultana
視頻videohttp://file.papertrans.cn/314/313321/313321.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Epistemic Uncertainty in Artificial Intelligence ; First International  Fabio Cuzzolin,Maryam Sultana Conference proceedings 2024 The Edito
描述.This LNCS 14523 conference volume constitutes the proceedings of the First International Workshop, Epi UAI 2023, in Pittsburgh, PA, USA, August 2023. The .8. full papers together included in this volume were carefully reviewed and selected from 16 submissions...Epistemic AI focuses, in particular, on some of the most important areas of machine learning: unsupervised learning, supervised learning, and reinforcement learning..
出版日期Conference proceedings 2024
關(guān)鍵詞Epistemic Uncertainty; Bayesian Deep Learning; Probabilistic Machine Learning; Variational Autoencoder;
版次1
doihttps://doi.org/10.1007/978-3-031-57963-9
isbn_softcover978-3-031-57962-2
isbn_ebook978-3-031-57963-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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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
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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
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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
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,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
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,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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