標(biāo)題: Titlebook: Epistemic Uncertainty in Artificial Intelligence ; First International Fabio Cuzzolin,Maryam Sultana Conference proceedings 2024 The Edito [打印本頁(yè)] 作者: 生長(zhǎng)變吼叫 時(shí)間: 2025-3-21 16:10
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書(shū)目名稱(chēng)Epistemic Uncertainty in Artificial Intelligence 被引頻次
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書(shū)目名稱(chēng)Epistemic Uncertainty in Artificial Intelligence 讀者反饋
書(shū)目名稱(chēng)Epistemic Uncertainty in Artificial Intelligence 讀者反饋學(xué)科排名
作者: annexation 時(shí)間: 2025-3-21 21:25 作者: cartilage 時(shí)間: 2025-3-22 02:24
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作者: ordain 時(shí)間: 2025-3-22 06:29
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作者: HPA533 時(shí)間: 2025-3-22 12:22
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作者: 輕快帶來(lái)危險(xiǎn) 時(shí)間: 2025-3-22 16:44 作者: 輕快帶來(lái)危險(xiǎn) 時(shí)間: 2025-3-22 20:46 作者: rods366 時(shí)間: 2025-3-23 00:50
,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作者: GRILL 時(shí)間: 2025-3-23 05:04
,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作者: Fluctuate 時(shí)間: 2025-3-23 09:16 作者: oracle 時(shí)間: 2025-3-23 10:45 作者: animated 時(shí)間: 2025-3-23 15:52
,Towards Offline Reinforcement Learning with?Pessimistic Value Priors,heuristic policy constraints, value regularisation or uncertainty penalties to achieve successful offline RL policies in a toy environment. An additional consequence of our work is a principled quantification of Bayesian uncertainty in off-policy returns in model-free RL. While we are able to presen作者: circumvent 時(shí)間: 2025-3-23 21:13
,A Novel Bayes’ Theorem for?Upper Probabilities,lies in a class of probability measures . and the likelihood is precise. They also give a sufficient condition for such upper bound to hold with equality. In this paper, we introduce a generalization of their result by additionally addressing uncertainty related to the likelihood. We give an upper b作者: 分期付款 時(shí)間: 2025-3-23 23:34 作者: 極大痛苦 時(shí)間: 2025-3-24 04:06 作者: Pander 時(shí)間: 2025-3-24 08:06
,Defensive Perception: Estimation and?Monitoring of?Neural Network Performance Under Deployment,entation in autonomous driving. Our approach is based on the idea that deep learning-based perception for autonomous driving is uncertain and best represented as a probability distribution. As autonomous vehicles’ safety is paramount, it is crucial for perception systems to recognize when the vehicl作者: Defraud 時(shí)間: 2025-3-24 13:44
,Towards Understanding the?Interplay of?Generative Artificial Intelligence and?the?Internet,, have put the societal impacts of these technologies at the center of public debate. These tools are possible due to the massive amount of data (text and images) that is publicly available through the Internet. At the same time, these generative AI tools become content creators that are already con作者: Keratin 時(shí)間: 2025-3-24 18:49 作者: 泛濫 時(shí)間: 2025-3-24 19:28
,Towards Offline Reinforcement Learning with?Pessimistic Value Priors,y interacting with the environment. As the agent tries to improve on the policy present in the dataset, it can introduce distributional shift between the training data and the suggested agent’s policy which can lead to poor performance. To avoid the agent assigning high values to out-of-distribution作者: 畏縮 時(shí)間: 2025-3-24 23:53
,Semantic Attribution for?Explainable Uncertainty Quantification,reting and explaining the origins and reasons for uncertainty presents a significant challenge. In this paper, we present semantic uncertainty attribution as a tool for pinpointing the primary factors contributing to uncertainty. This approach allows us to explain why a particular image carries high作者: 小蟲(chóng) 時(shí)間: 2025-3-25 03:34
0302-9743 rticular, on some of the most important areas of machine learning: unsupervised learning, supervised learning, and reinforcement learning..978-3-031-57962-2978-3-031-57963-9Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 親愛(ài) 時(shí)間: 2025-3-25 10:48 作者: dura-mater 時(shí)間: 2025-3-25 14:59
Laminated Glass Subjected to Blast Load,tors within the latent space and link the uncertainty to corresponding semantic factors for an explanation. The proposed techniques can also enhance explainable out-of-distribution (OOD) detection. We can not only identify OOD samples via their uncertainty, but also provide reasoning rooted in a semantic concept.作者: 白楊魚(yú) 時(shí)間: 2025-3-25 19:44
,A Novel Bayes’ Theorem for?Upper Probabilities,n for this upper bound to become an equality. This result is interesting on its own, and has the potential of being applied to various fields of engineering (e.g. model predictive control), machine learning, and artificial intelligence.作者: Cloudburst 時(shí)間: 2025-3-25 23:32 作者: 上坡 時(shí)間: 2025-3-26 02:34 作者: Parabola 時(shí)間: 2025-3-26 06:28 作者: Psa617 時(shí)間: 2025-3-26 08:47 作者: 抱負(fù) 時(shí)間: 2025-3-26 15:12
https://doi.org/10.1007/978-1-4419-6947-7 models have emerged as the state-of-the-art approach for disease mapping since they are able to both capture structure in the data and robustly characterise uncertainty. When working with areal data, e.g.?aggregates at the administrative unit level such as district or province, current models rely 作者: exceed 時(shí)間: 2025-3-26 16:49 作者: perimenopause 時(shí)間: 2025-3-26 23:19
https://doi.org/10.1007/978-1-4471-5460-0entation in autonomous driving. Our approach is based on the idea that deep learning-based perception for autonomous driving is uncertain and best represented as a probability distribution. As autonomous vehicles’ safety is paramount, it is crucial for perception systems to recognize when the vehicl作者: 委屈 時(shí)間: 2025-3-27 04:22 作者: BORE 時(shí)間: 2025-3-27 06:51 作者: ADORE 時(shí)間: 2025-3-27 11:39
Analysis of Genetic Association Studiesy interacting with the environment. As the agent tries to improve on the policy present in the dataset, it can introduce distributional shift between the training data and the suggested agent’s policy which can lead to poor performance. To avoid the agent assigning high values to out-of-distribution作者: AIL 時(shí)間: 2025-3-27 16:07 作者: Aviary 時(shí)間: 2025-3-27 18:17
https://doi.org/10.1007/978-3-031-57963-9Epistemic Uncertainty; Bayesian Deep Learning; Probabilistic Machine Learning; Variational Autoencoder; 作者: Needlework 時(shí)間: 2025-3-27 23:57
Conference proceedings 2024 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..
