Results 11 to 20 of about 51,507 (261)
Minimax Bayesian Neural Networks
EntropyRobustness is an important issue in deep learning, and Bayesian neural networks (BNNs) provide means of robustness analysis, while the minimax method is a conservative choice in the classical Bayesian field.
Junping Hong, Ercan Engin Kuruoglu
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Bayesian Neural Networks [PDF]
, 2022 In recent times, neural networks have become a powerful tool for the analysis of complex and abstract data models. However, their introduction intrinsically increases our uncertainty about which features of the analysis are model-related and which are due to the neural network.
Tom Charnock +2 more
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Neural Bayesian Network Understudy
CoRR, 2022 Bayesian Networks may be appealing for clinical decision-making due to their inclusion of causal knowledge, but their practical adoption remains limited as a result of their inability to deal with unstructured data. While neural networks do not have this limitation, they are not interpretable and are inherently unable to deal with causal structure in ...
Rabaey, Paloma +2 more
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Bayesian Quantum Neural Networks
IEEE Access, 2022 Bayesian Quantum Neural ...
Nam Nguyen 0003, Kwang-Cheng Chen
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Bayesian Reasoning with Trained Neural Networks [PDF]
Entropy, 2021 We showed how to use trained neural networks to perform Bayesian reasoning in order to solve tasks outside their initial scope. Deep generative models provide prior knowledge, and classification/regression networks impose constraints. The tasks at hand were formulated as Bayesian inference problems, which we approximately solved through variational or ...
Jakob Knollmüller, Torsten A. Enßlin
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Explaining Bayesian Neural Networks
CoRR, 2021 To advance the transparency of learning machines such as Deep Neural Networks (DNNs), the field of Explainable AI (XAI) was established to provide interpretations of DNNs' predictions. While different explanation techniques exist, a popular approach is given in the form of attribution maps, which illustrate, given a particular data point, the relevant ...
Kirill Bykov +6 more
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Micromachines, 2023 Microsystems are widely used in 5G, the Internet of Things, smart electronic devices and other fields, and signal integrity (SI) determines their performance.
Guangbao Shan +5 more
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Nature Communications, 2023 Safety-critical sensory applications, like medical diagnosis, demand accurate decisions from limited, noisy data. Bayesian neural networks excel at such tasks, offering predictive uncertainty assessment.
Djohan Bonnet +12 more
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Compressed CNN Plant Leaf Ｒecognition Model Fused with Bayesian
Journal of Harbin University of Science and Technology, 2021 Aiming at the problem that there are many parameters in the process of plant leaf recognition and it is easy to produce over-fitting，in order to reduce the cost of storage and calculation，this paper proposes a plant leaf recognition convolutional ...
YAN Ming, ZHU Liang-kuan, JING Wei-peng
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Scaling Up Bayesian Neural Networks with Neural Networks
Trans. Mach. Learn. Res., 2023 25 ...
Zahra Moslemi +3 more
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