Results 11 to 20 of about 236,656 (279)
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 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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Continual Learning Using Bayesian Neural Networks [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2021 Continual learning models allow to learn and adapt to new changes and tasks over time. However, in continual and sequential learning scenarios in which the models are trained using different data with various distributions, neural networks tend to forget the previously learned knowledge.
Honglin Li +3 more
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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.
Charnock, Tom +2 more
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Neural Bayesian Network Understudy
, 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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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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Bayesian Neural Networks for Aroma Classification [PDF]
Journal of Chemical Information and Computer Sciences, 2002 Bayesian Neural Networks (BNNs) are investigated to test their potential to distinguish between different aroma impressions. Special attention is thereby drawn on mixed aroma impressions, resulting from the flavor description of a single compound with more than one aroma quality.
Klocker, Johanna +3 more
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Non-linear carbon dioxide determination using infrared gas sensors and neural networks with Bayesian regularization [PDF]
, 2009 Carbon dioxide gas concentration determination using infrared gas sensors combined with Bayesian regularizing neural networks is presented in this work.
Almeida +27 more
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Winsorization for Robust Bayesian Neural Networks [PDF]
Entropy, 2021 With the advent of big data and the popularity of black-box deep learning methods, it is imperative to address the robustness of neural networks to noise and outliers. We propose the use of Winsorization to recover model performances when the data may have outliers and other aberrant observations.
Somya Sharma, Snigdhansu Chatterjee
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