Results 21 to 30 of about 224,776 (267)
Neural Network Parameterizations of Electromagnetic Nucleon Form Factors [PDF]
, 2010 The electromagnetic nucleon form-factors data are studied with artificial feed forward neural networks. As a result the unbiased model-independent form-factor parametrizations are evaluated together with uncertainties.
A Bodek +78 more
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Bayesian Neural Networks for Reversible Steganography
IEEE Access, 2022 Recent advances in deep learning have led to a paradigm shift in the field of reversible steganography. A fundamental pillar of reversible steganography is predictive modelling which can be realised via deep neural networks.
Ching-Chun Chang
doaj +1 more source
On Sequential Bayesian Inference for Continual Learning
Entropy, 2023 Sequential Bayesian inference can be used for continual learning to prevent catastrophic forgetting of past tasks and provide an informative prior when learning new tasks.
Samuel Kessler +4 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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Simple Direct Uncertainty Quantification Technique Based on Machine Learning Regression
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022 Epistemic uncertainty quantification provides useful insight into both deep and shallow neural networks' understanding of the relationships between their training distributions and unseen instances and can serve as an estimate of classification ...
Katherine E. Brown, Douglas A. Talbert
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Advanced obstacle avoidance for a laser based wheelchair using optimised Bayesian neural networks. [PDF]
, 2008 In this paper we present an advanced method of obstacle avoidance for a laser based intelligent wheelchair using optimized Bayesian neural networks. Three neural networks are designed for three separate sub-tasks: passing through a door way, corridor and
Nguyen, HT, Trieu, HT, Willey, K
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Quantum Bayesian Neural Networks
, 2021 17 pages, 11 ...
Berner, Noah +2 more
openaire +2 more sources
Explaining Bayesian Neural Networks
, 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 ...
Bykov, Kirill +6 more
openaire +2 more sources
Prediction of concrete fatigue durability using Bayesian neural networks
Computer Assisted Methods in Engineering and Science, 2022 The utility of Bayesian neural networks to predict concrete fatigue durability as a function of concrete mechanical parameters of a specimen and characteristics of the loading cycle is investigated.
Marek Słoński
doaj
On the determination of probability density functions by using Neural Networks [PDF]
, 1998 It is well known that the output of a Neural Network trained to disentangle between two classes has a probabilistic interpretation in terms of the a-posteriori Bayesian probability, provided that a unary representation is taken for the output patterns ...
Aurelio Juste +11 more
core +3 more sources