Results 21 to 30 of about 227,027 (269)
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
core +1 more source
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
core +1 more source
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
Stochasticity from function -- why the Bayesian brain may need no noise [PDF]
, 2019 An increasing body of evidence suggests that the trial-to-trial variability of spiking activity in the brain is not mere noise, but rather the reflection of a sampling-based encoding scheme for probabilistic computing.
Baumbach, Andreas +8 more
core +2 more sources
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
doaj +1 more source
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
doaj +1 more source
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
Personalizing gesture recognition using hierarchical bayesian neural networks [PDF]
, 2017 Building robust classifiers trained on data susceptible to group or subject-specific variations is a challenging pattern recognition problem. We develop hierarchical Bayesian neural networks to capture subject-specific variations and share statistical ...
Betke, Margrit +4 more
core +1 more source