Cited by 879Gaussian Processes and Bayesian InferenceAdversarial Robustness in Machine LearningNeural Networks and Applications

The DOI matches its Crossref record; 135 of the 141 references it cites were found in a registry or an index.

Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users

Open access

Jospin, Laurent Valentin, Laga, Hamid, Boussaid, Farid, Buntine, Wray, Bennamoun, Mohammed·2022·IEEE Computational Intelligence Magazine

DOI 10.1109/mci.2022.3155327

Open this paper in CoChat — free account Open PDF

Abstract

Modern deep learning methods constitute incredibly powerful tools to tackle a myriad of challenging problems. However, since deep learning methods operate as black boxes, the uncertainty associated with their predictions is often challenging to quantify. Bayesian statistics offer a formalism to understand and quantify the uncertainty associated with deep neural network predictions. This tutorial provides deep learning practitioners with an overview of the relevant literature and a complete toolset to design, implement, train, use and evaluate Bayesian neural networks,i.e., stochastic artificial neural networks trained using Bayesian methods.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1109/mci.2022.3155327

Cites

135 of 135 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
A Mathematical Theory of Communication
Shannon, C. E. (1948) · The MIT Press eBooks · doi:10.1002/j.1538-7305.1948.tb00917.x · Cited by 50,387
Untitled work
(2014) · Cited by 33,419
A survey on transfer learning
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
On Information and Sufficiency
Kullback, S., & Leibler, R. A. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729694 · Cited by 20,193
Bagging predictors
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Monte Carlo sampling methods using Markov chains and their applications.
Hastings, W. K. (1970) · Biometrika · doi:10.1093/biomet/57.1.97 · Cited by 15,195
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204

Show all 141 references

Research works printed but not linked: 5

depeweg, 0, Decomposition of uncertainty in Bayesian deep learning for efficient and risk-sensitive learning, Proc 35th Int Conf Mach Learn, 80, 1184
robert, 2007, The Bayesian Choice From Decision-Theoretic Foundations to Computational Implementation
gelman, 2020, Prior choice recommendations
lee, 0, Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks, Proc Workshop Challenges Representation Learn ICML, 3
goodfellow, 2016, Deep Learning

Sorted by citations

Items 401–500 of 878

Bayesian Machine Learning for Predicting Wing Pressure Distributions at Transonic Flow Conditions
Anhichem, M., Timme, S., Castagna, J., Peace, A. J., & Maina, M. (2025) · AIAA Journal · doi:10.2514/1.j064617 · Cited by 5
Uncertainty Quantification for Safety of the Intended Functionality of Autonomous Driving: A Comprehensive Survey
Wang, K., Ma, Q., Jiang, Y., & Lǚ, J. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3602549 · Cited by 5
Deep Probabilistic Binary Embedding via Learning Reliable Uncertainty for Cross-Modal Retrieval
Cheng, K., Qin, Q., Zhang, W., Huang, L., & Nie, J. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3754811 · Cited by 9
Quantitative performance evaluation of object detectors in hazy environments
Hodges, C., Bennamoun, M., & Boussaïd, F. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.10.001 · Cited by 4
Self-citation
A Review of Variational Inference for Bayesian Neural Network
Bakhouya, M., Ramchoun, H., Mohammed, H., & Masrour, T. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-43520-1_20 · Cited by 4
Sequential reversible jump MCMC for dynamic Bayesian neural networks
Nguyen, N.-M., Tran, M., & Chandra, R. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126960 · Cited by 5
Gibbs sampling the posterior of neural networks
Piccioli, G., Troiani, E., & Zdeborová, L. (2024) · Journal of Physics A Mathematical and Theoretical · doi:10.1088/1751-8121/ad2c26 · Cited by 4
Evaluation of Video Masked Autoencoders’ Performance and Uncertainty Estimations for Driver Action and Intention Recognition
Vellenga, K., Steinhauer, H. J., Falkman, G., & Björklund, T. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00726 · Cited by 7
Bayesian Inference Accelerator for Spiking Neural Networks
Katti, P., Nimbekar, A., Li, C., Acharyya, A., Al‐Hashimi, B. M., & Rajendran, B. (2024) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas58744.2024.10558608 · Cited by 4
Uncertainty Quantification in the Prediction of Remaining Useful Life Considering Multiple Failure Modes
Gandur, N. L., & Ekwaro-Osire, S. (2024) · ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B Mechanical Engineering · doi:10.1115/1.4066722 · Cited by 4
ANN uncertainty estimates in assessing fatty liver content from ultrasound data
Corso, G. M. D., Pascali, M. A., Caudai, C., Rosa, L. D., Salvati, A., Mancini, M., Ghiadoni, L., Bonino, F., Brunetto, M. R., Colantonio, S., et al. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.09.021 · Cited by 4
BayWatch: Leveraging Bayesian Neural Networks for Hardware Fault Tolerance and Monitoring
Hoefer, J., Stammler, M., Kreß, F., Hotfilter, T., Harbaum, T., & Becker, J. (2024) · Proceedings · doi:10.1109/dft63277.2024.10753546 · Cited by 4
a priori uncertainty quantification of reacting turbulence closure models using Bayesian neural networks
Pash, G., Hassanaly, M., & Yellapantula, S. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109821 · Cited by 4
A deep learning framework for multiplet splitting classification in 1H NMR
Fischetti, G., Schmid, N., Bruderer, S., Heitmann, B., Henrici, A., Scarso, A., Caldarelli, G., & WILHELM, D. (2025) · Journal of Magnetic Resonance · doi:10.1016/j.jmr.2025.107851 · Cited by 4
Bayesian RG flow in neural network field theories
Howard, J. N., Klinger, M. S., Maiti, A., & Stapleton, A. G. (2025) · SciPost Physics Core · doi:10.21468/scipostphyscore.8.1.027 · Cited by 11
Uncertainty-Aware and Reliable Neural MIMO Receivers via Modular Bayesian Deep Learning
Raviv, T., Park, S., Simeone, O., & Shlezinger, N. (2025) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2025.3579989 · Cited by 4
Bayesian inference
Liang, W., & Dai, H. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90049-2.00005-6 · Cited by 4
Uncertainty Analysis for Seismic Salt Interpretation by Convolutional Neural Networks
Jiang, F., Osypov, K. S., & Toms, J. (2022) · Abu Dhabi International Petroleum Exhibition & Conference (ADIPEC) · doi:10.2118/211654-ms · Cited by 4
Structured Bayesian Federated Learning for Green AI: A Decentralized Model Compression Using Turbo-VBI-Based Approach
Xia, C., Tsang, D. H. K., & Lau, V. K. N. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3335971 · Cited by 4
Building a Fully-Automatized Active Learning Framework for the Semantic Segmentation of Geospatial 3D Point Clouds
Kölle, M., Walter, V., & Sörgel, U. (2024) · PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science · doi:10.1007/s41064-024-00281-3 · Cited by 6
Automated tariff design for energy supply–demand matching based on Bayesian optimization: Technical framework and policy implications
Lee, H.-S. (2024) · Energy Policy · doi:10.1016/j.enpol.2024.114102 · Cited by 4
Enhanced Malware Prediction and Containment Using Bayesian Neural Networks
Jamadi, Z., & Aghdam, A. G. (2024) · IEEE Journal of Radio Frequency Identification · doi:10.1109/jrfid.2024.3410881 · Cited by 4
Density uncertainty quantification with NeRF-Ensembles: Impact of data and scene constraints
Jäger, M., Landgraf, S., & Jutzi, B. (2025) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2025.104406 · Cited by 4
Bayesian neural networks for the probabilistic forecasting of wind direction and speed using ocean data
Clare, M., & Piggott, M. (2022) · CRC Press eBooks · doi:10.1201/9781003360773-61 · Cited by 4
Prediction of Energy-Efficient Production of Coalbed Methane Based on Chaotic Time Series and Bayes-Least Squares-Support Vector Machine
Wang, Y., Man, Z., & Lu, M. (2020) · International Journal of Heat and Technology · doi:10.18280/ijht.380420 · Cited by 4
BayesBeat: A Bayesian Deep Learning Approach for Atrial Fibrillation Detection from Noisy Photoplethysmography Data.Preprint
Das, S. S. S., Shanto, S. K., Rahman, M., Islam, M. S., Rahman, A. H., Masud, M. M., & Ali, M. E. (2020) · arXiv (Cornell University) · Cited by 4
Quantum Computing for Climate Change Detection, Climate Modeling, and Climate Digital TwinPreprint
Otgonbaatar, S., Nurmi, O., Johansson, M. P., Mäkelä, J., kocman, T., Gawron, P., Puchała, Z., Mielzcarek, J., Miroszewski, A., & Dumitru, C. O. (2023) · doi:10.36227/techrxiv.24478663 · Cited by 4
Toward ambulatory monitoring of vocal behavior at the physiological level using deep ensembles and Bayesian neural networks
Zhang, Z. (2025) · JASA Express Letters · doi:10.1121/10.0039842 · Cited by 3
A Decision Tree-based Monitoring and Recovery Framework for Autonomous Robots with Decision Uncertainties
Peddi, R., & Bezzo, N. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10342207 · Cited by 4
Multi-task Bayesian model combining FDG-PET/CT imaging and clinical data for interpretable high-grade prostate cancer prognosis
Larose, M., Archambault, L., Touma, N., Brodeur, R., Desroches, F., Raymond, N., Bédard‐Tremblay, D., Leblanc, D., Rasekh, F., Hovington, H., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-77498-0 · Cited by 4
Differentiation between model error and measurement uncertainty in machine learning modeling for measurement processes
Huber, M., Müller, T., & Schmitt, R. H. (2025) · Measurement Sensors · doi:10.1016/j.measen.2024.101608 · Cited by 3
Uncertainty-Aware Artificial Intelligence for Gear Fault Diagnosis in Motor Drives
Sahoo, S., Wang, H., & Blaabjerg, F. (2025) · Conference proceedings/Conference proceedings - IEEE Applied Power Electronics Conference and Exposition · doi:10.1109/apec48143.2025.10977491 · Cited by 3
Deep inference of simulated strong lenses in ground-based surveys
Poh, J., Samudre, A., Ćiprijanović, A., Frieman, J. A., Khullar, G., & Nord, B. (2025) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2025/05/053 · Cited by 3
Conformal deep forest for uncertainty-aware classification
Zhang, J., Qiu, Y., & Dong, L. (2025) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-025-00175-3 · Cited by 3
Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks
Bench, C. A., Desai, V., Moulaeifard, M., Strodthoff, N., Aston, P. J., & Thompson, A. (2025) · Machine Learning Health · doi:10.1088/3049-477x/ae0b74 · Cited by 3
A Framework for Including Uncertainty in Robustness Evaluation of Bayesian Neural Network Classifiers
Essbai, W., Bombarda, A., Bonfanti, S., & Gargantini, A. (2024) · IEEE/ACM International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest) · doi:10.1145/3643786.3648026 · Cited by 3
Bayesian Neural Network to Predict Antibiotic Resistance
Vouriot, L., Rebaudet, S., Gaudart, J., & Ureña, R. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-66538-7_2 · Cited by 3
Robust quantum dots charge autotuning using neural network uncertainty
Yon, V., Galaup, B., Rohrbacher, C., Rivard, J., Godfrin, C., Li, R., Kubicek, S., Greve, K. D., Gaudreau, L., Dupont-Ferrier, E., et al. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad88d5 · Cited by 4
RBNN with cycle-based parameter updates and its application to RUL prediction
Guo, R., Wang, S., & Luo, X. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad9bd2 · Cited by 3
Artificial intelligence in the geothermal energy systems
Omrani, P. S., Poort, J., & Shahmohammadi, S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-21662-6.00004-9 · Cited by 3
Data-driven electricity price calibration based on Bayesian inference
Yang, H., Plathottam, S. J., Schell, K. R., Levin, T., & Zhou, Z. (2025) · Energy Systems · doi:10.1007/s12667-024-00718-z · Cited by 3
Machine Learning-Based probabilistic prediction of glacial lake formation using erosional and topographic features
Vashistha, A., Dashora, A., & Shah, A. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-17401-7 · Cited by 3
Probabilistic Transfer Learning through Ensemble Probabilistic Deep Neural Network
Ravi, S. K., Pandita, P., Ghosh, S., Bhaduri, A., Andreoli, V., & Wang, L. (2023) · AIAA SciTech Forum · doi:10.2514/6.2023-1479 · Cited by 3
Bayesian Posterior Approximation With Stochastic Ensembles
Balabanov, O., Mehlig, B., & Linander, H. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01317 · Cited by 5
Homodyned K-Distribution: Parameter Estimation and Uncertainty Quantification Using Bayesian Neural Networks
Tehrani, A. K. Z., Rosado-Méndez, I. M., & Rivaz, H. J. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230797 · Cited by 3
Uncertainty quantification for goal-oriented inverse problems via variational encoder-decoder networks
Afkham, B. M., Chung, J., & Chung, M. (2024) · Inverse Problems · doi:10.1088/1361-6420/ad5373 · Cited by 3
Securing Machine Learning: Understanding Adversarial Attacks and Bias Mitigation
Lakhani, A., & Rohit, N. (2024) · International Journal of Innovative Science and Research Technology (IJISRT) · doi:10.38124/ijisrt/ijisrt24jun1671 · Cited by 3
Learning to Optimize State Estimation in Multi-Agent Reinforcement Learning-Based Collaborative Detection
Zhou, T., Shi, T., Gao, H., & Rao, W. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3445583 · Cited by 3
Continual learning with hebbian plasticity in sparse and predictive coding networks: a survey and perspective
Safa, A. (2024) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/ada08b · Cited by 3
A Comparative Study of Network-Based Machine Learning Approaches for Binary Classification in Metabolomics
Dlugas, H., & Kim, S. (2025) · Metabolites · doi:10.3390/metabo15030174 · Cited by 3
Bayes2IMC: In-Memory Computing for Bayesian Binary Neural Networks
Katti, P., Ruah, C., Simeone, O., Al‐Hashimi, B. M., & Rajendran, B. (2025) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2025.3543065 · Cited by 3
A Comparative Study of Bayesian Neural Networks and Machine Learning Based on COVID-19 Image Classification
Yu, C.-H., & Wang, S. (2025) · Statistics and data science in imaging. · doi:10.1080/29979676.2025.2497555 · Cited by 3
A sector-specific probabilistic approach for 4D aircraft trajectory generation
Pepper, N., Ath, G. D., Carvell, B., Hodgkin, A., Dodwell, T. J., Thomas, M., & Everson, R. (2025) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2025.105291 · Cited by 3
Stochasticity in Motion: An Information-Theoretic Approach to Trajectory Prediction
Distelzweig, A., Look, A., Kosman, E., Janjoš, F., Wagner, J., & Valada, A. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11245653 · Cited by 3
Towards Nucleation of GoA3+ Approval Process
Tagiew, R., Buder, T., Hofmann, K., Klotz, C., & Tilly, R. (2021) · High Performance Computing and Cluster Technologies Conference (HPCCT) · doi:10.1145/3497737.3497742 · Cited by 3
A generalizable, uncertainty-aware neural network potential for GeSbTe with Monte Carlo dropout
Lee, S.-H., Olevano, V., & Sklénard, B. (2022) · Solid-State Electronics · doi:10.1016/j.sse.2022.108508 · Cited by 3
CoV-TI-Net: Transferred Initialization with Modified End Layer for COVID-19 Diagnosis
Khanam, S., Qazani, M. R. C., Mondal, S. K., Kabir, H. M. D., Sabyasachi, A. S., Asadi, H., Kumar, K., Tabarsinezhad, F., Mohamed, S. M. K., Khorsavi, A., et al. (2022) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53654.2022.9945615 · Cited by 3
Concentration Inequalities and Optimal Number of Layers for Stochastic Deep Neural Networks
Caprio, M., & Mukherjee, S. (2023) · IEEE Access · doi:10.1109/access.2023.3268034 · Cited by 3
Probabilistic partition of unity networks: clustering based deep approximationPreprint
Trask, N., Gulian, M., Huang, A., & Lee, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.03066 · Cited by 3
Machine Learning-Based Analysis of Bird Vocalizations
Ghani, B. (2022) · doi:10.53846/goediss-9147 · Cited by 3
A Survey of Uncertainty Quantification in Deep Learning for Financial Time Series PredictionPreprint
Blasco, T., Sánchez, J. S., & García, V. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4506769 · Cited by 3
Quantifying Uncertainty of Portfolios using Bayesian Neural Networks
Esener, S., Wegner, E., Almeida, R., Baştürk, N., & Rodrigues, P. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650382 · Cited by 2
Forward geometric model prediction of a 6-RSU parallel manipulator using a modified NARX Bayesian neural network
Joumah, A. A., Jafar, A., & Albitar, C. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e41047 · Cited by 2
Unveiling the power of uncertainty: A journey into Bayesian Neural Networks for stellar dating
Rodero, V. T., Moya, A., Sarro, L. M., & López-Sastre, R. J. (2025) · Astronomy and Computing · doi:10.1016/j.ascom.2025.100957 · Cited by 3
Distribution free uncertainty quantification for neuroscience-inspired deep neural operators
Garg, S., & Chakraborty, S. (2025) · Journal of Computational Physics · doi:10.1016/j.jcp.2025.114012 · Cited by 3
Reconstructing illusory camouflage patterns on moth wings using computer vision
Jospin, L. V., Porter, J. W., Boussaïd, F., Bennamoun, M., & Kelley, J. L. (2025) · Journal of The Royal Society Interface · doi:10.1098/rsif.2024.0757 · Cited by 2
Self-citation
Safe model based optimization balancing exploration and reliability for protein sequence design
Takizawa, S., Mori, K. P., Tanishiki, N., Yoshimura, D., Ohta, A., & Teramoto, R. (2025) · Scientific Reports · doi:10.1038/s41598-025-12568-5 · Cited by 3
Wasserstein generative regression
Song, S., Wang, T., Shen, G., Lin, Y., & Huang, J. (2025) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1093/jrsssb/qkaf053 · Cited by 2
From ANN to BNN: Inferring reionization parameters using uncertainty-aware emulators of 21-cm summaries
Mahida, Y., Yadav, S. K., Majumdar, S., Noble, L., Murmu, C. S., Dasgupta, S., Dutta, S., Tiwari, H., & Shaw, A. K. (2025) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2025/12/055 · Cited by 2
BayNNgine: Hardware-Enabled Bayesian Neural Network Support for Dependable AI Inference
Hoefer, J., Lesniak, F., Gutermann, A., Wöran, F., Harbaum, T., & Becker, J. (2025) · IEEE International System-on-Chip Conference (SOCC) · doi:10.1109/socc66126.2025.11235419 · Cited by 2
Gaussian process-based data augmentable deep Bayesian neural network: Application to estimate the stress transfer length in pretensioned concrete beams
Woo, B.-H., Choi, D.-H., Park, C.-H., Kim, J.-S., Ryou, J.-S., & Yoo, K.-S. (2025) · Engineering Structures · doi:10.1016/j.engstruct.2025.121809 · Cited by 2
Physics-Informed Machine Learning for Transformer Condition Monitoring – Part II: Physics-Informed Neural Networks and Uncertainty Quantification
Aizpurua, J. I. (2025) · International Advanced Research Workshop on Transformers (ARWtr) · doi:10.23919/arwtr66130.2025.11261232 · Cited by 2
Using artificial neural networks to reveal the human confidence computation
Shekhar, M., Fung, H., Saxena, K., Rafiei, F., & Rahnev, D. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013827 · Cited by 3
Conformalized Generative Bayesian Imaging: An Uncertainty Quantification Framework for Computational Imaging
Ekmekci, C., & Çetin, M. (2025) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2025.3649389 · Cited by 2
Bayesian neural networks for genomic prediction: uncertainty quantification and SNP interpretation with SHAP and GWAS
Sun, J., Zhang, X., You, X., Montesinos-López, O. A., Montesinos‐López, A., Crossa, J. L., & Sorrells, M. E. (2026) · Theoretical and Applied Genetics · doi:10.1007/s00122-025-05127-z · Cited by 2
Improved bayesian network with graph attention and prior algorithm for aircraft engine fault root cause analysis
Yuan, L., Han, G., & Dong, P. (2026) · Scientific Reports · doi:10.1038/s41598-026-36883-7 · Cited by 2
Integrating DWT and Bayesian Neural Networks for Effective Bearing Fault Detection With Uncertainty Evaluation in Induction Machines
Rengifo, J., A., R. A. G., & Aller, J. M. (2026) · IEEE Access · doi:10.1109/access.2026.3655166 · Cited by 2
Prediction of mechanical properties and inner-outer loop control strategy for galvannealed steel strips
Zhang, J., Wang, Z., Wang, Z., Yuan, H., Cheng, L., & Bai, Z. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.131453 · Cited by 2
Assessment and Compliance of Personalized Machine-Learning Pharmacokinetic Models in the European Regulatory Environment
Metto, S. C., Magnani, F., & Castellani, G. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-17216-7_7 · Cited by 2
Efficient RF Passive Components Modeling with Bayesian Online Learning and Uncertainty Aware Sampling
Zhang, H., & Zhou, P. (2026) · Asia and South Pacific Design Automation Conference (ASP-DAC) · doi:10.1109/asp-dac66049.2026.11420493 · Cited by 2
CIFLE: A physics-based machine learning framework with probabilistic inference for low-cycle fatigue life prediction
Yu, J., Cheon, S., Lee, S. H., Kim, Y. I., Park, S. H., & Lee, T. (2026) · Journal of Magnesium and Alloys · doi:10.1016/j.jma.2026.102028 · Cited by 2
Reliable uncertainty estimates in deep learning with efficient Metropolis-Hastings algorithms
Schmal, M., & Mäder, P. (2026) · Nature Communications · doi:10.1038/s41467-026-70015-z · Cited by 2
Visual impedance control using reinforcement learning for moving part robot assembly
Zhang, S., Liu, T., Wan, X., Cheng, P., & Yang, Q. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114610 · Cited by 2
LEON+: towards robust ML-aided query optimization
Chen, X., Zeng, X., Wang, Y., Liang, Z., Zeng, K., Su, H., & Zheng, K. (2026) · The VLDB Journal · doi:10.1007/s00778-026-00973-9 · Cited by 2
A comprehensive empirical evaluation of Bayesian evidential deep learning for uncertainty quantification of image-based medical classification
Tchokponhoue, G. A. D., & Idri, A. (2026) · Results in Engineering · doi:10.1016/j.rineng.2026.110453 · Cited by 2
AI for Lithium-ion battery research under data-scarce scenarios
Zhang, J., Zhai, X., Zhang, Q. (., Li, P., Li, M., Wu, K., Yang, L. I. U., & Wan, J. (2026) · The Innovation Materials · doi:10.59717/j.xinn-mater.2026.100209 · Cited by 2
Enhancing protein immunogenicity prediction via uncertainty weighted deep ensemble
Qayyum, A. B. A., Rahmati, A. H., Qian, X., & Yoon, B.-J. (2026) · Oxford Open Immunology · doi:10.1093/oxfimm/iqag008 · Cited by 2
Model-Assisted Prioritization of Gelatin Methacryloyl (GelMA) Hydrogel Formulations for Three-Dimensional Cell Culture
Yin, P., Wang, X., Liu, S., Huang, Y. Q., Huang, M., Zhang, Y., Peng, R., Sun, J., Wu, Y., & Wang, L. (2026) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.6c11606 · Cited by 2
Explainable AI for Deep Visual Recognition: Evaluation, Methods, and Open Challenges
Alharbi, K. N. (2026) · Electronics · doi:10.3390/electronics15143222 · Cited by 2
A multi-physics regularized transformer framework for tunnel settlement prediction: Integrating domain knowledge constraints with deep learning under limited data
Liang, W., Xu, W., Chen, X., Han, Q., Xiao, X., Cao, X., Xu, T., & Zhang, J. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.133779 · Cited by 3
NEW OPPORTUNITIES AND BENEFITS IN THE PRODUCT DEVELOPMENT PROCESS USING THE MACHINE LEARNING BASED DIRECT INVERSE METHOD FOR MATERIAL PARAMETER IDENTIFICATION
Meißner, P., & Vietor, T. (2023) · Proceedings of the Design Society · doi:10.1017/pds.2023.279 · Cited by 2
Adopting Two Supervisors for Efficient Use of Large-Scale Remote Deep Neural Networks
Weiß, M., & Tonella, P. (2023) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3617593 · Cited by 2
Using Noisy Evaluation to Accelerate Parameter Optimization of Medical Image Segmentation Ensembles
Tóth, J. A., Tomán, H., Hajdu, G., & Hajdú, A. (2023) · Mathematics · doi:10.3390/math11183992 · Cited by 2
Estimation of redshift and associated uncertainty of Fermi/LAT extragalactic sources with Deep Learning
Gharat, S., Borthakur, A., & Bhatta, G. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad3622 · Cited by 2
LEARNING ON THE EDGE: BENCHMARKING ACTIVE LEARNING FOR THE SEMANTIC SEGMENTATION OF ALS POINT CLOUDS
Kölle, M., Walter, V., Schmohl, S., & Soergel, U. (2023) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-x-1-w1-2023-945-2023 · Cited by 2
Artificial neural network for inferring radiative property variations across advanced high strength steel coils
Narayanan, N. S., Suleiman, F. K., Prada, W. M., Zuijderwijk, M., & Daun, K. J. (2024) · Journal of Quantitative Spectroscopy and Radiative Transfer · doi:10.1016/j.jqsrt.2024.108928 · Cited by 2
Sparse subnetwork inference for neural network epistemic uncertainty estimation with improved Hessian approximation
Chen, Y., Yu, S. S., Eshraghian, J. K., & Lim, C. P. (2024) · APL Machine Learning · doi:10.1063/5.0193951 · Cited by 2
Approximate Bayesian Computation with Deep Learning and Conformal prediction
Baragatti, M. C., Casenave, C., Cloez, B., Métivier, D., & Sanchez, I. (2025) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2025.2546446 · Cited by 3
Weight fluctuations in deep linear neural networks and a derivation of the inverse-variance flatness relation
Gross, M., Raulf, A., & Rath, C. W. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.033103 · Cited by 2
Neural networks for the analysis of 2D radio-xenon beta gamma spectra
Hall, R., Carpency, T., Scoville, J., Vincent, R. A., & Caldwell, J. (2024) · Journal of Radioanalytical and Nuclear Chemistry · doi:10.1007/s10967-024-09657-6 · Cited by 2

Authors: Jospin, L. V., Laga, H., Boussaid, F., Buntine, W., & Bennamoun, M.
Affiliations: The University of Western Australia (AU)
Murdoch University (AU)
University of Western Australia, Australia
ORCID: Jospin, Laurent Valentin
Laga, Hamid
Buntine, Wray
Bennamoun, Mohammed
Venue: IEEE Computational Intelligence Magazine
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2022-05
Volume: 17
Issue: 2
Pages: 29-48
ISSN: 1556-603X (ISSN-L)
1556-6048
DOI: 10.1109/mci.2022.3155327
OpenAlex: W3043426275
Cited by: 879
References: 141
Citations by year: 2026: 171 · 2025: 217 · 2024: 192 · 2023: 170 · 2022: 80
FWCI: 54.00
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Computational Intelligence Magazine (publishedVersion)
arXiv (Cornell University) (submittedVersion)
UWA Profiles and Research Repository (UWA) (submittedVersion, other-oa)
Murdoch University Research Portal (submittedVersion)
arXiv (Cornell University)
Funders: Australian Research Council (DP150100294, DP150104251, DP210101682)
Air Force Research Laboratory (FA8750-19-2-0501)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Keywords: Bayesian neural networks, uncertainty quantification, forecast uncertainty, black-box models, Bayesian inference, deep learning, stochastic neural networks

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks