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Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users
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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.
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“BNNs can be designed in different ways regarding the selection of stochastic components, distribution approximation, and inference approaches [74].”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [74]
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Cites
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
(2014) · Cited by 33,419
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
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
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
Cited by
Sorted by citations
A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U. R., et al. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.008 · Cited by 3,111
“BNNs/BDL are robust to overfitting problem and can be trained on both small and big datasets [27].”
MethodUncertainty quantification in scientific machine learning: Methods, metrics, and comparisons
Psaros, A. F., Meng, X., Zou, Z., Guo, L., & Karniadakis, G. E. (2023) · Journal of Computational Physics · doi:10.1016/j.jcp.2022.111902 · Cited by 464
Improving the open cluster census
Hunt, E. L., & Reffert, S. (2023) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202346285 · Cited by 355
Cheng, S. L., Quilodrán-Casas, C., Ouala, S., Farchi, A., Liu, C., Tandeo, P., Fablet, R., Lucor, D., Iooss, B., Brajard, J., et al. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123537 · Cited by 351
Ma, Y., Zhang, Z., Kang, Y., & Özdoğan, M. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112408 · Cited by 304
Lambert, B., Forbes, F., Doyle, S., Dehaene, H., & Michel Dojat, D. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102830 · Cited by 353
Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework
Zhou, T., Han, T., & Droguett, E. L. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108525 · Cited by 268
Nemani, V. P., Biggio, L., Huan, X., Hu, Z. Y., Fink, O., Tran, A., Wang, Y., Zhang, X., & Hu, C. K. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110796 · Cited by 278
Borys, K., Schmitt, Y. A., Nauta, M., Seifert, C., Krämer, N., Friedrich, C. M., & Nensa, F. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.110786 · Cited by 233
Evidential Deep Learning for Guided Molecular Property Prediction and Discovery
Soleimany, A. P., Amini, A., Goldman, S., Rus, D. L., Bhatia, S. N., & Coley, C. W. (2021) · ACS Central Science · doi:10.1021/acscentsci.1c00546 · Cited by 238
Deep Learning for Time Series Forecasting: Advances and Open Problems
Casolaro, A., Capone, V., Iannuzzo, G., & Camastra, F. (2023) · Information · doi:10.3390/info14110598 · Cited by 226
Bayesian learning for neural networks: an algorithmic survey
Magris, M., & Iosifidis, A. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10443-1 · Cited by 182
Machine learning for nanoplasmonics
Masson, J., Biggins, J. S., & Ringe, E. (2023) · Nature Nanotechnology · doi:10.1038/s41565-022-01284-0 · Cited by 155
Constructing Neural Network Based Models for Simulating Dynamical Systems
Legaard, C. M., Schranz, T., Schweiger, G., Drgoňa, J., Falay, B., Gomes, C. Â. G., Iosifidis, A., Abkar, M., & Larsen, P. G. (2022) · ACM Computing Surveys · doi:10.1145/3567591 · Cited by 159
Priors in Bayesian Deep Learning: A Review
Fortuin, V. (2022) · International Statistical Review · doi:10.1111/insr.12502 · Cited by 126
Huang, L., Ruan, S., Xing, Y., & Feng, M. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103223 · Cited by 152
Untrained Neural Network Priors for Inverse Imaging Problems: A Survey
Qayyum, A., Ilahi, I., Shamshad, F., Boussaïd, F., Bennamoun, M., & Qadir, J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3204527 · Cited by 126
Self-citationStochastic‐Deep Learning Parameterization of Ocean Momentum Forcing
Guillaumin, A. P., & Zanna, L. (2021) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2021ms002534 · Cited by 114
Recent Advances for Quantum Neural Networks in Generative Learning
Tian, J., Sun, X., Du, Y., Zhao, S., Liu, Q., Zhang, K., Yi, W., Huang, W., Wang, C., Wu, X., et al. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3272029 · Cited by 147
Machine learning-guided high throughput nanoparticle design
Ortiz‐Perez, A., van Tilborg, D., van der Meel, R., Grisoni, F., & Albertazzi, L. (2024) · Digital Discovery · doi:10.1039/d4dd00104d · Cited by 131
Implicit Seismic Full Waveform Inversion With Deep Neural Representation
Sun, J., Innanen, K. A., Zhang, T., & Trad, D. O. (2023) · Journal of Geophysical Research Solid Earth · doi:10.1029/2022jb025964 · Cited by 117
Recent advances in earthquake seismology using machine learning
Kubo, H., Naoi, M., & Kano, M. (2024) · Earth Planets and Space · doi:10.1186/s40623-024-01982-0 · Cited by 127
Liu, Y., Liu, B., Rehman, M. U., Imran, M. A., Akinsolu, M. O., Excell, P. S., & Hua, Q. (2022) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2022.3211732 · Cited by 101
Moradi, R., Cofre-Martel, S., Droguett, E. L., Modarres, M., & Groth, K. M. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108433 · Cited by 100
A Survey on Uncertainty Quantification Methods for Deep Learning
He, W., Jiang, Z., Xiao, T., Xu, Z., & Li, Y. (2025) · ACM Computing Surveys · doi:10.1145/3786319 · Cited by 218
Ensemble Methods for Neural Network‐Based Weather Forecasts
Scher, S., & Messori, G. (2020) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2020ms002331 · Cited by 105
Going deep into schizophrenia with artificial intelligence
Cortes-Briones, J., Tapia, N. I., D’Souza, D. C., & Estévez, P. A. (2021) · Schizophrenia Research · doi:10.1016/j.schres.2021.05.018 · Cited by 102
Artificial neural networks for photonic applications—from algorithms to implementation: tutorial
Freire, P. J., Manuylovich, E. S., Prilepsky, J. E., & Turitsyn, S. K. (2023) · Advances in Optics and Photonics · doi:10.1364/aop.484119 · Cited by 92
Data Analytics for Non-Life Insurance PricingPreprint
Wüthrich, M. V., & Buser, C. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2870308 · Cited by 87
A survey on epistemic (model) uncertainty in supervised learning: Recent advances and applications
Zhou, X., Liu, H., Pourpanah, F., Zeng, T., & Wang, X. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.10.119 · Cited by 87
Evaluating fatigue onset in metallic materials: Problem, current focus and future perspectives
Salvati, E. (2024) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2024.108487 · Cited by 79
Artificial intelligence for science in quantum, atomistic, and continuum systems
Zhang, X., Wang, L., Helwig, J., Luo, Y., Fu, C., Xie, Y., Liu, M., Lin, Y., Zhao, X., Yan, K., et al. (2025) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000115 · Cited by 214
Bonnet, D., Hirtzlin, T., Majumdar, A., Dalgaty, T., Esmanhotto, E., Meli, V., Castellani, N., Martin, S., Nodin, J.-F., Bourgeois, G., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43317-9 · Cited by 75
Yu, C., Deng, Y., Qin, Z., Yang, C., & Yuan, Q. (2023) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2023.103935 · Cited by 71
Development and challenges of object detection: A survey
Li, Z., Dong, Y., Shen, L., Liu, Y., Pei, Y., Yang, H., Zheng, L., & Ma, J. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128102 · Cited by 79
Uncertainty-driven ensembles of multi-scale deep architectures for image classification
Arco, J. E., Ortíz, A., Ramı́rez, J., Martínez-Murcia, F. J., Zhang, Y., & Górriz, J. M. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.08.010 · Cited by 71
Evidential deep learning-based drug-target interaction prediction
Zhao, Y., Xing, Y., Zhang, Y., Wang, Y., Wan, M., Yi, D., Wu, C., Li, S., Xu, H., Zhang, H., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62235-6 · Cited by 74
Deep Learning in Diverse Intelligent Sensor Based Systems
Zhu, Y., Wang, M., Yin, X., Zhang, J., Meijering, E. H. W., & Hu, J. (2022) · Sensors · doi:10.3390/s23010062 · Cited by 73
Harnessing deep learning for population genetic inference
Huang, X., Rymbekova, A., Dolgova, O., Lao, Ó., & Kuhlwilm, M. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00636-3 · Cited by 79
Forest-Fire Response System Using Deep-Learning-Based Approaches With CCTV Images and Weather Data
Tran, D. Q., Park, M., Jeon, Y., Bak, J., & Park, S. (2022) · IEEE Access · doi:10.1109/access.2022.3184707 · Cited by 67
Camera, LiDAR, and Radar Sensor Fusion Based on Bayesian Neural Network (CLR-BNN)
Ravindran, R., Santora, M. J., & Jamali, M. M. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3154980 · Cited by 66
Machine learning and Bayesian inference in nuclear fusion research: an overview
Pavone, A., Merlo, A., Kwak, S., & Svensson, J. (2023) · Plasma Physics and Controlled Fusion · doi:10.1088/1361-6587/acc60f · Cited by 72
Badjie, B., Cecílio, J., & Casimiro, A. (2024) · ACM Computing Surveys · doi:10.1145/3691625 · Cited by 73
Shorinwa, O., Mei, Z., Lidard, J., Ren, A. Z., & Majumdar, A. (2025) · ACM Computing Surveys · doi:10.1145/3744238 · Cited by 163
Out-of-Distribution Detection for Automotive Perception
Nitsch, J., Itkina, M., Senanayake, R., Nieto, J. I., Schmidt, M. T., Siegwart, R., Kochenderfer, M. J., & Cadena, C. (2021) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc48978.2021.9564545 · Cited by 66
Liu, X., Yao, W., Peng, W., & Zhou, W. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126425 · Cited by 58
Theocharides, S., Theristis, M., Makrides, G., Kynigos, M., Spanias, C., & Georghiou, G. E. (2021) · Energies · doi:10.3390/en14041081 · Cited by 56
Clare, M., Sonnewald, M., Lguensat, R., Deshayes, J., & Balaji, V. (2022) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2022ms003162 · Cited by 63
VB-DeepONet: A Bayesian operator learning framework for uncertainty quantification
Garg, S., & Chakraborty, S. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105685 · Cited by 55
When not to use machine learning: A perspective on potential and limitations
Carbone, M. R. (2022) · MRS Bulletin · doi:10.1557/s43577-022-00417-z · Cited by 58
Probabilistic SAR-based water segmentation with adapted Bayesian convolutional neural network
Hertel, V., Chow, C., Wani, O., Wieland, M., & Martinis, S. (2022) · Remote Sensing of Environment · doi:10.1016/j.rse.2022.113388 · Cited by 53
Taloma, R. L., Cuomo, F., Comminiello, D., & Pisani, P. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-024-11093-7 · Cited by 71
Vanitha, C. N., Sathyamoorthy, M., Dhanaraj, R. K., & Nayyar, A. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.109228 · Cited by 52
LEON: A New Framework for ML-Aided Query Optimization
Xu, C., Chen, H., Liang, Z., Liu, S., Wang, J., Zeng, K., Su, H., & Zheng, K. (2023) · Proceedings of the VLDB Endowment · doi:10.14778/3598581.3598597 · Cited by 52
Zhang, X., Zhang, X., & Wang, S. (2023) · Springer eBooks · doi:10.1007/978-981-99-6449-9_2 · Cited by 57
Haynes, K., Lagerquist, R., McGraw, M. C., Musgrave, K. D., & Ebert‐Uphoff, I. (2023) · Artificial Intelligence for the Earth Systems · doi:10.1175/aies-d-22-0061.1 · Cited by 56
Ni, L., Chen, J., Chen, G., Zhao, D., Wang, G., & Aphale, S. S. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108904 · Cited by 51
Wang, Y., Ke, S., An, C., Lu, Z., & Xia, J. (2023) · KSCE Journal of Civil Engineering · doi:10.1007/s12205-023-2457-y · Cited by 50
A survey on uncertainty quantification in deep learning for financial time series prediction
Blasco, T., Sánchez, J. S., & García, V. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127339 · Cited by 47
Kim, K. S. (2025) · Advanced Energy Materials · doi:10.1002/aenm.202503356 · Cited by 52
Recent Deep Learning Based NLP Techniques for Chatbot Development: An Exhaustive Survey
Rawat, B., Bist, A. S., Rahardja, U., Aini, Q., & Sanjaya, Y. P. A. (2022) · International Conference on Cyber and IT Service Management (CITSM) · doi:10.1109/citsm56380.2022.9935858 · Cited by 46
Uncertainty-aware point cloud segmentation for infrastructure projects using Bayesian deep learning
Vassilev, H. V. H., Laska, M., & Blankenbach, J. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105419 · Cited by 45
Reliability and Interpretability in Science and Deep Learning
Scorzato, L. (2024) · Minds and Machines · doi:10.1007/s11023-024-09682-0 · Cited by 51
Bayesian neural networks for predicting uncertainty in full-field material response
Pasparakis, G. D., Graham-Brady, L. L., & Shields, M. D. (2024) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2024.117486 · Cited by 51
A Variational Neural Architecture for Skill-based Team Formation
Rad, R. H., Fani, H., Bagheri, E., Kargar, M., Srivastava, D., & Szlichta, J. (2023) · ACM Transactions on Information Systems · doi:10.1145/3589762 · Cited by 47
A comprehensive review on uncertainty modeling methods in modern power systems
Wang, Z., Bu, S., Wen, J., & Huang, C. (2025) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2025.110534 · Cited by 44
Prediction and modeling of water quality using deep neural networks
El-Shebli, M., Sharrab, Y. O., & Al-Fraihat, D. (2023) · Environment Development and Sustainability · doi:10.1007/s10668-023-03335-5 · Cited by 42
Rahu, M. A., Shaikh, M. M., Karim, S., Soomro, S. A., Hussain, D., & Ali, S. M. (2024) · Water Resources Management · doi:10.1007/s11269-024-03899-5 · Cited by 42
Probabilistic machine learning approach for structural reliability analysis
Zhang, W., Zhao, M., Du, X., Gao, Z., & Ni, P. (2023) · Probabilistic Engineering Mechanics · doi:10.1016/j.probengmech.2023.103502 · Cited by 41
Scientific machine learning for closure models in multiscale problems: A review
Sanderse, B., Stinis, P., Maulik, R., & Ahmed, S. E. (2024) · Foundations of Data Science · doi:10.3934/fods.2024043 · Cited by 94
Nguyen, D. Q., Tran, K. Q., Le, T. D., Wahab, M. M. A., & Nguyen‐Xuan, H. (2024) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2024.117603 · Cited by 41
Predicting Cryptocurrency Fraud Using ChaosNet: The Ethereum Manifestation
Dutta, A., Voumik, L. C., Ramamoorthy, A., Ray, S., & Raihan, A. (2023) · Journal of risk and financial management · doi:10.3390/jrfm16040216 · Cited by 47
Liu, X., Liu, J. P., Zhao, Y. L., Ding, T., Liu, X., & Liu, J. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3278873 · Cited by 40
Daw, A., Maruf, M., & Karpatne, A. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467449 · Cited by 40
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Zeng, X. M., Zhang, S., Liu, Y., Hu, X., Ostrikov, K., & Shao, T. (2023) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.2c06259 · Cited by 40
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Sarwar, M. Z., & Cantero, D. (2022) · Measurement · doi:10.1016/j.measurement.2022.112216 · Cited by 39
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Liu, S., Xiao, T. P., Kwon, J., Debusschere, B., Agarwal, S., Incorvia, J. A. C., & Bennett, C. H. (2022) · Frontiers in Nanotechnology · doi:10.3389/fnano.2022.1021943 · Cited by 39
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Zhang, L., Shi, Y., Chang, Y., & Lin, C. (2023) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2023.3241170 · Cited by 39
Fail-Safe Execution of Deep Learning based Systems through Uncertainty Monitoring
Weiß, M., & Tonella, P. (2021) · IEEE International Conference on Software Testing, Verification and Validation (ICST) · doi:10.1109/icst49551.2021.00015 · Cited by 38
Machine learning in coastal bridge hydrodynamics: A state-of-the-art review
Xu, G., Ji, C., Xu, Y. B., Yu, E., Cao, Z., Wu, Q., Lin, P., & Wang, J. (2023) · Applied Ocean Research · doi:10.1016/j.apor.2023.103511 · Cited by 39
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Nguyen, N., & Chen, K. (2022) · IEEE Access · doi:10.1109/access.2022.3168675 · Cited by 42
Mao, J., Li, Z., Yu, Z., Lian-jun, H., Khan, M., & Wu, J. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.110827 · Cited by 37
Ghobadi, F., & Kang, D. (2022) · Water · doi:10.3390/w14223672 · Cited by 43
Peivaste, I., Belouettar, S., Mercuri, F., Fantuzzi, N., Dehghani, H., Izadi, R., Ibrahim, H., Lengiewicz, J., Belouettar-Mathis, M., Bendine, K., et al. (2025) · Composite Structures · doi:10.1016/j.compstruct.2025.119419 · Cited by 51
Nastos, C. V., Komninos, P., & Zarouchas, D. S. (2023) · Composite Structures · doi:10.1016/j.compstruct.2023.116815 · Cited by 36
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Drakoulas, G., Gortsas, T. V., Polyzos, E., Tsinopoulos, S. V., Pyl, L., & Polyzos, D. K. (2024) · Biomechanics and Modeling in Mechanobiology · doi:10.1007/s10237-024-01817-7 · Cited by 34
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Janßen, A., Leebeek, F. W. G., Cnossen, M. H., & Mathôt, R. A. A. (2022) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12808 · Cited by 34
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Nguyen, T. C. H., & Diab, A. (2023) · Nuclear Engineering and Technology · doi:10.1016/j.net.2023.05.025 · Cited by 35
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Starr, G. G. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1146083 · Cited by 43
Gao, F., Xu, Z., & Yin, L. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.122106 · Cited by 32
Willow, S. Y., Hajibabaei, A., Ha, M., Yang, D. C., Myung, C. W., Min, S. K., Lee, G., & Kim, K. S. (2024) · Chemical Physics Reviews · doi:10.1063/5.0231265 · Cited by 34
Bayesian Neural Ordinary Differential EquationsPreprint
Dandekar, R., Chung, K., Dixit, V., Tarek, M., Garcia-Valadez, A., Vemula, K. V., & Rackauckas, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.07244 · Cited by 81
Mohmed, G., Heynes, X., Naser, A., Sun, W., Hardy, K., Grundy, S., & Lu, C. (2023) · Scientific Reports · doi:10.1038/s41598-023-30846-y · Cited by 31
Chu, W. P., Zhang, C., Li, H., Zhang, L., Shen, D., & Li, R. (2024) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2024.103972 · Cited by 31
Chen, L., Meng, F., & Zhang, Y. (2023) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2023.3251302 · Cited by 30
Aseeri, A. O. (2021) · Computers · doi:10.3390/computers10060082 · Cited by 34
Metadata
- 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
- Venue
- IEEE Computational Intelligence Magazine
- Published
- 2022-05
- Volume
- 17
- Issue
- 2
- Pages
- 29-48
- ISSN
- 1556-603X (ISSN-L)
- 1556-6048
- 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
- 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 ›
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