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

Adam: A Method for Stochastic Optimization

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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

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A Mathematical Theory of Communication

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A survey on transfer learning

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On Information and Sufficiency

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Bagging predictors

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Monte Carlo sampling methods using Markov chains and their applications.

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Distilling the Knowledge in a Neural Network

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Uncertainty Estimation of Pedestrian Future Trajectory Using Bayesian Approximation

Nayak, A., Eskandarian, A., & Doerzaph, Z. R. (2022) · IEEE Open Journal of Intelligent Transportation Systems · doi:10.1109/ojits.2022.3205504 · Cited by 30

Surrogate model based on ANN for the evaluation of the fundamental frequency of offshore wind turbines supported on jackets

Quevedo-Reina, R., Álamo, G. M., Padrón, L. A., & Aznárez, J. J. (2022) · Computers & Structures · doi:10.1016/j.compstruc.2022.106917 · Cited by 30

Bayesian machine learning-aided approach bridges between dynamic elasticity and compressive strength in the cement-based mortars

Wang, N., Samavatian, M., Samavatian, V., & Sun, H. (2023) · Materials Today Communications · doi:10.1016/j.mtcomm.2023.106283 · Cited by 29

Towards trustworthy cybersecurity operations using Bayesian Deep Learning to improve uncertainty quantification of anomaly detection

Yang, T., Qiao, Y., & Lee, B. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103909 · Cited by 29

Probabilistic deep learning and transfer learning for robust cryptocurrency price prediction

Golnari, A., Komeili, M. H., & Azizi, Z. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124404 · Cited by 32

Low-order flow reconstruction and uncertainty quantification in disturbed aerodynamics using sparse pressure measurements

Mousavi, H., & Eldredge, J. D. (2025) · Journal of Fluid Mechanics · doi:10.1017/jfm.2025.10253 · Cited by 29

Cotton Yield Prediction Utilizing Unmanned Aerial Vehicles (Uav) and Bayesian Neural Networks

Zhao, L., Um, D., Nowka, K. J., Landivar, J., Landivar, J. A., & Bhandari, M. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109415 · Cited by 28

Antithetic Magnetic and Shadow Hamiltonian Monte Carlo

Mongwe, W. T., Mbuvha, R., & Marwala, T. (2021) · IEEE Access · doi:10.1109/access.2021.3069196 · Cited by 28

Uncertainty-aware predictions of molecular x-ray absorption spectra using neural network ensembles

Ghose, A. K., Segal, M., Meng, F., Liang, Z., Hybertsen, M. S., Qu, X., Stavitski, E., Yoo, S., Lu, D., & Carbone, M. R. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.013180 · Cited by 27

Sensitivity Analysis of Parameters Affecting Wetland Water Levels: A Study of Flood Detention Basin, Colombo, Sri Lanka

Herath, M., Jayathilaka, T., Azamathulla, H. M., Mandala, V., Rathnayake, N., & Rathnayake, U. (2023) · Sensors · doi:10.3390/s23073680 · Cited by 27

Using Constrained-Disorder Principle-Based Systems to Improve the Performance of Digital Twins in Biological Systems

Sigawi, T., & Ilan, Y. (2023) · Biomimetics · doi:10.3390/biomimetics8040359 · Cited by 27

The neural network RTNet exhibits the signatures of human perceptual decision-making

Rafiei, F., Shekhar, M., & Rahnev, D. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-01914-8 · Cited by 37

Decoding neutron star observations: Revealing composition through Bayesian neural networks

Carvalho, V., Ferreira, M., Malik, T., & Providência, C. (2023) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.108.043031 · Cited by 25

Bayesian Convolutional Neural Networks in Medical Imaging Classification: A Promising Solution for Deep Learning Limits in Data Scarcity Scenarios

Bargagna, F., Santi, L. A. D., Martini, N., Genovesi, D., Favilli, B., Vergaro, G., Emdin, M., Giorgetti, A., Positano, V., & Santarelli, M. F. (2023) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-023-00897-8 · Cited by 25

Robust deep reinforcement learning for inverter-based volt-var control in partially observable distribution networks

Liu, Q., Guo, Y., & Xu, T. Y. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.126445 · Cited by 24

A Comprehensive Review of Neuro-symbolic AI for Robustness, Uncertainty Quantification, and Intervenability

Acharya, K., & Song, H. (2025) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-025-10887-3 · Cited by 27

Improved Uncertainty Quantification for Neural Networks With Bayesian Last Layer

Fiedler, F., & Lucia, S. (2023) · IEEE Access · doi:10.1109/access.2023.3329685 · Cited by 31

A deep neural network: mechanistic hybrid model to predict pharmacokinetics in rat

Führer, F., Grüber, A., Diedam, H., Göller, A. H., Menz, S., & Schneckener, S. (2024) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-023-00547-9 · Cited by 24

Deep Ensembles from a Bayesian PerspectivePreprint

Hoffmann, L., & Elster, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.13283 · Cited by 51

Uncertainty in Data-Driven Kalman Filtering for Partially Known State-Space Models

Klein, I., Revach, G., Shlezinger, N., Mehr, J. E., van Sloun, R. J. G., & Eldar, Y. C. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746732 · Cited by 23

Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy

Bürkner, P., Scholz, M. M., & Radev, S. T. (2023) · Statistics Surveys · doi:10.1214/23-ss145 · Cited by 36

Integrating impulse excitation technique and machine learning to analyze elastic moduli in surface coatings

Shen, Z., Samavatian, M., & Samavatian, V. (2024) · European Journal of Mechanics - A/Solids · doi:10.1016/j.euromechsol.2024.105250 · Cited by 23

Machine Learning with High-Cardinality Categorical Features in Actuarial Applications

Avanzi, B., Taylor, G., Wang, M., & Wong, B. (2024) · Astin Bulletin · doi:10.1017/asb.2024.7 · Cited by 23

Jensen–Shannon divergence based novel loss functions for Bayesian neural networks

Thiagarajan, P., & Ghosh, S. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.129115 · Cited by 28

Gaussian Latent Representations for Uncertainty Estimation using Mahalanobis Distance in Deep Classifiers

Venkataramanan, A., Benbihi, A., Laviale, M., & Pradalier, C. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00483 · Cited by 22

Machine Learning-Aided Monte Carlo Simulation and Subset Simulation

Sabri, M. S., Ahmad, F., & Samui, P. (2024) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981241248166 · Cited by 24

Uncertainty quantification in real-time parameter updating for digital twins using Bayesian inverse mapping models

Kessels, B. M., Fey, R. H. B., & van de Wouw, N. (2024) · Nonlinear Dynamics · doi:10.1007/s11071-024-10608-9 · Cited by 24

Reliability engineering, risk management, and trustworthiness assurance for AI systems

Zhang, X., Wang, T., Ma, L., & Mahadevan, S. (2025) · Journal of Reliability Science and Engineering · doi:10.1088/3050-2454/adcef2 · Cited by 29

Combining data assimilation and machine learning to estimate parameters of a convective‐scale model

Legler, S., & Janjić, T. (2021) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.4235 · Cited by 22

Multilayer Perceptron and Bayesian Neural Network-Based Elastic Implicit Full Waveform Inversion

Zhang, T., Sun, J., Trad, D. O., & Innanen, K. A. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3265657 · Cited by 24

Farm-wide virtual load monitoring for offshore wind structures via Bayesian neural networks

Hlaing, N., Morato, P. G., Santos, F. D. N., Weijtjens, W., Devriendt, C., & Rigo, P. (2023) · Structural Health Monitoring · doi:10.1177/14759217231186048 · Cited by 22

Transformer CycleGAN with uncertainty estimation for CBCT based synthetic CT in adaptive radiotherapy

Rusanov, B., Hassan, G. M., Reynolds, M. A., Sabet, M., Rowshanfarzad, P., Bucknell, N. W., Gill, S., Dass, J., & Ebert, M. A. (2024) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad1cfc · Cited by 22

Bayesian-neural-network-based strain estimation approach for optical coherence elastography

Bai, Y., Zhang, K., Mo, R., Ni, Z., He, Z., Xie, S., & Dong, B. (2024) · Optica · doi:10.1364/optica.534933 · Cited by 21

AI-based framework for concrete durability assessment using generative adversarial networks and bayesian neural networks

Ragaa, A. B., Al-Neshawy, F., & Noureldin, M. (2025) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2025.140722 · Cited by 21

Planning Strategies in the Energy Sector: Integrating Bayesian Neural Networks and Uncertainty Quantification in Scenario Analysis & Optimization

Iseri, F., Iseri, H., Shah, H. B., Iakovou, E. T., & Pistikopoulos, E. N. (2025) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2025.109097 · Cited by 21

Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification

Weiß, M., & Tonella, P. (2021) · IEEE International Conference on Software Testing, Verification and Validation (ICST) · doi:10.1109/icst49551.2021.00056 · Cited by 21

Uncertainty Estimation via Monte Carlo Dropout in CNN-Based mmWave MIMO Localization

Sadr, M. A. M., Gante, J., Champagne, B., Falcão, G., & Sousa, L. A. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2021.3130504 · Cited by 21

Efficient Bayesian inference for stochastic agent-based models

Jørgensen, A. C. S., Ghosh, A., Sturrock, M., & Shahrezaei, V. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009508 · Cited by 30

A Bayesian Domain Adversarial Neural Network for Corn Yield Prediction

Ma, Y., & Zhang, Z. (2022) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2022.3211444 · Cited by 21

Do we really need a new theory to understand over-parameterization?

Oneto, L., Ridella, S., & Anguita, D. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126227 · Cited by 23

Bayesian DivideMix++ for Enhanced Learning with Noisy Labels

Nagarajan, B., Marques, R., Aguilar, E., & Radeva, P. I. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106122 · Cited by 22

Modeling unknowns: A vision for uncertainty-aware machine learning in healthcare

Campagner, A., Biganzoli, E. M., Balsano, C., Cereda, C., & Cabitza, F. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.106014 · Cited by 25

Solo: Data Discovery Using Natural Language Questions Via A Self-Supervised Approach

Wang, Q., & Fernandez, R. C. (2023) · Proceedings of the ACM on Management of Data · doi:10.1145/3626756 · Cited by 33

Uncertainty-aware Bayesian neural network with SHAP interpretability for data-driven assembly quality prediction in complex manufacturing systems

Wu, J., Sun, Y., Song, Y., Liu, L., Gao, Z., & Qin, W. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103730 · Cited by 20

Biologic formulation in a self-driving biomaterials lab

Tamasi, M., & Gormley, A. J. (2022) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2022.101041 · Cited by 23

Solution of physics-based inverse problems using conditional generative adversarial networks with full gradient penalty

Ray, D., Murgoitio-Esandi, J., Dasgupta, A., & Oberai, A. A. (2023) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2023.116338 · Cited by 20

Bayesian renormalization

Berman, D. S., Klinger, M. S., & Stapleton, A. G. (2023) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad0102 · Cited by 34

Risk assessment of gas pipeline using an integrated Bayesian belief network and GIS : Using Bayesian neural networks for external pitting corrosion modelling

Woldesellasse, H., & Tesfamariam, S. (2024) · The Canadian Journal of Chemical Engineering · doi:10.1002/cjce.25393 · Cited by 19

Predicting Asphalt Pavement Deterioration Under Climate Change Uncertainty Using Bayesian Neural Network

Cui, B., & Wang, H. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3505237 · Cited by 19

AI-powered exploration of molecular vibrations, phonons, and spectroscopy

Han, B., Okabe, R., Chotrattanapituk, A., Cheng, M., Li, M., & Cheng, Y. (2025) · Digital Discovery · doi:10.1039/d4dd00353e · Cited by 19

Deep Learning-based Uncertainty Quantification for spatio-temporal environmental Remote Sensing: A systematic literature review

Ferchichi, A., Ferchichi, A., Hendaoui, F. A., Chihaoui, M., & Toujani, R. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130242 · Cited by 23

Augmenting Softmax Information for Selective Classification with Out-of-Distribution Data

Xia, G., & Bouganis, C.-S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26351-4_40 · Cited by 19

Command filtered backstepping control of constrained flexible joint robotic manipulator

Arefi, M. M., Vafamand, N., Homayoun, B., & Davoodi, M. (2023) · IET Control Theory and Applications · doi:10.1049/cth2.12528 · Cited by 19

Learning to predict sustainable aviation fuel properties: A deep uncertainty quantification viewpoint

Oh, J.-H., Oldani, A., Solecki, A., & Lee, T. (2023) · Fuel · doi:10.1016/j.fuel.2023.129508 · Cited by 19

A Primer on Bayesian Neural Networks: Review and Debates

Arbel, J., Pitas, K., Vladimirova, M., & Fortuin, V. (2026) · Statistical Science · doi:10.1214/24-sts969 · Cited by 78

Spatial Bayesian neural networks

Zammit‐Mangion, A., Kaminski, M. D., Tran, B.-H., Filippone, M., & Cressie, N. (2024) · Spatial Statistics · doi:10.1016/j.spasta.2024.100825 · Cited by 19

Privacy-Preserved Framework for Short-Term Probabilistic Net Energy Forecasting

Razavi, S., Arefi, A., Smith, D. B., Ledwich, G. F., Nourbakhsh, G., & Minakshi, M. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3211328 · Cited by 18

Probabilistic Neural Data Fusion for Learning from an Arbitrary Number of Multi-Fidelity Data Sets

Mora, C., Eweis-Labolle, J. T., Johnson, T. B., Gadde, L., & Bostanabad, R. (2023) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2023.116207 · Cited by 20

Real-Time Bayesian Neural Networks for 6G Cooperative Positioning and Tracking

Tedeschini, B. C., Kwon, G., Nicoli, M., & Win, M. Z. (2024) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2024.3413950 · Cited by 18

Augmenting the Softmax with Additional Confidence Scores for Improved Selective Classification with Out-of-Distribution Data

Xia, G., & Bouganis, C.-S. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02029-3 · Cited by 17

A variational Bayesian deep reinforcement learning approach for resilient post-disruption recovery of distribution grids

Yin, Z., Fang, C., Fang, Y., & Xie, M. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.110840 · Cited by 17

Bayesian KalmanNet: Quantifying Uncertainty in Deep Learning Augmented Kalman Filter

Dahan, Y., Revach, G., Duník, J., & Shlezinger, N. (2025) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2025.3581703 · Cited by 17

Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution

Huang, Y., Chouzenoux, É., & Pesquet, J.-C. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3224322 · Cited by 17

NPBDREG: Uncertainty assessment in diffeomorphic brain MRI registration using a non-parametric Bayesian deep-learning based approach

Khawaled, S., & Freiman, M. (2022) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2022.102087 · Cited by 17

Prediction-Uncertainty-Aware Threat Detection for ADAS: A Case Study on Lane-Keeping Assistance

Dahl, J., de Campos, G. R., & Fredriksson, J. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3253555 · Cited by 19

Enhancement of virtual data quality using pre-trained Bayesian transfer learning under inaccurate and insufficient measurement data

Lee, J., Lee, J., & Heo, J. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102241 · Cited by 17

Real-time prediction of gas flow dynamics in diesel engines using a deep neural operator framework

Kumar, V., Goswami, S., Smith, D., & Karniadakis, G. E. (2023) · Applied Intelligence · doi:10.1007/s10489-023-05178-z · Cited by 17

Uncertainty-Aware Temporal Graph Convolutional Network for Traffic Speed Forecasting

Qian, W., Nielsen, T. D., Zhao, Y. L., Larsen, K. G., & Yu, J. J. Q. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3365721 · Cited by 20

From neutron star observations to nuclear matter properties: A machine learning approach

Carvalho, V., Ferreira, M., & Providência, C. (2024) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.109.123038 · Cited by 17

Stack performance classification and fault diagnosis optimization of solid oxide fuel cell system based on bayesian artificial neural network and feature selection

Wu, X., Mei, J., Xu, Y., Cheng, Y., Peng, J., Chi, B., Wang, Z., & Li, X. (2024) · Journal of Power Sources · doi:10.1016/j.jpowsour.2024.235198 · Cited by 17

A physics-informed Bayesian neural network model for probabilistic prediction of fatigue crack growth rate at different temperatures

Rongqiao, W. A. N. G., Kong, W., Liu, X., Chen, G., Chen, H., Hu, D., & Mao, J. (2025) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2025.109184 · Cited by 16

Compressed Particle-Based Federated Bayesian Learning and Unlearning

Gong, J., Simeone, O., & Kang, J. (2022) · IEEE Communications Letters · doi:10.1109/lcomm.2022.3223655 · Cited by 16

Gradient boosting Bayesian neural networks via Langevin MCMC

Bai, G., & Chandra, R. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126726 · Cited by 16

Damage identification for railway tracks using ultrasound guided wave and hybrid probabilistic deep learning

Zhang, Y., Dang, D.-Z., Wang, Y., & Ni, Y. (2024) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2024.135466 · Cited by 16

Evaluations of artificial intelligence and machine learning algorithms in neurodiagnostics

Williams, K. S. (2024) · Journal of Neurophysiology · doi:10.1152/jn.00404.2023 · Cited by 16

Pedestrian Trajectory Forecasting Using Deep Ensembles Under Sensing Uncertainty

Nayak, A., Eskandarian, A., Doerzaph, Z. R., & Ghorai, P. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3382201 · Cited by 16

Uncertainty-Aware Incremental Automatic Modulation Classification With Bayesian Neural Network

Luu, V.-C., Park, J., & Hong, J. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3390038 · Cited by 16

Expertise-informed Bayesian convolutional neural network for oil production forecasting

Zang, J., Wang, J., Zhang, K., El-Alfy, E.-S. M., & Mańdziuk, J. (2024) · Geoenergy Science and Engineering · doi:10.1016/j.geoen.2024.213061 · Cited by 17

A Bayesian deep learning approach for video-based estimation and uncertainty quantification of urban rainfall intensity

Zheng, F., Yin, H., Zhang, J., Duan, H., & Gupta, H. V. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.131706 · Cited by 19

Deep Neural Network Confidence Calibration from Stochastic Weight Averaging

Cao, Z., Li, Y., Kim, D., & Shin, B. (2024) · Electronics · doi:10.3390/electronics13030503 · Cited by 15

Explainable Multi-Fidelity Bayesian Neural Network for Distribution System State Estimation

Zhang, J., Zhao, J., Cheng, G., Rouhani, A., & Chen, X. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.125972 · Cited by 15

Learning Groundwater Contaminant Diffusion‐Sorption Processes With a Finite Volume Neural Network

Praditia, T., Karlbauer, M., Otte, S., Oladyshkin, S., Butz, M. V., & Nowak, W. (2022) · Water Resources Research · doi:10.1029/2022wr033149 · Cited by 15

Atlas

Liu, Q., Choi, N., & Han, T. (2022) · ACM International Conference on emerging Networking EXperiments and Technologies (CoNEXT) · doi:10.1145/3555050.3569115 · Cited by 15

Addressing uncertainty challenges for autonomous driving in real-world environments

Suk, H., Lee, Y., Kim, T., & Kim, S. (2023) · Advances in computers · doi:10.1016/bs.adcom.2023.06.004 · Cited by 18

UMat: Uncertainty-Aware Single Image High Resolution Material Capture

Rodríguez-Pardo, C., Dominguez-Elvira, H., Pascual-Hernández, D., & Garcés, E. (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/cvpr52729.2023.00558 · Cited by 17

Using Bayesian Neural Networks for Uncertainty Assessment of Ore Type Boundaries in Complex Geological Models

Jordão, H., Sousa, A. J., & Soares, A. (2023) · Natural Resources Research · doi:10.1007/s11053-023-10265-6 · Cited by 15

Urban Traffic Flow Prediction Based on Bayesian Deep Learning Considering Optimal Aggregation Time Interval

Fu, F., Wang, D., Sun, M. J., Xie, R., & Cai, Z. (2024) · Sustainability · doi:10.3390/su16051818 · Cited by 18

Physics-based reduced order modeling for uncertainty quantification of guided wave propagation using Bayesian optimization

Drakoulas, G. I., Gortsas, T. V., & Polyzos, D. K. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108531 · Cited by 15

AI4Mobile: Use Cases and Challenges of AI-based QoS Prediction for High-Mobility Scenarios

Kulzer, D. F., Kasparick, M., Palaios, A., Sattiraju, R., Ramos-Cantor, O. D., Wieruch, D., Tchouankem, H., Göttsch, F., Geuer, P., Schwardmann, J., et al. (2021) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2021-spring51267.2021.9449059 · Cited by 15

DIGS: deep inference of galaxy spectra with neural posterior estimation

Khullar, G., Nord, B., Ćiprijanović, A., Poh, J., & Xu, F. (2022) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ac98f4 · Cited by 15

Uncertainty Assessment-Based Active Learning for Reliable Fire Detection Systems

Kim, Y.-J., & Kim, W.-T. (2022) · IEEE Access · doi:10.1109/access.2022.3190852 · Cited by 14

Learning safety in model-based Reinforcement Learning using MPC and Gaussian Processes

Airaldi, F., Schutter, B. D., & Dabiri, A. (2023) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2023.10.563 · Cited by 14

Evaluation of Machine Learning Architectures on the Quantification Of Epistemic and Aleatoric Uncertainties In Complex Dynamical Systems

Guth, S., Mojahed, A., & Sapsis, T. P. (2023) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2024.116760 · Cited by 19

Parameter probabilistic prediction for satellite power system based on unsupervised multi-dimensional sequence segmentation

Kang, S., Gao, Y., Song, Y., Zhou, R., & Pang, J. (2024) · Aerospace Science and Technology · doi:10.1016/j.ast.2024.108933 · Cited by 14

DEUCE v1.0: a neural network for probabilistic precipitation nowcasting with aleatoric and epistemic uncertainties

Harnist, B., Pulkkinen, S., & Mäkinen, T. (2024) · Geoscientific model development · doi:10.5194/gmd-17-3839-2024 · Cited by 17

The Role of Uncertainty Quantification for Trustworthy AI

Deuschel, J., Foltyn, A., Roscher, K., & Scheele, S. (2024) · Springer eBooks · doi:10.1007/978-3-031-64832-8_5 · Cited by 17

Adversarially Robust Fault Zone Prediction in Smart Grids With Bayesian Neural Networks

Efatinasab, E., Sinigaglia, A., Azadi, N., Susto, G. A., & Rampazzo, M. (2024) · IEEE Access · doi:10.1109/access.2024.3452476 · Cited by 14

Physics-Informed Active Learning for Accelerating Quantum Chemical Simulations

Hou, Y.-F., Zhang, L., Zhang, Q., Ge, F., & Dral, P. O. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c00821 · Cited by 18

Incorporating modelling uncertainty and prior knowledge into landslide susceptibility mapping using Bayesian neural networks

Chen, C., Shen, Z., Fang, L., Lin, J., Li, S., & Wang, K. (2024) · Georisk Assessment and Management of Risk for Engineered Systems and Geohazards · doi:10.1080/17499518.2024.2422498 · Cited by 14

From theoretical models to practical deployment: A perspective and case study of opportunities and challenges in AI-driven cardiac auscultation research for low-income settings

Krones, F., & Walker, B. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000437 · Cited by 17

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

Measuring Uncertainty through Bayesian Learning of Deep Neural Network Structure

Deng, Z., Luo, Y., Zhu, J., & Zhang, B. (2019) · arXiv · doi:10.48550/arxiv.1911.09804

Bayesian Neural Networks: EssentialsPreprint

Chang, D. T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.13594 · Cited by 7

Estimating uncertainty in neural networks at scale

Denlinger, R. (2024) · Texas Digital Library (University of Texas) · doi:10.26153/tsw/55791

Measuring the Uncertainty of Predictions in Deep Neural Networks with Variational Inference

Steinbrener, J., Posch, K., & Pilz, J. (2020) · Sensors · doi:10.3390/s20216011 · Cited by 52