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

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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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PT-HMC: Optimization-based Pre-Training with Hamiltonian Monte-Carlo Sampling for Driver Intention Recognition

Vellenga, K., Karlsson, A., Steinhauer, H. J., Falkman, G., & Sjögren, A. (2024) · ACM transactions on probabilistic machine learning. · doi:10.1145/3688573 · Cited by 2

Characterising borderline areas in bladder tumour grading with Bayesian graph neural networks

Gao, S., Browning, L., Alham, N. K., Protheroe, A. S., Edwards, K., Hamblin, J., Rittscher, J., & Verrill, C. L. (2024) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi56570.2024.10635485 · Cited by 2

Empowering 6G Positioning and Tracking with Bayesian Neural Networks

Tedeschini, B. C., Kwon, G., Nicoli, M., & Win, M. Z. (2024) · International Conference on Communications · doi:10.1109/icc51166.2024.10622691 · Cited by 2

Natural gradient hybrid variational inference with application to deep mixed models

Zhang, W., Smith, M. S., Maneesoonthorn, W. O., & Loaiza‐Maya, R. (2024) · Statistics and Computing · doi:10.1007/s11222-024-10488-4 · Cited by 2

Probabilistic neural networks for improved analyses with phenomenological R -matrix

Kim, C.-H., Chae, K. Y., Smith, M. S., Bardayan, D. W., Brune, C. R., deBoer, R. J., Lu, D., & Odell, D. (2024) · Physical Review C · doi:10.1103/physrevc.110.054609 · Cited by 2

A Bayesian Inference-Based Method for Uncertainty Analysis in Raman Spectroscopy

Zhou, H. (2024) · IEEE Access · doi:10.1109/access.2024.3510927 · Cited by 2

Robust estimation of skin physiological parameters from hyperspectral images using Bayesian neural networks

Manojlović, T., Tomanič, T., Štajduhar, I., & Milanič, M. (2025) · Journal of Biomedical Optics · doi:10.1117/1.jbo.30.1.016004 · Cited by 2

Predicting ground-level nitrogen dioxide concentrations using the BaYesian attention-based deep neural network

Casolaro, A., Capone, V., & Camastra, F. (2025) · Ecological Informatics · doi:10.1016/j.ecoinf.2025.103097 · Cited by 4

Minimax Bayesian Neural Networks

Hong, J., & Kuruoğlu, E. E. (2025) · Entropy · doi:10.3390/e27040340 · Cited by 2

Tiny Deep Ensemble: Uncertainty Estimation in Edge AI Accelerators via Ensembling Normalization Layers with Shared Weights

Ahmed, S. T., Hefenbrock, M., & Tahoori, M. B. (2024) · doi:10.1145/3676536.3676804 · Cited by 2

In search of global 21-cm signal using artificial neural network in light of ARCADE 2

Mohapatra, V., Johnny, J., Natwariya, P. K., Goswami, J., & Nayak, A. (2025) · The European Physical Journal C · doi:10.1140/epjc/s10052-025-14256-0 · Cited by 2

Noise balance and stationary distribution of stochastic gradient descent

Ziyin, L., Li, H., & Ueda, M. (2025) · Physical review. E · doi:10.1103/physreve.111.065303 · Cited by 2

Sparsity-Aware Optimization of In-Memory Bayesian Binary Neural Network Accelerators

Katti, P., Al‐Hashimi, B. M., & Rajendran, B. (2025) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas56072.2025.11043768 · Cited by 2

A Planner-Agnostic Monitor for Behaviour Feasibility of Autonomous Vehicles Using a Bayesian Discriminator

Zhang, Y., Hu, Z., Yang, H., Lou, S., & Lv, C. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3582573 · Cited by 2

Future Residential Water Use and Management Under Climate Change Using Bayesian Neural Networks

Seo, Y.-H., Sung, J. H., Park, J.-S., Kim, B. S., & Park, J. (2025) · Water · doi:10.3390/w17152179 · Cited by 2

A Fourth-Order Moment Method Based on Back Propagation Neural Network for High-Dimensional Nonlinear Reliability Analysis

Yang, K., Li, W., Xun, J., Yang, X.-T., Wang, Y., & Shiyuan, E. (2025) · Applied Sciences · doi:10.3390/app15169046 · Cited by 2

Local Calibration Testing in Supervised Machine Learning Models Using Input Space Kernels

Walker, M., Reith‐Braun, M., & Hanebeck, U. D. (2025) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion65864.2025.11123977 · Cited by 2

Assessing Deep Neural Networks as Probability Estimators

Pan, Y. R., Kuo, K., Rilee, M. L., & Yu, H. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671328 · Cited by 2

Considering User Interests to Provid an Event Base News Stream Framework

Charmchi, M. R. H., Hassanpour, H., & Cami, B. R. (2022) · Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS) · doi:10.1109/icspis56952.2022.10043990 · Cited by 2

Control Chart Patterns (CCPs) Forecasting Using Probabilistic Deep Learning

Trong-Dung, N., Vu, H. C., Khac-Tuan, H., Anh-Cuong, H., & Boudaoud, N. (2022) · International Conference on System Reliability and Safety (ICSRS) · doi:10.1109/icsrs56243.2022.10067718 · Cited by 2

Streaming LifeLong Learning With Any-Time Inference

Banerjee, S., Verma, V. K., & Namboodiri, V. P. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160358 · Cited by 3

Data-Driven Based Corrective Actions and Detection of Intrusive Sensors Behavior in Power Electronic Dominated Grids

Alvand, H., Zare, A., Shadmand, M. B., & Abu‐Rub, H. (2023) · IEEE Power and Energy Conference at Illinois (PECI) · doi:10.1109/peci57361.2023.10197837 · Cited by 2

Bayesian Neural Network-Based Demand Forecasting for Express Transportation

Chen, R., Zhong, X., & Xu, X. (2023) · Highlights in Science Engineering and Technology · doi:10.54097/hset.v68i.12078 · Cited by 2

A Bayesian Neural Network-based approach for multistate reliability assessment of solder joints exposed to various failure mechanisms

Li, Y., Askar, S., Sullca, S. P., Burga-Falla, J.-M., & Asaad, R. R. (2024) · Vacuum · doi:10.1016/j.vacuum.2024.113004 · Cited by 2

Artificial intelligence in catalysis

Rangarajan, S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-99135-3.00002-6 · Cited by 2

Uncertainty quantification in LV state estimation under high shares of flexible resources

Müller, N., Chevalier, S. C., Heinrich, C., Heussen, K., & Ziras, C. (2022) · Electric Power Systems Research · doi:10.1016/j.epsr.2022.108479 · Cited by 2

Seeking New Physics in Cosmology with Bayesian Neural Networks I: Dark Energy and Modified GravityPreprint

Mancarella, M., Kennedy, J., Bose, B., & Lombriser, L. (2020) · arXiv (Cornell University) · Cited by 2

Soft Expectation and Deep Maximization for Image Feature DetectionPreprint

Mai, A., Yang, A. Y., & Meyer, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.10291 · Cited by 2

Comparison of Graphcore IPUs and Nvidia GPUsfor cosmology applicationsPreprint

Arcelin, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.02465 · Cited by 2

Structured Stochastic Gradient MCMCPreprint

Alexos, A., Boyd, A., & Mandt, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.09028 · Cited by 2

Neural Networks-based Image Denoising MethodsPreprint

Wang, M. (2023) · Preprints.org · doi:10.20944/preprints202310.0838.v1 · Cited by 2

Non-invasive arterial input function estimation using an MRI atlas and machine learningPreprint

Vashistha, R., Moradi, H., Hammond, A., O’Brien, K., Rominger, A., Sari, H., Shi, K., Vegh, V., & Reutens, D. C. (2023) · Research Square · doi:10.21203/rs.3.rs-3784721/v1 · Cited by 2

Aircraft Bird Strike Risk Prediction Using LSTM-RNN, XGBoost, and BNNPreprint

Powell, J., Liu, D., Liu, Y., & Washburn, B. E. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6340226 · Cited by 2

Probabilistic machine learning for breast cancer classification

Leventi-Peetz, A.-M., & Weber, K. (2022) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2023029 · Cited by 1

Exploring Node Classification Uncertainty in Graph Neural Networks

Islam, M. F., Zabeen, S., Rahman, F. B., Islam, M. A., Kibria, F. B., Manab, M. A., Karim, D. Z., & Rasel, A. A. (2023) · ACM Southeast Regional Conference (ACMSE) · doi:10.1145/3564746.3587019 · Cited by 1

Incertidumbre epistémica y aleatoria en soft metrología: una perspectiva desde el aseguramiento de la validez de los resultados

Agudelo-Cardona, V., Barbosa, I. N., Vallejo, M., Cortés, N. B., & Delgado‐Trejos, E. (2023) · Ingeniería · doi:10.14483/23448393.18883 · Cited by 1

Information Bound and Its Applications in Bayesian Neural Networks

Zhang, J., Yang, H., Song, T., Wang, H., Xue, Z., Ma, R., & Guan, H. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230617 · Cited by 1

Actively learning dynamical systems using Bayesian neural networks

Tang, S., Fujimoto, K., & Maruta, I. (2023) · Applied Intelligence · doi:10.1007/s10489-023-05044-y · Cited by 1

Method for Medium- and Long-Term Reservoir Streamflow Prediction by Coupling Group Search Optimizer with Deep Learning

Zhou, B., Chen, K., Zhu, M., Wang, Y., & Zhang, C. (2023) · International Symposium on Smart and Healthy Cities (ISHC) · doi:10.1109/ishc61216.2023.00024 · Cited by 1

Machine Learning for Lithology Analysis using a Multi-Modal Approach of Integrating XRF and XCT data

Neelakantan, S., Hansson, A., Norell, J., Schött, J., Längkvist, M., & Loutfi, A. (2024) · Linköping electronic conference proceedings · doi:10.3384/ecp208021 · Cited by 1

Enhancing Predictive Process Monitoring with Conformal Prediction

Skouvas, F., Papadopoulos, H., & Andreou, A. S. (2024) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-63215-0_15 · Cited by 1

Machine Learning and Human Unlearning

Ficarra, F. V. C. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-67426-6_4 · Cited by 1

Model Mismatch Detection in Wireless Resources Allocation for SCMA Video Communication Systems Using Bayesian Neural Network Confidence

Tseng, S., Wang, Y., & Hsu, Y.-T. (2024) · IEEE Symposium on Wireless Technology & Applications (ISWTA) · doi:10.1109/iswta62130.2024.10651710 · Cited by 1

Hardware-Aware Neural Dropout Search for Reliable Uncertainty Prediction on FPGA

Zhang, Z., Fan, H., Chen, H., Dudziak, Ł., & Luk, W. W. (2024) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/3649329.3656528 · Cited by 1

Challenges and Algorithms for Connected and Autonomous Vehicle Safety and Mobility in Smart Cities

Eskandarian, A., Mehr, G., Nayak, A., & Shang, X. (2024) · Springer tracts on transportation and traffic · doi:10.1007/978-3-031-64769-7_9 · Cited by 1

Transfer Learning for Data Fusion for Electromagnetic and Ultrasound Breast Imaging

Noël, V., Rodet, T., & Lesselier, D. (2025) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2025.3541934 · Cited by 1

Uncertainty-aware hand gesture recognition for safety-critical and emergency human-robot interaction

Wang, S., Li, K., Zhang, T., & Zhang, Z. (2024) · Sensors and Actuators A Physical · doi:10.1016/j.sna.2024.116178 · Cited by 1

Scalable and robust bayesian transfer learning for in-situ qualification in laser powder bed fusion additive manufacturing

Li, Z.-H., Fu, K.-N., Bevans, B., Rao, P. K., Wang, R.-X., Carrington, A., Riensche, A., Barrett, C., Halliday, S., & Kim, M. (2026) · Journal of QualityTechnology · doi:10.1080/00224065.2026.2664176 · Cited by 2

Accurate Classification of Dysplasia in Inflammatory Bowel Disease Patients Using Deep Learning

Hamad, A. T., Suthar, P., Laziuk, K., Rao, D. S., & Rao, P. R. (2024) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi62660.2024.10913548 · Cited by 1

Descriptor: Not-A-DAtabase of Synthetic Shapes Benchmarking Dataset (NADA-SynShapes)

Corso, G. D., Volpini, F., Caudai, C., Moroni, D., & Colantonio, S. (2025) · IEEE data descriptions. · doi:10.1109/ieeedata.2025.3562805 · Cited by 1

Carbon Nanotube Interconnect Optimizations With Bayesian Neural Network and Bayesian Optimization

Wang, Z., Xing, W. W., Xu, N., & Cheng, Y. (2025) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2025.3571336 · Cited by 1

Feature Preserving Shrinkage on Bayesian Neural Networks Via the R2D2 Prior

Chan, T. H., Zhang, D., Yin, G., & Yu, L. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3572766 · Cited by 1

Non-invasive arterial input function estimation using an MRA atlas and machine learning

Vashistha, R., Moradi, H., Hammond, A., O’Brien, K., Rominger, A., Sari, H., Shi, K., Vegh, V., & Reutens, D. C. (2025) · EJNMMI Research · doi:10.1186/s13550-025-01253-3 · Cited by 1

A Surrogate-Assisted High-Dimensional Mixed-Variable Evolutionary Framework and its Application to Vehicle Lightweighting Design

Tan, S., Wang, Y., Sun, G., & Pang, T. (2025) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2025.3573768 · Cited by 3

Bayesian Neural Networks With Physics-Informed Priors With Application to Boundary Layer Velocity

Menicali, L., Richter, D. H., & Castruccio, S. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3577508 · Cited by 1

Quantifying Geotechnical Uncertainty in Ground Motion Predictions: Bayesian Generalized Linear Model Framework

Chala, A. T., Mayassah, M., Vîlceanu, C.-B., & Ray, R. (2025) · Advances in Civil Engineering · doi:10.1155/adce/6678669 · Cited by 1

Underspecification and uncertainty in deep learning models: Is there a connection?

Ferreira, F. M. M. R., & Rossetti, R. J. F. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11415-y · Cited by 1

Pruning-Optimized Bayesian Neural Networks for Image Classification

Wang, C., & Xu, H. (2025) · IEEE International Conference on Electronic Technology, Communication and Information (ICETCI) · doi:10.1109/icetci64844.2025.11084096 · Cited by 1

Machine learning based energy confinement time extrapolation via multi-tokamak database

Nam, H., & Seo, J. (2025) · Fusion Engineering and Design · doi:10.1016/j.fusengdes.2025.115369 · Cited by 1

BayeSiamMTL: Uncertainty-aware multitask learning for post-disaster building damage assessment

Hertel, V., Wani, O., Geiß, C., Wieland, M., & Taubenböck, H. (2025) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2025.104759 · Cited by 1

Deep learning-based enhancement of integrated process control performance in nuclear reactor operations

Hsieh, H.-Y., Abuqudaira, T., & Tsvetkov, P. V. (2025) · Annals of Nuclear Energy · doi:10.1016/j.anucene.2025.111765 · Cited by 1

Multimodal vs. unimodal approaches to uncertainty in 3D image segmentation under distribution shifts

Javanbakhat, M., Hasan, M. T., & Lippert, C. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104473 · Cited by 5

Few-Shot Class-Incremental Audio Classification Using Pseudo-Incrementally Trained Embedding Learner and Continually Updated Stochastic Classifier

Li, Y., Cao, W., Tan, J., Li, Q., & Chen, G. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3610050 · Cited by 1

Assessing glacial lake outburst flood risk in the Eastern Himalayas: a Bayesian neural network framework

Vashistha, A., Dashora, A., & Shah, A. A. (2025) · Natural Hazards · doi:10.1007/s11069-025-07668-4 · Cited by 1

Improving Data Curation of Software Vulnerability Patches through Uncertainty Quantification

Chen, H., Zhao, Y., & Damevski, K. (2025) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2025.3612278 · Cited by 1

Instantaneous power prediction for industrial robots using tree-based machine learning methods

Lențoiu, I., Răileanu, S., Borangiu, T., Constantinescu, M., & Morariu, O. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112339 · Cited by 2

Estimation of uncertainty in electrical loads due to electric vehicle charging

Shamarova, N., Shushpanov, I., Федосов, Д. С., Suslov, K., & Batukhtin, A. G. (2025) · Power engineering research equipment technology · doi:10.30724/1998-9903-2025-27-3-147-161 · Cited by 1

Active Learning-Based Multiobjective Optimization of Electric Machines Using Probabilistic Metamodeling

Chen, N., Digel, C., Brodatzki, M., & Doppelbauer, M. (2025) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2025.3618253 · Cited by 1

Multi-step ahead streamflow and uncertainty forecasting using a HyMoLAP rainfall-runoff model-based framework integrated with Bayesian neural networks in the Ouémé river basin, Benin

Houénafa, S. E., Johnson, O., Ronoh, E. K., & Moore, S. E. (2025) · PLoS ONE · doi:10.1371/journal.pone.0333590 · Cited by 1

Automatic Modulation Classification based on Bayesian Learning

Wu, T.-C. (2025) · IEICE Communications Express · doi:10.23919/comex.2025xbl0088 · Cited by 1

Efficient Video Representation via Hybrid Key Frame Reconstruction: A Test-Time Data Augmentation Approach

Hu, J., Feng, Y., Huang, H., Chen, H., & Lyu, K. (2025) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2025.3626256 · Cited by 1

Nonlinear dynamics and uncertainty-aware control of prosthetic systems using Bayesian Neural Networks and finite-time disturbance compensation

Alsaadi, F. E., Alharbi, N. S., & Al-Barakati, A. A. (2025) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2025.117590 · Cited by 1

DnD Filter: Differentiable State Estimation for Dynamic Systems using Diffusion Models

Wan, Z., & Zhao, L. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246156 · Cited by 1

Global Optimization of Stochastic Black-Box Functions with Arbitrary Noise Distributions using Wilson Score Kernel Density Estimation

Iversen, T. M., Sørensen, L. C., Mathiesen, S., & Petersen, H. G. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247469 · Cited by 1

Generative Multiobjective Bayesian Optimization with Scalable Batch Evaluations for Sample-Efficient De Novo Molecular Design

Muthyala, M. R., Sorourifar, F., Tan, T., Peng, Y., & Paulson, J. A. (2025) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.5c03166 · Cited by 3

A Bayesian Neural Network Approach for Probabilistic-Based Damage Detection of Monitored Bridges

Mariani, F., Ierimonti, L., Ubertini, F., & Venanzi, I. (2025) · Lecture notes in civil engineering · doi:10.1007/978-3-031-96110-6_48 · Cited by 1

FEDERATED LEARNING ON STOCHASTIC NEURAL NETWORKS

Tang, J., Bausback, R., Bao, F., & Archibald, R. K. (2025) · Journal of Machine Learning for Modeling and Computing · doi:10.1615/jmachlearnmodelcomput.2025060047 · Cited by 1

Energy-Efficient Autonomous Driving With Adaptive Perception and Robust Decision

Xia, Y., Liang, Z., Deng, L., Zhao, Y., Su, H., & Zheng, K. (2026) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde65706.2026.00057 · Cited by 1

Vulnerability Analysis of Construction Safety System for Tropical Island Building Projects Based on GV-IB Model

Huang, B., Wang, J., & Huang, J. H. (2026) · Systems · doi:10.3390/systems14010070 · Cited by 1

Robust extrapolation in nuclear mass predictions using domain-informed activation functions

Kim, C.-H., Chae, K. Y., & Smith, M. S. (2026) · Physical Review C · doi:10.1103/mcxf-d32x · Cited by 1

DiffPF: Differentiable Particle Filtering With Generative Sampling via Conditional Diffusion Models

Wan, Z., & Zhao, L. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3655302 · Cited by 1

Variational Bayesian Gaussian Mixture Last Layer of Convolutional Neural Network

Bakhouya, M., Elghazi, K., Ramchoun, H., Hadda, M., & Masrour, T. (2026) · SN Computer Science · doi:10.1007/s42979-026-04736-9 · Cited by 1

Learning with constraints: A physics-guided Bayesian framework for reliable geothermal temperature mapping in northeastern Italy

Dashtgoli, D. S., Giustiniani, M., Busetti, M., Cherubini, C., & Narsilio, G. A. (2026) · Geothermics · doi:10.1016/j.geothermics.2026.103816 · Cited by 1

Flexural toughness characteristic of steel fiber reinforced concrete under high temperatures: Experimental, numerical, and machine learning study

Yang, H., Li, F., Yang, Q., Chai, W. H., & Li, M. (2026) · Materials Today Communications · doi:10.1016/j.mtcomm.2026.114805 · Cited by 1

A comprehensive approach for model-based SHM of bridges using Bayesian neural networks

Mariani, F., Ierimonti, L., Ubertini, F., & Venanzi, I. (2026) · Structure and Infrastructure Engineering · doi:10.1080/15732479.2026.2626406 · Cited by 1

Towards Trustworthy Vital Sign Forecasting: Leveraging Uncertainty for Prediction Intervals

Wang, L. R., Henderson, T. C., Ong, Y.-S., Ng, Y. Y., & Fan, X. (2025) · Proceedings · doi:10.1109/icdm65498.2025.00173 · Cited by 1

The value of satellite time series and uncertainty quantification for field-scale wheat yield prediction in a multi-climate nation like Iran

Aram, S. H., & Molajou, A. (2026) · Smart Agricultural Technology · doi:10.1016/j.atech.2026.101934 · Cited by 1

Trustworthy Compound–Protein Interaction Prediction with Interpretable and Conformalized Cross-Attention Transformers

Li, P., Hua, L., Liu, Y., & Zhu, J. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02709 · Cited by 1

UGSCE-BNN: An uncertainty-guided symmetric cross-entropy Bayesian neural network for industrial time series forecasting

Long, J., & Jia, X. D. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115630 · Cited by 1

Sparse Variational Student-t Processes for Heavy-Tailed Modeling

Xu, J., Zeng, D., & Paisley, J. W. (2026) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2026.3673350 · Cited by 1

Physics-informed Bayesian Neural Network for groundwater recharge estimation in data-scarce arid regions

Khan, M. S., Fahs, M., Hadidi, A., & Baalousha, H. M. (2026) · Frontiers in Water · doi:10.3389/frwa.2026.1787659 · Cited by 1

Batch active learning for reliability-based model validation

Lämmle, S., Stecher, J., Hodrius, T., Haselhoff, A., & Roos, D. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.112631 · Cited by 1

Neural Network-Based LoRa Received Signal Strength Indicator Fingerprint Identification for Indoor Localization of Mobile Robots

Barai, C., Sarkar, M., Sarkar, U., Mazumder, S., Chandra, A., Samanta, T., & Pandey, H. K. (2026) · Sensors · doi:10.3390/s26072127 · Cited by 1

Probabilistic Machine Learning Framework for Predicting Critical Heat Flux in Uniformly Heated Vertical Tubes with Uncertainty Quantification

Mondal, A., & Sharma, S. (2026) · Nuclear Technology · doi:10.1080/00295450.2026.2629142 · Cited by 1

Data-driven explainable artificial intelligence models for predicting CO2 uptake in metal–organic frameworks

Shaida, M. A., Saleem, L., Ahmad, S. A. W., Ansari, S. S., & Ibrahim, S. M. (2026) · Environmental Science and Pollution Research · doi:10.1007/s11356-026-37725-9 · Cited by 1

Information-Geometric Barycenters for Bayesian Federated Learning

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