mlsanityData to 7 Oct 2026

Bayesian Neural Networks: An Introduction and Survey

arXiv 2006.12024 ↗First posted 22 Jun 2020stat.ML cs.LGcited by 130

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Abstract (arXiv)

Neural Networks (NNs) have provided state-of-the-art results for many challenging machine learning tasks such as detection, regression and classification across the domains of computer vision, speech recognition and natural language processing. Despite their success, they are often implemented in a frequentist scheme, meaning they are unable to reason about uncertainty in their predictions. This article introduces Bayesian Neural Networks (BNNs) and the seminal research regarding their implementation. Different approximate inference methods are compared, and used to highlight where future research can improve on current methods.

Key facts. Cited by 130 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Jun 2020.

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Citing papers per year: 2021: 7; 2022: 20; 2023: 29; 2024: 26; 2025: 28; 2026: 20.

130 papers.

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  3. 2021Tractable Regularization of Probabilistic Circuitscited by 39
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  9. 2026Neural Thickets: Diverse Task Experts Are Dense Around Pretrained Weightscited by 25
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  11. 2023ExPT: Synthetic Pretraining for Few-Shot Experimental Designcited by 22
  12. 2024Experts Don't Cheat: Learning What You Don't Know By Predicting Pairscited by 18
38 more
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  2. 2023Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimizationcited by 15
  3. 2023Make Me a BNN: A Simple Strategy for Estimating Bayesian Uncertainty from Pre-trained Modelscited by 15
  4. 2021Bayesian Deep Learning for Partial Differential Equation Parameter Discovery with Sparse and Noisy Datacited by 12
  5. 2022Bayesian Physics-Informed Extreme Learning Machine for Forward and Inverse PDE Problems with Noisy Datacited by 12
  6. 2023A Symmetry-Aware Exploration of Bayesian Neural Network Posteriorscited by 12
  7. 2024Confidence Calibration of Classifiers with Many Classescited by 11
  8. 2025Decoding-based Regressioncited by 11
  9. 2022Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processescited by 10
  10. 2022Bayesian Physics Informed Neural Networks for Data Assimilation and Spatio-Temporal Modelling of Wildfirescited by 10
  11. 2023Gaussian Latent Representations for Uncertainty Estimation using Mahalanobis Distance in Deep Classifierscited by 10
  12. 2023Molecule Joint Auto-Encoding: Trajectory Pretraining with 2D and 3D Diffusioncited by 10
  13. 2024Uncertainty Quantification via Stable Distribution Propagationcited by 10
  14. 2024Composite Bayesian Optimization In Function Spaces Using NEON -- Neural Epistemic Operator Networkscited by 10
  15. 2025A Unified Evaluation Framework for Epistemic Predictionscited by 10
  16. 2022Quantification of Deep Neural Network Prediction Uncertainties for VVUQ of Machine Learning Modelscited by 9
  17. 2022Machine Learning in Orbit Estimation: a Surveycited by 8
  18. 2023Adaptive Multi-head Contrastive Learningcited by 8
  19. 2024Path-Guided Particle-based Samplingcited by 8
  20. 2025Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know'cited by 8
  21. 2022Physics-informed Neural Networks with Unknown Measurement Noisecited by 7
  22. 2021Certifying Emergency Landing for Safe Urban UAVcited by 6
  23. 2022Jensen-Shannon Divergence Based Novel Loss Functions for Bayesian Neural Networkscited by 6
  24. 2022Prediction and Uncertainty Quantification of SAFARI-1 Axial Neutron Flux Profiles with Neural Networkscited by 6
  25. 2023Conformalized Multimodal Uncertainty Regression and Reasoningcited by 6
  26. 2024Bridging the Sim-to-Real Gap with Bayesian Inferencecited by 6
  27. 2022Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Marketscited by 5
  28. 2022How to Combine Variational Bayesian Networks in Federated Learningcited by 5
  29. 2022Improved conformalized quantile regressioncited by 5
  30. 2024Calibrating Bayesian Generative Machine Learning for Bayesiamplificationcited by 5
  31. 2025Contextual Thompson Sampling via Generation of Missing Datacited by 5
  32. 2021SUPER-Net: Trustworthy Image Segmentation via Uncertainty Propagation in Encoder-Decoder Networkscited by 4
  33. 2024Conformalized Teleoperation: Confidently Mapping Human Inputs to High-Dimensional Robot Actionscited by 4
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  35. 2025Split Conformal Prediction in the Function Space with Neural Operatorscited by 4
  36. 2025Contextual Similarity Distillation: Ensemble Uncertainties with a Single Modelcited by 3
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  38. 2022EXACT: How to Train Your Accuracycited by 2

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