贝叶斯深度学习论文趋势 2023–2026

主要结论

• 贝叶斯深度学习论文从 2023 年的 112 篇增长到 2026 年的 112 篇，三年间扩大到约 1.0 倍。
• NeurIPS 是发贝叶斯深度学习论文最多的会议，2023–2026 年共 165 篇，占四个会议总量的 40%。
• 交叉方向里涨得最快的是驾驶仿真：同时属于两个主题的论文从 2023 年的 5 篇增至 2026 年的 11 篇。
• 在「概率与贝叶斯」的 5 个主题里，它的 2026 年论文数排第 4；同组增长最快的高斯过程是 2.2 倍，贝叶斯深度学习是 1.0 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• NeurIPS 2026Approximate Bayesian inference with exchangeable distributions for neurosymbolic AI
• ICML 2026A Bayesian Approach to Quantify the Uncertainty of Human Ratings in a Single-Instance Multimodal Framework
• ICLR 2026Bayesian Neural Networks for Functional ANOVA Model
• CVPR 2026CGU-Bayes Causal Graph Uncertainty-Guided Bayesian Inference for Domain Generalization
• NeurIPS 2025A Bayesian Approach to Contextual Dynamic Pricing using the Proportional Hazards Model with Discrete Price Data
• ICML 2025BARNN: A Bayesian Autoregressive and Recurrent Neural Network
• ICLR 2025Bayesian Image Regression with Soft-thresholded Conditional Autoregressive Prior
• CVPR 2025HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories
• NeurIPS 2024Bayesian Identification of the Hamiltonian Inductive Bias in Dynamical Systems
• ICML 2024Bayesian Knowledge Distillation: A Bayesian Perspective of Distillation with Uncertainty Quantification
• ICLR 2024A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors
• CVPR 2024Make Me a BNN: A Simple Strategy for Estimating Bayesian Uncertainty from Pre-trained Models
• NeurIPS 2023A Bayesian Approach To Analysing Training Data Attribution In Deep Learning
• ICML 2023Are Random Decompositions all we need in High Dimensional Bayesian Optimisation?
• ICLR 2023Accurate Bayesian Meta-Learning by Accurate Task Posterior Inference
• CVPR 2023Bayesian Posterior Approximation With Stochastic Ensembles
• NeurIPS 2026BO-Arena: An Evolving Benchmark for High-Dimensional Bayesian Optimisation
• ICML 2026Bayesian Gated Non-Negative Contrastive Learning
• ICLR 2026Complementing Self-Consistency with Cross-Model Disagreement for Uncertainty Quantification
• CVPR 2026Evidential Deep Partial Label Learning to Quantify Disambiguation Uncertainty

全部贝叶斯深度学习论文

共 25 篇，第 1 / 1 页

1. CVPR 2026AdaPrior Bayesian-Inspired Adaptive Prior Correction for Long-Tailed Continual Learning
2. CVPR 2026Adapting Point Cloud Analysis via Multimodal Bayesian Distribution Learning
3. CVPR 2026CGU-Bayes Causal Graph Uncertainty-Guided Bayesian Inference for Domain Generalization
4. CVPR 2026Defending Unauthorized Model Merging via Dual-Stage Weight Protection
5. CVPR 2026Enhancing Accuracy of Uncertainty Estimation in Appearance-based Gaze Tracking with
6. CVPR 2026Evidential Deep Partial Label Learning to Quantify Disambiguation Uncertainty
7. CVPR 2026PhysGS Bayesian-Inferred Gaussian Splatting for Physical Property Estimation
8. CVPR 2026ReBaPL Repulsive Bayesian Prompt Learning
9. CVPR 2026Spectral Conformal Risk Control Distribution-Free Tail Guarantees via Bayesian Quadrature
10. CVPR 2025HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories
11. CVPR 2025Investigating the Role of Weight Decay in Enhancing Nonconvex SGD
12. CVPR 2025LoTUS: Large-Scale Machine Unlearning with a Taste of Uncertainty
13. CVPR 2024Bayes' Rays: Uncertainty Quantification for Neural Radiance Fields
14. CVPR 2024Bayesian Differentiable Physics for Cloth Digitalization
15. CVPR 2024Bayesian Diffusion Models for 3D Shape Reconstruction
16. CVPR 2024Epistemic Uncertainty Quantification For Pre-Trained Neural Networks
17. CVPR 2024Make Me a BNN: A Simple Strategy for Estimating Bayesian Uncertainty from Pre-trained Models
18. CVPR 2024Quantifying Uncertainty in Motion Prediction with Variational Bayesian Mixture
19. CVPR 2024Uncertainty Visualization via Low-Dimensional Posterior Projections
20. CVPR 2023Bayesian Posterior Approximation With Stochastic Ensembles
21. CVPR 2023Deep Deterministic Uncertainty: A New Simple Baseline
22. CVPR 2023Gradient-Based Uncertainty Attribution for Explainable Bayesian Deep Learning
23. CVPR 2023Improving Robust Generalization by Direct PAC-Bayesian Bound Minimization
24. CVPR 2023Spectral Bayesian Uncertainty for Image Super-Resolution
25. CVPR 2023Training Debiased Subnetworks With Contrastive Weight Pruning

常见问题

2023–2026 年四大会议一共有多少篇贝叶斯深度学习论文？

共 408 篇，按年份分别是 2023 年 112 篇、2024 年 100 篇、2025 年 84 篇、2026 年 112 篇。

NeurIPS 2026 有多少篇贝叶斯深度学习论文？

NeurIPS 2026 有 40 篇贝叶斯深度学习论文，上一届是 27 篇。

ICLR 2026 有多少篇贝叶斯深度学习论文？

ICLR 2026 有 22 篇贝叶斯深度学习论文，上一届是 30 篇。

ICML 2026 有多少篇贝叶斯深度学习论文？

ICML 2026 有 41 篇贝叶斯深度学习论文，上一届是 24 篇。

CVPR 2026 有多少篇贝叶斯深度学习论文？

CVPR 2026 有 9 篇贝叶斯深度学习论文，上一届是 3 篇。

哪个会议的贝叶斯深度学习论文最多？

NeurIPS，2023–2026 年共 165 篇。