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 22 篇，第 1 / 1 页

1. ICLR 2026A Fair Bayesian Inference through Matched Gibbs Posterior
2. ICLR 2026Bayesian Neural Networks for Functional ANOVA Model
3. ICLR 2026Bayesian Post Training Enhancement of Regression Models with Calibrated Rankings
4. ICLR 2026Complementing Self-Consistency with Cross-Model Disagreement for Uncertainty Quantification
5. ICLR 2026Compositional amortized inference for large-scale hierarchical Bayesian models
6. ICLR 2026Don’t Pass@$k$: A Bayesian Framework for Large Language Model Evaluation
7. ICLR 2026Don't Throw Away Your Beams: Improving Consistency-based Uncertainties in LLMs via Beam Search
8. ICLR 2026Enhancing Hallucination Detection through Noise Injection
9. ICLR 2026Ensembling Pruned Attention Heads For Uncertainty-Aware Efficient Transformers
10. ICLR 2026EUBRL: Epistemic Uncertainty Directed Bayesian Reinforcement Learning
11. ICLR 2026Federated ADMM from Bayesian Duality
12. ICLR 2026Optimizing Data Augmentation through Bayesian Model Selection
13. ICLR 2026Rethinking Uncertainty Estimation in LLMs: A Principled Single-Sequence Measure
14. ICLR 2026Robust Amortized Bayesian Inference with Self-Consistency Losses on Unlabeled Data
15. ICLR 2026Robust Decision-Making with Partially Calibrated Forecasters
16. ICLR 2026Semantic Uncertainty Quantification of Hallucinations in LLMs: A Quantum Tensor Network Based Method
17. ICLR 2026Singleton-Optimized Conformal Prediction
18. ICLR 2026Textual Bayes: Quantifying Uncertainty in LLM-Based Systems
19. ICLR 2026The Alignment Auditor: A Bayesian Framework for Verifying and Refining LLM Objectives
20. ICLR 2026Uncertainty as Feature Gaps: Epistemic Uncertainty Quantification of LLMs in Contextual Question-Answering
21. ICLR 2026Uncertainty Estimation via Hyperspherical Confidence Mapping
22. ICLR 2026Variational Deep Learning via Implicit Regularization

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。