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 408 篇，第 1 / 9 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. ICLR 2026A Fair Bayesian Inference through Matched Gibbs Posterior
11. ICLR 2026Bayesian Neural Networks for Functional ANOVA Model
12. ICLR 2026Bayesian Post Training Enhancement of Regression Models with Calibrated Rankings
13. ICLR 2026Complementing Self-Consistency with Cross-Model Disagreement for Uncertainty Quantification
14. ICLR 2026Compositional amortized inference for large-scale hierarchical Bayesian models
15. ICLR 2026Don’t Pass@$k$: A Bayesian Framework for Large Language Model Evaluation
16. ICLR 2026Don't Throw Away Your Beams: Improving Consistency-based Uncertainties in LLMs via Beam Search
17. ICLR 2026Enhancing Hallucination Detection through Noise Injection
18. ICLR 2026Ensembling Pruned Attention Heads For Uncertainty-Aware Efficient Transformers
19. ICLR 2026EUBRL: Epistemic Uncertainty Directed Bayesian Reinforcement Learning
20. ICLR 2026Federated ADMM from Bayesian Duality
21. ICLR 2026Optimizing Data Augmentation through Bayesian Model Selection
22. ICLR 2026Rethinking Uncertainty Estimation in LLMs: A Principled Single-Sequence Measure
23. ICLR 2026Robust Amortized Bayesian Inference with Self-Consistency Losses on Unlabeled Data
24. ICLR 2026Robust Decision-Making with Partially Calibrated Forecasters
25. ICLR 2026Semantic Uncertainty Quantification of Hallucinations in LLMs: A Quantum Tensor Network Based Method
26. ICLR 2026Singleton-Optimized Conformal Prediction
27. ICLR 2026Textual Bayes: Quantifying Uncertainty in LLM-Based Systems
28. ICLR 2026The Alignment Auditor: A Bayesian Framework for Verifying and Refining LLM Objectives
29. ICLR 2026Uncertainty as Feature Gaps: Epistemic Uncertainty Quantification of LLMs in Contextual Question-Answering
30. ICLR 2026Uncertainty Estimation via Hyperspherical Confidence Mapping
31. ICLR 2026Variational Deep Learning via Implicit Regularization
32. ICML 2026A Bayesian Approach to Quantify the Uncertainty of Human Ratings in a Single-Instance Multimodal Framework
33. ICML 2026Active Continual Learning with Metaplastic Binary Bayesian Neural Networks
34. ICML 2026BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields
35. ICML 2026Bayesian Gated Non-Negative Contrastive Learning
36. ICML 2026Bayesian Tensor Decomposition with Diffusion Model Prior
37. ICML 2026Bayesian-LoRA: Probabilistic Low-Rank Adaptation of Large Language Models
38. ICML 2026BFTS: Thompson Sampling with Bayesian Additive Regression Trees
39. ICML 2026BLIPs: Bayesian Learned Interatomic Potentials
40. ICML 2026CalPro: Prior-Aware Evidential Conformal Prediction with Structure-Aware Sensitivity Bounds for Protein Structures
41. ICML 2026Cooperative Variance Estimation and Bayesian Neural Networks for Disentangling Aleatoric and Epistemic Uncertainties
42. ICML 2026Copula-SVI: Vine-Copula Variational Inference with Stein Refining for Instance-Level Correlation Capturing
43. ICML 2026Disentangling Latent Risk Pathways via Bayesian Hypergraph Inference
44. ICML 2026Distribution Transformers: Fast Approximate Bayesian Inference With On-The-Fly Prior Adaptation
45. ICML 2026Efficient Bayesian Inference from Noisy Pairwise Comparisons
46. ICML 2026GPan-LoRA: Gaussian Process Amortized Networks for Bayesian Low-Rank Adaptation in Large Language Models
47. ICML 2026In-Context Learning Is Provably Bayesian Inference: A Generalization Theory for Meta-Learning
48. ICML 2026Interventional Processes For Causal Uncertainty Quantification
49. ICML 2026Is the Last Layer Sufficient for Uncertainty Quantification?
50. ICML 2026Learning in Bayesian Stackelberg Games With Unknown Follower's Types

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。