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 41 篇，第 1 / 1 页

1. ICML 2026A Bayesian Approach to Quantify the Uncertainty of Human Ratings in a Single-Instance Multimodal Framework
2. ICML 2026Active Continual Learning with Metaplastic Binary Bayesian Neural Networks
3. ICML 2026BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields
4. ICML 2026Bayesian Gated Non-Negative Contrastive Learning
5. ICML 2026Bayesian Tensor Decomposition with Diffusion Model Prior
6. ICML 2026Bayesian-LoRA: Probabilistic Low-Rank Adaptation of Large Language Models
7. ICML 2026BFTS: Thompson Sampling with Bayesian Additive Regression Trees
8. ICML 2026BLIPs: Bayesian Learned Interatomic Potentials
9. ICML 2026CalPro: Prior-Aware Evidential Conformal Prediction with Structure-Aware Sensitivity Bounds for Protein Structures
10. ICML 2026Cooperative Variance Estimation and Bayesian Neural Networks for Disentangling Aleatoric and Epistemic Uncertainties
11. ICML 2026Copula-SVI: Vine-Copula Variational Inference with Stein Refining for Instance-Level Correlation Capturing
12. ICML 2026Disentangling Latent Risk Pathways via Bayesian Hypergraph Inference
13. ICML 2026Distribution Transformers: Fast Approximate Bayesian Inference With On-The-Fly Prior Adaptation
14. ICML 2026Efficient Bayesian Inference from Noisy Pairwise Comparisons
15. ICML 2026GPan-LoRA: Gaussian Process Amortized Networks for Bayesian Low-Rank Adaptation in Large Language Models
16. ICML 2026In-Context Learning Is Provably Bayesian Inference: A Generalization Theory for Meta-Learning
17. ICML 2026Interventional Processes For Causal Uncertainty Quantification
18. ICML 2026Is the Last Layer Sufficient for Uncertainty Quantification?
19. ICML 2026Learning in Bayesian Stackelberg Games With Unknown Follower's Types
20. ICML 2026LILO: Bayesian Optimization with Natural Language Feedback
21. ICML 2026Local Constrained Bayesian Optimization
22. ICML 2026Log-Normal Multiplicative Dynamics for Stable Low-Precision Deep Learning
23. ICML 2026Mitigating Reward Hacking in RLHF via Bayesian Non-negative Reward Modeling
24. ICML 2026On the Epistemic Uncertainty of Overparametrized Neural Networks
25. ICML 2026On the Expressive Power of Permutation-Equivariant Weight-Space Networks
26. ICML 2026Posterior Concentration of Bayesian Physics-Informed Neural Networks for Elliptic PDEs
27. ICML 2026Predictive variational inference: Learn the predictively optimal posterior distribution
28. ICML 2026Probabilistically-routed Bayesian Additive Spanning Trees for Learning on Constrained Domains
29. ICML 2026Proximal-IMH: Proximal Posterior Proposals for Independent Metropolis–Hastings with Approximate Operators
30. ICML 2026Regularized Offline Policy Optimization with Posterior Hybrid Bayesian Belief
31. ICML 2026Richer Bayesian Last Layers with Subsampled NTK Features
32. ICML 2026Robust Bayesian Optimisation with Unbounded Corruptions
33. ICML 2026Robust Stochastic Gradient Posterior Sampling with Lattice Based Discretisation
34. ICML 2026SIKA-GP: Accelerating Gaussian Process Inference with Sparse Inducing Kernel Approximations for Bayesian Deep Learning
35. ICML 2026Singular Bayesian Neural Networks
36. ICML 2026Sparse Bayesian Deep Functional Learning with Structured Region Selection
37. ICML 2026Stochastic Gradient Variational Inference with Price's Gradient Estimator from Bures-Wasserstein to Parameter Space
38. ICML 2026Symmetries in PAC-Bayesian Learning
39. ICML 2026TabMGP: Martingale Posterior with TabPFN
40. ICML 2026Taking the GP Out of the Loop
41. ICML 2026Variational Routing: A Scalable Bayesian Framework for Calibrated Mixture-of-Experts Transformers

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。