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 26 篇，第 1 / 1 页

1. ICLR 2024A Hierarchical Bayesian Model for Few-Shot Meta Learning
2. ICLR 2024A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors
3. ICLR 2024A Unified Framework for Bayesian Optimization under Contextual Uncertainty
4. ICLR 2024Adversarial Causal Bayesian Optimization
5. ICLR 2024BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference
6. ICLR 2024Bayesian Bi-clustering of Neural Spiking Activity with Latent Structures
7. ICLR 2024Bayesian Low-rank Adaptation for Large Language Models
8. ICLR 2024Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation
9. ICLR 2024Conformal Risk Control
10. ICLR 2024Convolutional Deep Kernel Machines
11. ICLR 2024Enhancing Transfer Learning with Flexible Nonparametric Posterior Sampling
12. ICLR 2024Entropy-MCMC: Sampling from Flat Basins with Ease
13. ICLR 2024Fast Ensembling with Diffusion Schrödinger Bridge
14. ICLR 2024Function-space Parameterization of Neural Networks for Sequential Learning
15. ICLR 2024Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data
16. ICLR 2024Implicit Neural Representation Inference for Low-Dimensional Bayesian Deep Learning
17. ICLR 2024In-Context Learning through the Bayesian Prism
18. ICLR 2024Learning model uncertainty as variance-minimizing instance weights
19. ICLR 2024Leveraging Uncertainty Estimates To Improve Classifier Performance
20. ICLR 2024Non-Exchangeable Conformal Risk Control
21. ICLR 2024PAC Prediction Sets Under Label Shift
22. ICLR 2024R-EDL: Relaxing Nonessential Settings of Evidential Deep Learning
23. ICLR 2024Sparse Weight Averaging with Multiple Particles for Iterative Magnitude Pruning
24. ICLR 2024Training Bayesian Neural Networks with Sparse Subspace Variational Inference
25. ICLR 2024Uncertainty Quantification via Stable Distribution Propagation
26. ICLR 2024Variational Bayesian Last Layers

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。