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 27 篇，第 1 / 1 页

1. NeurIPS 2025A Bayesian Approach to Contextual Dynamic Pricing using the Proportional Hazards Model with Discrete Price Data
2. NeurIPS 2025A Bayesian Fast-Slow Framework to Mitigate Interference in Non-Stationary Reinforcement Learning
3. NeurIPS 2025Bayesian Concept Bottleneck Models with LLM Priors
4. NeurIPS 2025BayeSQP: Bayesian Optimization through Sequential Quadratic Programming
5. NeurIPS 2025Diffusion Models Meet Contextual Bandits
6. NeurIPS 2025DSCS: Fast CPDAG-Based Verification of Collapsible Submodels in High-Dimensional Bayesian Networks
7. NeurIPS 2025Geometric Algebra-Enhanced Bayesian Flow Network for RNA Inverse Design
8. NeurIPS 2025Large Language Bayes
9. NeurIPS 2025Markov Persuasion Processes: Learning to Persuade From Scratch
10. NeurIPS 2025Model-Informed Flows for Bayesian Inference
11. NeurIPS 2025MOF-BFN: Metal-Organic Frameworks Structure Prediction via Bayesian Flow Networks
12. NeurIPS 2025Multidimensional Bayesian Utility Maximization: Tight Approximations to Welfare
13. NeurIPS 2025NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification
14. NeurIPS 2025One Filters All: A Generalist Filter For State Estimation
15. NeurIPS 2025Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregation
16. NeurIPS 2025Posterior Contraction for Sparse Neural Networks in Besov Spaces with Intrinsic Dimensionality
17. NeurIPS 2025PRESCRIBE: Predicting Single-Cell Responses with Bayesian Estimation
18. NeurIPS 2025Quantifying Uncertainty in the Presence of Distribution Shifts
19. NeurIPS 2025Squared families are useful conjugate priors
20. NeurIPS 2025Statistical Analysis of an Adversarial Bayesian Weak Supervision Method
21. NeurIPS 2025The Quotient Bayesian Learning Rule
22. NeurIPS 2025Torch-Uncertainty: Deep Learning Uncertainty Quantification
23. NeurIPS 2025Training-Free Bayesianization for Low-Rank Adapters of Large Language Models
24. NeurIPS 2025TreeGen: A Bayesian Generative Model for Hierarchies
25. NeurIPS 2025Uncertainty Quantification with the Empirical Neural Tangent Kernel
26. NeurIPS 2025Uncertainty-Sensitive Privileged Learning
27. NeurIPS 2025VIKING: Deep variational inference with stochastic projections

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。