贝叶斯深度学习论文趋势 2023–2026

权重不确定性

统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 408 篇被归入该主题的论文。

主要结论

每年论文数

各会议论文数

按年份和会议统计

贝叶斯深度学习论文数，按年份和会议
年份NeurIPSICLRICMLCVPR合计
20236819196112
20243026377100
2025273024384
20264022419112

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

全部贝叶斯深度学习论文

年份全部2023202420252026
会议全部NeurIPSICLRICMLCVPR

共 408 篇，第 6 / 9 页

  1. ICML 2024MALIBO: Meta-learning for Likelihood-free Bayesian Optimization
  2. ICML 2024More Flexible PAC-Bayesian Meta-Learning by Learning Learning Algorithms
  3. ICML 2024On the Weight Dynamics of Deep Normalized Networks
  4. ICML 2024PAC-Bayesian Error Bound, via Rényi Divergence, for a Class of Linear Time-Invariant State-Space Models
  5. ICML 2024PAC-Bayesian Generalization Bounds for Knowledge Graph Representation Learning
  6. ICML 2024Partially Stochastic Infinitely Deep Bayesian Neural Networks
  7. ICML 2024Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
  8. ICML 2024Probability Distribution of Hypervolume Improvement in Bi-objective Bayesian Optimization
  9. ICML 2024Reparameterized Importance Sampling for Robust Variational Bayesian Neural Networks
  10. ICML 2024Rotational Equilibrium: How Weight Decay Balances Learning Across Neural Networks
  11. ICML 2024Second-Order Uncertainty Quantification: A Distance-Based Approach
  12. ICML 2024The Effect of Weight Precision on the Neuron Count in Deep ReLU Networks
  13. ICML 2024Transitional Uncertainty with Layered Intermediate Predictions
  14. ICML 2024Uncertainty Estimation by Density Aware Evidential Deep Learning
  15. ICML 2024Variational Linearized Laplace Approximation for Bayesian Deep Learning
  16. ICML 2024WARM: On the Benefits of Weight Averaged Reward Models
  17. NeurIPS 2024A Bayesian Approach for Personalized Federated Learning in Heterogeneous Settings
  18. NeurIPS 2024A Metalearned Neural Circuit for Nonparametric Bayesian Inference
  19. NeurIPS 2024Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?
  20. NeurIPS 2024Bayesian Identification of the Hamiltonian Inductive Bias in Dynamical Systems
  21. NeurIPS 2024Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to Optimal
  22. NeurIPS 2024Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks
  23. NeurIPS 2024Block Sparse Bayesian Learning: A Diversified Scheme
  24. NeurIPS 2024BMRS: Bayesian Model Reduction for Structured Pruning
  25. NeurIPS 2024Conjugate Bayesian Two-step Change Point Detection for Hawkes Process
  26. NeurIPS 2024Enhancing Diversity in Bayesian Deep Learning via Hyperspherical Energy Minimization of CKA
  27. NeurIPS 2024Fixed Confidence Best Arm Identification in the Bayesian Setting
  28. NeurIPS 2024FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning
  29. NeurIPS 2024Gradients of Functions of Large Matrices
  30. NeurIPS 2024Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models
  31. NeurIPS 2024Hierarchical Selective Classification
  32. NeurIPS 2024Noether's Razor: Learning Conserved Quantities
  33. NeurIPS 2024Nonstationary Sparse Spectral Permanental Process
  34. NeurIPS 2024Poisson Variational Autoencoder
  35. NeurIPS 2024Practical Bayesian Algorithm Execution via Posterior Sampling
  36. NeurIPS 2024Quasi-Bayes meets Vines
  37. NeurIPS 2024Reparameterization invariance in approximate Bayesian inference
  38. NeurIPS 2024Sample-efficient Bayesian Optimisation Using Known Invariances
  39. NeurIPS 2024SEL-BALD: Deep Bayesian Active Learning with Selective Labels
  40. NeurIPS 2024Set-based Neural Network Encoding Without Weight Tying
  41. NeurIPS 2024Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks using the Marginal Likelihood
  42. NeurIPS 2024The Bayesian sampling in a canonical recurrent circuit with a diversity of inhibitory interneurons
  43. NeurIPS 2024The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof
  44. NeurIPS 2024Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data Corruptions
  45. NeurIPS 2024Unrolled denoising networks provably learn to perform optimal Bayesian inference
  46. NeurIPS 2024Unveiling The Matthew Effect Across Channels: Assessing Layer Width Sufficiency via Weight Norm Variance
  47. CVPR 2023Bayesian Posterior Approximation With Stochastic Ensembles
  48. CVPR 2023Deep Deterministic Uncertainty: A New Simple Baseline
  49. CVPR 2023Gradient-Based Uncertainty Attribution for Explainable Bayesian Deep Learning
  50. CVPR 2023Improving Robust Generalization by Direct PAC-Bayesian Bound Minimization

在 Claude、Cursor 里直接问这个趋势

接上 Paperlist MCP 后，写综述、挑方向或准备投稿时，不用离开对话框就能拿到这页的数字：每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文，AI 会按你的问题整理。

可以直接复制这句话问 AI

用 paperlist 查贝叶斯深度学习在 NeurIPS 2023–2026 的论文趋势，哪一年涨得最快？

AI 会调用的工具：get_topic_stats {"topic": "bayesian-deep-learning"}

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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。

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