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 5 / 9 页

  1. CVPR 2024Make Me a BNN: A Simple Strategy for Estimating Bayesian Uncertainty from Pre-trained Models
  2. CVPR 2024Quantifying Uncertainty in Motion Prediction with Variational Bayesian Mixture
  3. CVPR 2024Uncertainty Visualization via Low-Dimensional Posterior Projections
  4. ICLR 2024A Hierarchical Bayesian Model for Few-Shot Meta Learning
  5. ICLR 2024A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors
  6. ICLR 2024A Unified Framework for Bayesian Optimization under Contextual Uncertainty
  7. ICLR 2024Adversarial Causal Bayesian Optimization
  8. ICLR 2024BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference
  9. ICLR 2024Bayesian Bi-clustering of Neural Spiking Activity with Latent Structures
  10. ICLR 2024Bayesian Low-rank Adaptation for Large Language Models
  11. ICLR 2024Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation
  12. ICLR 2024Conformal Risk Control
  13. ICLR 2024Convolutional Deep Kernel Machines
  14. ICLR 2024Enhancing Transfer Learning with Flexible Nonparametric Posterior Sampling
  15. ICLR 2024Entropy-MCMC: Sampling from Flat Basins with Ease
  16. ICLR 2024Fast Ensembling with Diffusion Schrödinger Bridge
  17. ICLR 2024Function-space Parameterization of Neural Networks for Sequential Learning
  18. ICLR 2024Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data
  19. ICLR 2024Implicit Neural Representation Inference for Low-Dimensional Bayesian Deep Learning
  20. ICLR 2024In-Context Learning through the Bayesian Prism
  21. ICLR 2024Learning model uncertainty as variance-minimizing instance weights
  22. ICLR 2024Leveraging Uncertainty Estimates To Improve Classifier Performance
  23. ICLR 2024Non-Exchangeable Conformal Risk Control
  24. ICLR 2024PAC Prediction Sets Under Label Shift
  25. ICLR 2024R-EDL: Relaxing Nonessential Settings of Evidential Deep Learning
  26. ICLR 2024Sparse Weight Averaging with Multiple Particles for Iterative Magnitude Pruning
  27. ICLR 2024Training Bayesian Neural Networks with Sparse Subspace Variational Inference
  28. ICLR 2024Uncertainty Quantification via Stable Distribution Propagation
  29. ICLR 2024Variational Bayesian Last Layers
  30. ICML 2024A Bayesian Approach to Online Planning
  31. ICML 2024A Rate-Distortion View of Uncertainty Quantification
  32. ICML 2024A2Q+: Improving Accumulator-Aware Weight Quantization
  33. ICML 2024Accelerating Convergence in Bayesian Few-Shot Classification
  34. ICML 2024Bayesian Exploration Networks
  35. ICML 2024Bayesian Knowledge Distillation: A Bayesian Perspective of Distillation with Uncertainty Quantification
  36. ICML 2024Bayesian Program Learning by Decompiling Amortized Knowledge
  37. ICML 2024Bayesian Regret Minimization in Offline Bandits
  38. ICML 2024Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning
  39. ICML 2024BayOTIDE: Bayesian Online Multivariate Time Series Imputation with Functional Decomposition
  40. ICML 2024Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
  41. ICML 2024Deep Functional Factor Models: Forecasting High-Dimensional Functional Time Series via Bayesian Nonparametric Factorization
  42. ICML 2024Enabling Uncertainty Estimation in Iterative Neural Networks
  43. ICML 2024Equivariant Deep Weight Space Alignment
  44. ICML 2024High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Sampling
  45. ICML 2024Improving Instruction Following in Language Models through Proxy-Based Uncertainty Estimation
  46. ICML 2024Improving Neural Additive Models with Bayesian Principles
  47. ICML 2024Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?
  48. ICML 2024Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective
  49. ICML 2024Latent Optimal Paths by Gumbel Propagation for Variational Bayesian Dynamic Programming
  50. ICML 2024Learning to Continually Learn with the Bayesian Principle

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"}

两分钟接入 Paperlist MCP（Claude Code、Cursor、Codex、ChatGPT）

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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