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 97 篇，第 2 / 2 页

51. ICLR 2025Variational Bayesian Pseudo-Coreset
52. ICLR 2025When narrower is better: the narrow width limit of Bayesian parallel branching neural networks
53. ICLR 2024A Hierarchical Bayesian Model for Few-Shot Meta Learning
54. ICLR 2024A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors
55. ICLR 2024A Unified Framework for Bayesian Optimization under Contextual Uncertainty
56. ICLR 2024Adversarial Causal Bayesian Optimization
57. ICLR 2024BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference
58. ICLR 2024Bayesian Bi-clustering of Neural Spiking Activity with Latent Structures
59. ICLR 2024Bayesian Low-rank Adaptation for Large Language Models
60. ICLR 2024Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation
61. ICLR 2024Conformal Risk Control
62. ICLR 2024Convolutional Deep Kernel Machines
63. ICLR 2024Enhancing Transfer Learning with Flexible Nonparametric Posterior Sampling
64. ICLR 2024Entropy-MCMC: Sampling from Flat Basins with Ease
65. ICLR 2024Fast Ensembling with Diffusion Schrödinger Bridge
66. ICLR 2024Function-space Parameterization of Neural Networks for Sequential Learning
67. ICLR 2024Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data
68. ICLR 2024Implicit Neural Representation Inference for Low-Dimensional Bayesian Deep Learning
69. ICLR 2024In-Context Learning through the Bayesian Prism
70. ICLR 2024Learning model uncertainty as variance-minimizing instance weights
71. ICLR 2024Leveraging Uncertainty Estimates To Improve Classifier Performance
72. ICLR 2024Non-Exchangeable Conformal Risk Control
73. ICLR 2024PAC Prediction Sets Under Label Shift
74. ICLR 2024R-EDL: Relaxing Nonessential Settings of Evidential Deep Learning
75. ICLR 2024Sparse Weight Averaging with Multiple Particles for Iterative Magnitude Pruning
76. ICLR 2024Training Bayesian Neural Networks with Sparse Subspace Variational Inference
77. ICLR 2024Uncertainty Quantification via Stable Distribution Propagation
78. ICLR 2024Variational Bayesian Last Layers
79. ICLR 2023Accurate Bayesian Meta-Learning by Accurate Task Posterior Inference
80. ICLR 2023Active Learning for Object Detection with Evidential Deep Learning and Hierarchical Uncertainty Aggregation
81. ICLR 2023Active Learning in Bayesian Neural Networks with Balanced Entropy Learning Principle
82. ICLR 2023Approximate Bayesian Inference with Stein Functional Variational Gradient Descent
83. ICLR 2023Batch Multivalid Conformal Prediction
84. ICLR 2023BSTT: A Bayesian Spatial-Temporal Transformer for Sleep Staging
85. ICLR 2023Calibrating Transformers via Sparse Gaussian Processes
86. ICLR 2023Deep Variational Implicit Processes
87. ICLR 2023Latent Neural ODEs with Sparse Bayesian Multiple Shooting
88. ICLR 2023Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial Examples
89. ICLR 2023MARS: Meta-learning as Score Matching in the Function Space
90. ICLR 2023Martingale Posterior Neural Processes
91. ICLR 2023Model-based Causal Bayesian Optimization
92. ICLR 2023Robustness to corruption in pre-trained Bayesian neural networks
93. ICLR 2023SAM as an Optimal Relaxation of Bayes
94. ICLR 2023Sampling-based inference for large linear models, with application to linearised Laplace
95. ICLR 2023Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class Annealing
96. ICLR 2023Scale-invariant Bayesian Neural Networks with Connectivity Tangent Kernel
97. ICLR 2023Transfer NAS with Meta-learned Bayesian Surrogates

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。