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 408 篇，第 3 / 9 页

101. NeurIPS 2026Preconditioned Implicit Midpoint Langevin Sampling for Non-Smooth Bayesian Imaging
102. NeurIPS 2026PRIM: Meta-Learned Bayesian Root Cause Analysis
103. NeurIPS 2026Proper Scoring Rules for Agentic Uncertainty Quantification
104. NeurIPS 2026Rethinking Bayesian Optimization for Co-Optimizing LLM Training Configurations
105. NeurIPS 2026Robust Latent Space Bayesian Optimization with Marginalized Kernel
106. NeurIPS 2026SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference
107. NeurIPS 2026TabK: Amortized Bayesian Estimation of the Number of Clusters in Tabular Data
108. NeurIPS 2026Task-Driven Bayesian Experimental Design Yields Singly Intractable Objectives for Joint Policy Training
109. NeurIPS 2026The Bayesian Learning Rule Beyond KL Geometry
110. NeurIPS 2026The Bayesian Origin of the Probability Weighting Function in Human Representation of Probabilities
111. NeurIPS 2026To discretize continually: Mean shift interacting particle systems for Bayesian inference
112. NeurIPS 2026Very Fast Bayesian Additive Regression Trees on GPU
113. CVPR 2025HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories
114. CVPR 2025Investigating the Role of Weight Decay in Enhancing Nonconvex SGD
115. CVPR 2025LoTUS: Large-Scale Machine Unlearning with a Taste of Uncertainty
116. ICLR 2025A Meta-Learning Approach to Bayesian Causal Discovery
117. ICLR 2025Analysis of Linear Mode Connectivity via Permutation-Based Weight Matching: With Insights into Other Permutation Search Methods
118. ICLR 2025Bayesian Image Regression with Soft-thresholded Conditional Autoregressive Prior
119. ICLR 2025Bayesian Optimization via Continual Variational Last Layer Training
120. ICLR 2025Calibrating Expressions of Certainty
121. ICLR 2025Causal Discovery via Bayesian Optimization
122. ICLR 2025CBMA: Improving Conformal Prediction through Bayesian Model Averaging
123. ICLR 2025Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification
124. ICLR 2025Deep Kernel Posterior Learning under Infinite Variance Prior Weights
125. ICLR 2025Deep Random Features for Scalable Interpolation of Spatiotemporal Data
126. ICLR 2025Dimension Agnostic Neural Processes
127. ICLR 2025Distribution-Free Data Uncertainty for Neural Network Regression
128. ICLR 2025ELBOing Stein: Variational Bayes with Stein Mixture Inference
129. ICLR 2025Enhancing Uncertainty Estimation and Interpretability with Bayesian Non-negative Decision Layer
130. ICLR 2025From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation
131. ICLR 2025Hierarchical Uncertainty Estimation for Learning-based Registration in Neuroimaging
132. ICLR 2025KinPFN: Bayesian Approximation of RNA Folding Kinetics using Prior-Data Fitted Networks
133. ICLR 2025Laplace Sample Information: Data Informativeness Through a Bayesian Lens
134. ICLR 2025Microcanonical Langevin Ensembles: Advancing the Sampling of Bayesian Neural Networks
135. ICLR 2025Parameter Expanded Stochastic Gradient Markov Chain Monte Carlo
136. ICLR 2025Provable Uncertainty Decomposition via Higher-Order Calibration
137. ICLR 2025Random-Set Neural Networks
138. ICLR 2025Residual Deep Gaussian Processes on Manifolds
139. ICLR 2025Robust Conformal Prediction with a Single Binary Certificate
140. ICLR 2025Scalable Bayesian Learning with posteriors
141. ICLR 2025Searching for Optimal Solutions with LLMs via Bayesian Optimization
142. ICLR 2025Streamlining Prediction in Bayesian Deep Learning
143. ICLR 2025Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations
144. ICLR 2025Variational Bayesian Pseudo-Coreset
145. ICLR 2025When narrower is better: the narrow width limit of Bayesian parallel branching neural networks
146. ICML 2025BARNN: A Bayesian Autoregressive and Recurrent Neural Network
147. ICML 2025Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks
148. ICML 2025Bayesian Weight Enhancement with Steady-State Adaptation for Test-time Adaptation in Dynamic Environments
149. ICML 2025BILBO: BILevel Bayesian Optimization
150. ICML 2025Can Transformers Learn Full Bayesian Inference in Context?

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。