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 7 / 9 页

301. CVPR 2023Spectral Bayesian Uncertainty for Image Super-Resolution
302. CVPR 2023Training Debiased Subnetworks With Contrastive Weight Pruning
303. ICLR 2023Accurate Bayesian Meta-Learning by Accurate Task Posterior Inference
304. ICLR 2023Active Learning for Object Detection with Evidential Deep Learning and Hierarchical Uncertainty Aggregation
305. ICLR 2023Active Learning in Bayesian Neural Networks with Balanced Entropy Learning Principle
306. ICLR 2023Approximate Bayesian Inference with Stein Functional Variational Gradient Descent
307. ICLR 2023Batch Multivalid Conformal Prediction
308. ICLR 2023BSTT: A Bayesian Spatial-Temporal Transformer for Sleep Staging
309. ICLR 2023Calibrating Transformers via Sparse Gaussian Processes
310. ICLR 2023Deep Variational Implicit Processes
311. ICLR 2023Latent Neural ODEs with Sparse Bayesian Multiple Shooting
312. ICLR 2023Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial Examples
313. ICLR 2023MARS: Meta-learning as Score Matching in the Function Space
314. ICLR 2023Martingale Posterior Neural Processes
315. ICLR 2023Model-based Causal Bayesian Optimization
316. ICLR 2023Robustness to corruption in pre-trained Bayesian neural networks
317. ICLR 2023SAM as an Optimal Relaxation of Bayes
318. ICLR 2023Sampling-based inference for large linear models, with application to linearised Laplace
319. ICLR 2023Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class Annealing
320. ICLR 2023Scale-invariant Bayesian Neural Networks with Connectivity Tangent Kernel
321. ICLR 2023Transfer NAS with Meta-learned Bayesian Surrogates
322. ICML 2023Adversarial robustness of amortized Bayesian inference
323. ICML 2023Are Random Decompositions all we need in High Dimensional Bayesian Optimisation?
324. ICML 2023BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic Programming
325. ICML 2023Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
326. ICML 2023Differentially Private Distributed Bayesian Linear Regression with MCMC
327. ICML 2023Distortion and Uncertainty Aware Loss for Panoramic Depth Completion
328. ICML 2023End-to-End Learning for Stochastic Optimization: A Bayesian Perspective
329. ICML 2023Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes
330. ICML 2023Learning Noisy OR Bayesian Networks with Max-Product Belief Propagation
331. ICML 2023Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation
332. ICML 2023MEWL: Few-shot multimodal word learning with referential uncertainty
333. ICML 2023On the Complexity of Bayesian Generalization
334. ICML 2023PAC-Bayesian Offline Contextual Bandits With Guarantees
335. ICML 2023PFNs4BO: In-Context Learning for Bayesian Optimization
336. ICML 2023Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs
337. ICML 2023Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Bounds
338. ICML 2023Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances
339. ICML 2023spred: Solving L1 Penalty with SGD
340. ICML 2023Uncertainty Estimation by Fisher Information-based Evidential Deep Learning
341. NeurIPS 2023“Why Not Looking backward?” A Robust Two-Step Method to Automatically Terminate Bayesian Optimization
342. NeurIPS 2023A Bayesian Approach To Analysing Training Data Attribution In Deep Learning
343. NeurIPS 2023A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods
344. NeurIPS 2023Adaptive Uncertainty Estimation via High-Dimensional Testing on Latent Representations
345. NeurIPS 2023Asymptotics of Bayesian Uncertainty Estimation in Random Features Regression
346. NeurIPS 2023Bayesian Extensive-Rank Matrix Factorization with Rotational Invariant Priors
347. NeurIPS 2023Bayesian Learning of Optimal Policies in Markov Decision Processes with Countably Infinite State-Space
348. NeurIPS 2023Bayesian Learning via Q-Exponential Process
349. NeurIPS 2023Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval
350. NeurIPS 2023Bayesian nonparametric (non-)renewal processes for analyzing neural spike train variability

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。