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
- 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。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 68 | 19 | 19 | 6 | 112 |
| 2024 | 30 | 26 | 37 | 7 | 100 |
| 2025 | 27 | 30 | 24 | 3 | 84 |
| 2026 | 40 | 22 | 41 | 9 | 112 |
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 40 篇,第 1 / 1 页
- NeurIPS 2026A Revisit of Hamiltonian Monte Carlo Efficiency on Bayesian Neural Networks
- NeurIPS 2026Approximate Bayesian inference with exchangeable distributions for neurosymbolic AI
- NeurIPS 2026Bayes-pFCL:Bayesian Personalized Federated Continual Learning
- NeurIPS 2026Bayesian Causal Experimental Design for CATE Estimation under Noncompliance
- NeurIPS 2026Bayesian Causal Stress Testing: Posterior Fragility of Treatment-Effect Conclusions
- NeurIPS 2026Bayesian Low-Rank Posteriors for Scalable Membership Inference
- NeurIPS 2026Bayesian Preference Learning for Test-Time Steerable Reward Models
- NeurIPS 2026Bifurcation Models: Learning Set-Valued Solution Maps with Weight-Tied Dynamics
- NeurIPS 2026BO-Arena: An Evolving Benchmark for High-Dimensional Bayesian Optimisation
- NeurIPS 2026CLEAR: Complementary Tripartite Play with Bayesian Calibration for Semi-Supervised Edge Classification
- NeurIPS 2026CLUE: Correlated Latent Uncertainty for Single-Pass Deep Uncertainty Estimation
- NeurIPS 2026DnNP: Denoising Input Uncertainty in Neural Processes
- NeurIPS 2026Does Weight Decay Enhance Training Stability?
- NeurIPS 2026DT-PBO: an Interpretable Tree-based Surrogate Model for Preferential Bayesian Optimization
- NeurIPS 2026Dynamically Structured Diffusion Language Model Decoding via Bayesian Inference
- NeurIPS 2026Ensemble Distributionally Robust Bayesian Optimisation
- NeurIPS 2026ESS-Flow: training-free guidance as Bayesian inference in source space
- NeurIPS 2026Extrapolative Weight Averaging Reveals Correctness–Efficiency Frontiers in Code RL
- NeurIPS 2026Generalised Linear Models in Deep Bayesian RL with Learnable Basis Functions
- NeurIPS 2026Generative Active Learning via Bayesian Acquisition for Improving the Efficiency of Synthetic Data
- NeurIPS 2026Incorporating Neural Network Structure in the Bayesian Learning Rule
- NeurIPS 2026Maximin Robust Bayesian Experimental Design
- NeurIPS 2026Neural Bayesian Filtering
- NeurIPS 2026On the Complexity of Discounted Robust MDPs with $L_p$ Uncertainty Sets
- NeurIPS 2026On the Construction and Implications of Low-Loss Valleys in LoRA-based Bayesian Inference
- NeurIPS 2026On the Existence of Uniformly Optimal Policies in MDPs under Epistemic Uncertainty
- NeurIPS 2026Outlier-robust Diffusion Posterior Sampling for Bayesian Inverse Problems
- NeurIPS 2026Position: Semantic Uncertainty Measures Disagreement, Not Reliability
- NeurIPS 2026Preconditioned Implicit Midpoint Langevin Sampling for Non-Smooth Bayesian Imaging
- NeurIPS 2026PRIM: Meta-Learned Bayesian Root Cause Analysis
- NeurIPS 2026Proper Scoring Rules for Agentic Uncertainty Quantification
- NeurIPS 2026Rethinking Bayesian Optimization for Co-Optimizing LLM Training Configurations
- NeurIPS 2026Robust Latent Space Bayesian Optimization with Marginalized Kernel
- NeurIPS 2026SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference
- NeurIPS 2026TabK: Amortized Bayesian Estimation of the Number of Clusters in Tabular Data
- NeurIPS 2026Task-Driven Bayesian Experimental Design Yields Singly Intractable Objectives for Joint Policy Training
- NeurIPS 2026The Bayesian Learning Rule Beyond KL Geometry
- NeurIPS 2026The Bayesian Origin of the Probability Weighting Function in Human Representation of Probabilities
- NeurIPS 2026To discretize continually: Mean shift interacting particle systems for Bayesian inference
- NeurIPS 2026Very Fast Bayesian Additive Regression Trees on GPU
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"}
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。