变分推断论文趋势 2023–2026

主要结论

• 变分推断论文从 2023 年的 106 篇降到 2026 年的 91 篇，热度在回落。
• NeurIPS 是发变分推断论文最多的会议，2023–2026 年共 124 篇，占四个会议总量的 35%。
• 交叉方向里涨得最快的是采样方法：同时属于两个主题的论文从 2023 年的 1 篇增至 2026 年的 6 篇。
• 在「概率与贝叶斯」的 5 个主题里，它的 2026 年论文数排第 5；同组增长最快的高斯过程是 2.2 倍，变分推断是 0.9 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 2026A Reproducible Evaluation Protocol for Quantum Non-Linearity in Variational Quantum Models
• ICML 2026Beyond Independence: Learning Correlated Views for Variational Incomplete Multi-View Clustering
• ICLR 2026Optimizing Data Augmentation through Bayesian Model Selection
• CVPR 2026BrepVGAE Variational Graph Autoencoder with Unified Latent Representation for B-rep
• NeurIPS 2025Covariate-moderated Empirical Bayes Matrix Factorization
• ICML 2025A Variational Information Theoretic Approach to Out-of-Distribution Detection
• ICLR 2025Bridging the Gap between Variational Inference and Stochastic Gradient MCMC in Function Space
• CVPR 2025Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders
• NeurIPS 2024Training Binary Neural Networks via Gaussian Variational Inference and Low-Rank Semidefinite Programming
• ICML 2024Amortized Variational Deep Kernel Learning
• ICLR 2024Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder
• CVPR 2024WorDepth: Variational Language Prior for Monocular Depth Estimation
• NeurIPS 2023[Re] Variational Neural Cellular Automata
• ICML 2023Coupled Variational Autoencoder
• ICLR 2023A VAE for Transformers with Nonparametric Variational Information Bottleneck
• CVPR 2023Confidence-Aware Personalized Federated Learning via Variational Expectation Maximization
• NeurIPS 2026Adaptive Delayed-Update Cyclic Algorithm for Variational Inequalities
• ICML 2026Dual-stage Contrastive Learning-enhanced Multi-view Variational Clustering
• ICLR 2026Pareto Variational Autoencoder
• CVPR 2026Variational Graph-based Normal Integration

全部变分推断论文

共 26 篇，第 1 / 1 页

1. ICML 2024$\mathtt{VITS}$ : Variational Inference Thompson Sampling for contextual bandits
2. ICML 2024Adaptive Observation Cost Control for Variational Quantum Eigensolvers
3. ICML 2024Amortized Variational Deep Kernel Learning
4. ICML 2024Batch and match: black-box variational inference with a score-based divergence
5. ICML 2024Beyond ELBOs: A Large-Scale Evaluation of Variational Methods for Sampling
6. ICML 2024Efficient Mixture Learning in Black-Box Variational Inference
7. ICML 2024Generalized Smooth Variational Inequalities: Methods with Adaptive Stepsizes
8. ICML 2024High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise
9. ICML 2024Kernel Semi-Implicit Variational Inference
10. ICML 2024Language-guided Skill Learning with Temporal Variational Inference
11. ICML 2024Latent Optimal Paths by Gumbel Propagation for Variational Bayesian Dynamic Programming
12. ICML 2024Learning Latent Structures in Network Games via Data-Dependent Gated-Prior Graph Variational Autoencoders
13. ICML 2024Logistic Variational Bayes Revisited
14. ICML 2024Online Variational Sequential Monte Carlo
15. ICML 2024Provably Scalable Black-Box Variational Inference with Structured Variational Families
16. ICML 2024Purify Unlearnable Examples via Rate-Constrained Variational Autoencoders
17. ICML 2024Reparameterized Importance Sampling for Robust Variational Bayesian Neural Networks
18. ICML 2024Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes
19. ICML 2024Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians
20. ICML 2024Understanding Stochastic Natural Gradient Variational Inference
21. ICML 2024Variational Inference with Coverage Guarantees in Simulation-Based Inference
22. ICML 2024Variational Learning is Effective for Large Deep Networks
23. ICML 2024Variational Linearized Laplace Approximation for Bayesian Deep Learning
24. ICML 2024Variational Partial Group Convolutions for Input-Aware Partial Equivariance of Rotations and Color-Shifts
25. ICML 2024Variational Schrödinger Diffusion Models
26. ICML 2024Why do Variational Autoencoders Really Promote Disentanglement?

常见问题

2023–2026 年四大会议一共有多少篇变分推断论文？

共 359 篇，按年份分别是 2023 年 106 篇、2024 年 76 篇、2025 年 86 篇、2026 年 91 篇。

NeurIPS 2026 有多少篇变分推断论文？

NeurIPS 2026 有 30 篇变分推断论文，上一届是 28 篇。

ICLR 2026 有多少篇变分推断论文？

ICLR 2026 有 23 篇变分推断论文，上一届是 28 篇。

ICML 2026 有多少篇变分推断论文？

ICML 2026 有 35 篇变分推断论文，上一届是 28 篇。

CVPR 2026 有多少篇变分推断论文？

CVPR 2026 有 3 篇变分推断论文，上一届是 2 篇。

哪个会议的变分推断论文最多？

NeurIPS，2023–2026 年共 124 篇。