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 111 篇，第 1 / 3 页

1. ICML 2026A hitchhiker's guide to Poisson gradient estimation
2. ICML 2026A Unifying View of Variational Generative Wasserstein Flows
3. ICML 2026Accelerated and Stable Convergence with Anchored Generalized Optimistic Method
4. ICML 2026Amortized Variational Inference for Partial-Label Learning: A Probabilistic Approach to Label Disambiguation
5. ICML 2026Beyond Independence: Learning Correlated Views for Variational Incomplete Multi-View Clustering
6. ICML 2026Copula-SVI: Vine-Copula Variational Inference with Stein Refining for Instance-Level Correlation Capturing
7. ICML 2026Disentangling Latent Risk Pathways via Bayesian Hypergraph Inference
8. ICML 2026Distribution Matching Variational AutoEncoder
9. ICML 2026Do-Prompt: Causal Interventions Meet Variational Prompt Bottlenecks
10. ICML 2026Dual-stage Contrastive Learning-enhanced Multi-view Variational Clustering
11. ICML 2026Efficient Online Variational Estimation via Monte Carlo Sampling
12. ICML 2026Gaussian Mean Field Variational Inference can Overestimate Predictive Variance
13. ICML 2026Hölder++: Improving Quality-Coherence Trade-off in Multimodal VAEs
14. ICML 2026HVAE: Hyperbolic Variational Autoencoder For Flexible Knowledge Transfer Across Multiple Domains
15. ICML 2026Last-Iterate Convergence of Regularized Gradient Methods for Stochastic Monotone Variational Inequalities
16. ICML 2026MAVRL: Learning Reward Functions from Multiple Feedback Types with Amortized Variational Inference
17. ICML 2026Monotonic Variational Gaussian Process for Efficient Data Collection
18. ICML 2026Native Spatio-Temporal 4D Variational Autoencoder
19. ICML 2026Particles Don’t Care About Z: Towards Scaling Entropy Estimation of Unnormalized Densities
20. ICML 2026Phase-Type Variational Autoencoders for Heavy-Tailed Data
21. ICML 2026Predictive variational inference: Learn the predictively optimal posterior distribution
22. ICML 2026Rényi Diffusion Models
23. ICML 2026Solving Stochastic Variational Inequalities without the Bounded Variance Assumption
24. ICML 2026Spectral Bridge Variational Inference: Dynamic LoRA via Bures-Wasserstein Gradient Flows
25. ICML 2026Stochastic Gradient Variational Inference with Price's Gradient Estimator from Bures-Wasserstein to Parameter Space
26. ICML 2026Unsupervised Disentanglement Without Compromises : How Functional Orthogonality Enforces Identifiability
27. ICML 2026Var-JEPA: A Variational Formulation of the Joint-Embedding Predictive Architecture – Bridging Predictive and Generative Self-Supervised Learning
28. ICML 2026Variational Adapter for Cross-modal Similarity Representation
29. ICML 2026Variational Entropic Optimal Transport
30. ICML 2026Variational Inference for Uncertain Optimal Transport via Sinkhorn Parametrization
31. ICML 2026Variational inference via Gaussian interacting particles in the Bures-Wasserstein geometry
32. ICML 2026Variational Learning of Disentangled Representations
33. ICML 2026Variational Routing: A Scalable Bayesian Framework for Calibrated Mixture-of-Experts Transformers
34. ICML 2026Variational Speculative Decoding: Rethinking Draft Training from Token Likelihood to Sequence Acceptance
35. ICML 2026VJEPA: Variational Joint Embedding Predictive Architectures as Probabilistic World Models
36. ICML 2025$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting
37. ICML 2025A Variational Information Theoretic Approach to Out-of-Distribution Detection
38. ICML 2025Aggregation of Dependent Expert Distributions in Multimodal Variational Autoencoders
39. ICML 2025Be a Goldfish: Forgetting Bad Conditioning in Sparse Linear Regression via Variational Autoencoders
40. ICML 2025Censor Dependent Variational Inference
41. ICML 2025Continuous Semi-Implicit Models
42. ICML 2025Cross-Modal Alignment via Variational Copula Modelling
43. ICML 2025Deep Bayesian Filter for Bayes-Faithful Data Assimilation
44. ICML 2025Expected Variational Inequalities
45. ICML 2025Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework
46. ICML 2025Flexible Tails for Normalizing Flows
47. ICML 2025Global-Local Dirichlet Processes for Clustering Grouped Data in the Presence of Group-Specific Idiosyncratic Variables
48. ICML 2025Identifying Causal Direction via Variational Bayesian Compression
49. ICML 2025Layer-wise Quantization for Quantized Optimistic Dual Averaging
50. ICML 2025Learning Likelihood-Free Reference Priors

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。