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 110 篇，第 2 / 3 页

51. ICLR 2025When do GFlowNets learn the right distribution?
52. ICLR 2024$t^3$-Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power Divergence
53. ICLR 2024A Variational Framework for Estimating Continuous Treatment Effects with Measurement Error
54. ICLR 2024A Variational Perspective on Solving Inverse Problems with Diffusion Models
55. ICLR 2024Beyond Vanilla Variational Autoencoders: Detecting Posterior Collapse in Conditional and Hierarchical Variational Autoencoders
56. ICLR 2024Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder
57. ICLR 2024Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates
58. ICLR 2024Conditional Variational Diffusion Models
59. ICLR 2024Decoupled Marked Temporal Point Process using Neural Ordinary Differential Equations
60. ICLR 2024DiffEnc: Variational Diffusion with a Learned Encoder
61. ICLR 2024Disentangling Time Series Representations via Contrastive Independence-of-Support on l-Variational Inference
62. ICLR 2024Fast Ensembling with Diffusion Schrödinger Bridge
63. ICLR 2024Fiber Monte Carlo
64. ICLR 2024Forward $\chi^2$ Divergence Based Variational Importance Sampling
65. ICLR 2024Input-gradient space particle inference for neural network ensembles
66. ICLR 2024Kernelised Normalising Flows
67. ICLR 2024Lewis's Signaling Game as beta-VAE For Natural Word Lengths and Segments
68. ICLR 2024MINDE: Mutual Information Neural Diffusion Estimation
69. ICLR 2024R-EDL: Relaxing Nonessential Settings of Evidential Deep Learning
70. ICLR 2024Rethinking the symmetry-preserving circuits for constrained variational quantum algorithms
71. ICLR 2024Stochastic Gradient Descent for Gaussian Processes Done Right
72. ICLR 2024Training Bayesian Neural Networks with Sparse Subspace Variational Inference
73. ICLR 2024Transport meets Variational Inference: Controlled Monte Carlo Diffusions
74. ICLR 2024Uncertainty Quantification via Stable Distribution Propagation
75. ICLR 2024Variational Bayesian Last Layers
76. ICLR 2024Variational Inference for SDEs Driven by Fractional Noise
77. ICLR 2023A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games
78. ICLR 2023A VAE for Transformers with Nonparametric Variational Information Bottleneck
79. ICLR 2023Approximate Bayesian Inference with Stein Functional Variational Gradient Descent
80. ICLR 2023Benchmarking Constraint Inference in Inverse Reinforcement Learning
81. ICLR 2023Calibrating Transformers via Sparse Gaussian Processes
82. ICLR 2023Classically Approximating Variational Quantum Machine Learning with Random Fourier Features
83. ICLR 2023DAVA: Disentangling Adversarial Variational Autoencoder
84. ICLR 2023Deep Variational Implicit Processes
85. ICLR 2023Denoising Diffusion Samplers
86. ICLR 2023Domain-Indexing Variational Bayes: Interpretable Domain Index for Domain Adaptation
87. ICLR 2023Explicitly Minimizing the Blur Error of Variational Autoencoders
88. ICLR 2023Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach
89. ICLR 2023Function-space regularized Rényi divergences
90. ICLR 2023GFlowNets and variational inference
91. ICLR 2023Gromov-Wasserstein Autoencoders
92. ICLR 2023Interpretations of Domain Adaptations via Layer Variational Analysis
93. ICLR 2023Learning Continuous Normalizing Flows For Faster Convergence To Target Distribution via Ascent Regularizations
94. ICLR 2023Learning Group Importance using the Differentiable Hypergeometric Distribution
95. ICLR 2023Learning on Large-scale Text-attributed Graphs via Variational Inference
96. ICLR 2023Learning with Logical Constraints but without Shortcut Satisfaction
97. ICLR 2023Optimal Activation Functions for the Random Features Regression Model
98. ICLR 2023Particle-based Variational Inference with Preconditioned Functional Gradient Flow
99. ICLR 2023Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information
100. ICLR 2023Real-time variational method for learning neural trajectory and its dynamics

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。