采样方法论文趋势 2023–2026

主要结论

• 采样方法论文从 2023 年的 122 篇增长到 2026 年的 195 篇，三年间扩大到约 1.6 倍。
• NeurIPS 是发采样方法论文最多的会议，2023–2026 年共 221 篇，占四个会议总量的 35%。
• 交叉方向里涨得最快的是MCTS：同时属于两个主题的论文从 2023 年的 4 篇增至 2026 年的 31 篇。
• 在「概率与贝叶斯」的 5 个主题里，它的 2026 年论文数排第 2；同组增长最快的高斯过程是 2.2 倍，采样方法是 1.6 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 Model of Diverse Sampling from Language Models
• ICML 2026BFTS: Thompson Sampling with Bayesian Additive Regression Trees
• ICLR 2026Cactus: Accelerating Auto-Regressive Decoding with Constrained Acceptance Speculative Sampling
• CVPR 2026Active Inference for Micro-Gesture Recognition EFE-Guided Temporal Sampling and Adaptive
• NeurIPS 2025Antidistillation Sampling
• ICML 2025Connecting Thompson Sampling and UCB: Towards More Efficient Trade-offs Between Privacy and Regret
• ICLR 2025Bridging the Gap between Variational Inference and Stochastic Gradient MCMC in Function Space
• CVPR 2025AutoLUT: LUT-Based Image Super-Resolution with Automatic Sampling and Adaptive Residual Learning
• NeurIPS 2024In-and-Out: Algorithmic Diffusion for Sampling Convex Bodies
• ICML 2024Closing the Gap: Achieving Global Convergence (Last Iterate) of Actor-Critic under Markovian Sampling with Neural Network Parametrization
• ICLR 2024Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label Learning
• CVPR 2024G-FARS: Gradient-Field-based Auto-Regressive Sampling for 3D Part Grouping
• NeurIPS 2023A Dynamical System View of Langevin-Based Non-Convex Sampling
• ICML 2023Adaptive Annealed Importance Sampling with Constant Rate Progress
• ICLR 2023Any-scale Balanced Samplers for Discrete Space
• CVPR 2023Change-Aware Sampling and Contrastive Learning for Satellite Images
• NeurIPS 2026A Semantic-Sampling Framework for Evaluating Calibration in Open-Ended Question Answering
• ICML 2026Box Thirding: Anytime Best Arm Identification under Insufficient Sampling
• ICLR 2026On Coreset for LASSO Regression Problem with Sensitivity Sampling
• CVPR 2026Designing Instance-Level Sampling Schedules via REINFORCE with James-Stein Shrinkage

全部采样方法论文

共 19 篇，第 1 / 1 页

1. CVPR 2024Accelerating Diffusion Sampling with Optimized Time Steps
2. CVPR 2024Accelerating Neural Field Training via Soft Mining
3. CVPR 2024Boosting Diffusion Models with Moving Average Sampling in Frequency Domain
4. CVPR 2024Deep Equilibrium Diffusion Restoration with Parallel Sampling
5. CVPR 2024Density-Guided Semi-Supervised 3D Semantic Segmentation with Dual-Space Hardness Sampling
6. CVPR 2024Fast ODE-based Sampling for Diffusion Models in Around 5 Steps
7. CVPR 2024FastMAC: Stochastic Spectral Sampling of Correspondence Graph
8. CVPR 2024G-FARS: Gradient-Field-based Auto-Regressive Sampling for 3D Part Grouping
9. CVPR 2024Gear-NeRF: Free-Viewpoint Rendering and Tracking with Motion-aware Spatio-Temporal Sampling
10. CVPR 2024HashPoint: Accelerated Point Searching and Sampling for Neural Rendering
11. CVPR 2024Improving Graph Contrastive Learning via Adaptive Positive Sampling
12. CVPR 2024KD-DETR: Knowledge Distillation for Detection Transformer with Consistent Distillation Points Sampling
13. CVPR 2024L0-Sampler: An L0 Model Guided Volume Sampling for NeRF
14. CVPR 2024LTA-PCS: Learnable Task-Agnostic Point Cloud Sampling
15. CVPR 2024Neural Redshift: Random Networks are not Random Functions
16. CVPR 2024Pick-or-Mix: Dynamic Channel Sampling for ConvNets
17. CVPR 2024Posterior Distillation Sampling
18. CVPR 2024Probabilistic Sampling of Balanced K-Means using Adiabatic Quantum Computing
19. CVPR 2024Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Sampling

常见问题

2023–2026 年四大会议一共有多少篇采样方法论文？

共 640 篇，按年份分别是 2023 年 122 篇、2024 年 156 篇、2025 年 167 篇、2026 年 195 篇。

NeurIPS 2026 有多少篇采样方法论文？

NeurIPS 2026 有 64 篇采样方法论文，上一届是 67 篇。

ICLR 2026 有多少篇采样方法论文？

ICLR 2026 有 46 篇采样方法论文，上一届是 48 篇。

ICML 2026 有多少篇采样方法论文？

ICML 2026 有 71 篇采样方法论文，上一届是 36 篇。

CVPR 2026 有多少篇采样方法论文？

CVPR 2026 有 14 篇采样方法论文，上一届是 16 篇。

哪个会议的采样方法论文最多？

NeurIPS，2023–2026 年共 221 篇。