通用机器学习 · unsupervised and semisupervised learning论文趋势 2023–2026
通用机器学习：unsupervised_and_semisupervised_learning
统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 2,683 篇被归入该主题的论文。
主要结论
- 通用机器学习 · unsupervised and semisupervised learning论文从 2023 年的 649 篇增长到 2026 年的 799 篇，三年间扩大到约 1.2 倍。
- ICML 是发通用机器学习 · unsupervised and semisupervised learning论文最多的会议，2023–2026 年共 859 篇，占四个会议总量的 32%。
- 交叉方向里涨得最快的是深度学习 · self supervised learning：同时属于两个主题的论文从 2023 年的 23 篇增至 2026 年的 86 篇。
- 在「通用机器学习」的 6 个主题里，它的 2026 年论文数排第 1；同组增长最快的通用机器学习 · evaluation是 4.3 倍，通用机器学习 · unsupervised and semisupervised learning是 1.2 倍。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 318 | 87 | 157 | 87 | 649 |
| 2024 | 118 | 236 | 115 | 84 | 553 |
| 2025 | 203 | 174 | 239 | 66 | 682 |
| 2026 | 194 | 187 | 348 | 70 | 799 |
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 general kernel framework for non-CND distance measures using $|\mathcal{D}|$-dimensional sparse landmark embeddings
- ICML 2026$\mu$pscaling small models: Principled warm starts and hyperparameter transfer
- ICLR 2026A Generalized Geometric Theoretical Framework of Centroid Discriminant Analysis for Linear Classification of Multi-dimensional Data
- CVPR 2026COG Confidence-aware Optimal Geometric Correspondence for Unsupervised Single-reference Novel Object
- NeurIPS 2025$O(\sqrt{T})$ Static Regret and Instance Dependent Constraint Violation for Constrained Online Convex Optimization
- ICML 2025A Square Peg in a Square Hole: Meta-Expert for Long-Tailed Semi-Supervised Learning
- ICLR 2025A General Framework for Producing Interpretable Semantic Text Embeddings
- CVPR 2025A General Adaptive Dual-level Weighting Mechanism for Remote Sensing Pansharpening
- NeurIPS 2024GlotCC: An Open Broad-Coverage CommonCrawl Corpus and Pipeline for Minority Languages
- ICML 2024CKGConv: General Graph Convolution with Continuous Kernels
- ICLR 2024A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
- CVPR 2024Dual Prototype Attention for Unsupervised Video Object Segmentation
- NeurIPS 2023$H$-Consistency Bounds: Characterization and Extensions
- ICML 2023A Mathematical Model for Curriculum Learning for Parities
- ICLR 2023A General Framework For Proving The Equivariant Strong Lottery Ticket Hypothesis
- CVPR 2023A General Regret Bound of Preconditioned Gradient Method for DNN Training
- NeurIPS 2026Attention-based PCA
- ICML 2026A Control-Theoretic View of Mamba on Stability and Robustness
- ICLR 2026A Single Architecture for Representing Invariance Under Any Space Group
- CVPR 2026CROWn A Unified Framework for Anti-Aliased Downsampling and Phase-Calibrated Fusion
全部通用机器学习 · unsupervised and semisupervised learning论文
共 649 篇，第 7 / 13 页
- ICML 2023Revisiting the Linear-Programming Framework for Offline RL with General Function Approximation
- ICML 2023Reward-Mixing MDPs with Few Latent Contexts are Learnable
- ICML 2023Rockmate: an Efficient, Fast, Automatic and Generic Tool for Re-materialization in PyTorch
- ICML 2023Sample and Predict Your Latent: Modality-free Sequential Disentanglement via Contrastive Estimation
- ICML 2023Sample Complexity Bounds for Learning High-dimensional Simplices in Noisy Regimes
- ICML 2023Sampling-Based Accuracy Testing of Posterior Estimators for General Inference
- ICML 2023Scalable Set Encoding with Universal Mini-Batch Consistency and Unbiased Full Set Gradient Approximation
- ICML 2023Self-Repellent Random Walks on General Graphs - Achieving Minimal Sampling Variance via Nonlinear Markov Chains
- ICML 2023SGD with AdaGrad Stepsizes: Full Adaptivity with High Probability to Unknown Parameters, Unbounded Gradients and Affine Variance
- ICML 2023Shape-Guided Dual-Memory Learning for 3D Anomaly Detection
- ICML 2023Sharper Bounds for $\ell_p$ Sensitivity Sampling
- ICML 2023SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
- ICML 2023SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series
- ICML 2023Stable and Consistent Prediction of 3D Characteristic Orientation via Invariant Residual Learning
- ICML 2023State and parameter learning with PARIS particle Gibbs
- ICML 2023STEP: Learning N:M Structured Sparsity Masks from Scratch with Precondition
- ICML 2023Tensor Decompositions Meet Control Theory: Learning General Mixtures of Linear Dynamical Systems
- ICML 2023The Edge of Orthogonality: A Simple View of What Makes BYOL Tick
- ICML 2023Theory on Forgetting and Generalization of Continual Learning
- ICML 2023Toward Large Kernel Models
- ICML 2023Towards Understanding Generalization of Macro-AUC in Multi-label Learning
- ICML 2023Transformers as Algorithms: Generalization and Stability in In-context Learning
- ICML 2023UMD: Unsupervised Model Detection for X2X Backdoor Attacks
- ICML 2023Underspecification Presents Challenges for Credibility in Modern Machine Learning
- ICML 2023Understanding and Generalizing Contrastive Learning from the Inverse Optimal Transport Perspective
- ICML 2023Unearthing InSights into Mars: Unsupervised Source Separation with Limited Data
- ICML 2023Unlocking Slot Attention by Changing Optimal Transport Costs
- ICML 2023Unsupervised Out-of-Distribution Detection with Diffusion Inpainting
- ICML 2023Unsupervised Skill Discovery for Learning Shared Structures across Changing Environments
- ICML 2023Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills
- ICML 2023Which Features are Learnt by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppression
- NeurIPS 2023(Almost) Provable Error Bounds Under Distribution Shift via Disagreement Discrepancy
- NeurIPS 2023$H$-Consistency Bounds: Characterization and Extensions
- NeurIPS 2023$L_2$-Uniform Stability of Randomized Learning Algorithms: Sharper Generalization Bounds and Confidence Boosting
- NeurIPS 2023$p$-value Adjustment for Monotonous, Unbiased, and Fast Clustering Comparison
- NeurIPS 2023$SE(3)$ Equivariant Convolution and Transformer in Ray Space
- NeurIPS 2023A Combinatorial Algorithm for Approximating the Optimal Transport in the Parallel and MPC Settings
- NeurIPS 2023A Competitive Algorithm for Agnostic Active Learning
- NeurIPS 2023A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games
- NeurIPS 2023A Framework for Fast and Stable Representations of Multiparameter Persistent Homology Decompositions
- NeurIPS 2023A General Framework for Equivariant Neural Networks on Reductive Lie Groups
- NeurIPS 2023A General Framework for Robust G-Invariance in G-Equivariant Networks
- NeurIPS 2023A General Theory of Correct, Incorrect, and Extrinsic Equivariance
- NeurIPS 2023A Novel Framework for Policy Mirror Descent with General Parameterization and Linear Convergence
- NeurIPS 2023A Simple Solution for Offline Imitation from Observations and Examples with Possibly Incomplete Trajectories
- NeurIPS 2023A Theory of Transfer-Based Black-Box Attacks: Explanation and Implications
- NeurIPS 2023A Theory of Unsupervised Translation Motivated by Understanding Animal Communication
- NeurIPS 2023A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning
- NeurIPS 2023A Unified Algorithm Framework for Unsupervised Discovery of Skills based on Determinantal Point Process
- NeurIPS 2023A Unified Approach to Domain Incremental Learning with Memory: Theory and Algorithm
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 查通用机器学习 · unsupervised and semisupervised learning在 ICML 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": "gml-unsupervised-and-semisupervised-learning"}
常见问题
- 2023–2026 年四大会议一共有多少篇通用机器学习 · unsupervised and semisupervised learning论文？
- 共 2,683 篇，按年份分别是 2023 年 649 篇、2024 年 553 篇、2025 年 682 篇、2026 年 799 篇。
- NeurIPS 2026 有多少篇通用机器学习 · unsupervised and semisupervised learning论文？
- NeurIPS 2026 有 194 篇通用机器学习 · unsupervised and semisupervised learning论文，上一届是 203 篇。
- ICLR 2026 有多少篇通用机器学习 · unsupervised and semisupervised learning论文？
- ICLR 2026 有 187 篇通用机器学习 · unsupervised and semisupervised learning论文，上一届是 174 篇。
- ICML 2026 有多少篇通用机器学习 · unsupervised and semisupervised learning论文？
- ICML 2026 有 348 篇通用机器学习 · unsupervised and semisupervised learning论文，上一届是 239 篇。
- CVPR 2026 有多少篇通用机器学习 · unsupervised and semisupervised learning论文？
- CVPR 2026 有 70 篇通用机器学习 · unsupervised and semisupervised learning论文，上一届是 66 篇。
- 哪个会议的通用机器学习 · unsupervised and semisupervised learning论文最多？
- ICML，2023–2026 年共 859 篇。