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 833 篇，第 15 / 17 页
- NeurIPS 2023Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM
- NeurIPS 2023Model Shapley: Equitable Model Valuation with Black-box Access
- NeurIPS 2023Model Sparsity Can Simplify Machine Unlearning
- NeurIPS 2023Multi-body SE(3) Equivariance for Unsupervised Rigid Segmentation and Motion Estimation
- NeurIPS 2023Multi-Object Representation Learning via Feature Connectivity and Object-Centric Regularization
- NeurIPS 2023Multi-Prompt Alignment for Multi-Source Unsupervised Domain Adaptation
- NeurIPS 2023Mutual Information Regularized Offline Reinforcement Learning
- NeurIPS 2023Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise
- NeurIPS 2023Necessary and Sufficient Conditions for Optimal Decision Trees using Dynamic Programming
- NeurIPS 2023Neural Harmonics: Bridging Spectral Embedding and Matrix Completion in Self-Supervised Learning
- NeurIPS 2023Neural Modulation for Flash Memory: An Unsupervised Learning Framework for Improved Reliability
- NeurIPS 2023NeuralGF: Unsupervised Point Normal Estimation by Learning Neural Gradient Function
- NeurIPS 2023No-Regret Learning in Dynamic Competition with Reference Effects Under Logit Demand
- NeurIPS 2023Nonparametric Teaching for Multiple Learners
- NeurIPS 2023OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents
- NeurIPS 2023Object-centric Learning with Cyclic Walks between Parts and Whole
- NeurIPS 2023Object-Centric Slot Diffusion
- NeurIPS 2023On Proper Learnability between Average- and Worst-case Robustness
- NeurIPS 2023On Robust Streaming for Learning with Experts: Algorithms and Lower Bounds
- NeurIPS 2023On skip connections and normalisation layers in deep optimisation
- NeurIPS 2023On the Convergence of No-Regret Learning Dynamics in Time-Varying Games
- NeurIPS 2023On the Importance of Feature Separability in Predicting Out-Of-Distribution Error
- NeurIPS 2023On the Interplay between Social Welfare and Tractability of Equilibria
- NeurIPS 2023On the Robustness of Removal-Based Feature Attributions
- NeurIPS 2023On the Role of Entanglement and Statistics in Learning
- NeurIPS 2023One Fits All: Power General Time Series Analysis by Pretrained LM
- NeurIPS 2023Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms
- NeurIPS 2023Operator Learning with Neural Fields: Tackling PDEs on General Geometries
- NeurIPS 2023Optimal and Fair Encouragement Policy Evaluation and Learning
- NeurIPS 2023Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization
- NeurIPS 2023Optimal Parameter and Neuron Pruning for Out-of-Distribution Detection
- NeurIPS 2023Optimal privacy guarantees for a relaxed threat model: Addressing sub-optimal adversaries in differentially private machine learning
- NeurIPS 2023Optimality in Mean Estimation: Beyond Worst-Case, Beyond Sub-Gaussian, and Beyond $1+\alpha$ Moments
- NeurIPS 2023Overcoming Recency Bias of Normalization Statistics in Continual Learning: Balance and Adaptation
- NeurIPS 2023Parallel Spiking Neurons with High Efficiency and Ability to Learn Long-term Dependencies
- NeurIPS 2023Parameterizing Context: Unleashing the Power of Parameter-Efficient Fine-Tuning and In-Context Tuning for Continual Table Semantic Parsing
- NeurIPS 2023Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model
- NeurIPS 2023Partial Label Learning with Dissimilarity Propagation guided Candidate Label Shrinkage
- NeurIPS 2023Partial Multi-Label Learning with Probabilistic Graphical Disambiguation
- NeurIPS 2023PETAL: Physics Emulation Through Averaged Linearizations for Solving Inverse Problems
- NeurIPS 2023Physics-Driven ML-Based Modelling for Correcting Inverse Estimation
- NeurIPS 2023PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learning
- NeurIPS 2023Posterior Sampling for Competitive RL: Function Approximation and Partial Observation
- NeurIPS 2023Practical Differentially Private Hyperparameter Tuning with Subsampling
- NeurIPS 2023Prediction and Control in Continual Reinforcement Learning
- NeurIPS 2023Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression
- NeurIPS 2023Promises and Pitfalls of Threshold-based Auto-labeling
- NeurIPS 2023Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization
- NeurIPS 2023Provably Efficient Offline Goal-Conditioned Reinforcement Learning with General Function Approximation and Single-Policy Concentrability
- NeurIPS 2023QuACK: Accelerating Gradient-Based Quantum Optimization with Koopman Operator Learning
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。