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 348 篇，第 5 / 7 页
- ICML 2026nD-RoPE: A Generalized RoPE for n-Dimensional Position Embedding
- ICML 2026Near-Optimal Regret for Policy Optimization in Contextual MDPs with General Offline Function Approximation
- ICML 2026Negatives-Dominant Contrastive Learning for Generalization in Imbalanced Domains
- ICML 2026NeuralFLoC: Neural Flow-Based Joint Registration and Clustering of Functional Data
- ICML 2026New Algorithms for Fully-Dynamic k-center with Outliers
- ICML 2026Newton-coupled Dual-Teacher Semi-supervised Learning Framework
- ICML 2026Noise-Robust Density Estimation for Tabular Data Anomaly Detection
- ICML 2026Nonlinear Covariate Balance in Experimental Design
- ICML 2026Not All Prefills Are Equal: PPD Disaggregation for Multi-turn LLM Serving
- ICML 2026Omitted Variable Bias in Language Models Under Distribution Shift
- ICML 2026On Contraction of Sequential and Offset Rademacher Complexities
- ICML 2026On Learnability and Disambiguation of Multiclass Partial Concept Classes
- ICML 2026On Testing Conditional Mean Independence for Manifold-Valued Data
- ICML 2026On the Difficulty of Learning a Meta-network for Training Data Selection
- ICML 2026On the Interaction of Batch Noise, Adaptivity, and Compression, under $(L_0,L_1)$-Smoothness: An SDE Approach
- ICML 2026On the Learnability of Test-Time Adaptation: A Recovery Complexity Perspective
- ICML 2026On the Plasticity and Stability for Post-Training Large Language Models
- ICML 2026On the Power of Statistics in Class-Incremental Learning with Pretrained Models
- ICML 2026One Batch Is Enough: A Unified Dataset Condensation Framework for General Time Series Analysis
- ICML 2026One Coin Has Two Sides: Single Poistive Multi Label Learning from Salient Annotations
- ICML 2026One-shot Conditional Sampling: MMD meets Nearest Neighbors
- ICML 2026Online Learning and Inference for Cox Proportional Hazards Model Using Renewable Sieve Estimation
- ICML 2026OPIC: Enhancing Language Model Merging via Optimizing In-Context Capability
- ICML 2026Optimal Learning from Label Proportions with General Loss Functions
- ICML 2026Optimal structure learning and conditional independence testing
- ICML 2026Optimal Top-$k$ Identification from Pairwise Comparisons
- ICML 2026Optimal Unconstrained Self-Distillation in Ridge Regression: Strict Improvements, Precise Asymptotics, and One-Shot Tuning
- ICML 2026OPTION: Optimal Transport–Guided Flow Matching for Incomplete and Unaligned Multi-View Clustering
- ICML 2026Orthogonal Model Merging
- ICML 2026OvisOCR: End-to-End Document Parsing via Aligning Specialized Perception with General Reasoning
- ICML 2026PACE: Parameter Change for Unsupervised Environment Design
- ICML 2026PartCo: Part-Level Correspondence Priors Enhance Category Discovery
- ICML 2026Partial Fusion of Neural Networks: Efficient Tradeoffs Between Ensembles and Weight Aggregation
- ICML 2026Partial Identification under High-Dimensional Potential Outcomes and Confounders via Optimal Transport
- ICML 2026PipeSD: An Efficient Cloud-Edge Collaborative Pipeline Inference Framework with Speculative Decoding
- ICML 2026Positive-Unlabeled Learning with Extreme Scarcity of Labeled Positives
- ICML 2026PowerFlow: Unlocking the Dual Nature of LLMs via Principled Distribution Matching
- ICML 2026Powerful and Theoretically Guaranteed Independence Testing on Heterogeneous Federated Clients
- ICML 2026Predictive variational inference: Learn the predictively optimal posterior distribution
- ICML 2026Prior Diffusiveness and Regret in the Linear-Gaussian Bandit
- ICML 2026PyHealth 2.0: A Comprehensive Open-Source Toolkit for Accessible and Reproducible Clinical Deep Learning
- ICML 2026Quadratically Regularized Optimal Transport: Localization Bounds and Affine Case Analysis
- ICML 2026Quantifying Frontier LLM Capabilities for Container Sandbox Escape
- ICML 2026Quantitative Estimation of Target Task Performance from Unsupervised Pretext Task in Semi/Self-Supervised Learning
- ICML 2026Rank-guided Diffusion for Noise Few-Shot Learning
- ICML 2026Real-World Unsupervised Models Generalize to Predict Brain Responses to Out-of-Distribution Stimuli
- ICML 2026Realizable Bayes-Consistency for General Metric Losses
- ICML 2026Refining Context-Entangled Content Segmentation via Curriculum Selection and Anti-Curriculum Promotion
- ICML 2026Refining Dual Spectral Sparsity in Transformed Tensor Singular Values
- ICML 2026Relevance-Based Embeddings: Lightweight Candidate Retrieval via Heavy-Ranker Calls
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。