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 318 篇,第 6 / 7 页
- NeurIPS 2023Scenario Diffusion: Controllable Driving Scenario Generation With Diffusion
- NeurIPS 2023Schema-learning and rebinding as mechanisms of in-context learning and emergence
- NeurIPS 2023Selective Sampling and Imitation Learning via Online Regression
- NeurIPS 2023Self-Supervised Learning with Lie Symmetries for Partial Differential Equations
- NeurIPS 2023Self-supervised Object-Centric Learning for Videos
- NeurIPS 2023SEVA: Leveraging sketches to evaluate alignment between human and machine visual abstraction
- NeurIPS 2023SG×P : A Sorghum Genotype × Phenotype Prediction Dataset and Benchmark
- NeurIPS 2023Shape Non-rigid Kinematics (SNK): A Zero-Shot Method for Non-Rigid Shape Matching via Unsupervised Functional Map Regularized Reconstruction
- NeurIPS 2023Should I Stop or Should I Go: Early Stopping with Heterogeneous Populations
- NeurIPS 2023SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning
- NeurIPS 2023SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization
- NeurIPS 2023Simple and Asymmetric Graph Contrastive Learning without Augmentations
- NeurIPS 2023Simple, Scalable and Effective Clustering via One-Dimensional Projections
- NeurIPS 2023Sketching Algorithms for Sparse Dictionary Learning: PTAS and Turnstile Streaming
- NeurIPS 2023Slot-guided Volumetric Object Radiance Fields
- NeurIPS 2023SmooSeg: Smoothness Prior for Unsupervised Semantic Segmentation
- NeurIPS 2023Smoothed Online Learning for Prediction in Piecewise Affine Systems
- NeurIPS 2023Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds
- NeurIPS 2023StableFDG: Style and Attention Based Learning for Federated Domain Generalization
- NeurIPS 2023Statistical Insights into HSIC in High Dimensions
- NeurIPS 2023Swap Agnostic Learning, or Characterizing Omniprediction via Multicalibration
- NeurIPS 2023Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds
- NeurIPS 2023Tanimoto Random Features for Scalable Molecular Machine Learning
- NeurIPS 2023Temporal Robustness against Data poisoning
- NeurIPS 2023Temporally Disentangled Representation Learning under Unknown Nonstationarity
- NeurIPS 2023Tester-Learners for Halfspaces: Universal Algorithms
- NeurIPS 2023The Equivalence of Dynamic and Strategic Stability under Regularized Learning in Games
- NeurIPS 2023The Grand Illusion: The Myth of Software Portability and Implications for ML Progress.
- NeurIPS 2023The Pick-to-Learn Algorithm: Empowering Compression for Tight Generalization Bounds and Improved Post-training Performance
- NeurIPS 2023The Pursuit of Human Labeling: A New Perspective on Unsupervised Learning
- NeurIPS 2023The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance
- NeurIPS 2023Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics
- NeurIPS 2023Topological Obstructions and How to Avoid Them
- NeurIPS 2023Towards a Unified Analysis of Kernel-based Methods Under Covariate Shift
- NeurIPS 2023Towards Free Data Selection with General-Purpose Models
- NeurIPS 2023Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation
- NeurIPS 2023Towards robust and generalizable representations of extracellular data using contrastive learning
- NeurIPS 2023Trade-off Between Efficiency and Consistency for Removal-based Explanations
- NeurIPS 2023Transfer learning for atomistic simulations using GNNs and kernel mean embeddings
- NeurIPS 2023Transfer Learning with Affine Model Transformation
- NeurIPS 2023Trial matching: capturing variability with data-constrained spiking neural networks
- NeurIPS 2023UltraRE: Enhancing RecEraser for Recommendation Unlearning via Error Decomposition
- NeurIPS 2023Unbalanced Low-rank Optimal Transport Solvers
- NeurIPS 2023Uncoupled and Convergent Learning in Two-Player Zero-Sum Markov Games with Bandit Feedback
- NeurIPS 2023Understanding and Improving Feature Learning for Out-of-Distribution Generalization
- NeurIPS 2023Understanding Few-Shot Learning: Measuring Task Relatedness and Adaptation Difficulty via Attributes
- NeurIPS 2023Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems
- NeurIPS 2023Universality and Limitations of Prompt Tuning
- NeurIPS 2023Unlocking Deterministic Robustness Certification on ImageNet
- NeurIPS 2023UNSSOR: Unsupervised Neural Speech Separation by Leveraging Over-determined Training Mixtures
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。