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 1 / 7 页
- 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
- NeurIPS 2023A Unified Generalization Analysis of Re-Weighting and Logit-Adjustment for Imbalanced Learning
- NeurIPS 2023A Unified Model and Dimension for Interactive Estimation
- NeurIPS 2023A Unified Solution for Privacy and Communication Efficiency in Vertical Federated Learning
- NeurIPS 2023Accelerating Reinforcement Learning with Value-Conditional State Entropy Exploration
- NeurIPS 2023Accessing Higher Dimensions for Unsupervised Word Translation
- NeurIPS 2023Active representation learning for general task space with applications in robotics
- NeurIPS 2023Adaptive Linear Estimating Equations
- NeurIPS 2023Adaptive Topological Feature via Persistent Homology: Filtration Learning for Point Clouds
- NeurIPS 2023Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation
- NeurIPS 2023Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions
- NeurIPS 2023Adversarially Robust Learning with Uncertain Perturbation Sets
- NeurIPS 2023Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventions
- NeurIPS 2023An Adaptive Algorithm for Learning with Unknown Distribution Drift
- NeurIPS 2023Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning
- NeurIPS 2023Auditing for Human Expertise
- NeurIPS 2023Balancing memorization and generalization in RNNs for high performance brain-machine Interfaces
- NeurIPS 2023Balancing Risk and Reward: A Batched-Bandit Strategy for Automated Phased Release
- NeurIPS 2023Batchnorm Allows Unsupervised Radial Attacks
- NeurIPS 2023Bayesian Learning via Q-Exponential Process
- NeurIPS 2023Benchmark of Machine Learning Force Fields for Semiconductor Simulations: Datasets, Metrics, and Comparative Analysis
- NeurIPS 2023Beyond Black-Box Advice: Learning-Augmented Algorithms for MDPs with Q-Value Predictions
- NeurIPS 2023Beyond Myopia: Learning from Positive and Unlabeled Data through Holistic Predictive Trends
- NeurIPS 2023Beyond Normal: On the Evaluation of Mutual Information Estimators
- NeurIPS 2023Bicriteria Multidimensional Mechanism Design with Side Information
- NeurIPS 2023Bilevel Coreset Selection in Continual Learning: A New Formulation and Algorithm
- NeurIPS 2023Black-Box Differential Privacy for Interactive ML
- NeurIPS 2023Block-Coordinate Methods and Restarting for Solving Extensive-Form Games
- NeurIPS 2023Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information
- NeurIPS 2023BubbleML: A Multiphase Multiphysics Dataset and Benchmarks for Machine Learning
- NeurIPS 2023CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning
- NeurIPS 2023Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents
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。