通用机器学习 · unsupervised and semisupervised learning论文趋势 2023–2026

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。

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 5 / 7 页

201. NeurIPS 2023Object-centric Learning with Cyclic Walks between Parts and Whole
202. NeurIPS 2023Object-Centric Slot Diffusion
203. NeurIPS 2023On Proper Learnability between Average- and Worst-case Robustness
204. NeurIPS 2023On Robust Streaming for Learning with Experts: Algorithms and Lower Bounds
205. NeurIPS 2023On skip connections and normalisation layers in deep optimisation
206. NeurIPS 2023On the Convergence of No-Regret Learning Dynamics in Time-Varying Games
207. NeurIPS 2023On the Importance of Feature Separability in Predicting Out-Of-Distribution Error
208. NeurIPS 2023On the Interplay between Social Welfare and Tractability of Equilibria
209. NeurIPS 2023On the Robustness of Removal-Based Feature Attributions
210. NeurIPS 2023On the Role of Entanglement and Statistics in Learning
211. NeurIPS 2023One Fits All: Power General Time Series Analysis by Pretrained LM
212. NeurIPS 2023Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms
213. NeurIPS 2023Operator Learning with Neural Fields: Tackling PDEs on General Geometries
214. NeurIPS 2023Optimal and Fair Encouragement Policy Evaluation and Learning
215. NeurIPS 2023Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization
216. NeurIPS 2023Optimal Parameter and Neuron Pruning for Out-of-Distribution Detection
217. NeurIPS 2023Optimal privacy guarantees for a relaxed threat model: Addressing sub-optimal adversaries in differentially private machine learning
218. NeurIPS 2023Optimality in Mean Estimation: Beyond Worst-Case, Beyond Sub-Gaussian, and Beyond $1+\alpha$ Moments
219. NeurIPS 2023Overcoming Recency Bias of Normalization Statistics in Continual Learning: Balance and Adaptation
220. NeurIPS 2023Parallel Spiking Neurons with High Efficiency and Ability to Learn Long-term Dependencies
221. NeurIPS 2023Parameterizing Context: Unleashing the Power of Parameter-Efficient Fine-Tuning and In-Context Tuning for Continual Table Semantic Parsing
222. NeurIPS 2023Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model
223. NeurIPS 2023Partial Label Learning with Dissimilarity Propagation guided Candidate Label Shrinkage
224. NeurIPS 2023Partial Multi-Label Learning with Probabilistic Graphical Disambiguation
225. NeurIPS 2023PETAL: Physics Emulation Through Averaged Linearizations for Solving Inverse Problems
226. NeurIPS 2023Physics-Driven ML-Based Modelling for Correcting Inverse Estimation
227. NeurIPS 2023PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learning
228. NeurIPS 2023Posterior Sampling for Competitive RL: Function Approximation and Partial Observation
229. NeurIPS 2023Practical Differentially Private Hyperparameter Tuning with Subsampling
230. NeurIPS 2023Prediction and Control in Continual Reinforcement Learning
231. NeurIPS 2023Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression
232. NeurIPS 2023Promises and Pitfalls of Threshold-based Auto-labeling
233. NeurIPS 2023Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization
234. NeurIPS 2023Provably Efficient Offline Goal-Conditioned Reinforcement Learning with General Function Approximation and Single-Policy Concentrability
235. NeurIPS 2023QuACK: Accelerating Gradient-Based Quantum Optimization with Koopman Operator Learning
236. NeurIPS 2023ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction
237. NeurIPS 2023Recovering from Out-of-sample States via Inverse Dynamics in Offline Reinforcement Learning
238. NeurIPS 2023Reducing Blackwell and Average Optimality to Discounted MDPs via the Blackwell Discount Factor
239. NeurIPS 2023Regularity as Intrinsic Reward for Free Play
240. NeurIPS 2023ReHLine: Regularized Composite ReLU-ReHU Loss Minimization with Linear Computation and Linear Convergence
241. NeurIPS 2023Reinforcement Learning with Fast and Forgetful Memory
242. NeurIPS 2023Reliable learning in challenging environments
243. NeurIPS 2023Representation Learning via Consistent Assignment of Views over Random Partitions
244. NeurIPS 2023Resilient Constrained Learning
245. NeurIPS 2023Responsible AI (RAI) Games and Ensembles
246. NeurIPS 2023Reward Finetuning for Faster and More Accurate Unsupervised Object Discovery
247. NeurIPS 2023Robust and Actively Secure Serverless Collaborative Learning
248. NeurIPS 2023Rotating Features for Object Discovery
249. NeurIPS 2023SALSA VERDE: a machine learning attack on LWE with sparse small secrets
250. NeurIPS 2023Sample based Explanations via Generalized Representers

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。