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 194 篇，第 4 / 4 页

151. NeurIPS 2026SemGeo-Gen: Unsupervised Generation of Approximate Cross-Instance Semantic-Geometric Correspondences
152. NeurIPS 2026Settling Pure Differentially Private Covariance Estimation
153. NeurIPS 2026ShiftRAG: Bypassing the Textual Bottleneck via Decoupled Learning and Continuous Soft Tokens
154. NeurIPS 2026Simulation-free Unbalanced Dynamic Optimal Transport with General Growth Penalty
155. NeurIPS 2026Sparse Koopman Autoencoders Identify Local Dynamical Regimes in Multibasin Systems
156. NeurIPS 2026Sparsifying Correlation Clustering: Edge Coresets, Triangle Witnesses, and Observation Lower Bounds
157. NeurIPS 2026SPATIALEPIBENCH: Benchmarking Spatial Information and Epidemic Priors in Forecasting
158. NeurIPS 2026State-Action Selection in General Stochastic Games under Regularized Policy Gradient Methods
159. NeurIPS 2026State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives
160. NeurIPS 2026Subdata Selection: A Unified Framework for Optimal Selection and Statistical Efficiency Assessment
161. NeurIPS 2026Submodular Benchmark Selection
162. NeurIPS 2026Subspace-Guided Continual Learning: Hessian Based Stable–Plastic Decomposition for Exemplar-Free Class-Incremental Learning
163. NeurIPS 2026Symmetry Guarantees Statistic Recovery in Variational Inference
164. NeurIPS 2026Synthetic Pre-Pre-Training Improves Language Model Robustness to Noisy Pre-Training Data
165. NeurIPS 2026TabClustPFN: A Prior-Fitted Network for Tabular Data Clustering
166. NeurIPS 2026TabK: Amortized Bayesian Estimation of the Number of Clusters in Tabular Data
167. NeurIPS 2026TENET: Time-point Encoding Network for Multivariate Time Series Anomaly Detection
168. NeurIPS 2026Testable Learning of General Halfspaces under Massart Noise
169. NeurIPS 2026The BV4 Benchmark for Unsupervised Anomaly Detection in High-Dimensional Spectral Data Streams
170. NeurIPS 2026The parameters in weight-sparse transformers are interpretable
171. NeurIPS 2026The Query Complexity of Local Search in Rounds on General Graphs
172. NeurIPS 2026The Sample Complexity of Multiple Change Point Identification under Bandit Feedback
173. NeurIPS 2026The Sampling Complexity of Condorcet Winner Identification in Dueling Bandits
174. NeurIPS 2026TILT: Target-induced loss tilting under covariate shift
175. NeurIPS 2026ToMAToMP: Robust and Multi-Parameter Topological Clustering
176. NeurIPS 2026TopoPrune: Robust Data Pruning via Unified Latent Space Topology
177. NeurIPS 2026Towards Identifiable Latent Additive Noise Models
178. NeurIPS 2026Towards more general control of diffusion models using Jeffrey Guidance
179. NeurIPS 2026Towards Understanding the Power and Limits of the Muon Optimizer: A River-Valley Perspective
180. NeurIPS 2026Tracing Actual Causes with Counterfactual Witness Maps
181. NeurIPS 2026Tree Search With Predictions
182. NeurIPS 2026Trust Region Continual Learning as an Implicit Meta-Learner
183. NeurIPS 2026UGGRH: Unsupervised Generative Completion and Graph-attention Refinement for Incomplete Cross-modal Hashing
184. NeurIPS 2026UniRAP: Towards Unified Part-level Physical Affordance Reasoning and Actionable Perception
185. NeurIPS 2026Unpaired Canonical Correlation Analysis
186. NeurIPS 2026Unrolled gradients in disguise: bridging interpolation-based and Jacobian regularization for stable neural dynamics
187. NeurIPS 2026Unsupervised Concept Discovery with Dirichlet Concept Diffusion Models
188. NeurIPS 2026Virtual-Flow: Virtual Microphone-based Speech Enhancement via Unsupervised Flow Matching
189. NeurIPS 2026What the Geometry of Good Models Tells Us
190. NeurIPS 2026What Transformer FFNs Never See: Theory, Diagnosis, and Lightweight Remediation
191. NeurIPS 2026When and Why is Optimistic Multiplicative Weights Slow? The Geometry of Energy Dissipation
192. NeurIPS 2026Words That Make Language Models Perceive
193. NeurIPS 2026You Don’t Need Aligned Representations: Knowledge Distillation via Random Prototype Spaces
194. NeurIPS 2026Your Embedding Model Is SMARTer Than You Think

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。