低秩适配论文趋势 2023–2026

主要结论

• 低秩适配论文从 2023 年的 282 篇增长到 2026 年的 841 篇，三年间扩大到约 3.0 倍。
• NeurIPS 是发低秩适配论文最多的会议，2023–2026 年共 781 篇，占四个会议总量的 34%。
• 交叉方向里涨得最快的是扩散模型：同时属于两个主题的论文从 2023 年的 4 篇增至 2026 年的 61 篇。
• 在「优化与缩放」的 5 个主题里，它的 2026 年论文数排第 1；同组增长最快的缩放定律是 4.1 倍，低秩适配是 3.0 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 2026$\delta$-Mem: : Efficient Online Memory For Large Language Models
• ICML 2026A3: an Analytical Low-Rank Approximation Framework for Attention
• ICLR 2026A Guardrail for Safety Preservation: When Safety-Sensitive Subspace Meets Harmful-Resistant Null-Space
• CVPR 20262-Shots in the Dark Low-Light Denoising with Minimal Data Acquisition
• NeurIPS 2025A geometric framework for momentum-based optimizers for low-rank training
• ICML 2025Beyond Low-rank Decomposition: A Shortcut Approach for Efficient On-Device Learning
• ICLR 2025A Quantum Circuit-Based Compression Perspective for Parameter-Efficient Learning
• CVPR 2025AdaDARE-gamma: Balancing Stability and Plasticity in Multi-modal LLMs through Efficient Adaptation
• NeurIPS 2024Bridging The Gap between Low-rank and Orthogonal Adaptation via Householder Reflection Adaptation
• ICML 2024A Tensor Decomposition Perspective on Second-order RNNs
• ICLR 2024A Hard-to-Beat Baseline for Training-free CLIP-based Adaptation
• CVPR 2024DePT: Decoupled Prompt Tuning
• NeurIPS 2023A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression
• ICML 2023A Three-regime Model of Network Pruning
• ICLR 2023A General Rank Preserving Framework for Asymmetric Image Retrieval
• CVPR 20231% VS 100%: Parameter-Efficient Low Rank Adapter for Dense Predictions
• NeurIPS 2026A low-rank decoder bottleneck bounds reliability in foundation-model perturbation prediction
• ICML 2026Adaptive Utilization of Low-Rank Adaptation via Conditioned Gating
• ICLR 2026ABBA-Adapters: Efficient and Expressive Fine-Tuning of Foundation Models
• CVPR 2026Basis-Oriented Low-rank Transfer for Few-Shot and Test-Time Adaptation

全部低秩适配论文

共 78 篇，第 1 / 2 页

1. NeurIPS 2023(Almost) Provable Error Bounds Under Distribution Shift via Disagreement Discrepancy
2. NeurIPS 2023A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression
3. NeurIPS 2023A Unified Framework for Rank-based Loss Minimization
4. NeurIPS 2023Adaptive whitening with fast gain modulation and slow synaptic plasticity
5. NeurIPS 2023Adversarial Examples Exist in Two-Layer ReLU Networks for Low Dimensional Linear Subspaces
6. NeurIPS 2023Algorithmic Regularization in Tensor Optimization: Towards a Lifted Approach in Matrix Sensing
7. NeurIPS 2023Bayes beats Cross Validation: Efficient and Accurate Ridge Regression via Expectation Maximization
8. NeurIPS 2023Bayesian Extensive-Rank Matrix Factorization with Rotational Invariant Priors
9. NeurIPS 2023Block Low-Rank Preconditioner with Shared Basis for Stochastic Optimization
10. NeurIPS 2023Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language Models
11. NeurIPS 2023Combinatorial Optimization with Policy Adaptation using Latent Space Search
12. NeurIPS 2023Composing Parameter-Efficient Modules with Arithmetic Operation
13. NeurIPS 2023Context-guided Embedding Adaptation for Effective Topic Modeling in Low-Resource Regimes
14. NeurIPS 2023Context-lumpable stochastic bandits
15. NeurIPS 2023Cross-Domain Policy Adaptation via Value-Guided Data Filtering
16. NeurIPS 2023DäRF: Boosting Radiance Fields from Sparse Input Views with Monocular Depth Adaptation
17. NeurIPS 2023Data Selection for Language Models via Importance Resampling
18. NeurIPS 2023Decentralized Matrix Sensing: Statistical Guarantees and Fast Convergence
19. NeurIPS 2023DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method
20. NeurIPS 2023Efficient Adaptation of Large Vision Transformer via Adapter Re-Composing
21. NeurIPS 2023Efficient Hyper-parameter Optimization with Cubic Regularization
22. NeurIPS 2023Efficient Low-rank Backpropagation for Vision Transformer Adaptation
23. NeurIPS 2023Efficient Model-Free Exploration in Low-Rank MDPs
24. NeurIPS 2023Efficient Test-Time Adaptation for Super-Resolution with Second-Order Degradation and Reconstruction
25. NeurIPS 2023Efficient Testable Learning of Halfspaces with Adversarial Label Noise
26. NeurIPS 2023Errors-in-variables Fr\'echet Regression with Low-rank Covariate Approximation
27. NeurIPS 2023Fast and Regret Optimal Best Arm Identification: Fundamental Limits and Low-Complexity Algorithms
28. NeurIPS 2023Fast Rank-1 Lattice Targeted Sampling for Black-box Optimization
29. NeurIPS 2023Feature Adaptation for Sparse Linear Regression
30. NeurIPS 2023Geometric Analysis of Matrix Sensing over Graphs
31. NeurIPS 2023Globally solving the Gromov-Wasserstein problem for point clouds in low dimensional Euclidean spaces
32. NeurIPS 2023Hardness of Low Rank Approximation of Entrywise Transformed Matrix Products
33. NeurIPS 2023Hypernetwork-based Meta-Learning for Low-Rank Physics-Informed Neural Networks
34. NeurIPS 2023Kissing to Find a Match: Efficient Low-Rank Permutation Representation
35. NeurIPS 2023Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback
36. NeurIPS 2023Learning List-Level Domain-Invariant Representations for Ranking
37. NeurIPS 2023Low Tensor Rank Learning of Neural Dynamics
38. NeurIPS 2023Lower Bounds on Adaptive Sensing for Matrix Recovery
39. NeurIPS 2023Matrix Compression via Randomized Low Rank and Low Precision Factorization
40. NeurIPS 2023Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models
41. NeurIPS 2023MKOR: Momentum-Enabled Kronecker-Factor-Based Optimizer Using Rank-1 Updates
42. NeurIPS 2023Multi-Head Adapter Routing for Cross-Task Generalization
43. NeurIPS 2023Neural Priming for Sample-Efficient Adaptation
44. NeurIPS 2023Non-Stationary Bandits with Auto-Regressive Temporal Dependency
45. NeurIPS 2023On the spectral bias of two-layer linear networks
46. NeurIPS 2023Parameter-efficient Tuning of Large-scale Multimodal Foundation Model
47. NeurIPS 2023Parameterizing Context: Unleashing the Power of Parameter-Efficient Fine-Tuning and In-Context Tuning for Continual Table Semantic Parsing
48. NeurIPS 2023PDP: Parameter-free Differentiable Pruning is All You Need
49. NeurIPS 2023Perceptual adjustment queries and an inverted measurement paradigm for low-rank metric learning
50. NeurIPS 2023Preconditioning Matters: Fast Global Convergence of Non-convex Matrix Factorization via Scaled Gradient Descent

常见问题

2023–2026 年四大会议一共有多少篇低秩适配论文？

共 2,273 篇，按年份分别是 2023 年 282 篇、2024 年 518 篇、2025 年 632 篇、2026 年 841 篇。

NeurIPS 2026 有多少篇低秩适配论文？

NeurIPS 2026 有 269 篇低秩适配论文，上一届是 241 篇。

ICLR 2026 有多少篇低秩适配论文？

ICLR 2026 有 184 篇低秩适配论文，上一届是 152 篇。

ICML 2026 有多少篇低秩适配论文？

ICML 2026 有 240 篇低秩适配论文，上一届是 136 篇。

CVPR 2026 有多少篇低秩适配论文？

CVPR 2026 有 148 篇低秩适配论文，上一届是 103 篇。

哪个会议的低秩适配论文最多？

NeurIPS，2023–2026 年共 781 篇。