{"total":87,"rows":[{"id":"2402.12354","title":"LoRA+: Efficient Low Rank Adaptation of Large Models","created":"2024-02-19","cited_by_count":377,"area":null},{"id":"2405.09673","title":"LoRA Learns Less and Forgets Less","created":"2024-05-15","cited_by_count":328,"area":null},{"id":"2312.12148","title":"Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment","created":"2023-12-19","cited_by_count":230,"area":null},{"id":"2402.16842","title":"Asymmetry in Low-Rank Adapters of Foundation Models","created":"2024-02-26","cited_by_count":86,"area":null},{"id":"2407.11046","title":"A Survey on LoRA of Large Language Models","created":"2024-07-08","cited_by_count":67,"area":null},{"id":"2402.11867","title":"LoRA Training in the NTK Regime has No Spurious Local Minima","created":"2024-02-19","cited_by_count":48,"area":null},{"id":"2407.15720","title":"Do Large Language Models Have Compositional Ability? An Investigation into Limitations and Scalability","created":"2024-07-22","cited_by_count":40,"area":null},{"id":"2501.00365","title":"Low-Rank Adaptation for Foundation Models: A Comprehensive Review","created":"2024-12-31","cited_by_count":37,"area":null},{"id":"2505.24717","title":"PDE-Transformer: Efficient and Versatile Transformers for Physics Simulations","created":"2025-05-30","cited_by_count":32,"area":null},{"id":"2406.03136","title":"Computational Limits of Low-Rank Adaptation (LoRA) Fine-Tuning for Transformer Models","created":"2024-06-05","cited_by_count":31,"area":null},{"id":"2501.06425","title":"Tensor Product Attention Is All You Need","created":"2025-01-11","cited_by_count":31,"area":null},{"id":"2402.04497","title":"The Fine-Grained Complexity of Gradient Computation for Training Large Language Models","created":"2024-02-07","cited_by_count":30,"area":null},{"id":"2407.00066","title":"Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead","created":"2024-06-17","cited_by_count":24,"area":null},{"id":"2405.16747","title":"Understanding Linear Probing then Fine-tuning Language Models from NTK Perspective","created":"2024-05-27","cited_by_count":22,"area":null},{"id":"2409.16670","title":"GraphLoRA: Structure-Aware Contrastive Low-Rank Adaptation for Cross-Graph Transfer Learning","created":"2024-09-25","cited_by_count":21,"area":null},{"id":"2502.09376","title":"LoRA Training Provably Converges to a Low-Rank Global Minimum or It Fails Loudly (But it Probably Won't Fail)","created":"2025-02-13","cited_by_count":20,"area":null},{"id":"2506.03133","title":"PoLAR: Polar-Decomposed Low-Rank Adapter Representation","created":"2025-06-03","cited_by_count":20,"area":null},{"id":"2402.18039","title":"ResLoRA: Identity Residual Mapping in Low-Rank Adaption","created":"2024-02-28","cited_by_count":18,"area":null},{"id":"2410.15821","title":"The effect of fine-tuning on language model toxicity","created":"2024-10-21","cited_by_count":16,"area":null},{"id":"2505.23194","title":"Beyond Zero Initialization: Investigating the Impact of Non-Zero Initialization on LoRA Fine-Tuning Dynamics","created":"2025-05-29","cited_by_count":16,"area":null},{"id":"2405.17258","title":"$\\textit{Trans-LoRA}$: towards data-free Transferable Parameter Efficient Finetuning","created":"2024-05-27","cited_by_count":13,"area":null},{"id":"2503.06982","title":"Understanding the Learning Dynamics of LoRA: A Gradient Flow Perspective on Low-Rank Adaptation in Matrix Factorization","created":"2025-03-10","cited_by_count":13,"area":null},{"id":"2406.09679","title":"Exploring Training on Heterogeneous Data with Mixture of Low-rank Adapters","created":"2024-06-14","cited_by_count":12,"area":null},{"id":"2506.14530","title":"Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters","created":"2025-06-17","cited_by_count":11,"area":null},{"id":"2406.14036","title":"Towards Infinite-Long Prefix in Transformer","created":"2024-06-20","cited_by_count":10,"area":null},{"id":"2512.18248","title":"On the Convergence Rate of LoRA Gradient Descent","created":"2025-12-20","cited_by_count":10,"area":null},{"id":"2412.20645","title":"YOLO-UniOW: Efficient Universal Open-World Object Detection","created":"2024-12-30","cited_by_count":9,"area":null},{"id":"2505.11892","title":"Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform","created":"2025-05-17","cited_by_count":9,"area":null},{"id":"2602.06204","title":"Learning Rate Scaling across LoRA Ranks and Transfer to Full Finetuning","created":"2026-02-05","cited_by_count":9,"area":null},{"id":"2505.00598","title":"Fast and Low-Cost Genomic Foundation Models via Outlier Removal","created":"2025-05-01","cited_by_count":8,"area":null},{"id":"2502.05087","title":"Mitigating Unintended Memorization with LoRA in Federated Learning for LLMs","created":"2025-02-07","cited_by_count":7,"area":null},{"id":"2505.16284","title":"Only Large Weights (And Not Skip Connections) Can Prevent the Perils of Rank Collapse","created":"2025-05-22","cited_by_count":7,"area":null},{"id":"2507.06558","title":"The Primacy of Magnitude in Low-Rank Adaptation","created":"2025-07-09","cited_by_count":7,"area":null},{"id":"2407.04173","title":"Quantifying Prediction Consistency Under Fine-Tuning Multiplicity in Tabular LLMs","created":"2024-07-04","cited_by_count":6,"area":null},{"id":"2503.12576","title":"RaSA: Rank-Sharing Low-Rank Adaptation","created":"2025-03-16","cited_by_count":6,"area":null},{"id":"2510.23123","title":"Beyond Higher Rank: Token-wise Input-Output Projections for Efficient Low-Rank Adaptation","created":"2025-10-27","cited_by_count":6,"area":null},{"id":"2404.15384","title":"FL-TAC: Enhanced Fine-Tuning in Federated Learning via Low-Rank, Task-Specific Adapter Clustering","created":"2024-04-23","cited_by_count":5,"area":null},{"id":"2407.19342","title":"Parameter-Efficient Fine-Tuning via Circular Convolution","created":"2024-07-27","cited_by_count":4,"area":null},{"id":"2506.00630","title":"Probabilistic Forecasting for Building Energy Systems using Time-Series Foundation Models","created":"2025-05-31","cited_by_count":4,"area":null},{"id":"2507.07694","title":"SAS: Simulated Attention Score","created":"2025-07-10","cited_by_count":4,"area":null},{"id":"2508.06953","title":"BoRA: Towards More Expressive Low-Rank Adaptation with Block Diversity","created":"2025-08-09","cited_by_count":4,"area":null},{"id":"2508.17232","title":"Curvature Learning for Generalization of Hyperbolic Neural Networks","created":"2025-08-24","cited_by_count":4,"area":null},{"id":"2409.01688","title":"Differentially Private Kernel Density Estimation","created":"2024-09-03","cited_by_count":3,"area":null},{"id":"2411.15804","title":"LoRA-Mini : Adaptation Matrices Decomposition and Selective Training","created":"2024-11-24","cited_by_count":3,"area":null},{"id":"2505.18494","title":"FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation","created":"2025-05-24","cited_by_count":3,"area":null},{"id":"2602.00084","title":"Why LoRA Resists Label Noise: A Theoretical Framework for Noise-Robust Parameter-Efficient Fine-Tuning","created":"2026-01-22","cited_by_count":3,"area":null},{"id":"2603.14706","title":"AdapterTune: Zero-Initialized Low-Rank Adapters for Frozen Vision Transformers","created":"2026-03-16","cited_by_count":3,"area":null},{"id":"2604.21905","title":"Low-Rank Adaptation Redux for Large Models","created":"2026-04-23","cited_by_count":3,"area":null},{"id":"2605.31484","title":"Balanced LoRA: Removing Parameter Invariance to Accelerate Convergence","created":"2026-05-29","cited_by_count":3,"area":null},{"id":"2606.05899","title":"High-Dimensional Theory of LoRA Fine-Tuning in a Solvable Attention Model","created":"2026-06-04","cited_by_count":3,"area":null},{"id":"2408.17383","title":"MoRe Fine-Tuning with 10x Fewer Parameters","created":"2024-08-30","cited_by_count":2,"area":null},{"id":"2410.22264","title":"Provable Meta-Learning with Low-Rank Adaptations","created":"2024-10-29","cited_by_count":2,"area":null},{"id":"2503.04325","title":"GBT-SAM: A Parameter-Efficient Depth-Aware Model for Generalizable Brain tumour Segmentation on mp-MRI","created":"2025-03-06","cited_by_count":2,"area":null},{"id":"2505.00333","title":"Two Stage Wireless Federated LoRA Fine-Tuning with Sparsified Orthogonal Updates","created":"2025-05-01","cited_by_count":2,"area":null},{"id":"2505.17030","title":"Distillation-Enabled Knowledge Alignment Protocol for Semantic Communication in AI Agent Networks","created":"2025-05-07","cited_by_count":2,"area":null},{"id":"2602.02855","title":"When pre-training hurts LoRA fine-tuning: a dynamical analysis via single-index models","created":"2026-02-02","cited_by_count":2,"area":null},{"id":"2605.19018","title":"LoRA vs. Full Fine-Tuning: A Theoretical Perspective","created":"2026-05-18","cited_by_count":2,"area":null},{"id":"2607.27680","title":"Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection","created":"2026-07-30","cited_by_count":2,"area":null},{"id":"2312.00639","title":"RefinedFields: Radiance Fields Refinement for Planar Scene Representations","created":"2023-12-01","cited_by_count":1,"area":null},{"id":"2407.12074","title":"Enhancing Parameter Efficiency and Generalization in Large-Scale Models: A Regularized and Masked Low-Rank Adaptation Approach","created":"2024-07-16","cited_by_count":1,"area":null},{"id":"2502.07739","title":"HRP: High-Rank Preheating for Superior LoRA Initialization","created":"2025-02-11","cited_by_count":1,"area":null},{"id":"2503.07748","title":"AdaptSR: Low-Rank Adaptation for Efficient and Scalable Real-World Super-Resolution","created":"2025-03-10","cited_by_count":1,"area":null},{"id":"2506.00236","title":"Localized LoRA: A Structured Low-Rank Approximation for Efficient Fine-Tuning","created":"2025-05-30","cited_by_count":1,"area":null},{"id":"2506.16456","title":"Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation","created":"2025-06-19","cited_by_count":1,"area":null},{"id":"2603.29824","title":"Curvature-Guided LoRA: Matching Full Fine-Tuning in Function Space","created":"2026-03-31","cited_by_count":1,"area":null},{"id":"2605.05659","title":"Structural Correspondence and Universal Approximation in Diagonal plus Low-Rank Neural Networks","created":"2026-05-07","cited_by_count":1,"area":null},{"id":"2607.21975","title":"On the Convergence of Stochastic Low-Rank Adaptation","created":"2026-07-24","cited_by_count":1,"area":null},{"id":"2408.00359","title":"Memorization Capacity for Additive Fine-Tuning with Small ReLU Networks","created":"2024-08-01","cited_by_count":0,"area":null},{"id":"2408.10923","title":"LBC: Language-Based-Classifier for Out-Of-Variable Generalization","created":"2024-08-20","cited_by_count":0,"area":null},{"id":"2410.19694","title":"Less is More: Extreme Gradient Boost Rank-1 Adaption for Efficient Finetuning of LLMs","created":"2024-10-25","cited_by_count":0,"area":null},{"id":"2502.06300","title":"The impact of allocation strategies in subset learning on the expressive power of neural networks","created":"2025-02-10","cited_by_count":0,"area":null},{"id":"2506.13479","title":"Position: Pause Recycling LoRAs and Prioritize Mechanisms to Uncover Limits and Effectiveness","created":"2025-06-16","cited_by_count":0,"area":null},{"id":"2507.01806","title":"LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs","created":"2025-07-02","cited_by_count":0,"area":null},{"id":"2507.01841","title":"Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization","created":"2025-07-02","cited_by_count":0,"area":null},{"id":"2511.18306","title":"Table Comprehension in Building Codes using Vision Language Models and Domain-Specific Fine-Tuning","created":"2025-11-23","cited_by_count":0,"area":null},{"id":"2602.07479","title":"ODELoRA: Training Low-Rank Adaptation by Solving Ordinary Differential Equations","created":"2026-02-07","cited_by_count":0,"area":null},{"id":"2602.10770","title":"LOREN: Low Rank-Based Code-Rate Adaptation in Neural Receivers","created":"2026-02-11","cited_by_count":0,"area":null},{"id":"2603.19611","title":"Demonstrations, CoT, and Prompting: A Theoretical Analysis of ICL","created":"2026-03-20","cited_by_count":0,"area":null},{"id":"2605.12207","title":"Not How Many, But Which: Parameter Placement in Low-Rank Adaptation","created":"2026-05-12","cited_by_count":0,"area":null},{"id":"2606.31397","title":"Mixture-of-Control: State-Aware Fine-Tuning for Transformer-based Models","created":"2026-06-30","cited_by_count":0,"area":null},{"id":"2608.26052","title":"How Much Rank Does LoRA Need? Rank-Error Bounds for Transformer Attention","created":"2026-08-26","cited_by_count":0,"area":null},{"id":"2609.32002","title":"What Does the Rank Buy? A Spectral and Distributional Analysis of Low-Rank Adaptation","created":"2026-09-25","cited_by_count":0,"area":null},{"id":"2609.32271","title":"Certification Frontiers for Gaussian LoRA: Independent Priors, Posterior Risk, and Prediction-Preserving Balancing","created":"2026-09-26","cited_by_count":0,"area":null},{"id":"2609.33687","title":"Resource-Aware Parameter-Efficient Model Adaptation for Onboard High-Dimensional Data","created":"2026-09-27","cited_by_count":0,"area":null},{"id":"2609.36766","title":"When Can Prefixes Compile LoRA? Exact Resource-Capped Tests for Frozen Attention","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2609.37134","title":"SQUARE: Structured Quantum Representation Adapters as Compact Quadratic Feature Maps for Frozen Language Models","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2610.04507","title":"LoRA's Second Descent Extends Beyond Parameter Parity","created":"2026-10-03","cited_by_count":0,"area":null}],"next_cursor":null}