{"total":71,"rows":[{"id":"2408.05517","title":"SWIFT:A Scalable lightWeight Infrastructure for Fine-Tuning","created":"2024-08-10","cited_by_count":355,"area":"mllm"},{"id":"2407.11046","title":"A Survey on LoRA of Large Language Models","created":"2024-07-08","cited_by_count":67,"area":null},{"id":"2410.08114","title":"Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning","created":"2024-10-10","cited_by_count":43,"area":null},{"id":"2411.01327","title":"Visual Fourier Prompt Tuning","created":"2024-11-02","cited_by_count":30,"area":null},{"id":"2505.18738","title":"AuroRA: Breaking Low-Rank Bottleneck of LoRA with Nonlinear Mapping","created":"2025-05-24","cited_by_count":23,"area":null},{"id":"2504.07448","title":"LoRI: Reducing Cross-Task Interference in Multi-Task Low-Rank Adaptation","created":"2025-04-10","cited_by_count":19,"area":null},{"id":"2503.04543","title":"Keeping Yourself is Important in Downstream Tuning Multimodal Large Language Model","created":"2025-03-06","cited_by_count":16,"area":"lr"},{"id":"2405.17484","title":"Bridging The Gap between Low-rank and Orthogonal Adaptation via Householder Reflection Adaptation","created":"2024-05-24","cited_by_count":15,"area":null},{"id":"2406.08798","title":"FouRA: Fourier Low Rank Adaptation","created":"2024-06-13","cited_by_count":15,"area":"diffusion"},{"id":"2410.18965","title":"On the Crucial Role of Initialization for Matrix Factorization","created":"2024-10-24","cited_by_count":15,"area":null},{"id":"2505.00976","title":"Attack and defense techniques in large language models: A survey and new perspectives","created":"2025-05-02","cited_by_count":14,"area":null},{"id":"2409.05926","title":"SVFit: Parameter-Efficient Fine-Tuning of Large Pre-Trained Models Using Singular Values","created":"2024-09-09","cited_by_count":11,"area":null},{"id":"2407.09946","title":"Low-Rank Interconnected Adaptation across Layers","created":"2024-07-13","cited_by_count":10,"area":null},{"id":"2503.18225","title":"DeLoRA: Decoupling Angles and Strength in Low-rank Adaptation","created":"2025-03-23","cited_by_count":10,"area":null},{"id":"2505.20355","title":"GraLoRA: Granular Low-Rank Adaptation for Parameter-Efficient Fine-Tuning","created":"2025-05-26","cited_by_count":10,"area":null},{"id":"2602.04998","title":"Learning Rate Matters: Vanilla LoRA May Suffice for LLM Fine-tuning","created":"2026-02-04","cited_by_count":10,"area":null},{"id":"2505.18877","title":"RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models","created":"2025-05-24","cited_by_count":9,"area":null},{"id":"2410.01870","title":"PEANuT: Parameter-Efficient Adaptation with Weight-aware Neural Tweakers","created":"2024-10-02","cited_by_count":8,"area":null},{"id":"2412.06071","title":"KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language Models","created":"2024-12-08","cited_by_count":8,"area":null},{"id":"2505.12532","title":"Exploring Sparsity for Parameter Efficient Fine Tuning Using Wavelets for Vision","created":"2025-05-18","cited_by_count":7,"area":null},{"id":"2506.00799","title":"Uni-LoRA: One Vector is All You Need","created":"2025-06-01","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":"2410.13618","title":"LoLDU: Low-Rank Adaptation via Lower-Diag-Upper Decomposition for Parameter-Efficient Fine-Tuning","created":"2024-10-17","cited_by_count":6,"area":null},{"id":"2503.18368","title":"MoST: Efficient Monarch Sparse Tuning for 3D Representation Learning","created":"2025-03-24","cited_by_count":6,"area":null},{"id":"2506.05713","title":"Come Together, But Not Right Now: A Progressive Strategy to Boost Low-Rank Adaptation","created":"2025-06-06","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":"2410.09758","title":"BiDoRA: Bi-level Optimization-Based Weight-Decomposed Low-Rank Adaptation","created":"2024-10-13","cited_by_count":4,"area":null},{"id":"2502.06820","title":"LoCA: Location-Aware Cosine Adaptation for Parameter-Efficient Fine-Tuning","created":"2025-02-05","cited_by_count":4,"area":null},{"id":"2505.11235","title":"Efficient Orthogonal Fine-Tuning with Principal Subspace Adaptation","created":"2025-05-16","cited_by_count":4,"area":null},{"id":"2506.13187","title":"Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence","created":"2025-06-16","cited_by_count":4,"area":null},{"id":"2501.08727","title":"Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models","created":"2025-01-15","cited_by_count":3,"area":null},{"id":"2504.02012","title":"Instruction-Guided Autoregressive Neural Network Parameter Generation","created":"2025-04-02","cited_by_count":3,"area":null},{"id":"2505.00582","title":"Block Circulant Adapter for Large Language Models","created":"2025-05-01","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":"2505.00580","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","created":"2025-05-01","cited_by_count":2,"area":null},{"id":"2510.21883","title":"Language Ranker: A Lightweight Ranking framework for LLM Decoding","created":"2025-10-23","cited_by_count":2,"area":"alignment"},{"id":"2512.00940","title":"Memory-Integrated Reconfigurable Adapters: A Unified Framework for Settings with Multiple Tasks","created":"2025-11-30","cited_by_count":2,"area":null},{"id":"2512.03056","title":"Delta Sampling: Data-Free Knowledge Transfer Across Diffusion Models","created":"2025-11-25","cited_by_count":2,"area":"diffusion"},{"id":"2503.22352","title":"Meta-LoRA: Meta-Learning LoRA Components for Domain-Aware ID Personalization","created":"2025-03-28","cited_by_count":1,"area":"diffusion"},{"id":"2505.23782","title":"4,500 Seconds: Small Data Training Approaches for Deep UAV Audio Classification","created":"2025-05-21","cited_by_count":1,"area":null},{"id":"2506.24092","title":"WaRA: Wavelet Low-Rank Adaptation for Medical Image Classification","created":"2025-06-25","cited_by_count":1,"area":null},{"id":"2509.17428","title":"QWHA: Quantization-Aware Walsh-Hadamard Adaptation for Parameter-Efficient Fine-Tuning on Large Language Models","created":"2025-09-22","cited_by_count":1,"area":null},{"id":"2510.23818","title":"ScaLoRA: Optimally Scaled Low-Rank Adaptation for Efficient High-Rank Fine-Tuning","created":"2025-10-27","cited_by_count":1,"area":null},{"id":"2510.24037","title":"Kernelized Sparse Fine-Tuning with Bi-level Parameter Competition for Vision Models","created":"2025-10-28","cited_by_count":1,"area":null},{"id":"2512.12295","title":"Near-Zero-Overhead Freshness for Recommendation Systems via Inference-Side Model Updates","created":"2025-12-13","cited_by_count":1,"area":null},{"id":"2602.05709","title":"Nonlinearity as Rank: Generative Low-Rank Adapter with Radial Basis Functions","created":"2026-02-05","cited_by_count":1,"area":null},{"id":"2603.17558","title":"Zipper-LoRA: Dynamic Parameter Decoupling for Speech-LLM based Multilingual Speech Recognition","created":"2026-03-18","cited_by_count":1,"area":null},{"id":"2603.26929","title":"Live Interactive Training for Video Segmentation","created":"2026-03-27","cited_by_count":1,"area":null},{"id":"2605.08589","title":"S2FT: Parameter-Efficient Fine-Tuning in Sparse Spectrum Domain","created":"2026-05-09","cited_by_count":1,"area":null},{"id":"2605.14841","title":"GPart: End-to-End Isometric Fine-Tuning via Global Parameter Partitioning","created":"2026-05-14","cited_by_count":1,"area":null},{"id":"2409.19733","title":"Pear: Pruning and Sharing Adapters in Visual Parameter-Efficient Fine-Tuning","created":"2024-09-29","cited_by_count":0,"area":null},{"id":"2506.11042","title":"GenFT: A Generative Parameter-Efficient Fine-Tuning Method for Pretrained Foundation Models","created":"2025-05-21","cited_by_count":0,"area":null},{"id":"2508.10312","title":"Beyond Semantic Understanding: Preserving Collaborative Frequency Components in LLM-based Recommendation","created":"2025-08-14","cited_by_count":0,"area":null},{"id":"2511.01385","title":"Memory-Efficient Training with In-Place FFT Implementation","created":"2025-11-03","cited_by_count":0,"area":null},{"id":"2604.01762","title":"FourierMoE: Fourier Mixture-of-Experts Adaptation of Large Language Models","created":"2026-04-02","cited_by_count":0,"area":"moe"},{"id":"2604.04440","title":"Training Transformers in Cosine Coefficient Space","created":"2026-04-06","cited_by_count":0,"area":null},{"id":"2604.05934","title":"Leveraging Image Editing Foundation Models for Data-Efficient CT Metal Artifact Reduction","created":"2026-04-07","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":"2605.22869","title":"FuRA: Full-Rank Parameter-Efficient Fine-Tuning with Spectral Preconditioning","created":"2026-05-19","cited_by_count":0,"area":null},{"id":"2607.19171","title":"Point Ladder Tuning: Parameter-Efficient Hierarchical Adaptation for 3D Point Cloud Understanding","created":"2026-07-21","cited_by_count":0,"area":null},{"id":"2607.29462","title":"MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification","created":"2026-07-31","cited_by_count":0,"area":"moe"},{"id":"2608.19680","title":"Frequency-Aware Continual Learning for Smart Contract Vulnerability Detection with Large Language Models","created":"2026-08-20","cited_by_count":0,"area":null},{"id":"2608.26430","title":"Fine-Tuning of Transformer models with Frames","created":"2026-08-26","cited_by_count":0,"area":null},{"id":"2608.26531","title":"FAN-LoRA: A Fourier-Adaptive Nonlinear Low-Rank Adaptor for Medical Foundation Model Domain Adaptation","created":"2026-08-27","cited_by_count":0,"area":null},{"id":"2609.18056","title":"Position Anchor Tuning: Towards Efficient Adaptation of Pre-Trained Point Cloud Transformers","created":"2026-09-16","cited_by_count":0,"area":null},{"id":"2609.19716","title":"GAPrompt++: Multi-Granular Geometry-Aware Point Cloud Prompt for 3D Vision Model","created":"2026-09-17","cited_by_count":0,"area":null},{"id":"2609.29592","title":"QINA: Quantum-Inspired Nonlinear Adapters for Pretrained Vision Models","created":"2026-09-01","cited_by_count":0,"area":null},{"id":"2609.37027","title":"Beyond Low-Rank Parameterization: Narrowing the Gap Between LoRA and Full Fine-Tuning via Gradient Decomposition","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2610.04226","title":"PaLoRA: Paced Low-Rank Adaptation for Continual Learning","created":"2026-10-03","cited_by_count":0,"area":null},{"id":"2610.05394","title":"The Hidden States Cookbook: A Large-Scale Ablation Study for Noise-Robust Conversational Intent Classification in Industry","created":"2026-10-04","cited_by_count":0,"area":null},{"id":"2610.07848","title":"Dynamic Positional Attention Modulation for Parameter-Efficient Fine-Tuning of Large Language Models","created":"2026-10-06","cited_by_count":0,"area":null}],"next_cursor":null}