:: Library Catalog

Imagen de Portada

Guardado en:

Detalles Bibliográficos
Autores principales:	Wang, Xujia, Qi, Yunjia, Xu, Bin
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Artificial Intelligence
Acceso en línea:	https://arxiv.org/abs/2507.04487
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Localized LoRA: A Structured Low-Rank Approximation for Efficient Fine-Tuning
por: Barazandeh, Babak, et al.
Publicado: (2025)

Rethinking the Rank Threshold for LoRA Fine-Tuning
por: Park, Juneyoung
Publicado: (2026)

GraLoRA: Granular Low-Rank Adaptation for Parameter-Efficient Fine-Tuning
por: Jung, Yeonjoon, et al.
Publicado: (2025)

Memory-Efficient Fine-Tuning via Low-Rank Activation Compression
por: Shi, Jiang-Xin, et al.
Publicado: (2025)

mLoRA: Fine-Tuning LoRA Adapters via Highly-Efficient Pipeline Parallelism in Multiple GPUs
por: Ye, Zhengmao, et al.
Publicado: (2023)

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation
por: Liu, Jun, et al.
Publicado: (2025)

AR1-ZO: Topology-Aware Rank-1 Zeroth-Order Queries for High-Rank LoRA Fine-Tuning
por: Chen, Ziye, et al.
Publicado: (2026)

Echo-LoRA: Parameter-Efficient Fine-Tuning via Cross-Layer Representation Injection
por: Peng, Yihang, et al.
Publicado: (2026)

ARD-LoRA: Dynamic Rank Allocation for Parameter-Efficient Fine-Tuning of Foundation Models with Heterogeneous Adaptation Needs
por: Shinwari, Haseeb Ullah Khan, et al.
Publicado: (2025)

ElaLoRA: Elastic & Learnable Low-Rank Adaptation for Efficient Model Fine-Tuning
por: Chang, Huandong, et al.
Publicado: (2025)

LoRAFusion: Efficient LoRA Fine-Tuning for LLMs
por: Zhu, Zhanda, et al.
Publicado: (2025)

A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning
por: Xu, Junzhou, et al.
Publicado: (2025)

Hybrid-LoRA: Bridging Full Fine-Tuning and Low-Rank Adaptation for Post-Training
por: Zhang, Chengqian, et al.
Publicado: (2026)

FedTreeLoRA: Reconciling Statistical and Functional Heterogeneity in Federated LoRA Fine-Tuning
por: Bian, Jieming, et al.
Publicado: (2026)

FRoD: Full-Rank Efficient Fine-Tuning with Rotational Degrees for Fast Convergence
por: Wan, Guoan, et al.
Publicado: (2025)

FedALT: Federated Fine-Tuning through Adaptive Local Training with Rest-of-World LoRA
por: Bian, Jieming, et al.
Publicado: (2025)

LoCA: Location-Aware Cosine Adaptation for Parameter-Efficient Fine-Tuning
por: Du, Zhekai, et al.
Publicado: (2025)

Computational Limits of Low-Rank Adaptation (LoRA) Fine-Tuning for Transformer Models
por: Hu, Jerry Yao-Chieh, et al.
Publicado: (2024)

Polynomial Expansion Rank Adaptation: Enhancing Low-Rank Fine-Tuning with High-Order Interactions
por: Zhang, Wenhao, et al.
Publicado: (2026)

LoRA+: Efficient Low Rank Adaptation of Large Models
por: Hayou, Soufiane, et al.
Publicado: (2024)

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models
por: Zhang, Longteng, et al.
Publicado: (2026)

KD-LoRA: A Hybrid Approach to Efficient Fine-Tuning with LoRA and Knowledge Distillation
por: Azimi, Rambod, et al.
Publicado: (2024)

MatryoshkaLoRA: Learning Accurate Hierarchical Low-Rank Representations for LLM Fine-Tuning
por: Modoranu, Ionut-Vlad, et al.
Publicado: (2026)

MetaLoRA: Tensor-Enhanced Adaptive Low-Rank Fine-tuning
por: Wang, Maolin, et al.
Publicado: (2025)

$α$-LoRA: Effective Fine-Tuning via Base Model Rescaling
por: Firdoussi, Aymane El, et al.
Publicado: (2025)

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization
por: Deng, Yanxia, et al.
Publicado: (2025)

Wireless Federated Multi-Task LLM Fine-Tuning via Sparse-and-Orthogonal LoRA
por: Yang, Nuocheng, et al.
Publicado: (2026)

Parameter-Efficient Fine-Tuning for HAR: Integrating LoRA and QLoRA into Transformer Models
por: Seregina, Irina, et al.
Publicado: (2025)

On-the-Fly Adaptation to Quantization: Configuration-Aware LoRA for Efficient Fine-Tuning of Quantized LLMs
por: Ye, Rongguang, et al.
Publicado: (2025)

Bi-LoRA: Efficient Sharpness-Aware Minimization for Fine-Tuning Large-Scale Models
por: Liu, Yuhang, et al.
Publicado: (2025)

SC-LoRA: Balancing Efficient Fine-tuning and Knowledge Preservation via Subspace-Constrained LoRA
por: Luo, Minrui, et al.
Publicado: (2025)

A Stronger Mixture of Low-Rank Experts for Fine-Tuning Foundation Models
por: Sun, Mengyang, et al.
Publicado: (2025)

TT-LoRA MoE: Unifying Parameter-Efficient Fine-Tuning and Sparse Mixture-of-Experts
por: Kunwar, Pradip, et al.
Publicado: (2025)

RPO:Reinforcement Fine-Tuning with Partial Reasoning Optimization
por: Yi, Hongzhu, et al.
Publicado: (2026)

LoTA-QAF: Lossless Ternary Adaptation for Quantization-Aware Fine-Tuning
por: Chen, Junyu, et al.
Publicado: (2025)

FedMomentum: Preserving LoRA Training Momentum in Federated Fine-Tuning
por: Yan, Peishen, et al.
Publicado: (2026)

Robust and Efficient Zeroth-Order LLM Fine-Tuning via Adaptive Bayesian Subspace Optimizer
por: Feng, Jian, et al.
Publicado: (2026)

Fine-Tuned In-Context Learners for Efficient Adaptation
por: Bornschein, Jorg, et al.
Publicado: (2025)

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning
por: Bian, Jieming, et al.
Publicado: (2025)

LoRDO: Distributed Low-Rank Optimization with Infrequent Communication
por: Jovanović, Andrej, et al.
Publicado: (2026)