Results 181 to 190 of about 9,649,421 (248)

A Dual-Side Synergistic LoRA Framework for Full-Chain Fine-Tuning of Qwen2.5-VL for Plant Disease Diagnosis. [PDF]

Plants (Basel)
Zhang Z, Feng Q.
europepmc   +1 more source

SAM3-AgeSeg: an adaptive segmentation model for bone tumors in the aging population. [PDF]

Front Med (Lausanne)
Zhang F, Yuan X, Song H, Chen Y, Bai T.
europepmc   +1 more source

LoRA-Mini : Adaptation Matrices Decomposition and Selective Training

The rapid advancements in large language models (LLMs) have revolutionized natural language processing, creating an increased need for efficient, task-specific fine-tuning methods.
Singh, Ayush, Garg, Shivank, Aher, Rajdeep   +2 more
core   +1 more source

Sequential multi-site fine-tuning for incremental deployment of large language models for mobility functional status extraction. [PDF]

JAMIA Open
Liu X, Garg M, Jeon E, Jia H, Crowson CS, Sauver JS, Pagali SR, Sohn S.   +7 more
europepmc   +1 more source

U-SplitDoRA: an improved privacy-preserved U-shaped split parameter-efficient fine-tuning framework through weight decomposition for large language models. [PDF]

Front Artif Intell
Singh S, Subburaj B, Alagewaran R, Alphonse S.   +3 more
europepmc   +1 more source

Fine-Tuning, Retrieval-Augmented Generation, and Hybrid Adaptation of Language Models for Clinical Decision-Making in Health Care: Systematic Review. [PDF]

J Med Internet Res
Patel A, Khand Y, Vallamchetla SK, Li P, Tao C, Cheung J.   +5 more
europepmc   +1 more source

PRILoRA: Pruned and Rank-Increasing Low-Rank Adaptation

With the proliferation of large pre-trained language models (PLMs), fine-tuning all model parameters becomes increasingly inefficient, particularly when dealing with numerous downstream tasks that entail substantial training and storage costs.
Benedek, Nadav, Wolf, Lior
core  

TC3-VLM: A Vision-Language Model for Tactical Combat Casualty Care. [PDF]

IEEE Access
Kim J, Chng J, Ali A, Moas V, Hwang S, Kamaleswaran R.   +5 more
europepmc   +1 more source

Bayesian Low-rank Adaptation for Large Language Models

Low-rank adaptation (LoRA) has emerged as a new paradigm for cost-efficient fine-tuning of large language models (LLMs). However, fine-tuned LLMs often become overconfident especially when fine-tuned on small datasets.
Aitchison, Laurence, Robeyns, Maxime, Yang, Adam X., Wang, Xi   +3 more
core  

Foundation Models for Remote Sensing Semantic Segmentation: A Review of Architectures, Adaptations, and Prospects. [PDF]

Sensors (Basel)
Deng M, Chen Y, Ding G, Li S, Xie X.
europepmc   +1 more source