Results 71 to 80 of about 124,133 (290)
DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning [PDF]
Prompt tuning (PT), where a small amount of trainable soft (continuous) prompt vectors is affixed to the model input, has shown promising results across various tasks and model architecture for parameter-efficient fine-tuning (PEFT).
Shi, Z, Lipani, A
core +2 more sources
Parameter-Efficient Fine-Tuning of Large Pretrained Models for Instance Segmentation Tasks
Machine Learning and Knowledge ExtractionResearch and applications in artificial intelligence have recently shifted with the rise of large pretrained models, which deliver state-of-the-art results across numerous tasks.
Nermeen Abou Baker +2 more
doaj +1 more source
Meta-Learning Hyperparameters for Parameter Efficient Fine-Tuning
2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Accepted by CVPR 2025 (Highlight).
Zichen Tian, Yaoyao Liu 0001, Qianru Sun
openaire +2 more sources
Near‐Field Electrospinning Micro‐Printhead Achieves Precise Control of Nanofiber Deposition
Advanced Engineering Materials, EarlyView.A micro‐printhead for near‐field electrospinning enables reproducible deposition of polymer nanofibers with diameters below 50 nm. Systematic parameter studies uncover the mechanisms linking operating conditions to fiber morphology, paving the way for precise and low‐cost nanoscale 3D manufacturing.As a high‐resolution, cost‐effective, and rapid ...
Han Xu, Dario Mager, Jan G. Korvink
wiley +1 more source
Efficient Side-Tuning for Remote Sensing: A Low-Memory Fine-Tuning Framework
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingFine-tuning pretrained models for remote sensing tasks often demands substantial computational resources. To reduce memory requirements and training costs, this article proposes a low-memory fine-tuning framework, called efficient side-tuning (EST), for ...
Haichen Yu +6 more
doaj +1 more source
Fine-Tune Smarter, Not Harder: Parameter-Efficient Fine-Tuning for Geospatial Foundation Models
Earth observation (EO) is crucial for monitoring environmental changes, responding to disasters, and managing natural resources. In this context, foundation models facilitate remote sensing image analysis to retrieve relevant geoinformation accurately and efficiently.
Francesc Marti Escofet +4 more
openaire +2 more sources
MambaPEFT: Exploring Parameter-Efficient Fine-Tuning for Mamba
CoRRAn ecosystem of Transformer-based models has been established by building large models with extensive data. Parameter-efficient fine-tuning (PEFT) is a crucial technology for deploying these models to downstream tasks with minimal cost while achieving effective performance. Recently, Mamba, a State Space Model (SSM)-based model, has attracted attention
Masakazu Yoshimura +2 more
openaire +3 more sources
Advanced Engineering Materials, EarlyView.Disordered (Fe50Co50)1−xPtx thin films exhibit a pronounced anomalous Nernst effect (ANE) with a strong composition dependence on both rigid and flexible substrates. The transverse thermoelectric response peaks near 22.5 at.% Pt, accompanied by enhanced αxy/σxy scaling, thermal transport, and ANE sensitivity.
Mojtaba Mohammadi +2 more
wiley +1 more source
Activation-Guided Low-Rank Parameter Adaptation for Efficient Model Fine-Tuning
IEEE AccessFine-tuning large language models is computationally expensive, and while existing parameter-efficient methods like Low-Rank Adaptation (LoRA) reduce computational costs, they are limited by suboptimal initialization strategies.
Qingchen Wang, Shengyu Shen
doaj +1 more source
Advanced Engineering Materials, EarlyView.This study demonstrates how optimizing laser power, scanning speed, and hatching distance in laser powder bed fusion can boost the productivity of Inconel 718 manufacturing by up to 29% while maintaining mechanical integrity. The work delivers a validated process window and cost–time analysis, offering industry‐ready guidelines for efficient additive ...
Amir Behjat +7 more
wiley +1 more source