Results 81 to 90 of about 124,133 (290)
Explore parameter efficient fine-tuning methods on Large Language Model
, 2023 Recent advancements in Large Language Models (LLMs) have enabled the development of a single model capable of performing a wide range of tasks. However, the cost of training and fine-tuning LLMs for unseen tasks is extremely high and time-consuming ...
Liu, Yufan
core +1 more source
Parameter-Efficient Fine-Tuning of State Space Models
CoRRAccepted at ICML 2025.
Kevin Galim +4 more
openaire +3 more sources
Advanced Engineering Materials, EarlyView.A unified research data management framework for heterogeneous materials data is presented. The system integrates multimodal datasets using ontologies and knowledge graphs, enabling interoperability and FAIR (findable, accessible, interoperable, reusable) data principles. By linking data across scales and workflows, it supports reproducible, Artifitial
Doaa Mohamed +6 more
wiley +1 more source
Symbiotic Tuning: A Simple Approach for Enhancing Task Performance of Side-Tuning
IEEE AccessReducing computational and memory overhead in fine-tuning large language models remains a significant challenge in natural language processing. While parameter-efficient fine-tuning (PEFT) methods, such as LoRA, have gained attention for reducing ...
Zhi-Quan Feng +3 more
doaj +1 more source
Advanced Engineering Materials, EarlyView.Additive manufacturing provides precise control over the placement of continuous fibres within polymer matrices, enabling customised mechanical performance in composite components. This article explores processing strategies, mechanical testing, and modelling approaches for additive manufactured continuous fibre‐reinforced composites.
Cherian Thomas, Amir Hosein Sakhaei
wiley +1 more source
Swin-TUNA: A Novel PEFT Approach for Accurate Food Image Segmentation
IEEE AccessIn food image processing, parameter-efficient semantic segmentation is important for high-performance applications under constrained training resources.
Haotian Chen, Zhiyong Xiao
doaj +1 more source
Parameter-Efficient Fine-Tuning via Circular Convolution
Findings of the Association for Computational Linguistics: ACL 2025ACL ...
Chen, Aochuan +6 more
openaire +3 more sources
Advanced Engineering Materials, EarlyView.Identified through the use of statistical design of experiments and metallographic investigation, this study exposes the stochastic origins of intergranular cracks in blown powder laser beam directed energy deposition additive manufacturing of pure molybdenum. It further demonstrates a successful crack mitigation approach with direct correlation to the
Nathaniel J. Lies +2 more
wiley +1 more source
Multimodal Data‐Driven Microstructure Characterization
Advanced Engineering Materials, EarlyView.A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang +4 more
wiley +1 more source
Towards Pruning and Parameter Efficient Fine-tuning of Deep Neural Networks
Deep Neural Networks (DNNs) have achieved significant success across various applications. However, the increasing number of parameters in state-of-the-art architectures presents challenges such as overfitting and high computational costs.
Li, Yang
core +1 more source