Results 41 to 50 of about 5,833,459 (242)

Improving Tire Pattern Recognition Using Parameter-Efficient Fine-Tuning Techniques

IEEE Access
Tire-tread classification plays a key role in forensic investigation and public safety. This work introduces a robust, efficient recognition system that integrates Discrete Wavelet Transform (DWT) with Weighted Local Gray-Level on Robust Local Binary ...
Parkpoom Chaisiriprasert, Khachonkit Chuiad   +1 more
doaj   +1 more source

Explore the Principles of Prompt Tuning and the Progress of Research [PDF]

ITM Web of Conferences
Prompt Tuning is a lightweight fine-tuning method that demonstrates efficient task adaptation and parameter efficiency for pre-trained language models (PLMs). Prompt Tuning highlights an important contribution to the advancement of NLP technology.
Zheng Tongxin
doaj   +1 more source

Deep Learning Pose Estimation for Phenotyping of Co‐Occurring Hyperkinetic Movement Disorders

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To explore whether routine outpatient video combined with deep learning‐based pose estimation and clinically interpretable kinematic features can support multi‐label phenotyping of co‐occurring hyperkinetic movement disorders (HMDs).
Laura Cif, Diane Demailly, Gabriella A. Horvàth, Juan Dario Ortigoza Escobar, Nathalie Dorison, Mayté Castro Jiménez, Cécile A. Hubsch, Thomas Wirth, Gun‐Marie Hariz, Sophie Huby, Morgan Dornadic, Zohra Souei, Muhammad Mushhood Ur Rehman, Simone Hemm, Mehdi Boulaymen, Eduardo M. Moraud, Jocelyne Bloch, Xavier Vasques   +17 more
wiley   +1 more source

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions

Advanced Engineering Materials, EarlyView.
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice, Krzysztof S. Stopka, Michael D. Sangid   +2 more
wiley   +1 more source

Field Report from Collaborative Research Center 1625: Heterogeneous Research Data Management Using Ontology Representations

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, Samuel García Vázquez, Behnam Mardani, Victor Dudarev, Alfred Ludwig, Maribel Acosta, Markus Stricker   +6 more
wiley   +1 more source

Comparison between parameter-efficient techniques and full fine-tuning: A case study on multilingual news article classification.

PLoS ONE
Adapters and Low-Rank Adaptation (LoRA) are parameter-efficient fine-tuning techniques designed to make the training of language models more efficient. Previous results demonstrated that these methods can even improve performance on some classification ...
Olesya Razuvayevskaya, Ben Wu, João A Leite, Freddy Heppell, Ivan Srba, Carolina Scarton, Kalina Bontcheva, Xingyi Song   +7 more
doaj   +1 more source

Is an Apple an Orange? A Large Language Model Benchmark for Candidate Term Extraction and Subclass Decisions Against Upper Ontologies in Engineering and Materials Science

Advanced Engineering Materials, EarlyView.
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch, Maximilian Holland, Klaus Drechsler, Johannes Schilp   +3 more
wiley   +1 more source

Parameter-Efficient Fine-Tuning of Large Pretrained Models for Instance Segmentation Tasks

Machine Learning and Knowledge Extraction
Research 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, David Rohrschneider, Uwe Handmann   +2 more
doaj   +1 more source

Layer-wise Rank Allocation for Parameter-Efficient Fine-Tuning in Vision Transformers [PDF]

ITM Web of Conferences
Vision Transformer (ViT) obtains state-of-the-art performance but is expensive to fine-tune because of the large number of parameters. Parameter-efficient tuning methods, such as LoRA, have been proposed.
Chen Qiuyu
doaj   +1 more source