Results 61 to 70 of about 29,104 (263)
Plain Template Insertion: Korean-Prompt-Based Engineering for Few-Shot Learners
IEEE Access, 2022 Prompt-based learning is a method used for language models to interpret natural language by remembering the prior knowledge acquired and the training objective.
Jaehyung Seo +7 more
doaj +1 more source
Screening Routine Clinical Notes for Epilepsy Surgery Candidates Using Large Language Models
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Epilepsy surgery is severely underutilized despite proven efficacy, with substantial under‐referral of eligible patients in routine clinical practice. This study evaluated the potential role of large language models (LLMs) as decision‐support tools for screening unstructured clinical notes to identify epilepsy surgery candidates and ...
Uriel Fennig +9 more
wiley +1 more source
Few-Shot Learning for Biometric Verification
CoRR, 2022 In machine learning applications, it is common practice to feed as much information as possible. In most cases, the model can handle large data sets that allow to predict more accurately. In the presence of data scarcity, a Few-Shot learning (FSL) approach aims to build more accurate algorithms with limited training data.
Umaid M. Zaffar +3 more
openaire +2 more sources
Looking Back to Lower-Level Information in Few-Shot Learning
Information, 2020 Humans are capable of learning new concepts from small numbers of examples. In contrast, supervised deep learning models usually lack the ability to extract reliable predictive rules from limited data scenarios when attempting to classify new examples ...
Zhongjie Yu, Sebastian Raschka
doaj +1 more source
CLIP-Driven Prototype Network for Few-Shot Semantic Segmentation
Entropy, 2023 Recent research has shown that visual–text pretrained models perform well in traditional vision tasks. CLIP, as the most influential work, has garnered significant attention from researchers.
Shi-Cheng Guo +4 more
doaj +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 +2 more
wiley +1 more source
Fostering Innovation: Streamlining Magnetocaloric Materials Research by Digitalization
Advanced Engineering Materials, EarlyView.Magnetocaloric cooling (MCE) is an environmentally friendly refrigeration method with great potential. Optimizing MCE materials involves the preparation and screening of large quantities of samples, which in turn generates a large amount of data. A digitalization approach is presented that uses ontologies, knowledge graphs, and digital workflows to ...
Simon Bekemeier +17 more
wiley +1 more source
Dynamic Knowledge Path Learning for Few-Shot Learning
Big Data Mining and AnalyticsFew-shot learning is a challenging task that aims to train a classifier with very limited training samples. Most existing few-shot learning methods mainly focus on mining knowledge from limited training samples as much as possible and ignore the learning
Jingzhu Li +4 more
doaj +1 more source
Enhancing Few-Shot Image Classification With Cosine Transformer
IEEE Access, 2023 This paper addresses the few-shot image classification problem, where the classification task is performed on unlabeled query samples given a small amount of labeled support samples only.
Quang-Huy Nguyen +3 more
doaj +1 more source
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 +3 more
wiley +1 more source