Results 71 to 80 of about 259,517 (255)
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
Probabilistic Meta-Conv1D Driving Energy Prediction for Mobile Robots in Unstructured Terrains
IEEE Access, 2022 Driving energy consumption plays an important role in the navigation of autonomous mobile robots in off-road scenarios. However, the accuracy of the driving energy predictions is often affected by a high degree of uncertainty due to unknown and ...
Marco Visca +3 more
doaj +1 more source
Meta-learning of Textual Representations [PDF]
, 2020 Recent progress in AutoML has lead to state-of-the-art methods (e.g., AutoSKLearn) that can be readily used by non-experts to approach any supervised learning problem. Whereas these methods are quite effective, they are still limited in the sense that they work for tabular (matrix formatted) data only. This paper describes one step forward in trying to
Madrid, Jorge +2 more
openaire +2 more sources
Advanced Engineering Materials, EarlyView.Herein, environmental scanning electron microscopy (ESEM) is discussed as a powerful extension of conventional SEM for life sciences. By combining high‐resolution imaging with variable pressure and humidity, ESEM allows the analysis of untreated biological materials, supports in situ monitoring of hydration‐driven changes, and advances the functional ...
Jendrian Riedel +6 more
wiley +1 more source
Multimodal meta-learning through meta-learned task representations
Neural Computing and ApplicationsAbstractFew-shot meta-learning involves training a model on multiple tasks to enable it to efficiently adapt to new, previously unseen tasks with only a limited number of samples. However, current meta-learning methods assume that all tasks are closely related and belong to a common domain, whereas in practice, tasks can be highly diverse and originate
Anna Vettoruzzo +2 more
openaire +3 more sources
Photoswitchable Conductive Metal–Organic Frameworks
Advanced Functional Materials, EarlyView.A conductive material where the conductivity can be modulated remotely by irradiation with light is presented. It is based on films of conductive metal–organic framework type Cu3(HHTP)2 with embedded photochromic molecules such as azobenzene, diarylethene, spiropyran, and hexaarylbiimidazole in the pores.
Yidong Liu +5 more
wiley +1 more source
IMETA-GNN: Meta Learning-Based Cold Start Optimization for Recommendation System
IEEE AccessRecommendation systems are becoming essential components of contemporary online goods and services, and they significantly affect customer satisfaction.
Nida Siddique +7 more
doaj +1 more source
Quantum Emitters in Hexagonal Boron Nitride: Principles, Engineering and Applications
Advanced Functional Materials, EarlyView.Quantum emitters in hexagonal boron nitride have emerged as a promising candidate for quantum information science. This review examines the fundamentals of these quantum emitters, including their level structures, defect engineering, and their possible chemical structures.
Thi Ngoc Anh Mai +8 more
wiley +1 more source
A Survey of Multi-Label Text Classification Under Few-Shot Scenarios
Applied SciencesMulti-label text classification is a fundamental and important task in natural language processing, with widespread applications in specialized domains such as sentiment analysis, legal document classification, and medical coding.
Wenlong Hu +5 more
doaj +1 more source