Zobrazeno 1 - 10
of 1 159
pro vyhledávání: '"pretraining"'
Publikováno v:
Big Earth Data, Vol 8, Iss 4, Pp 649-672 (2024)
The direct application of large language models (LLMs) to specific domain tasks frequently encounters challenges due to the scarcity of domain data, variations in domain semantics, and the complexity of domain knowledge. Further pretraining of advanc
Externí odkaz:
https://doaj.org/article/e0447887a3a5424ea7b84e34f3c86a22
Publikováno v:
Big Data Mining and Analytics, Vol 7, Iss 3, Pp 565-576 (2024)
The effectiveness of AI-driven drug discovery can be enhanced by pretraining on small molecules. However, the conventional masked language model pretraining techniques are not suitable for molecule pretraining due to the limited vocabulary size and t
Externí odkaz:
https://doaj.org/article/75dbf0c867db4ba6bab24df45ec47c91
Publikováno v:
Industrial Artificial Intelligence, Vol 2, Iss 1, Pp 1-20 (2024)
Abstract The amount of labelled data in industrial use cases is limited because the annotation process is time-consuming and costly. As in research, self-supervised pretraining such as MAE resulted in training segmentation models with fewer labels, t
Externí odkaz:
https://doaj.org/article/31276db40a1e4255ae268aa32982a857
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-19 (2024)
Abstract Semantic segmentation plays a crucial role in interpreting remote sensing images, especially in high-resolution scenarios where finer object details, complex spatial information and texture structures exist. To address the challenge of bette
Externí odkaz:
https://doaj.org/article/9aafaa829af543e98d756912be75e13c
Autor:
Vishal Dey, Xia Ning
Publikováno v:
Journal of Cheminformatics, Vol 16, Iss 1, Pp 1-13 (2024)
Abstract Pretrained Graph Neural Networks have been widely adopted for various molecular property prediction tasks. Despite their ability to encode structural and relational features of molecules, traditional fine-tuning of such pretrained GNNs on th
Externí odkaz:
https://doaj.org/article/f03615e6a918444db0a981439348f898
Autor:
Xiaoqiang Zhang, Ying Chen
Publikováno v:
PeerJ Computer Science, Vol 10, p e2359 (2024)
Field-road classification that automatically identifies the activity (either in-field or on-road) of each point in Global Navigation Satellite System (GNSS) trajectories is a critical process in the behavior analysis of agricultural vehicles. To capt
Externí odkaz:
https://doaj.org/article/be44cdd955864f38bb7408310fe11dfc
Autor:
Xiaofan Zheng, Yoichi Tomiura
Publikováno v:
Journal of Cheminformatics, Vol 16, Iss 1, Pp 1-9 (2024)
Abstract Among the various molecular properties and their combinations, it is a costly process to obtain the desired molecular properties through theory or experiment. Using machine learning to analyze molecular structure features and to predict mole
Externí odkaz:
https://doaj.org/article/430ba32fd7324bccafcdd7cb1c754733
Publikováno v:
IEEE Access, Vol 12, Pp 162861-162869 (2024)
In current Neural Machine Translation (NMT) research, translating low-resource language pairs remains a significant challenge. This work proposes an LGE-Transformer method for Chinese-Malay neural machine translation based on Gated Dynamic Encoding O
Externí odkaz:
https://doaj.org/article/b339588f35e1426dbb81564419cfe005
Publikováno v:
IEEE Access, Vol 12, Pp 159280-159295 (2024)
In Natural Language Processing, creating training data for question answering (QA) systems typically requires significant effort and expertise. This challenge is amplified in few-shot scenarios where only a limited number of training samples are avai
Externí odkaz:
https://doaj.org/article/3473289f9780449e885289d1a7c90ea8
Publikováno v:
IEEE Access, Vol 12, Pp 134133-134143 (2024)
Precise recognition of operator actions is crucial in industrial automation for enhancing production efficiency and ensuring safety standards. This study introduces a novel self-supervised pre-training framework using visual transformers to address t
Externí odkaz:
https://doaj.org/article/27a7e66e33f74f59883ef847ec27a22a