Zobrazeno 1 - 10
of 890
pro vyhledávání: '"model training"'
Autor:
Bo Liu
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-19 (2024)
Abstract This work aims to explore the application of an improved convolutional neural network (CNN) combined with Internet of Things (IoT) technology in art design education and teaching. The development of IoT technology has created new opportuniti
Externí odkaz:
https://doaj.org/article/a3fc7cc0c23a4489a0081fda127ad7d5
Autor:
Martin Correa-Luna, Juan Gargiulo, Peter Beale, David Deane, Jacob Leonard, Josh Hack, Zac Geldof, Chloe Wilson, Sergio Garcia
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)
Abstract Precision in grazing management is highly dependent on accurate pasture monitoring. Typically, this is often overlooked because existing approaches are labour-intensive, need calibration, and are commonly perceived as inaccurate. Machine-lea
Externí odkaz:
https://doaj.org/article/83ad940ff0ab435194c5e6cfbc10347e
Publikováno v:
Frontiers in Radiology, Vol 4 (2024)
IntroductionClinical research on smart health has an increasing demand for intelligent and clinic-oriented medical image computing algorithms and platforms that support various applications. However, existing research platforms for medical image info
Externí odkaz:
https://doaj.org/article/a581e755681946afaa44836f1f95df2a
Autor:
Weimin ZHENG
Publikováno v:
大数据, Vol 10, Pp 1-8 (2024)
There are three types of computer systems that support large model training, among which the ecosystem based on domestic AI chip systems is not very good.To change this situation, it is necessary to develop 10 key software such as AI compilers and pa
Externí odkaz:
https://doaj.org/article/0af5e3fb48ac4751ae8fc5cafbbce4f9
Publikováno v:
IEEE Access, Vol 12, Pp 135419-135450 (2024)
Machine learning (ML) and deep learning (DL) analyze raw data to extract valuable insights in specific phases. The rise of continuous practices in software projects emphasizes automating Continuous Integration (CI) with these learning-based methods,
Externí odkaz:
https://doaj.org/article/816a42e4912d42ef842c4ba6c349d379
Publikováno v:
Jisuanji kexue yu tansuo, Vol 18, Iss 1, Pp 111-126 (2024)
For computing-intensive artificial intelligence (AI) training tasks, the computational graph is more complex, and data loading, task division of the computational graph, and load balancing of task scheduling have become the key factors affecting the
Externí odkaz:
https://doaj.org/article/745e0537fcd04fd78f65f5f59263d8a7
Publikováno v:
IEEE Access, Vol 12, Pp 45423-45441 (2024)
Most computer vision applications that use deep learning on constrained device come from the Internet of Things (IoT) or robotics fields, where low-quality cameras are used to capture input images in real time. Since most pretrained models typically
Externí odkaz:
https://doaj.org/article/7ff1a65fdb44456685a2eb97e53f5a94
Autor:
Adam Hajek, Ales Horak
Publikováno v:
IEEE Access, Vol 12, Pp 34570-34581 (2024)
Automatic text summarization (ATS), alongside neural machine translation or question answering, is one of the leading tasks in Natural Language Processing (NLP). In recent years, ATS has experienced significant development, especially in the English
Externí odkaz:
https://doaj.org/article/1c01369a83204741a81cf2797c408fef
Autor:
Qifei Dong, Gang Luo
Publikováno v:
IEEE Access, Vol 12, Pp 18658-18684 (2024)
Deep learning is the best machine learning algorithm for numerous analytical tasks. On a large data set, training a deep learning model frequently lasts several days to several months. Throughout this long period, it would be helpful to show a progre
Externí odkaz:
https://doaj.org/article/9d18ea3d9baf454b921e0a4fedf781c0
Publikováno v:
Frontiers in Ecology and Evolution, Vol 12 (2024)
BackgroundCalcareous nannofossils are minute microfossils widely present in marine strata. Their identification holds significant value in studies related to stratigraphic dating, paleo-environmental evolution, and paleoclimate reconstruction. Howeve
Externí odkaz:
https://doaj.org/article/9edb419664a5454785da0e3ea8f137c0