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WANG Sili, ZHANG Ling, YANG Heng, LIU Wei • Nongye tushu qingbao xuebao
(2023)
[Purpose/Significance] Deep learning for language modeling is one of the major methods and advanced technologies to enhance language intelligence of machines at present, which has become an indispensable important technical means for automa...
DOAJ
Open Access (DOAJ)
DOI: 10.13998/j.cnki.issn1002-1248.23-0251
Hongming Chen, Ola Engkvist, Yinhai Wang, Marcus Olivecrona et al. • Drug Discovery Today
(2018)
Over the past decade, deep learning has achieved remarkable success in various artificial intelligence research areas. Evolved from the previous research on artificial neural networks, this technology has shown superior performance to other...
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DOI: https://doi.org/10.1016/j.drudis.2018.01.039
Jiasi Chen, Xukan Ran • Proceedings of the IEEE
(2019)
Deep learning is currently widely used in a variety of applications, including computer vision and natural language processing. End devices, such as smartphones and Internet-of-Things sensors, are generating data that need to be analyzed in...
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DOI: https://doi.org/10.1109/jproc.2019.2921977
Yihan Chen, Xingyu Gu, Zhen Liu, Jia Liang • Remote Sensing
(2022)
Traditional automatic pavement distress detection methods using convolutional neural networks (CNNs) require a great deal of time and resources for computing and are poor in terms of interpretability. Therefore, inspired by the successful a...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/rs14081877
Xuedan Du, Yinghao Cai, Shuo Wang, Leijie Zhang • Youth Academic Annual Conference of Chinese Association of Automation (YAC)
(2016)
In recent years, deep learning has achieved great success in many fields, such as computer vision and natural language processing. Compared to traditional machine learning methods, deep learning has a strong learning ability and can make be...
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DOI: https://doi.org/10.1109/yac.2016.7804882
Shuai Zhang, Lina Yao, Aixin Sun, Yi Tay • ACM Computing Surveys
(2019)
With the growing volume of online information, recommender systems have been an effective strategy to overcome information overload. The utility of recommender systems cannot be overstated, given their widespread adoption in many web applic...
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Open Access
DOI: https://doi.org/10.1145/3285029
Gabriel Chartrand, Phillip M. Cheng, Eugene Vorontsov, Michal Drozdzal et al. • Radiographics
(2017)
Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods prod...
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DOI: https://doi.org/10.1148/rg.2017170077
Emanuele Aiello, Mirko Agarla, Diego Valsesia, Paolo Napoletano et al. • IEEE Access
(2024)
Large-scale self-supervised pretraining of deep learning models is known to be critical in several fields, such as language processing, where its has led to significant breakthroughs. Indeed, it is often more impactful than architectural de...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2024.3396990
Yuwei Yin, Zhixian Tang, Huachun Weng • BioMedical Engineering OnLine
(2024)
Abstract Deep Self-Attention Network (Transformer) is an encoder–decoder architectural model that excels in establishing long-distance dependencies and is first applied in natural language processing. Due to its complementary nature with th...
DOAJ
Open Access (DOAJ)
DOI: 10.1186/s12938-024-01209-z
Kalaivani Sundararajan, Damon L. Woodard • ACM Computing Surveys
(2018)
In the recent past, deep learning methods have demonstrated remarkable success for supervised learning tasks in multiple domains including computer vision, natural language processing, and speech processing. In this article, we investigate ...
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DOI: https://doi.org/10.1145/3190618
Li Deng, Dong Yu • Foundations and Trends® in Signal Processing
(2014)
This monograph provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks. The application areas are chosen with the following three criteria in mind: (1) expertise...
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Open Access
DOI: https://doi.org/10.1561/2000000039
Samira Pouyanfar, Saad B Sadiq, Yilin Yan, Haiman Tian et al. • ACM Computing Surveys
(2018)
The field of machine learning is witnessing its golden era as deep learning slowly becomes the leader in this domain. Deep learning uses multiple layers to represent the abstractions of data to build computational models. Some key enabler d...
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DOI: https://doi.org/10.1145/3234150
Ian Goodfellow, Yoshua Bengio, Aaron C. Courville • The MIT Press eBooks
(2016)
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human c...
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DOI: https://doi.org/10.1016/b978-0-12-804291-5.00010-6
Jiying Han, Hongbiao Yin, Xin Yang, Feifei Wang • Frontiers in Psychology
(2021)
Following Grandey's integrative model of emotional labor and emotion regulation, this study examined the relationships between university teachers' reported use of various emotional labor strategies and some antecedents (i. e., perceived em...
DOAJ
Open Access (DOAJ)
DOI: 10.3389/fpsyg.2021.731099
Vishal Kumar Goar, Nagendra Singh Yadav • Advances in business information systems and analytics book series
(2024)
This chapter focuses on providing a complete grasp of the foundations of machine learning (ML). Machine learning is a rapidly evolving domain with wide-ranging applications, from natural language processing to autonomous vehicles. This chap...
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DOI: https://doi.org/10.4018/979-8-3693-1598-9.ch002
Aishat I. Lepshakova, Oleg V. Kurskikh • Актуальные проблемы филологии и педагогической лингвистики
(2025)
The article analyses different translation versions of science fiction films directed by Denis Villeneuve with the aim of determining their semantic similarity through NLP analysis. There are four groups of metrics used in text analysis: se...
DOAJ
Open Access (DOAJ)
DOI: 10.29025/2079-6021-2025-1-132-150
Róbert Skapinyecz • Machine Learning and Knowledge Extraction
(2026)
The paper presents a novel large language model-based neighbourhood search method for production scheduling using discrete-event simulation (DES) as the modelling and evaluation engine. The combination of large language models (LLMs) and DE...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/make8090284
Jeremy M. Watt, Reza Borhani, Aggelos K. Katsaggelos • Cambridge University Press eBooks
(2020)
With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuiti...
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DOI: https://doi.org/10.1017/9781108690935
Meghna Chaudhary, Kristin Kosyluk, Sylvia Thomas, Tempestt Neal • Scientific Reports
(2023)
Abstract According to data from the U.S. Center for Disease Control and Prevention, as of June 2020, a significant number of African Americans had been infected with the coronavirus disease, experiencing disproportionately higher death rate...
DOAJ
Open Access (DOAJ)
DOI: 10.1038/s41598-023-37592-1
Richard J. Roiger • Chapman & Hall/CRC eBooks
(2020)
R is one of the top-rated problem-solving tools for machine learning, data science, data analytics, data mining, and statistical analysis. R ’s popularity has resulted in the development of thousands of tutorials. Unfortunately, it’s easy t...
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DOI: https://doi.org/10.1201/9781003006695-1