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Academic Articles & Research Papers
Showing 20 of 480 academic articles
Jianqing Fan, Cong Ma, Yiqiao Zhong • Statistical Science
(2021)
Deep learning has achieved tremendous success in recent years. In simple words, deep learning uses the composition of many nonlinear functions to model the complex dependency between input features and labels. While neural networks have a l...
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Open Access
DOI: https://doi.org/10.1214/20-sts783
PMC: 8300482
Christopher D. Manning • Computational Linguistics
(2015)
Deep Learning waves have lapped at the shores of computational linguistics for several years now, but 2015 seems like the year when the full force of the tsunami hit the major Natural Language Processing (NLP) conferences. However, some pun...
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Open Access
DOI: https://doi.org/10.1162/coli_a_00239
Jing Li, Aixin Sun, Jianglei Han, Chenliang Li • IEEE Transactions on Knowledge and Data Engineering
(2020)
Named entity recognition (NER) is the task to identify mentions of rigid designators from text belonging to predefined semantic types such as person, location, organization etc. NER always serves as the foundation for many natural language ...
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Open Access
DOI: https://doi.org/10.1109/tkde.2020.2981314
Ruihui Mu, Xiaoqin Zeng • KSII Transactions on Internet and Information Systems
(2019)
With the advent of big data, deep learning technology has become an important research direction in the field of machine learning, which has been widely applied in the image processing, natural language processing, speech recognition and on...
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Open Access
DOI: https://doi.org/10.3837/tiis.2019.04.001
Xue-Wen Chen, Xiaotong Lin • IEEE Access
(2014)
Deep learning is currently an extremely active research area in machine learning and pattern recognition society. It has gained huge successes in a broad area of applications such as speech recognition, computer vision, and natural language...
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Open Access
DOI: https://doi.org/10.1109/access.2014.2325029
Connor Shorten, Taghi M. Khoshgoftaar, Borko Furht • Journal Of Big Data
(2021)
Natural Language Processing (NLP) is one of the most captivating applications of Deep Learning. In this survey, we consider how the Data Augmentation training strategy can aid in its development. We begin with the major motifs of Data Augme...
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DOI: https://doi.org/10.1186/s40537-021-00492-0
PMC: 8287113
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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cc-by
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
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
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
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