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Showing 20 of 1,126 academic articles
Fabien Charmet, Harry Chandra Tanuwidjaja, Solayman Ayoubi, Pierre-François Gimenez et al. • Annals of Telecommunications
(2022)
Abstract With the extensive application of deep learning (DL) algorithms in recent years, e.g., for detecting Android malware or vulnerable source code, artificial intelligence (AI) and machine learning (ML) are increasingly becoming essent...
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DOI: https://doi.org/10.1007/s12243-022-00926-7
Barbara J. Grosz • Artificial Intelligence
(1982)
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DOI: https://doi.org/10.1016/0004-3702(82)90032-7
Diane J. Litman • Proceedings of the AAAI Conference on Artificial Intelligence
(2016)
Advances in natural language processing (NLP) and educational technology, as well as the availability of unprecedented amounts of educationally-relevant text and speech data, have led to an increasing interest in using NLP to address the ne...
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Open Access
DOI: https://doi.org/10.1609/aaai.v30i1.9879
Mallory Selfridge • Artificial Intelligence in Engineering
(1987)
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DOI: https://doi.org/10.1016/0954-1810(87)90076-8
V Geetha, C.K. Gomathy, Mr.D.Sri Datta Vallab Yaratha Yagn, Sai Praneesh • INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
(2023)
Natural Language Processing (NLP) is a rapidly evolving field in the intersection of computer science, artificial intelligence, and linguistics. This article provides an overview of NLP, tracing its historical development from early rule-ba...
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Open Access
DOI: https://doi.org/10.55041/ijsrem27094
Yijun Xiao, William Yang Wang • Proceedings of the AAAI Conference on Artificial Intelligence
(2019)
Reliable uncertainty quantification is a first step towards building explainable, transparent, and accountable artificial intelligent systems. Recent progress in Bayesian deep learning has made such quantification realizable. In this paper,...
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Open Access
DOI: https://doi.org/10.1609/aaai.v33i01.33017322
Yang Liu, Meng Zhang • Computational Linguistics
(2017)
Deep learning has attracted dramatic attention in recent years, both in academia and industry. The popular term deep learning generally refers to neural network methods. Indeed, many core ideas and methods were born years ago in the era of ...
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DOI: https://doi.org/10.1162/coli_r_00312
James D. Pustejovsky, Branimir Boguraev • Artificial Intelligence
(1993)
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DOI: https://doi.org/10.1016/0004-3702(93)90017-6
Barry Walsh • Apress eBooks
(2022)
Any book on operationalizing Artificial Intelligence today cannot ignore growth in Natural Language Processing (NLP), with market size projected to be $43b in 2025. Ostensibly the branch of AI concerned with programming computers to underst...
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DOI: https://doi.org/10.1007/978-1-4842-8817-7_10
Santanu Pattanayak • Apress eBooks
(2022)
In the modern age of information and analytics, natural language processing (NLP) is one of the most important technologies out there. Making sense of complex structures in language and deriving insights and actions from it are crucial from...
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DOI: https://doi.org/10.1007/978-1-4842-8931-0_4
Nils J. Nilsson • Cambridge University Press eBooks
(2009)
B eyond pattern recognition of individual alphanumeric characters , whether they be of fixed font or handwritten, lies the problem of understanding strings of characters that form words, sentences, or larger assemblages of text in a “natura...
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DOI: https://doi.org/10.1017/cbo9780511819346.009
Tatwadarshi P. Nagarhalli, Vinod Moreshwar Vaze, Nirav Pravinsinh Rana • International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV)
(2021)
The recent times have seen unprecedented growth in research and development in the field of Artificial Intelligence and its allied fields, subfields, like Machine Learning, Deep Learning and Natural Language Processing. The new found excite...
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DOI: https://doi.org/10.1109/icicv50876.2021.9388380
Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, Jong‐Seok Lee et al. • Artificial Intelligence Review
(2023)
Abstract Over the last decade, neural networks have reached almost every field of science and become a crucial part of various real world applications. Due to the increasing spread, confidence in neural network predictions has become more a...
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DOI: https://doi.org/10.1007/s10462-023-10562-9
R. Daniel VanLuchene, ROUFEI SUN • Computer-Aided Civil and Infrastructure Engineering
(1990)
In the past few years literature on computational civil engineering has concentrated primarily on artificial intelligence (Al) applications involving expert system technology. This article discusses a different Al approach involving neural ...
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DOI: https://doi.org/10.1111/j.1467-8667.1990.tb00377.x
Igor I. Baskin, David Alan Winkler, Igor V. Tetko • Expert Opinion on Drug Discovery
(2016)
INTRODUCTION: Neural networks are becoming a very popular method for solving machine learning and artificial intelligence problems. The variety of neural network types and their application to drug discovery requires expert knowledge to cho...
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DOI: https://doi.org/10.1080/17460441.2016.1201262
Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler et al. • arXiv (Cornell University)
(2018)
Neural networks are known to be a class of highly expressive functions able to fit even random input-output mappings with $100\%$ accuracy. In this work, we present properties of neural networks that complement this aspect of expressivity. ...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1806.08734
Hongsik Jeong, Luping Shi • Journal of Physics D Applied Physics
(2018)
Abstract Neural network technologies have taken center stage owing to their powerful computing capability for supporting deep learning in artificial intelligence. However, conventional synaptic devices such as SRAM and DRAM are not satisfac...
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DOI: https://doi.org/10.1088/1361-6463/aae223
Manish Kumar Mishra, Monika Srivastava • International Conference on Advances in Engineering & Technology Research
(2014)
In this paper, An Artificial Neural Network or ANN, its various characteristics and business applications. In this paper also show that “what are neural networks” and “Why they are so important in today's Artificial intelligence?” Because v...
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DOI: https://doi.org/10.1109/icaetr.2014.7012785
Sebastian Kujawa, Gniewko Niedbała • Agriculture
(2021)
Artificial neural networks are one of the most important elements of machine learning and artificial intelligence. They are inspired by the human brain structure and function as if they are based on interconnected nodes in which simple proc...
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DOI: https://doi.org/10.3390/agriculture11060497
Qiming Zhang, Haoyi Yu, Martina Barbiero, Baokai Wang et al. • Light Science & Applications
(2019)
The growing demands of brain science and artificial intelligence create an urgent need for the development of artificial neural networks (ANNs) that can mimic the structural, functional and biological features of human neural networks. Nano...
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DOI: https://doi.org/10.1038/s41377-019-0151-0
PMC: 6504946