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Showing 20 of 962 academic articles
Gaurav Menghani • ACM Computing Surveys
(2023)
Deep learning has revolutionized the fields of computer vision, natural language understanding, speech recognition, information retrieval, and more. However, with the progressive improvements in deep learning models, their number of paramet...
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Open Access
DOI: https://doi.org/10.1145/3578938
Lili Li, Shujuan Zhang, Bin Wang • IEEE Access
(2021)
Deep learning is a branch of artificial intelligence. In recent years, with the advantages of automatic learning and feature extraction, it has been widely concerned by academic and industrial circles. It has been widely used in image and v...
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DOI: https://doi.org/10.1109/access.2021.3069646
Xiaozheng Li, Huazhen Wang, Huixin He, Jixiang Du et al. • BMC Bioinformatics
(2019)
Abstract Background Benefiting from big data, powerful computation and new algorithmic techniques, we have been witnessing the renaissance of deep learning, particularly the combination of natural language processing (NLP) and deep neural n...
DOAJ
Open Access (DOAJ)
DOI: 10.1186/s12859-019-2617-8
Li Deng • APSIPA Transactions on Signal and Information Processing
(2014)
In this invited paper, my overview material on the same topic as presented in the plenary overview session of APSIPA-2011 and the tutorial material presented in the same conference [1] are expanded and updated to include more recent develop...
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DOI: https://doi.org/10.1017/atsip.2013.9
Daniel W. Otter, Julian Richard Medina, Jugal Kumar Kalita • IEEE Transactions on Neural Networks and Learning Systems
(2020)
Over the last several years, the field of natural language processing has been propelled forward by an explosion in the use of deep learning models. This article provides a brief introduction to the field and a quick overview of deep learni...
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Open Access
DOI: https://doi.org/10.1109/tnnls.2020.2979670
Alexander Selvikvåg Lundervold, Arvid Lundervold • Zeitschrift für Medizinische Physik
(2018)
What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when...
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DOI: https://doi.org/10.1016/j.zemedi.2018.11.002
• Springer eBooks
(2018)
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DOI: https://doi.org/10.1007/978-981-10-5209-5
Marek L. Borowiec, Rebecca B. Dikow, Paul B. Frandsen, Alexander J. McKeeken et al. • Methods in Ecology and Evolution
(2022)
Abstract Deep learning is driving recent advances behind many everyday technologies, including speech and image recognition, natural language processing and autonomous driving. It is also gaining popularity in biology, where it has been use...
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DOI: https://doi.org/10.1111/2041-210x.13901
Ziwei Zhang, Peng Cui, Wenwu Zhu • IEEE Transactions on Knowledge and Data Engineering
(2020)
Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characte...
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Open Access
DOI: https://doi.org/10.1109/tkde.2020.2981333
Siripen Pongpaichet, Boonyapat Sukosit, Chitchaya Duangtanawat, Jiramed Jamjongdamrongkit et al. • IEEE Access
(2024)
Crimes result in not only loss to individuals but also hinder national economic growth. While crime rates have been reported to decrease in developed countries, underdeveloped and developing nations still suffer from prevalent crimes, espec...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2024.3363879
Sihan Zhou, Gene Thompson • Studies in Second Language Learning and Teaching
(2023)
Listening to academic content in English medium instruction (EMI) classrooms at university can be a demanding task for students who transition from first language (L1) instructed secondary schools. This longitudinal mixed methods study anal...
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Open Access (DOAJ)
DOI: 10.14746/ssllt.38281
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
Andreas Mayr, Günter Klambauer, Thomas Unterthiner, Sepp Hochreiter • Frontiers in Environmental Science
(2016)
The Tox21 Data Challenge has been the largest effort of the scientific community to compare computational methods for toxicity prediction. This challenge comprised 12,000 environmental chemicals and drugs which were measured for 12 differen...
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DOI: https://doi.org/10.3389/fenvs.2015.00080
Qiang Zhao, Rundong Guo, Xiaowei Feng, Weifeng Hu et al. • Mathematics
(2022)
Legal judgement prediction (LJP) is a crucial part of legal AI, and its goal is to predict the outcome of a case based on the information in the description of criminal facts. This paper proposes a decision prediction method based on causal...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/math10132281
Yoshua Bengio, Yann LeCun, Geoffrey E. Hinton • Communications of the ACM
(2021)
How can neural networks learn the rich internal representations required for difficult tasks such as recognizing objects or understanding language?...
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DOI: https://doi.org/10.1145/3448250
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