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Academic Articles & Research Papers
Showing 20 of 987 academic articles
Greg Zaharchuk, Enhao Gong, Max Wintermark, Daniel L. Rubin et al. • American Journal of Neuroradiology
(2018)
Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applic...
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DOI: https://doi.org/10.3174/ajnr.a5543
PMC: 7410723
Daniele Ravì, Charence Wong, Fani Deligianni, Melissa Berthelot et al. • IEEE Journal of Biomedical and Health Informatics
(2016)
With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on mac...
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DOI: https://doi.org/10.1109/jbhi.2016.2636665
Christof Angermueller, Tanel Pärnamaa, Leopold Parts, Oliver Stegle • Molecular Systems Biology
(2016)
Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples. This rapid increase in biological data dimension and acquisition rate is challenging conventiona...
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other-oa
DOI: https://doi.org/10.15252/msb.20156651
PMC: 4965871
Aston Zhang, Zachary C. Lipton, Li Mu, Alexander Johannes Smola • arXiv (Cornell University)
(2021)
This open-source book represents our attempt to make deep learning approachable, teaching readers the concepts, the context, and the code. The entire book is drafted in Jupyter notebooks, seamlessly integrating exposition figures, math, and...
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DOI: https://doi.org/10.48550/arxiv.2106.11342
Adam C. Mater, Michelle L. Coote • Journal of Chemical Information and Modeling
(2019)
Machine learning enables computers to address problems by learning from data. Deep learning is a type of machine learning that uses a hierarchical recombination of features to extract pertinent information and then learn the patterns repres...
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DOI: https://doi.org/10.1021/acs.jcim.9b00266
Hao Xing, Guigang Zhang, Shang Ma • International Journal of Semantic Computing
(2016)
Deep learning is a branch of machine learning that tries to model high-level abstractions of data using multiple layers of neurons consisting of complex structures or non-liner transformations. With the increase of the amount of data and th...
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DOI: https://doi.org/10.1142/s1793351x16500045
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
Luca Oneto, Silvia Chiappa • Studies in computational intelligence
(2020)
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Open Access
DOI: https://doi.org/10.1007/978-3-030-43883-8_7
Mohammed Javeed Zaki, Wagner Meira • Cambridge University Press eBooks
(2020)
The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business an...
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DOI: https://doi.org/10.1017/9781108564175
Daniel Kirk, E.J. Kok, Michele Tufano, Bedir Teki̇nerdoğan et al. • Advances in Nutrition
(2022)
Data currently generated in the field of nutrition are becoming increasingly complex and high-dimensional, bringing with them new methods of data analysis. The characteristics of machine learning (ML) make it suitable for such analysis and ...
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DOI: https://doi.org/10.1093/advances/nmac103
PMC: 9776646
Tom M. Mitchell, Bruce G. Buchanan, GERALD F. DEJONG, Thomas G. Dietterich et al. • Annual Review of Computer Science
(1990)
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DOI: https://doi.org/10.1146/annurev.cs.04.060190.002221
Frank Noé, Gianni De Fabritiis, Cecilia Clementi • Current Opinion in Structural Biology
(2019)
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Open Access
DOI: https://doi.org/10.1016/j.sbi.2019.12.005
Steven L. Brunton (2020)
This video gives an overview of how Machine Learning is being used in Fluid Mechanics. In fact, fluid mechanics is one of the original 'big data' sciences, and many advances in ML came out of fluids....
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DOI: https://doi.org/10.52843/cassyni.27tbdb
Jason Bell • Wiley eBooks
(2014)
Emotion recognition is an important research topic.Physiological signals seem to be an appropriate way for emotion recognition and specific sensors are required to collect these data.Therefore, laboratory sensors are commonly used while the...
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DOI: https://doi.org/10.1002/9781119183464
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
Donald Michie • Nature
(1968)
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DOI: https://doi.org/10.1038/218019a0
Kaizhu Huang, Haiqin Yang, Irwin King, Michael Rung-Tsong Lyu • Advanced topics in science and technology in China
(2008)
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DOI: https://doi.org/10.1007/978-3-540-79452-3
Mortaza Aghbashlo, Wanxi Peng, Meisam Tabatabaei, Soteris A. Kalogirou et al. • Progress in Energy and Combustion Science
(2021)
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Open Access
DOI: https://doi.org/10.1016/j.pecs.2021.100904
Simon J. Caton, Christian Stefan Haas • ACM Computing Surveys
(2023)
When Machine Learning technologies are used in contexts that affect citizens, companies as well as researchers need to be confident that there will not be any unexpected social implications, such as bias towards gender, ethnicity, and/or pe...
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DOI: https://doi.org/10.1145/3616865