1996

Kutz J. N. (2017). Deep learning in fluid dynamics / Journal of Fluid Mechanics, Vol. 814, 10 March 2017, pp. 1—4 // https://doi.org/10.1017/jfm.2016.803

1997

Zhang Y. G., Gajjar V., Foster G., Siemion A., Cordes J., Law C., Wang Y. (2018). Fast Radio Burst Pulse Detection and Periodicity: A Machine Learning Approach / The Astrophysical Journal, Vol. 866, No. 2 // https://doi.org/10.3847%2F1538-4357%2Faadf31

1998

Wei J. N., Duvenaud D., Aspuru-Guzik A. (2016). Neural Networks for the Prediction of Organic Chemistry Reactions / ACS Central Science, October 14, 2016, 2, 10, 725—732 // https://doi.org/10.1021/acscentsci.6b00219

1999

Rajpurkar P., Hannun A. Y., Haghpanahi M., Bourn C., Ng A. Y. (2017). Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks // https://arxiv.org/abs/1707.01836

2000

Schirrmeister R. T., Springenberg J. T., Fiederer L. D. J., Glasstetter M., Eggensperger K., Tangermann M., Hutter F., Burgard W., Ball T. (2017). Deep learning with convolutional neural networks for EEG decoding and visualization / Human Brain Mapping, Vol. 38, Iss. 11, November 2017, pp. 5391—5420 // https://doi.org/10.1002/hbm.23730

2001

Pyrkov T. V., Slipensky K., Barg M., Kondrashin A., Zhurov B., Zenin A., Pyatnitskiy M., Menshikov L., Markov S., Fedichev P. O. (2018). Extracting biological age from biomedical data via deep learning: too much of a good thing? / Scientific Reports, Vol. 8, Article num.: 5210 (2018) // https://doi.org/10.1038/s41598-018-23534-9

2002

Lin W., Tong T, Gao Q., Guo D., Du X., Yang Y., Guo G., Xiao M., Du M., Qu X. (2018). Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer’s Disease Prediction From Mild Cognitive Impairment / Frontiers in Neuroscience, 05 November 2018 // https://doi.org/10.3389/fnins.2018.00777

2003

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2004

Velas M., Spanel M., Hradis M., Herout A. (2018). CNN for very fast ground segmentation in velodyne LiDAR data / 2018 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Torres Vedras, 2018, pp. 97—103 // https://doi.org/10.1109/ICARSC.2018.8374167

2005

Martinsson E. (2017). WTTE-RNN: Weibull Time To Event Recurrent Neural Network. A model for sequential prediction of time-to-event in the case of discrete or continuous censored data, recurrent events or time-varying covariates. Master’s thesis in Engineering Mathematics & Computational Science // http://publications.lib.chalmers.se/records/fulltext/253611/253611.pdf

2006

Rebedea T. (2017). Deep Neural Networks for Matching Online Social Networking Profiles / Conference on Computational Collective Intelligence Technologies and Applications // https://doi.org/10.1007/978-3-319-67074-4_19

2007

Tan Q., Liu N., Hu X. (2019). Deep Representation Learning for Social Network Analysis / Frontiers in Big Data, 03 April 2019 // https://doi.org/10.3389/fdata.2019.00002

2008

Hamilton W. L, Ying R., Leskovec J. (2017). Representation Learning on Graphs: Methods and Applications / IEEE Data Engineering Bulletin // https://arxiv.org/abs/1709.05584

2009

Lample G., Charton F. (2019). Deep Learning for Symbolic Mathematics // https://arxiv.org/abs/1912.01412

2010

Palaskar S., Sanabria R., Metze F. (2018). End-to-End Multimodal Speech Recognition // https://arxiv.org/abs/1804.09713

2011

Nag N., Bharadwaj A., Rao A. N., Kulhalli A., Mehta K. S., Bhattacharya N., Ramkumar P., Sitaram D., Jain R. (2019). Flavour Enhanced Food Recommendation // https://arxiv.org/abs/1904.05331

2012

Lee B. K., Mayhew E. J., Sanchez-Lengeling B., Wei J. N., Qian W. W., Little K. A., Andres M., Nguyen B. B., Moloy T., Yasonik J., Parker J. K., Gerkin R. C., Mainland J. D., Wiltschko A. B. (2023). A principal odor map unifies diverse tasks in olfactory perception / Science, Vol. 381, pp. 999-1006 // https://doi.org/10.1126/science.ade4401

2013

Graves A., Wayne G., Danihelka I. (2014). Neural Turing Machines // https://arxiv.org/abs/1410.5401

2014

Graves A., Wayne G., Reynolds M., Harley T., Danihelka I., Grabska-Barwińska A., Colmenarejo S. G., Grefenstette E., Ramalho T., Agapiou J., Badia A. P., Hermann K. M., Zwols Y., Ostrovski G., Cain A., King H., Summerfield C., Blunsom P., Kavukcuoglu K., Hassabis D. (2016). Hybrid computing using a neural network with dynamic external memory / Nature, Vol. 538, pp. 471—476 (2016) // https://doi.org/10.1038/nature20101

2015

Collier M., Beel J. (2019). Memory-Augmented Neural Networks for Machine Translation // https://arxiv.org/abs/1909.08314

2016

* Пер. Н. Россова.

2017

Шаврина Т. О. (2017). Методы обнаружения и исправления опечаток: исторический обзор / Вопросы языкознания. № 4. С. 115—134 // https://doi.org/10.31857/S0373658X0001024-5

2018

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2019

Gardner W. D. (2008). Remembering Joe Weizenbaum, ELIZA Creator / InformationWeek // https://www.informationweek.com/remembering-joe-weizenbaum-eliza-creator-/d/d-id/1065648

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2021

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2022

Saygin A. P., Cicekli I., Akman V. (2003). Turing Test: 50 Years Later / Moor J. H. (2003). The Turing Test. The Elusive Standard of Artificial Intelligence. Studies in Cognitive Systems, Vol. 30, pp. 23–78 // https://doi.org/10.1007/978-94-010-0105-2_2

2023

Luiselli J. K., Fischer A. J. (2016). Computer-Assisted and Web-Based Innovations in Psychology, Special Education, and Health. Academic Press // https://books.google.ru/books?id=NwLSBgAAQBAJ

2024

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