Научный журнал ВолНЦ РАН (сетевое издание)
22.12.202412.2024с 01.01.2024
Просмотры
Посетители
* - в среднем в день за текущий месяц
RuEn

рубрика "Социально-экономические исследования"

Использование искусственных нейронных сетей в современном обществе

Алферьев Д.А.

Том 6, №3, 2020

Алферьев Д.А. Использование искусственных нейронных сетей в современном обществе // Социальное пространство. 2020. Т. 6. № 3. DOI: 10.15838/sa.2020.3.25.6 URL: http://socialarea-journal.ru/article/28618

DOI: 10.15838/sa.2020.3.25.6

  1. Алферьев Д.А. Технологии ИИ как метод прогнозной аналитики // Искусственные общества. 2018. T. 13. Вып. 4. DOI: 10.18254/S0000137-9-1
  2. Горбачевская Е.Н., Краснов С.С. История развития нейронных сетей // Вестн. Волж. ун-та им. В.Н. Татищева. 2015. № 1 (23). URL: https://cyberleninka.ru/article/n/istoriya-razvitiya-neyronnyh-setey (дата обращения 17.06.2020).
  3. МакКаллок У., Питтс В. Логическое исчисление идей, относящихся к нервной активности // Автоматы. М.: ИЛ, 1956. С. 363–384.
  4. McCulloch W.S., Pitts W. A logical calculus of ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 1943, no. 5, pp. 115–133.
  5. Венецкий С. Виды архитектур нейронных сетей // GeekBrains. 2019. URL: https://geekbrains.ru/events/1461 (дата обращения 05.06.2020).
  6. Розенблатт Ф. Принципы нейродинамики. Перцептроны и теория механизмов мозга. М.: Мир, 1965. 478 с.
  7. Rosenblatt F. Principles of Neurodynamics. Perceptrons and the Theory of Brain Mechanisms. Washington, D.C.: Spartan Books, 1962. 616 p.
  8. LeCun Y., Boser B., Denker J.S. [et al.]. Back-Propagation Applied to Handwritten Zip Code Recognition. Neural Computation, 1989, no. 1 (4), pp. 541–551. DOI: 10.1162/neco.1989.1.4.541
  9. Krizhevsky A., Sutskever I., Hinton G.E. ImageNet Classification with Deep Convolutional Neural Networks. Communications of the ACM, 2017, vol. 60, no. 6, pp. 84–90. DOI: 10.1145/3065386
  10. Raina R., Madhavan A., Ng A.Y. Large-Scale Deep Unsupervised Learning Using Graphics Processors. ICML'09: Proceedings of the 26th Annual International Conference on Machine Learning, 2009, pp. 873–880. DOI: 10.1145/1553374.1553486. Available at: https://dl.acm.org/doi/10.1145/1553374.1553486 (accessed 05.06.2020).
  11. Иванов С. Закон Мура больше не работает. Как развивает вычислительная техника сегодня // Хайтек. 2019. URL: https://hightech.fm/2019/08/19/moore (дата обращения 05.06.2020).
  12. Жерон О. Прикладное машинное обучение с помощью Scikit-Learn и TensorFlow: концепции, инструменты и техники для создания интеллектуальных систем: пер. с англ. СПб.: ООО «Альфа-книга», 2018. 688 с.
  13. Николенко С., Кадурин А., Архангельская Е. Глубокое обучение. СПб.: Питер, 2018. 480 с.
  14. Hinton G.E., Sabour S., Frosst N. Dynamic Routing Between Capsules. arXiv, 2017. Available at: https://arxiv.org/abs/1710.09829 (accessed 05.06.2020).
  15. Baker J.M., Deng L., Glass J. [et al.]. Developments and Directions in Speech Recognition and Understanding, Part 1. IEEE Signal Processing Magazine, 2009, no. 3 (26), pp. 75–80. DOI: 10.1109/MSP.2009.932166
  16. Kneser R., Ney H. Improved backing-off for M-gram language modeling. International Conference on Acoustics, Speech, and Signal Processing, 1995. DOI: 10.1109/ICASSP.1995.479394
  17. Boulanger-Lewandowski N., Bengio Y., Vincent P. Modeling Temporal Dependencies in High-Dimensional Sequenced: Application to Polyphonic Music Generation and Transcription. arXiv, 2012. Available at: https://arxiv.org/abs/1206.6392 (accessed 05.06.2020).
  18. LeCun Y., Boser B.E., Denker J.S. [et al.]. Handwritten Digit Recognition with a Back-Propagation Network. Advances in Neural Information Processing Systems 2, 1990, pp. 396–404. DOI: 10.5555/109230.109279
  19. Graves A., Liwicki M., Fernández S. [et al.]. A Novel Connectionist System for Unconstrained Handwriting Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, no. 31 (5), pp. 855–868. DOI: 10.1109/TPAMI.2008.137
  20. Jain V., Seung S. Natural Image Denoising with Convolutional Networks. NIPS'08: Proceedings of the 21st International Conference on Neural Information Processing Systems, 2008, pp. 769–776. DOI: 10.5555/2981780.2981876
  21. Prokhorov D.A. Convolutional Learning System for Object Classification in 3-D LIDAR Data. IEEE Transactions on Neural Networks, 2010, no. 21 (5), pp. 858–863. DOI: 10.1109/TNN.2010.2044802
  22. Silver D., Huang A., Maddison C. [et al.]. Mastering the game of Go with deep neural networks and tree search. Nature, 2016, no. 529, pp. 484–489. DOI: 10.1038/nature16961
  23. Brown N., Sandholm T. Safe and Nested Endgame Solving for Imperfect-Information Games. arXiv, 2017. Available at: https://arxiv.org/abs/1705.02955 (accessed 05.06.2020).
  24. Moravčík M., Schmid M., Burch N. DeepStack: Expert-level artificial intelligence in heads-up no-limit poker. Science, 2017, no. 356 (6337), pp. 508–513. DOI: 10.1126/science.aam6960
  25. Finn C., Levine S., Abbeel P. Guided Cost Learning: Deep Inverse Optimal Control via Policy. ICML'16: Proceedings of the 33rd International Conference on International Conference on Machine Learning, 2016, pp. 49–58. DOI: 10.5555/3045390.3045397
  26. Gu S., Holly E., Lillicrap T. [et al.]. Deep Reinforcement Learning for Robotic Manipulation. arXiv, 2016. Available at: https://arxiv.org/abs/1610.00633 (accessed 05.06.2020).
  27. Bojarski M., Testa D.D., Dworakowski D. End to End Learning for Self-Driving Cars. arXiv, 2016. Available at: https://arxiv.org/abs/1604.07316 (accessed 05.06.2020).
  28. Lake B.M., Ullman T.D., Tenenbaum J.B. Building Machines That Learn and Think Like People. arXiv, 2016. Available at: https://arxiv.org/abs/1604.00289 (accessed 05.06.2020).
  29. Spelke E.S., Kinzler K.D. Core Knowledge. Development Science. 2007, no. 10 (1), pp. 89–96. DOI: 10.1111/j.1467-7687.2007.00569.x
  30. Lerer A., Gross S., Fergus R. Learning Physical Intuition of Block Towers by Example. arXiv, 2016. Available at: https://arxiv.org/abs/1603.01312 (accessed 05.06.2020).
  31. Baker C.L., Saxe R., Tenenbaum J.B. Bayesian Theory of Mind: Modeling Joint Belief-Desire Attribution. ResearchGate, 2011. Available at: https://www.researchgate.net/publication/228727729_Bayesian_Theory_of_Mind_Modeling_Joint_Belief-Desire_Attribution (accessed 05.06.2020).
  32. Lake B.M., Salakhutdinov R., Tenenbaum J.B. Human-Level Concept Learning through Probabilistic Program Induction. Science, 2015, no. 350 (6266), pp. 1332–1338. DOI: 10.1126/science.aab3050
  33. Lake B.M, Lee C., Tenenbaum J.B. [et al.]. One-Shot Learning of Generative Speech Concepts. Semantic Scholar, 2014. Available at: https://www.semanticscholar.org/paper/One-shot-learning-of-generative-speech-concepts-Lake-Lee/fc362caf22c206d1d22df495c2bd4eef2f537e0c (accessed 05.06.2020).
  34. Karpathy A., Fei-Fei L. Deep Visual-Semantic Aligments for Generating Image Descriptions. Stanford Vision Lab., 2015. Available at: https://cs.stanford.edu/people/karpathy/deepimagesent (accessed 05.06.2020).
  35. Mikolov T., Joulin A., Baroni M. A Roadmap towards Machine Intelligence. arXiv, 2015. Available at: https://arxiv.org/abs/1511.08130 (accessed 05.06.2020).

Полная версия статьи