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Bayesian Sparsification of Recurrent Neural Networks
P. 1-8.
Ключевые слова: recurrent neural networks
Кодрян М. С., Грачев А. М., Игнатов Д. И. и др., , in : Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019). Issue W19-43.: Association for Computational Linguistics, 2019. P. 40-48.
Добавлено: 1 ноября 2019 г.
Добавлено: 12 июня 2019 г.
Лобачева Е. М., Чиркова Н. А., Markovich A. и др., , in : Thirty-Fourth AAAI Conference on Artificial Intelligence. Vol. 34.: AAAI Press, 2020. Ch. 5938. P. 4989-4996.
Добавлено: 29 октября 2020 г.
Depth estimation has been an essential task for many computer vision applications, especially in autonomous driving, where safety is paramount. Depth can be estimated not only with traditional supervised learning but also via a self-supervised approach that relies on camera motion and does not require ground truth depth maps. Recently, major improvements have been introduced ...
Добавлено: 1 февраля 2022 г.
Добавлено: 16 октября 2018 г.
Лобачева Е. М., Чиркова Н. А., Ветров Д. П., , in : Workshop on Compact Deep Neural Network Representation with Industrial Applications, Thirty-second Conference on Neural Information Processing Systems. : Montréal : [б.и.], 2018. P. 1-6.
Добавлено: 5 декабря 2018 г.
Leblond R., Alayrac J., Осокин А. А. и др., , in : Proceedings of the 6th International Conference on Learning Representations (ICLR 2018). : [б.и.], 2018. P. 1-16.
Добавлено: 29 октября 2018 г.
Чиркова Н. А., , in : 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL 2021). : Association for Computational Linguistics, 2021. P. 2679-2689.
Добавлено: 31 августа 2021 г.
Arefyev, N.V., Gratsianova T. Y., Popov K., , in : Computational Linguistics and Intellectual Technologies. International Conference "Dialogue 2018" Proceedings. : M. : Conference Proceedings Editorial board, 2018. P. 85-95.
Добавлено: 9 октября 2020 г.
Шпильман А. А., Sosin I., Kudenko D., , in : 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV). : IEEE, 2018. P. 1436-1441.
Movement control of artificial limbs has made big advances in recent years. New sensor and control technology enhanced the functionality and usefulness of artificial limbs to the point that complex movements, such as grasping, can be performed to a limited extent. To date, the most successful results were achieved by applying recurrent neural networks (RNNs), ...
Добавлено: 18 января 2019 г.
Sushentsev N., Abrego L., Colarieti A. и др., EUROPEAN UROLOGY OPEN SCIENCE 2023 Vol. 52 P. 36-39
Добавлено: 28 февраля 2024 г.