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of 36
pro vyhledávání: '"Kim, Jungi"'
Publikováno v:
In Journal of Alloys and Compounds 25 October 2024 1003
Autor:
Jang, Min-Sun, Park, Jong-Min, Kim, Jungi, Sun, Changhyo, Koo, Bonuk, Kim, Hea‐Ran, Kwon, Young-Tae, Yang, Sangsun, Lee, Jung Woo, Kim, Yunseok, Jeong, Jae Won
Publikováno v:
In Journal of Magnetism and Magnetic Materials 15 August 2023 580
Autor:
Deng, Yongchao, Kim, Jungi, Klein, Guillaume, Kobus, Catherine, Segal, Natalia, Servan, Christophe, Wang, Bo, Zhang, Dakun, Crego, Josep, Senellart, Jean
This paper describes SYSTRAN's systems submitted to the WMT 2017 shared news translation task for English-German, in both translation directions. Our systems are built using OpenNMT, an open-source neural machine translation system, implementing sequ
Externí odkaz:
http://arxiv.org/abs/1709.03814
Training efficiency is one of the main problems for Neural Machine Translation (NMT). Deep networks need for very large data as well as many training iterations to achieve state-of-the-art performance. This results in very high computation cost, slow
Externí odkaz:
http://arxiv.org/abs/1612.06138
Autor:
Crego, Josep, Kim, Jungi, Klein, Guillaume, Rebollo, Anabel, Yang, Kathy, Senellart, Jean, Akhanov, Egor, Brunelle, Patrice, Coquard, Aurelien, Deng, Yongchao, Enoue, Satoshi, Geiss, Chiyo, Johanson, Joshua, Khalsa, Ardas, Khiari, Raoum, Ko, Byeongil, Kobus, Catherine, Lorieux, Jean, Martins, Leidiana, Nguyen, Dang-Chuan, Priori, Alexandra, Riccardi, Thomas, Segal, Natalia, Servan, Christophe, Tiquet, Cyril, Wang, Bo, Yang, Jin, Zhang, Dakun, Zhou, Jing, Zoldan, Peter
Since the first online demonstration of Neural Machine Translation (NMT) by LISA, NMT development has recently moved from laboratory to production systems as demonstrated by several entities announcing roll-out of NMT engines to replace their existin
Externí odkaz:
http://arxiv.org/abs/1610.05540
Akademický článek
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Publikováno v:
Proceedings ECML/PKDD (2) 2014: 437-452
Neural networks have recently been proposed for multi-label classification because they are able to capture and model label dependencies in the output layer. In this work, we investigate limitations of BP-MLL, a neural network (NN) architecture that
Externí odkaz:
http://arxiv.org/abs/1312.5419
Pathological-Gait Recognition Using Spatiotemporal Graph Convolutional Networks and Attention Model.
Autor:
Kim, Jungi1 (AUTHOR) poui3737@ynu.ac.kr, Seo, Haneol2 (AUTHOR) haneol@yu.ac.kr, Naseem, Muhammad Tahir2 (AUTHOR) nmtahir@yu.ac.kr, Lee, Chan-Su3 (AUTHOR) chansu@ynu.ac.kr
Publikováno v:
Sensors (14248220). Jul2022, Vol. 22 Issue 13, p4863-N.PAG. 14p.
Publikováno v:
In Information Processing and Management 2007 43(5):1173-1182
Publikováno v:
International Journal of Computer Processing of Languages. Jun2009, Vol. 22 Issue 2/3, p205-218. 14p. 3 Charts, 4 Graphs.