Zobrazeno 1 - 10
of 548
pro vyhledávání: '"KIM SEYOUNG"'
Autor:
Yang, Seung Hyeon, Jeon, Hyejin, Kim, Seyoung, Muratbekova, Medina, Zhamankulova, Madina, Kurmanalieva, Zamira, Djumalieva, Gulmira, Shin, Hyunsook
Publikováno v:
In Nurse Education Today December 2024 143
Autor:
Onen, Murat, Gokmen, Tayfun, Todorov, Teodor K., Nowicki, Tomasz, del Alamo, Jesus A., Rozen, John, Haensch, Wilfried, Kim, Seyoung
Analog crossbar arrays comprising programmable nonvolatile resistors are under intense investigation for acceleration of deep neural network training. However, the ubiquitous asymmetric conductance modulation of practical resistive devices critically
Externí odkaz:
http://arxiv.org/abs/2201.13377
Autor:
Adduri, Abhinav, Kim, Seyoung
Publikováno v:
In The American Journal of Human Genetics 8 August 2024 111(8):1770-1781
Autor:
Yoon, Jun Ho, Kim, Seyoung
In many real-world problems, complex dependencies are present both among samples and among features. The Kronecker sum or the Cartesian product of two graphs, each modeling dependencies across features and across samples, has been used as an inverse
Externí odkaz:
http://arxiv.org/abs/2105.09872
Autor:
Lee, Chaeun, Kim, Seyoung
As deep neural networks require tremendous amount of computation and memory, analog computing with emerging memory devices is a promising alternative to digital computing for edge devices. However, because of the increasing simulation time for analog
Externí odkaz:
http://arxiv.org/abs/2101.07864
Publikováno v:
J. Emerg. Technol. Comput. Syst. 16, 4, Article 36 (August 2020)
Recent advances in deep neural network demand more than millions of parameters to handle and mandate the high-performance computing resources with improved efficiency. The cross-bar array architecture has been considered as one of the promising deep
Externí odkaz:
http://arxiv.org/abs/2002.04605
Akademický článek
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Autor:
Oh, Jeong-Won, Kim, Seyoung, Yoon, Jung-won, Kim, Taemi, Kim, Myoung-Hee, Ryu, Jia, Choe, Seung-Ah
Publikováno v:
In Safety and Health at Work September 2023 14(3):272-278
Autor:
Kim, Hyungjun, Rasch, Malte, Gokmen, Tayfun, Ando, Takashi, Miyazoe, Hiroyuki, Kim, Jae-Joon, Rozen, John, Kim, Seyoung
A resistive memory device-based computing architecture is one of the promising platforms for energy-efficient Deep Neural Network (DNN) training accelerators. The key technical challenge in realizing such accelerators is to accumulate the gradient in
Externí odkaz:
http://arxiv.org/abs/1907.10228
Autor:
Kim, Ok-Jin, Kim, Seyoung, Park, Eun Young, Oh, Jin Kyoung, Jung, Sun Kyoung, Park, Soyoung, Hong, Sooyeon, Jeon, Hye Li, Kim, Hyun-Jin, Park, Bohyun, Park, Bomi, Kim, Suejin, Kim, Byungmi
Publikováno v:
In Chemosphere May 2023 322