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pro vyhledávání: '"LEE, EUNJUNG"'
In the past few years, contrastive learning has played a central role for the success of visual unsupervised representation learning. Around the same time, high-performance non-contrastive learning methods have been developed as well. While most of t
Externí odkaz:
http://arxiv.org/abs/2401.05730
Autor:
Chae, Yu-Rim, Lee, Hye-Bin, Lee, Yu Ra, Yoo, Guijae, Lee, Eunjung, Park, Miri, Choi, Sang Yoon, Park, Ho-Young
Publikováno v:
In Journal of Ethnopharmacology 28 October 2024 333
Autor:
Naser, Jwan A., Lee, Eunjung, Lopez-Jimenez, Francisco, Noseworthy, Peter A., Latif, Omar S., Friedman, Paul A., Lin, Grace, Oh, Jae K., Scott, Christopher G., Pislaru, Sorin V., Attia, Zachi I., Pellikka, Patricia A.
Publikováno v:
In JACC: Advances September 2024 3(9) Part 2
Autor:
Lee, Jandee, Yoon, Jung Hyun, Lee, Eunjung, Lee, Hwa Young, Jeong, Seonhyang, Park, Sunmi, Jo, Young Suk, Kwak, Jin Young
Publikováno v:
In Journal of Advanced Research August 2024 62:219-228
Autor:
Lee, Eunjung, Shin, Youngmin
Publikováno v:
In Applied Numerical Mathematics January 2025 207:370-383
Autor:
Baek, Yae Jee1 (AUTHOR) shegets@schmc.ac.kr, Lee, Eunjung1 (AUTHOR) shegets@schmc.ac.kr, Jung, Jongtak1 (AUTHOR), Won, Sung Hun2 (AUTHOR), An, Chi Young2 (AUTHOR), Kang, Eun Myeong2 (AUTHOR), Park, Se Yoon3 (AUTHOR), Baek, Seung Lim2 (AUTHOR), Chun, Dong-il2 (AUTHOR), Kim, Tae Hyong1 (AUTHOR)
Publikováno v:
Open Forum Infectious Diseases. Jul2024, Vol. 11 Issue 7, p1-7. 7p.
Autor:
Lee, Hye-Bin, Lee, Yu Ra, Yoo, Guijae, Yim, Sangeun, Son, Hee-Kyoung, Kang, Choon Gil, Jo, Jae Hyeok, Lee, Eunjung, Park, Ho-Young
Publikováno v:
In Journal of Functional Foods November 2024 122
Autor:
Na, Hyesun, Lee, Eunjung
Publikováno v:
In Results in Applied Mathematics November 2024 24
Autor:
Kim, Da Hyun, Lee, Sungho, Ahn, Jisong, Kim, Jae Hwan, Lee, Eunjung, Lee, Insuk, Byun, Sanguine
Publikováno v:
In Environmental Research 15 May 2024 249
Publikováno v:
ICML 2021 Workshop on Adversarial Machine Learning
We propose an AID-purifier that can boost the robustness of adversarially-trained networks by purifying their inputs. AID-purifier is an auxiliary network that works as an add-on to an already trained main classifier. To keep it computationally light
Externí odkaz:
http://arxiv.org/abs/2107.06456