Zobrazeno 1 - 10
of 347
pro vyhledávání: '"Jung-Woo Ha"'
Autor:
Hee Won Seo, Jung Woo Ha, Jin Hye Kwak, Moo Keon Kim, Hayoung Byun, Seung Hwan Lee, Jae Ho Chung
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-9 (2023)
Abstract More people use the internet for medical information, especially YouTube. Nevertheless, no study has been conducted to analyze the quality of YouTube videos about tinnitus in Korea. This study aims to review the contents and quality of YouTu
Externí odkaz:
https://doaj.org/article/5f91ee7cd98f48cab74fb4e259ba708d
Autor:
Hyeonjin Park, Seunghun Lee, Dasol Hwang, Jisu Jeong, Kyung-Min Kim, Jung-Woo Ha, Hyunwoo J. Kim
Publikováno v:
IEEE Access, Vol 9, Pp 127961-127972 (2021)
Graph neural networks (GNNs) have demonstrated superior performance in various tasks on graphs. However, existing GNNs often suffer from weak-generalization due to sparsely labeled datasets. Here we propose a novel framework that learns to augment th
Externí odkaz:
https://doaj.org/article/c520d7cea1e74589b27eb9a7cd46ef8c
Publikováno v:
IEEE Access, Vol 9, Pp 144025-144034 (2021)
Graph neural networks have shown promising results on representing and analyzing diverse graph-structured data, such as social networks, traffic flow, drug discovery, and recommendation systems. Existing approaches for graph neural networks typically
Externí odkaz:
https://doaj.org/article/cce2ed80764148d7a7daa0e5f461e51e
Publikováno v:
IEEE Access, Vol 8, Pp 189587-189597 (2020)
Filter pruning is prevalent for pruning-based model compression. Most filter pruning methods have two main issues: 1) the pruned network capability depends on that of source pretrained models, and 2) they do not consider that filter weights follow a
Externí odkaz:
https://doaj.org/article/c462300c990b46858d5fc844cfedb8fd
Autor:
Tae-Uk Kim, Jung-Woo Ha
Publikováno v:
Journal of Tourism and Leisure Research. 35:43-64
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::15adec60d9dd28bc6001793283a24ad8
https://doi.org/10.31336/jtlr.2023.1.35.1.43
https://doi.org/10.31336/jtlr.2023.1.35.1.43
Autor:
Soo-Jin Kim, Sojeong Ka, Jung-Woo Ha, Jaemin Kim, DongAhn Yoo, Kwondo Kim, Hak-Kyo Lee, Dajeong Lim, Seoae Cho, Olivier Hanotte, Okeyo Ally Mwai, Tadelle Dessie, Stephen Kemp, Sung Jong Oh, Heebal Kim
Publikováno v:
BMC Genomics, Vol 18, Iss 1, Pp 1-18 (2017)
Abstract Background Indigenous cattle in Africa have adapted to various local environments to acquire superior phenotypes that enhance their survival under harsh conditions. While many studies investigated the adaptation of overall African cattle, ge
Externí odkaz:
https://doaj.org/article/731b8e51a00543578b3198effba7ca73
Autor:
Dasol Hwang, Jisu Jeong, Hyunwoo Kim, Seunghun Lee, Hyeonjin Park, Jung-Woo Ha, Kyung-Min Kim
Publikováno v:
IEEE Access, Vol 9, Pp 127961-127972 (2021)
Graph neural networks (GNNs) have demonstrated superior performance in various tasks on graphs. However, existing GNNs often suffer from weak-generalization due to sparsely labeled datasets. Here we propose a novel framework that learns to augment th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bd87a62b011d441d3552ba8fddc6c478
https://ieeexplore.ieee.org/document/9535521/
https://ieeexplore.ieee.org/document/9535521/
Publikováno v:
IEEE Access, Vol 8, Pp 189587-189597 (2020)
Filter pruning is prevalent for pruning-based model compression. Most filter pruning methods have two main issues: 1) the pruned network capability depends on that of source pretrained models, and 2) they do not consider that filter weights follow a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a590281100e5c49a956dd79029307f7d
https://doi.org/10.1109/access.2020.3031031
https://doi.org/10.1109/access.2020.3031031
Learning under a continuously changing data distribution with incorrect labels is a desirable real-world problem yet challenging. A large body of continual learning (CL) methods, however, assumes data streams with clean labels, and online learning sc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::33f4e4ee284877522016bf066693cc1f
http://arxiv.org/abs/2203.15355
http://arxiv.org/abs/2203.15355
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031198328
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a430a583d9ef3274f74d7a57e1277677
https://doi.org/10.1007/978-3-031-19833-5_10
https://doi.org/10.1007/978-3-031-19833-5_10