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Showing results 1 to 10 of 10

Conference
A GPU-based tensor decomposition method for large-scale tensors
  • Lee, Jihye ; 
  • Chon, Kang-Wook ; 
  • Kim, Min-Soo
  • 2023-02-14
  • Lee, Jihye. (2023-02-14). A GPU-based tensor decomposition method for large-scale tensors. IEEE International Conference on Big Data and Smart Computing (BigComp 2023), 77–80. doi: 10.1109/BigComp57234.2023.00020
  • IEEE Computer Society, Korean Institute of Information Scientists and Engineers (한국정보과학회)
  • View : 106
  • Download : 0
Article
Active learning using Generative Adversarial Networks for improving generalization and avoiding distractor points
  • Lim, Heechul ; 
  • Chon, Kang-Wook ; 
  • Kim, Min-Soo
  • 2023-10
  • Lim, Heechul. (2023-10). Active learning using Generative Adversarial Networks for improving generalization and avoiding distractor points. Expert Systems with Applications, 227. doi: 10.1016/j.eswa.2023.120193
  • Pergamon Press Ltd.
  • View : 63
  • Download : 0
Article
BIGMiner: a fast and scalable distributed frequent pattern miner for big data
  • Chon, Kang-Wook ; 
  • Kim, Min-Soo
  • 2018-09
  • Chon, Kang-Wook. (2018-09). BIGMiner: a fast and scalable distributed frequent pattern miner for big data. doi: 10.1007/s10586-018-1812-0
  • Springer New York LLC
  • View : 918
  • Download : 0
Article
ConnectomeNet: a Unified Deep Neural Network Modeling Framework for Multi-Task Learning
  • Lim, Heechul ; 
  • Chon, Kang-Wook ; 
  • KJim, Min-Soo
  • 2023-03
  • Lim, Heechul. (2023-03). ConnectomeNet: a Unified Deep Neural Network Modeling Framework for Multi-Task Learning. IEEE Access, 11, 34297–34308. doi: 10.1109/ACCESS.2023.3258975
  • Institute of Electrical and Electronics Engineers Inc.
  • View : 151
  • Download : 0
Thesis
Fast and Scalable Frequent Pattern Mining Methods without Pattern Generation at Intermediate Levels
  • Chon, Kang-Wook
  • 2018
  • Chon, Kang-Wook. (2018). Fast and Scalable Frequent Pattern Mining Methods without Pattern Generation at Intermediate Levels. doi: 10.22677/thesis.200000004241
  • DGIST
  • View : 656
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Conference
FedSTGNN: A Federated Spatio-Temporal Graph Neural Network
  • Yoon, Heeyong ; 
  • Chon, Kang-Wook ; 
  • Kim, Min-Soo
  • 2024-10-18
  • Yoon, Heeyong. (2024-10-18). FedSTGNN: A Federated Spatio-Temporal Graph Neural Network. 15th International Conference on Information and Communication Technology Convergence, ICTC 2024, 1863–1868. doi: 10.1109/ICTC62082.2024.10826762
  • IEEE Computer Society
  • View : 296
  • Download : 0
Article
GPUTucker: Large-Scale GPU-Based Tucker Decomposition Using Tensor Partitioning
  • Lee, Jihye ; 
  • Han, Donghyoung ; 
  • Kwon, Oh-Kyoung ; 
  • Chon, Kang-Wook ; 
  • Kim, Min-Soo
  • 2024-03
  • Lee, Jihye. (2024-03). GPUTucker: Large-Scale GPU-Based Tucker Decomposition Using Tensor Partitioning. Expert Systems with Applications, 237(Part A). doi: 10.1016/j.eswa.2023.121445
  • Elsevier
  • View : 248
  • Download : 0
Article
MRPrimer: a MapReduce-based method for the thorough design of valid and ranked primers for PCR
  • Kim, Hyerin ; 
  • Kang, NaNa ; 
  • Chon, Kang-Wook ; 
  • Kim, Seonho ; 
  • Lee, NaHye ; 
  • Koo, JaeHyung ; 
  • Kim, Min-Soo
  • 2015-11
  • Kim, Hyerin. (2015-11). MRPrimer: a MapReduce-based method for the thorough design of valid and ranked primers for PCR. Nucleic Acids Research, 43(20). doi: 10.1093/nar/gkv632
  • Oxford University Press
  • View : 1256
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Article
SGMiner: A Fast and Scalable GPU-Based Frequent Pattern Miner on SSDs
  • Chon, Kang-Wook ; 
  • Yi, Eunjeong ; 
  • Kim, Min-Soo
  • 2022-06
  • Chon, Kang-Wook. (2022-06). SGMiner: A Fast and Scalable GPU-Based Frequent Pattern Miner on SSDs. IEEE Access, 10, 62502–62519. doi: 10.1109/ACCESS.2022.3179592
  • Institute of Electrical and Electronics Engineers Inc.
  • View : 184
  • Download : 0
Article
SPATS: a practical system for comparative analysis of spatio-temporal graph neural networks
  • Yoon, Heeyong ; 
  • Chon, Kang-Wook ; 
  • Kim, Min-Soo
  • 2025-09
  • Cluster Computing, v.28, no.13
  • Baltzer Science Publishers B.V.
  • View : 24
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