Browsing by Titles

Showing results 1 to 17 of 17

  • 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 : 92
  • Download : 0
  • Jeon, Hajin
  • Bae, Jeongmin
  • Kim, Min-Soo
  • 2021-12-11
  • Jeon, Hajin. (2021-12-11). A Web-based Method for Designing and Validating Primer-probe Sets for SARS-CoV-2. IEEE International Conference on Bioinformatics and Biomedicine, 1339–1342. doi: 10.1109/BIBM52615.2021.9669619
  • Institute of Electrical and Electronics Engineers Inc.
  • View : 230
  • Download : 0
  • 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 : 47
  • Download : 0

An Effective and Efficient Method for Tweaking Deep Neural Networks

  • Jinwook Kim
  • 2021
  • Jinwook Kim. (2021). An Effective and Efficient Method for Tweaking Deep Neural Networks. doi: 10.22677/thesis.200000497154
  • DGIST
  • View : 253
  • Download : 105
  • Nam, Ju Gang
  • Kim, J.
  • Noh, K.
  • Yang, H.-L.
  • Choi, Hyewon
  • Kim, Da Som
  • Yoo, Seung-Jin
  • Hwang, Eui Jin
  • Goo, Jin Mo
  • Park, Eun-Ah
  • et al
  • 2021-11
  • Nam, Ju Gang. (2021-11). Automatic prediction of left cardiac chamber enlargement from chest radiographs using convolutional neural network. European Radiology, 31(11), 8130–8140. doi: 10.1007/s00330-021-07963-1
  • Springer Verlag
  • View : 329
  • Download : 0
  • Shim, Sungho
  • Yang, Hyun-Lim
  • Kim, Min-Soo
  • 2024-05
  • Shim, Sungho. (2024-05). Comparison of self-supervised learning methods for optical coherence tomography image classification. 8th International Conference on Medical and Health Informatics, ICMHI 2024, 41–46. doi: 10.1145/3673971.3673989
  • Association for Computing Machinery
  • View : 53
  • Download : 0

Development and validation of a multi-stage self-supervised learning model for optical coherence tomography image classification

  • Shim, Sungho
  • Kim, Min-Soo
  • Yae, Che Gyem
  • Kang, Yong Koo
  • Do, Jae Rock
  • Kim, Hong Kyun
  • Yang, Hyun-Lim
  • 2025-05
  • Journal of the American Medical Informatics Association : JAMIA, v.32, no.5, pp.800 - 810
  • Oxford University Press
  • View : 65
  • Download : 8

Development and Validation of an Arterial Pressure-Based Cardiac Output Algorithm Using a Convolutional Neural Network: Retrospective Study Based on Prospective Registry Data

  • Yang, Hyun-Lim
  • Jung, Chul-Woo
  • Yang, Seong Mi
  • Kim, Min-Soo
  • Shim, Sungho
  • Lee, Kook Hyun
  • Lee, Hyung-Chul
  • 2021-08
  • Yang, Hyun-Lim. (2021-08). Development and Validation of an Arterial Pressure-Based Cardiac Output Algorithm Using a Convolutional Neural Network: Retrospective Study Based on Prospective Registry Data. JMIR Medical Informatics, 9(8). doi: 10.2196/24762
  • JMIR Publications
  • View : 434
  • Download : 81
  • Kim, Jinwoong
  • Kim, Minsu
  • Kim, Min-Soo
  • Lee, Jemin
  • 2023-07
  • Kim, Jinwoong. (2023-07). Ensuring Data Freshness in Wireless Monitoring Networks: Age-of-Information Sensitive Coverage and Energy Efficiency Perspectives. IEEE Internet of Things Journal, 10(14), 12811–12825. doi: 10.1109/JIOT.2023.3257263
  • Institute of Electrical and Electronics Engineers
  • View : 227
  • Download : 0
  • Yi, Eunjeong
  • Kim, Min-Soo
  • 2022-05-18
  • Yi, Eunjeong. (2022-05-18). Exploiting Triangle Patterns for Heterogeneous Graph Attention Network. 1st International Workshop on Big data-driven Edge Cloud Services, BECS 2021, 71–81. doi: 10.1007/978-3-030-92231-3_7
  • Research Center for Big data Edge Cloud Services (BECS, KAIST)
  • View : 49
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Exploiting various patterns for heterogeneous graph attention network

  • Eunjeong Yi
  • 2021
  • Eunjeong Yi. (2021). Exploiting various patterns for heterogeneous graph attention network. doi: 10.22677/thesis.200000497156
  • DGIST
  • View : 199
  • Download : 121
  • 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 : 267
  • Download : 0

GPrimer: a fast GPU-based pipeline for primer design for qPCR experiments

  • Bae, Jeongmin
  • Jeon, Hajin
  • Kim, Min-Soo
  • 2021-04
  • Bae, Jeongmin. (2021-04). GPrimer: a fast GPU-based pipeline for primer design for qPCR experiments. BMC Bioinformatics, 22(1), 220. doi: 10.1186/s12859-021-04133-4
  • BioMed Central
  • View : 312
  • Download : 71
  • 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 : 224
  • Download : 0
  • Kim, Euihyeok
  • Kim, Min-Soo
  • 2013
  • Kim, Euihyeok. (2013). Performance analysis of cache-conscious hashing techniques for multi-core CPUs.
  • Security Engineering Research Support Center
  • View : 855
  • Download : 0
  • 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 : 161
  • Download : 0
  • Jeon, Hajin
  • Bae, Jeongmin
  • Kim, Hyerin
  • Kim, Min-Soo
  • 2023-01
  • Jeon, Hajin. (2023-01). VPrimer: A method of designing and updating primer and probe with high variant coverage for RNA virus detection. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 20(1), 775–784. doi: 10.1109/TCBB.2021.3138145
  • IEEE Computer Society
  • View : 483
  • Download : 0
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