About
I am Giung Nam, a Ph.D. student at the Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST AI), advised by Professor Juho Lee. I received my M.S. from KAIST AI and my B.S. from the Department of Computer Science and Engineering, Korea University.
Since March 2024, I have been working as a Technical Research Personnel, and I expect to complete my doctoral degree by February 2026.
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Publication
Parameter expanded stochastic gradient Markov chain Monte Carlo Hyunsu Kim, Giung Nam, Chulhee Yun, Hongseok Yang, Juho Lee International Conference on Learning Representations (ICLR), 2025 [pdf]
Ex uno pluria: insights on ensembling in low precision number systems Giung Nam, Juho Lee Neural Information Processing Systems (NeurIPS), 2024 [pdf, arXiv]
Sparse weight averaging with multiple particles for iterative magnitude pruning Moonseok Choi*, Hyungi Lee*, Giung Nam*, Juho Lee International Conference on Learning Representations (ICLR), 2024 [pdf, arXiv]
Enhancing transfer learning with flexible nonparametric posterior sampling Hyungi Lee*, Giung Nam*, Edwin Fong, Juho Lee International Conference on Learning Representations (ICLR), 2024 [pdf, arXiv]
Lipsum-FT: robust fine-tuning of zero-shot models using random text guidance Giung Nam, Byeongho Heo, Juho Lee International Conference on Learning Representations (ICLR), 2024 [pdf, arXiv]
Traversing between modes in function space for fast ensembling Eunggu Yun*, Hyungi Lee*, Giung Nam*, Juho Lee International Conference on Machine Learning (ICML), 2023 [pdf, arXiv]
Martingale posterior neural processes Hyungi Lee, Eunggu Yun, Giung Nam, Edwin Fong, Juho Lee International Conference on Learning Representations (ICLR), 2023, Spotlight [pdf, arXiv]
Decoupled training for long-tailed classification with stochastic representations Giung Nam*, Sunguk Jang*, Juho Lee International Conference on Learning Representations (ICLR), 2023 [pdf, arXiv]
Benefits of stochastic weight averaging in developing neural network radiation scheme for numerical weather prediction Hwan-Jin Song, Soonyoung Roh, Juho Lee, Giung Nam, Eunggu Yun, Jongmin Yoon, Park Sa Kim Journal of Advances in Modeling Earth Systems (JAMES), October 2022 [pdf, ESSOAr]
Improving ensemble distillation with weight averaging and diversifying perturbation Giung Nam, Hyungi Lee, Byeongho Heo, Juho Lee International Conference on Machine Learning (ICML), 2022 [pdf, arXiv]
Diversity matters when learning from ensembles Giung Nam*, Jongmin Yoon*, Yoonho Lee, Juho Lee Neural Information Processing Systems (NeurIPS), 2021 [pdf, arXiv]