More information can be found on [DBLP, Google Scholar, Semantic Scholar].

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, code]

Diversity matters when learning from ensembles Giung Nam*, Jongmin Yoon*, Yoonho Lee, Juho Lee Neural Information Processing Systems (NeurIPS), 2021 [pdf, arXiv, code]