Neural Processes with Stochastic Attention: Paying more attention to the context dataset. - Mingyu Kim 002; Kyeongryeol Go; Se-Young Yun (ICLR, 2022)

The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions. - Mingyu Kim 002; Jihwan Oh; Yongsik Lee; Joonkee Kim; Seonghwan Kim 004; Song Chong; Se-Young Yun (CoRR, 2022)

Meta-Learning Amidst Heterogeneity and Ambiguity. - Kyeongryeol Go; Mingyu Kim 002; Seyoung Yun (IEEE Access, 2023)

The StarCraft Multi-Agent Exploration Challenges: Learning Multi-Stage Tasks and Environmental Factors Without Precise Reward Functions. - Mingyu Kim 002; Jihwan Oh; Yongsik Lee; Joonkee Kim; Seonghwan Kim 004; Song Chong; Seyoung Yun (IEEE Access, 2023)

PolarGAN: Creating realistic Arctic sea ice concentration images with user-defined geometric preferences. - Mingyu Kim 002; Jaekyeong Lee; Leechan Choi; Minjoo Choi (Eng. Appl. Artif. Intell., 2023)

Synergistic Integration of Coordinate Network and Tensorial Feature for Improving Neural Radiance Fields from Sparse Inputs. - Mingyu Kim 002; Jun-Seong Kim; Se-Young Yun; Jin-Hwa Kim (ICML, 2024)

Factorized Multi-Resolution HashGrid for Efficient Neural Radiance Fields: Execution on Edge-Devices. - Kim Jun-Seong; Mingyu Kim 002; GeonU Kim; Tae-Hyun Oh; Jin-Hwa Kim (IEEE Robotics Autom. Lett., 2024)