| 2026 | AAAI | Expandable and Differentiable Dual Memories with Orthogonal Regularization for Exemplar-free Continual Learning. | Hyung-Jun Moon, Sung-Bae Cho |
| 2026 | PAKDD | Subgraph Plug-in Boosts up Graph Neural Networks. | Hyung-Jun Moon, Sung-Bae Cho |
| 2025 | HAIS | Knowledge Distillation of Class Activation Maps from Two Teachers for Continual Learning. | Minkai Sheng, Hyung-Jun Moon, Sung-Bae Cho |
| 2024 | CEC | Extended Generative Adversarial Imitation Learning for Autonomous Agents in Minecraft Game. | Hyung-Jun Moon, Sung-Bae Cho |
| 2024 | HAIS | Differentiable Prototypes with Distributed Memory Network for Continual Learning. | Min-Seo Kwak, Hyung-Jun Moon, Sung-Bae Cho |
| 2024 | HAIS | A Graph Neural Network with Multi-head Attention for Universal Brain Disease Diagnosis from fMRI Images. | Hyung-Jun Moon, Tae-Hoon Kang, Sung-Bae Cho |
| 2024 | IWINAC | Contrastive Learning of Multivariate Gaussian Distributions of Incremental Classes for Continual Learning. | Hyung-Jun Moon, Sung-Bae Cho |
| 2023 | ICDM | Exploiting Local Information with Subgraph Embedding for Graph Neural Networks. | Hyung-Jun Moon, Sung-Bae Cho |
| 2023 | IDEAL | A Subgraph Embedded GIN with Attention for Graph Classification. | Hyung-Jun Moon, Sung-Bae Cho |
| 2022 | IDEAL | Gradient Regularization with Multivariate Distribution of Previous Knowledge for Continual Learning. | Tae-Heon Kim, Hyung-Jun Moon, Sung-Bae Cho |
| 2021 | IDEAL | Directional Graph Transformer-Based Control Flow Embedding for Malware Classification. | Hyung-Jun Moon, Seok-Jun Bu, Sung-Bae Cho |
| 2020 | ICDM | Learning Disentangled Representation of Residential Power Demand Peak via Convolutional-Recurrent Triplet Network. | Hyung-Jun Moon, Seok-Jun Bu, Sung-Bae Cho |