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Hyung-Jun Moon
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Journal Articles
- 2023
- [j1]Hyung-Jun Moon, Seok-Jun Bu, Sung-Bae Cho:
A graph convolution network with subgraph embedding for mutagenic prediction in aromatic hydrocarbons. Neurocomputing 530: 60-68 (2023)
Conference and Workshop Papers
- 2024
- [c11]Hyung-Jun Moon, Sung-Bae Cho:
Extended Generative Adversarial Imitation Learning for Autonomous Agents in Minecraft Game. CEC 2024: 1-8 - [c10]Hyung-Jun Moon, Tae-Hoon Kang, Sung-Bae Cho:
A Graph Neural Network with Multi-head Attention for Universal Brain Disease Diagnosis from fMRI Images. HAIS (1) 2024: 85-97 - [c9]Min-Seo Kwak, Hyung-Jun Moon, Sung-Bae Cho:
Differentiable Prototypes with Distributed Memory Network for Continual Learning. HAIS (1) 2024: 286-298 - [c8]Hyung-Jun Moon, Sung-Bae Cho:
Contrastive Learning of Multivariate Gaussian Distributions of Incremental Classes for Continual Learning. IWINAC 2024: 518-527 - 2023
- [c7]Hyung-Jun Moon, Sung-Bae Cho:
Exploiting Local Information with Subgraph Embedding for Graph Neural Networks. ICDM (Workshops) 2023: 1113-1120 - [c6]Hyung-Jun Moon, Sung-Bae Cho:
A Subgraph Embedded GIN with Attention for Graph Classification. IDEAL 2023: 356-367 - 2022
- [c5]Tae-Heon Kim, Hyung-Jun Moon, Sung-Bae Cho:
Gradient Regularization with Multivariate Distribution of Previous Knowledge for Continual Learning. IDEAL 2022: 359-368 - 2021
- [c4]Seok-Jun Bu, Hyung-Jun Moon, Sung-Bae Cho:
Adversarial Signal Augmentation for CNN-LSTM to Classify Impact Noise in Automobiles. BigComp 2021: 60-64 - [c3]Hyung-Jun Moon, Seok-Jun Bu, Sung-Bae Cho:
Directional Graph Transformer-Based Control Flow Embedding for Malware Classification. IDEAL 2021: 426-436 - [c2]Hyung-Jun Moon, Seok-Jun Bu, Sung-Bae Cho:
Mutagenic Prediction for Chemical Compound Discovery with Partitioned Graph Convolution Network. SOCO 2021: 578-587 - 2020
- [c1]Hyung-Jun Moon, Seok-Jun Bu, Sung-Bae Cho:
Learning Disentangled Representation of Residential Power Demand Peak via Convolutional-Recurrent Triplet Network. ICDM (Workshops) 2020: 757-761
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