Hyung-Jun Moon

19 papers A* 1B 1C 3Misc 1Journal 5Unranked 8
YearRankTypeTitle / Venue / Authors
2026 J jnl
A 4D transformer with spatiotemporal attentions for universal diagnosis of brain disorders.
Neurocomputing
Hyung-Jun Moon, Sung-Bae Cho
2026 A* conf
Expandable and Differentiable Dual Memories with Orthogonal Regularization for Exemplar-free Continual Learning.
AAAI
Hyung-Jun Moon, Sung-Bae Cho
2025 — conf
An Efficient Method of Lifelong Learning with Differentiable Memory for Edge Computing.
HICSS
Hyung-Jun Moon, Sung-Bae Cho
2025 J jnl
Continual Learning by Contrastive Learning of Regularized Classes in Multivariate Gaussian Distributions.
Int. J. Neural Syst.
Hyung-Jun Moon, Sung-Bae Cho
2025 J jnl
Expandable and Differentiable Dual Memories with Orthogonal Regularization for Exemplar-free Continual Learning.
CoRR
Hyung-Jun Moon, Sung-Bae Cho
2025 — conf
Knowledge Distillation of Class Activation Maps from Two Teachers for Continual Learning.
HAIS (2)
Minkai Sheng, Hyung-Jun Moon, Sung-Bae Cho
2025 J jnl
Traffic prediction by graph transformer embedded with subgraphs.
Expert Syst. Appl.
Hyung-Jun Moon, Sung-Bae Cho
2024 — conf
A Graph Neural Network with Multi-head Attention for Universal Brain Disease Diagnosis from fMRI Images.
HAIS (1)
Hyung-Jun Moon, Tae-Hoon Kang, Sung-Bae Cho
2024 Misc conf
Contrastive Learning of Multivariate Gaussian Distributions of Incremental Classes for Continual Learning.
IWINAC
Hyung-Jun Moon, Sung-Bae Cho
2024 — conf
Differentiable Prototypes with Distributed Memory Network for Continual Learning.
HAIS (1)
Min-Seo Kwak, Hyung-Jun Moon, Sung-Bae Cho
2024 B conf
Extended Generative Adversarial Imitation Learning for Autonomous Agents in Minecraft Game.
CEC
Hyung-Jun Moon, Sung-Bae Cho
2023 C conf
A Subgraph Embedded GIN with Attention for Graph Classification.
IDEAL
Hyung-Jun Moon, Sung-Bae Cho
2023 J jnl
A graph convolution network with subgraph embedding for mutagenic prediction in aromatic hydrocarbons.
Neurocomputing
Hyung-Jun Moon, Seok-Jun Bu, Sung-Bae Cho
2023 — conf
Exploiting Local Information with Subgraph Embedding for Graph Neural Networks.
ICDM (Workshops)
Hyung-Jun Moon, Sung-Bae Cho
2022 C conf
Gradient Regularization with Multivariate Distribution of Previous Knowledge for Continual Learning.
IDEAL
Tae-Heon Kim, Hyung-Jun Moon, Sung-Bae Cho
2021 — conf
Adversarial Signal Augmentation for CNN-LSTM to Classify Impact Noise in Automobiles.
BigComp
Seok-Jun Bu, Hyung-Jun Moon, Sung-Bae Cho
2021 C conf
Directional Graph Transformer-Based Control Flow Embedding for Malware Classification.
IDEAL
Hyung-Jun Moon, Seok-Jun Bu, Sung-Bae Cho
2021 — conf
Mutagenic Prediction for Chemical Compound Discovery with Partitioned Graph Convolution Network.
SOCO
Hyung-Jun Moon, Seok-Jun Bu, Sung-Bae Cho
2020 — conf
Learning Disentangled Representation of Residential Power Demand Peak via Convolutional-Recurrent Triplet Network.
ICDM (Workshops)
Hyung-Jun Moon, Seok-Jun Bu, Sung-Bae Cho