P-PseudoLabel: Enhanced Pseudo-Labeling Framework With Network Pruning in Semi-Supervised Learning. - Gyeongdo Ham; Yucheol Cho; Jae-Hyeok Lee 001; Daeshik Kim (IEEE Access, 2022)

A Multiple-State Ion Synaptic Transistor Applicable to Abnormal Car Detection with Transfer Learning. - Ji-Man Yu; Gyeongdo Ham; Chungryeol Lee; Jae-Hyeok Lee 001; Joon-Kyu Han; Jin-Ki Kim; Donggon Jang; Nahyun Kim; Moon-Seok Kim; Sung Gap Im; Dae-Shik Kim; Yang-Kyu Choi (Adv. Intell. Syst., 2022)

Cosine Similarity Knowledge Distillation for Individual Class Information Transfer. - Gyeongdo Ham; Seonghak Kim; Suin Lee; Jae-Hyeok Lee 001; Daeshik Kim (CoRR, 2023)

Maximizing Discrimination Capability of Knowledge Distillation with Energy-based Score. - Seonghak Kim; Gyeongdo Ham; Suin Lee; Donggon Jang; Daeshik Kim (CoRR, 2023)

Ambiguity-aware robust teacher (ART): Enhanced self-knowledge distillation framework with pruned teacher network. - Yucheol Cho; Gyeongdo Ham; Jae-Hyeok Lee 001; Daeshik Kim (Pattern Recognit., 2023)

Burst super-resolution with adaptive feature refinement and enhanced group up-sampling. - Minchan Kang; Woojin Jeong; Sanghyeok Son; Gyeongdo Ham; Dae-Shik Kim (Appl. Intell., 2023)

Difficulty level-based knowledge distillation. - Gyeongdo Ham; Yucheol Cho; Jae-Hyeok Lee 001; Minchan Kang; Gyuwon Choi; Daeshik Kim (Neurocomputing, 2024)

Maximizing discrimination capability of knowledge distillation with energy function. - Seonghak Kim; Gyeongdo Ham; Suin Lee; Donggon Jang; Daeshik Kim (Knowl. Based Syst., 2024)

SemiH: DFT Hamiltonian neural network training with semi-supervised learning. - Yucheol Cho; Guenseok Choi; Gyeongdo Ham; Mincheol Shin; Daeshik Kim (Mach. Learn. Sci. Technol., 2024)

Robustness-Reinforced Knowledge Distillation With Correlation Distance and Network Pruning. - Seonghak Kim; Gyeongdo Ham; Yucheol Cho; Daeshik Kim (IEEE Trans. Knowl. Data Eng., 2024)

Generality-aware self-supervised transformer for multivariate time series anomaly detection. - Yucheol Cho; Jae-Hyeok Lee 001; Gyeongdo Ham; Donggon Jang; Dae-Shik Kim (Appl. Intell., 2025)