NEO-KD: Knowledge-Distillation-Based Adversarial Training for Robust Multi-Exit Neural Networks. - Seokil Ham; Jungwuk Park; Dong-Jun Han; Jaekyun Moon (NeurIPS, 2023)

Switch Diffusion Transformer: Synergizing Denoising Tasks with Sparse Mixture-of-Experts. - Byeongjun Park; Hyojun Go; Jin-Young Kim; Sangmin Woo; Seokil Ham; Changick Kim (ECCV (53), 2024)

Improving Low-Latency Predictions in Multi-Exit Neural Networks via Block-Dependent Losses. - Dong-Jun Han; Jungwuk Park; Seokil Ham; Namjin Lee; Jaekyun Moon (IEEE Trans. Neural Networks Learn. Syst., 2024)

Diffusion Model Patching via Mixture-of-Prompts. - Seokil Ham; Sangmin Woo; Jin-Young Kim; Hyojun Go; Byeongjun Park; Changick Kim (AAAI, 2025)

Parameter Efficient Mamba Tuning via Projector-targeted Diagonal-centric Linear Transformation. - Seokil Ham; Hee-Seon Kim; Sangmin Woo; Changick Kim (CVPR, 2025)

Refusal-Feature-guided Teacher for Safe Finetuning via Data Filtering and Alignment Distillation. - Seokil Ham; Yubin Choi; Seungju Cho; Yujin Yang; Younghun Kim; Changick Kim (CoRR, 2025)