Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation
Aecheon Jung, Seunghwan Lee, Dongyoon Han +1
Model merging combines independently trained models into a single multi-task model. However, most existing approaches focus primarily on avoiding task interference. We argue that i…
cs.LG2025
Dynamic Rank Adjustment for Accurate and Efficient Neural Network Training
Hyuntak Shin, Aecheon Jung, Sungeun Hong +1
Low-rank training methods reduce the number of trainable parameters by re-parameterizing the weights with matrix decompositions (e.g., singular value decomposition). However, enfor…
cs.LG2025
Task Vector Quantization for Memory-Efficient Model Merging
Youngeun Kim, Seunghwan Lee, Aecheon Jung +2
Model merging enables efficient multi-task models by combining task-specific fine-tuned checkpoints. However, storing multiple task-specific checkpoints requires significant memory…