4 papers
mSFT: Addressing Dataset Mixtures Overfitting Heterogeneously in Multi-task SFT
Woosung Koh, Jeyoung Jeon, Youngjin Song +4
Current language model training commonly applies multi-task Supervised Fine-Tuning (SFT) using a homogeneous compute budget across all sub-datasets. This approach is fundamentally…
Prune-then-Quantize or Quantize-then-Prune? Understanding the Impact of Compression Order in Joint Model Compression
Minjun Kim, Jaehyeon Choi, Hyunwoo Yang +3
What happens when multiple compression methods are combined-does the order in which they are applied matter? Joint model compression has emerged as a powerful strategy to achieve h…
Zero-shot Quantization: A Comprehensive Survey
Minjun Kim, Jaehyeon Choi, Jongkeun Lee +2
Network quantization has proven to be a powerful approach to reduce the memory and computational demands of deep learning models for deployment on resource-constrained devices. How…
AugWard: Augmentation-Aware Representation Learning for Accurate Graph Classification
Minjun Kim, Jaehyeon Choi, SeungJoo Lee +2
How can we accurately classify graphs? Graph classification is a pivotal task in data mining with applications in social network analysis, web analysis, drug discovery, molecular p…