15 citations · 21 across the 11 of their papers we have counts for
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cs.LG2023★ 2 cited
Fine tuning Pre trained Models for Robustness Under Noisy Labels
Sumyeong Ahn, Sihyeon Kim, Jongwoo Ko +1
The presence of noisy labels in a training dataset can significantly impact the performance of machine learning models. To tackle this issue, researchers have explored methods for…
cs.CL2023
NASH: A Simple Unified Framework of Structured Pruning for Accelerating Encoder-Decoder Language Models
Jongwoo Ko, Seungjoon Park, Yujin Kim +4
Structured pruning methods have proven effective in reducing the model size and accelerating inference speed in various network architectures such as Transformers. Despite the vers…
cs.CV2023★ 15 cited
CUDA: Curriculum of Data Augmentation for Long-Tailed Recognition
Sumyeong Ahn, Jongwoo Ko, Se-Young Yun
Class imbalance problems frequently occur in real-world tasks, and conventional deep learning algorithms are well known for performance degradation on imbalanced training datasets.…