38 citations · 63 across the 14 of their papers we have counts for
6 papers · 1 filter
Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language Models
Yongjin Yang, Jongwoo Ko, Se-Young Yun
Vision-language models (VLMs) like CLIP have demonstrated remarkable applicability across a variety of downstream tasks, including zero-shot image classification. Recently, the use…
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…
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…
Fast and Robust Early-Exiting Framework for Autoregressive Language Models with Synchronized Parallel Decoding
Sangmin Bae, Jongwoo Ko, Hwanjun Song +1
To tackle the high inference latency exhibited by autoregressive language models, previous studies have proposed an early-exiting framework that allocates adaptive computation path…
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.…
Revisiting Intermediate Layer Distillation for Compressing Language Models: An Overfitting Perspective
Jongwoo Ko, Seungjoon Park, Minchan Jeong +4
Knowledge distillation (KD) is a highly promising method for mitigating the computational problems of pre-trained language models (PLMs). Among various KD approaches, Intermediate…