4 citations · 5 across the 3 of their papers we have counts for
5 papers
Task-specific Compression for Multi-task Language Models using Attribution-based Pruning
Nakyeong Yang, Yunah Jang, Hwanhee Lee +2
Multi-task language models show outstanding performance for various natural language understanding tasks with only a single model. However, these language models utilize an unneces…
LINDA: Unsupervised Learning to Interpolate in Natural Language Processing
Yekyung Kim, Seohyeong Jeong, Kyunghyun Cho
Despite the success of mixup in data augmentation, its applicability to natural language processing (NLP) tasks has been limited due to the discrete and variable-length nature of n…
Learning Dynamic BERT via Trainable Gate Variables and a Bi-modal Regularizer
Seohyeong Jeong, Nojun Kwak
The BERT model has shown significant success on various natural language processing tasks. However, due to the heavy model size and high computational cost, the model suffers from…
Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Video Object Segmentation
Hyojin Park, Jayeon Yoo, Seohyeong Jeong +2
Current state-of-the-art approaches for Semi-supervised Video Object Segmentation (Semi-VOS) propagates information from previous frames to generate segmentation mask for the curre…
Self-supervised pre-training and contrastive representation learning for multiple-choice video QA
Seonhoon Kim, Seohyeong Jeong, Eunbyul Kim +2
Video Question Answering (Video QA) requires fine-grained understanding of both video and language modalities to answer the given questions. In this paper, we propose novel trainin…