858 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Samsung (South Korea)KR16 papers
- Samsung Medical CenterKR3 papers
- Seoul National UniversityKR3 papers
- Korea Astronomy and Space Science InstituteKR2 papers
- Ulsan National Institute of Science and TechnologyKR2 papers
- Australian Centre for Robotic VisionAU1 paper
- Beijing Academy of Artificial IntelligenceCN1 paper
- Beijing University of Chemical TechnologyCN1 paper
- Chinese Academy of SciencesCN1 paper
- Chinese University of Hong KongHK1 paper
- Christian Doppler Laboratory for ThermoelectricityAT1 paper
- City College of New YorkUS1 paper
6 papers · 1 filter
SEEN: Sharpening Explanations for Graph Neural Networks using Explanations from Neighborhoods
Hyeoncheol Cho, Youngrock Oh, Eunjoo Jeon
Explaining the foundations for predictions obtained from graph neural networks (GNNs) is critical for credible use of GNN models for real-world problems. Owing to the rapid growth…
DUET: Detection Utilizing Enhancement for Text in Scanned or Captured Documents
Eun-Soo Jung, HyeongGwan Son, Kyusam Oh +3
We present a novel deep neural model for text detection in document images. For robust text detection in noisy scanned documents, the advantages of multi-task learning are adopted…
Topology for Substrate Routing in Semiconductor Package Design
Rak-Kyeong Seong, Jaeho Yang, Sang-Hoon Han
In this work, we propose a new signal routing method for solving routing problems that occur in the design process of semiconductor package substrates. Our work uses a topological…
KoreALBERT: Pretraining a Lite BERT Model for Korean Language Understanding
Hyunjae Lee, Jaewoong Yoon, Bonggyu Hwang +3
A Lite BERT (ALBERT) has been introduced to scale up deep bidirectional representation learning for natural languages. Due to the lack of pretrained ALBERT models for Korean langua…
Analyzing Zero-shot Cross-lingual Transfer in Supervised NLP Tasks
Hyunjin Choi, Judong Kim, Seongho Joe +2
In zero-shot cross-lingual transfer, a supervised NLP task trained on a corpus in one language is directly applicable to another language without any additional training. A source…
Evaluation of BERT and ALBERT Sentence Embedding Performance on Downstream NLP Tasks
Hyunjin Choi, Judong Kim, Seongho Joe +1
Contextualized representations from a pre-trained language model are central to achieve a high performance on downstream NLP task. The pre-trained BERT and A Lite BERT (ALBERT) mod…