Pattern-preserving-based motion imitation for robots. - Bonggun Shin; Sungho Jo (URAI, 2011)

Bayesian Group Nonnegative Matrix Factorization for EEG Analysis - Bonggun Shin; Alice Oh (CoRR, 2012)

Classification of radiology reports using neural attention models. - Bonggun Shin; Falgun H. Chokshi; Timothy Lee; Jinho D. Choi (IJCNN, 2017)

Lexicon Integrated CNN Models with Attention for Sentiment Analysis. - Bonggun Shin; Timothy Lee; Jinho D. Choi (WASSA@EMNLP, 2017)

Multimodal Ensemble Approach to Incorporate Various Types of Clinical Notes for Predicting Readmission. - Bonggun Shin; Julien Hogan; Andrew B. Adams; Raymond J. Lynch; Rachel E. Patzer; Jinho D. Choi (BHI, 2019)

The Pupil Has Become the Master: Teacher-Student Model-Based Word Embedding Distillation with Ensemble Learning. - Bonggun Shin; Hao Yang; Jinho D. Choi (IJCAI, 2019)

Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction. - Bonggun Shin; Sungsoo Park; Keunsoo Kang; Joyce C. Ho (MLHC, 2019)

SMAT: An Attention-Based Deep Learning Solution to the Automation of Schema Matching. - Jing Zhang; Bonggun Shin; Jinho D. Choi; Joyce C. Ho (ADBIS, 2021)

Controlled molecule generator for optimizing multiple chemical properties. - Bonggun Shin; Sungsoo Park; JinYeong Bak; Joyce C. Ho (CHIL, 2021)

An Interpretable Framework for Drug-Target Interaction with Gated Cross Attention. - Yeachan Kim; Bonggun Shin (MLHC, 2021)

Improving Evidential Deep Learning via Multi-Task Learning. - Dongpin Oh; Bonggun Shin (AAAI, 2022)

In Defense of Core-set: A Density-aware Core-set Selection for Active Learning. - Yeachan Kim; Bonggun Shin (KDD, 2022)

Improving group robustness under noisy labels using predictive uncertainty. - Dongpin Oh; Dae Lee; Jeunghyun Byun; Bonggun Shin (CoRR, 2022)

Phase-shifted adversarial training. - Yeachan Kim; Seongyeon Kim; Ihyeok Seo; Bonggun Shin (UAI, 2023)

Boosting Convolutional Neural Networks' Protein Binding Site Prediction Capacity Using SE(3)-invariant transformers, Transfer Learning and Homology-based Augmentation. - Daeseok Lee; Bonggun Shin; Jeunghyun Byun (CoRR, 2023)

Learning From Drift: Federated Learning on Non-IID Data via Drift Regularization. - Yeachan Kim; Bonggun Shin (CoRR, 2023)

Turbocharging protein binding site prediction with geometric attention, inter-resolution transfer learning, and homology-based augmentation. - Daeseok Lee; Wonjun Hwang; Jeunghyun Byun; Bonggun Shin (BMC Bioinform., 2024)