HaDextract: Extracting HLA-Disease Interaction Information from Biomedical Literature. - Jeongmin Chae; Kinam Park; YoungHee Jung; Soonyoung Jung; JiEun Chae; Heung-Bum Oh (FGCN (3: BSBT), 2008)

CopyCaptor : Plagiarized Source Retrieval System using Global Word Frequency and Local Feedback Notebook for PAN at CLEF 2013. - Taemin Lee; Jeongmin Chae; Kinam Park; Soonyoung Jung (CLEF (Working Notes), 2013)

Identifying non-elliptical entity mentions in a coordinated NP with ellipses. - Jeongmin Chae; YoungHee Jung; Taemin Lee; Soonyoung Jung; Chan Huh; Gilhan Kim; Hyeoncheol Kim; Heung-Bum Oh (J. Biomed. Informatics, 2014)

Design and implementation of a system for creating multimedia linked data and its applications in education. - Jeongmin Chae; Yoonah Cho; Minkyung Lee; Sangmi Lee; Munsuk Choi; Seongbin Park (Multim. Tools Appl., 2016)

A corpus-based approach to classifying emotions using Korean linguistic features. - YoungHee Jung; Kinam Park; Taemin Lee; Jeongmin Chae; Soonyoung Jung (Clust. Comput., 2017)

Automatic scoring system for short descriptive answer written in Korean using lexico-semantic pattern. - Jeong-Eun Kim; Kinam Park; Jeongmin Chae; Hong-Jun Jang; Byoung Wook Kim; Soon-Young Jung (Soft Comput., 2018)

Greedy Sparse Signal Recovery Algorithm Based on Bit-wise MAP detection. - Jeongmin Chae; Song-Nam Hong 001 (CoRR, 2019)

A Novel B-MAP Proxy for Greedy Sparse Signal Recovery Algorithms. - Jeongmin Chae; Song-Nam Hong 001 (ISIT, 2020)

Machine Learning Detectors for MU-MIMO Systems With One-Bit ADCs. - Seonho Kim; Jeongmin Chae; Song-Nam Hong 001 (IEEE Access, 2020)

Pool-based sequential active learning with multi kernels. - Jeongmin Chae; Song-Nam Hong 001 (CoRR, 2020)

Stacked Bayesian Matching Pursuit for One-Bit Compressed Sensing. - Jeongmin Chae; Seonho Kim; Song-Nam Hong 001 (IEEE Signal Process. Lett., 2020)

Greedy Algorithms for Sparse and Positive Signal Recovery Based on Bit-Wise MAP Detection. - Jeongmin Chae; Song-Nam Hong 001 (IEEE Trans. Signal Process., 2020)

Fully-Decentralized Multi-Kernel Online Learning over Networks. - Jeongmin Chae; Urbashi Mitra; Songnam Hong 001 (GLOBECOM, 2021)

Stream-Based Active Learning with Multiple Kernels. - Jeongmin Chae; Songnam Hong 001 (ICOIN, 2021)

Multiple Kernel-Based Online Federated Learning. - Jeongmin Chae; Song-Nam Hong 001 (CoRR, 2021)

Communication-Efficient Randomized Algorithm for Multi-Kernel Online Federated Learning. - Songnam Hong 001; Jeongmin Chae (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Active Learning With Multiple Kernels. - Songnam Hong 001; Jeongmin Chae (IEEE Trans. Neural Networks Learn. Syst., 2022)

Column-Based Matrix Approximation with Quasi-Polynomial Structure. - Jeongmin Chae; Praneeth Narayanamurthy; Selin Bac; Shaama Mallikarjun Sharada; Urbashi Mitra (ICASSP, 2023)

Distributed Online Learning With Multiple Kernels. - Songnam Hong 001; Jeongmin Chae (IEEE Trans. Neural Networks Learn. Syst., 2023)

Sketched Column-Based Matrix Approximation With Side Information. - Jeongmin Chae; Praneeth Narayanamurthy; Selin Bac; Shaama Mallikarjun Sharada; Urbashi Mitra (ICASSP, 2024)

Matrix Approximation With Side Information: When Column Sampling Is Enough. - Jeongmin Chae; Praneeth Narayanamurthy; Selin Bac; Shaama Mallikarjun Sharada; Urbashi Mitra (IEEE Trans. Signal Process., 2024)

Quasi Polynomial and Interpolative Models for Tensor Approximation. - Jeongmin Chae; Selin Bac; Usama Saleem; Shaama Mallikarjun Sharada; Urbashi Mitra (ICASSP, 2025)