Deep-learning-based recognition of symbols and texts at an industrially applicable level from images of high-density piping and instrumentation diagrams. - Hyungki Kim; Wonyong Lee; Mijoo Kim; Yoochan Moon; Taekyong Lee; Mincheol Cho; Duhwan Mun (Expert Syst. Appl., 2021)

Cracking the Code of Negative Transfer: A Cooperative Game Theoretic Approach for Cross-Domain Sequential Recommendation. - Chung Park; Taesan Kim; Taekyoon Choi; Junui Hong; Yelim Yu; Mincheol Cho; Kyunam Lee; Sungil Ryu; Hyungjun Yoon; Minsung Choi; Jaegul Choo (CIKM, 2023)

FedGeo: Privacy-Preserving User Next Location Prediction with Federated Learning. - Chung Park; Taekyoon Choi; Taesan Kim; Mincheol Cho; Junui Hong; Minsung Choi; Jaegul Choo (SIGSPATIAL/GIS, 2023)

t3-Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power Divergence. - Juno Kim; Jaehyuk Kwon; Mincheol Cho; Hyunjong Lee; Joong-Ho Won (CoRR, 2023)

$t^3$-Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power Divergence. - Juno Kim; Jaehyuk Kwon; Mincheol Cho; Hyunjong Lee; Joong-Ho Won (ICLR, 2024)

Pacer and Runner: Cooperative Learning Framework between Single- and Cross-Domain Sequential Recommendation. - Chung Park; Taesan Kim; Hyungjun Yoon; Junui Hong; Yelim Yu; Mincheol Cho; Minsung Choi; Jaegul Choo (SIGIR, 2024)