Min-Oh Heo

Biointelligence Lab (BI)
School of Computer Sci. and Eng.
Seoul National University
Seoul 151-744, Korea

Office: 302-314-1
Phone: +82-2-880-1847
Fax: +82-2-875-2240
E-mail: moheo (at) bi.snu.ac.kr
Personal home page: http://bi.snu.ac.kr/~moheo

Research interests:Cognitive Machine Learning, Deep Learning, Computational Video Narrative Intelligence, Associative Learning, Online Learning, Human Activity Structure Learning from Smartphone lifelogs
Selected Publications
* Video Narrative Intelligence with Deep Learning
  • K.-M. Kim, M.-O. Heo, S.-H. Choi, and B.-T. Zhang, DeepStory: Video story QA by deep embedded memory networks, The 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), 2017. (Acceptance Ratio 25.9%, Travel Award)
    * Invited paper to UAI 2017 MLTrain's Neural Abstract Machines session
    [code]
  • J.-H. Kim, S.-W. Lee, D.-H. Kwak, M.-O. Heo, J. Kim, J.-W. Ha, and B.-T. Zhang, Multimodal residual learning for visual QA, Advances in Neural Information Processing Systems 29 (NIPS 2016), 2016.
  • K. Kim, C. Nan, M.-O. Heo, S.-H. Choi, and B.-T. Zhang, PororoQA: Cartoon video series dataset for story understanding, NIPS 2016 Workshop on Large Scale Computer Vision System, 2016.
  • M.-O. Heo, K.-M. Kim, and B.-T. Zhang, Deep Learning-based Techniques for Learning Video Stories, Communications of the Korea Multimedia Society, vol. 20 (3), 2016.
  • M.-O. Heo, M. Kang, and B.-T. Zhang, Visual Query Expansion via Incremental Hypernetwork Models of Image and Text, Proceedings of the Eleventh Pacific Rim International Conference on AI (PRICAI2010), Lecture Notes in Artificial Intelligence, 6230:88-99, 2010.
* Online Learning
  • M.-O. Heo, B.-T. Zhang, Predictive convolutional networks for learning stream data, KIISE Transactions on Computing Practices, 22(11):614-618, 2016.
  • S.-W. Lee, M.-O. Heo, J. Kim, J. Kim, and B.-T. Zhang, Dual memory architectures for fast deep learning of stream data via an oline-incremental-transfer strategy, International Conference on Machine Learning (ICML) Workshop on Deep Learning, 2015.
  • S.-W. Lee, M.-O. Heo, and B.-T. Zhang, An Online incremental associative feature construction method via maximizing entropy, Journal of the Korea Information Science Society: Software and Applications, 41(3):177-182, 2014.
  • S.-W. Lee, M.-O. Heo, and B.-T. Zhang, Online incremental structure learning of sum-product networks, In Proceedings of the 20th International Conference on Neural Information Processing (ICONIP 2013), Lecture Notes in Computer Science, 8227:220-227, 2013.
  • M.-O. Heo, S.-W. Lee, and B.-T. Zhang, A sequential prediction model for online incremental learning of mutidimensional stream data, Journal of the Korean Institute of Information Scientists and Engineers: Computing Practices and Letters, 19(8):419-423, 2013.
* Human Activity Structure Learning from Smartphone lifelogs
  • M.-O. Heo, S.-H. Jo, S.-W. Lee and B.-T. Zhang, Learning sparse higher-order Markov random fields for human activity analysis, Proceedings of KIIS Spring Conference 2015, Vol. 25, No. 1., pp. 62-63, 2015.
  • B.-J. Lee, J. Kim, J.-H. Ryu, M.-O. Heo, J.-S. Kim and B.-T. Zhang, Smartphone-User Interactive based Self Developing Place-Time-Activity Coupled Prediction Method for Daily Routine Planning System, Journal of the Korean Institute of Information Scientists and Engineers: Computing Practices, 21(2):154-159, 2015.
  • M.-O. Heo, S.-W. Lee, J. Lee, and B.-T. Zhang, Learning global-to-local discrete components with nonparametric Bayesian feature construction, Constructive Machine Learning workshop in Neural Information Processing Systems (NIPS), 2013.
  • S.-W. Lee, M.-O. Heo, and B.-T. Zhang, Multiswitch hidden Markov models for route and destination prediction of mobile phone users, Journal of the Korean Institute of Information Scientists and Engineers: Computing Practices and Letters, 19(6):351-355, 2013.
  • Min-Oh Heo, Myounggu Kang, Byoung-Kwon Lim, Kyu-Baek Hwang Young-Tack Park, Byoung-Tak Zhang, A real-time Route Inference and Learning for Smartphone Users using Probabilistic Graphical Models , Journal of the Korea Information Science Society: Software and Applications, 2012.