Maoying Qiao
| Contact category | Researcher (past) |
|---|---|
| Job title | Lecturer |
| Research institute | Peter Faber Business School |
| Faculty of Law and Business |
Research outputs
Improving stochastic block models by incorporating power-law degree characteristic
Qiao, Maoying, Yu, Jun, Bian, Wei, Li, Qiang and Tao, Dacheng. (2017). Improving stochastic block models by incorporating power-law degree characteristic. Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17). Melbourne, Australia 19 - 25 Aug 2017 International Joint Conferences on Artificial Intelligence Organization. pp. 2620-2626 https://doi.org/10.24963/ijcai.2017/365Conference paper
Conditional graphical lasso for multi-label image classification
Li, Qiang, Qiao, Maoying, Bian, Wei and Tao, Dacheng. (2016). Conditional graphical lasso for multi-label image classification. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, Nevada, United States of America 27 - 30 Jun 2016 Computer Vision Foundation. pp. 2977-2986 https://doi.org/10.1109/CVPR.2016.325Conference paper
Deep learning methods applied to electronic monitoring data : Automated catch event detection for longline fishing
Qiao, Maoying, Wang, Dadong, Tuck, Geoffrey N., Little, L. Richard, Punt, Andre E. and Gerner, Mike. (2021). Deep learning methods applied to electronic monitoring data : Automated catch event detection for longline fishing. ICES Journal of Marine Science: journal du conseil. 78(1), pp. 25-35. https://doi.org/10.1093/icesjms/fsaa158Journal article
Diversified Bayesian nonnegative matrix factorization
Qiao, Maoying, Jun,Yu, Tongliang, Liu, Xinchao, Wang and Dacheng, Tao. (2020). Diversified Bayesian nonnegative matrix factorization. The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20). New York Hilton Midtown, New York, New York, United States of America 07 - 12 Feb 2020 AAAI Press. pp. 5420-5427 https://doi.org/10.1609/aaai.v34i04.5991Conference paper
Adapting stochastic block models to power-law degree distributions
Qiao, Maoying, Yu, Jun, Bian, Wei, Li, Qiang and Tao, Dacheng. (2019). Adapting stochastic block models to power-law degree distributions. IEEE Transactions on Cybernetics. 49(2), pp. 626-637. https://doi.org/10.1109/TCYB.2017.2783325YJournal article
Diversified dictionaries for multi-instance learning
Qiao, Maoying, Liu, Liu, Yu, Jun, Xu, Chang and Tao, Dacheng. (2017). Diversified dictionaries for multi-instance learning. Pattern Recognition. 64, pp. 407-416. https://doi.org/10.1016/j.patcog.2016.08.026Journal article
Fast sampling for time-varying determinantal point processes
Qiao, Maoying, Xu, Richard Yi Da, Bian, Wei and Tao, Dacheng. (2016). Fast sampling for time-varying determinantal point processes. ACM Transactions on Knowledge Discovery from Data. 11(1), p. 8. https://doi.org/1556-4681Journal article
Diversified hidden Markov models for sequential labeling
Qiao, Maoying, Bian, Wei, Da Xu, Richard Yi and Tao, Dacheng. (2015). Diversified hidden Markov models for sequential labeling. IEEE Transactions on Knowledge and Data Engineering. 27(11), pp. 2947-2960. https://doi.org/10.1109/TKDE.2015.2433262Journal article
Biview learning for human posture segmentation from 3D points cloud
Qiao, Maoying, Cheng, Jun, Bian, Wei and Tao, Dacheng. (2014). Biview learning for human posture segmentation from 3D points cloud. PLoS ONE. 9(1), p. e85811. https://doi.org/10.1371/journal.pone.0085811Journal article
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