| Multilevel clustering via Wasserstein means N Ho, XL Nguyen, M Yurochkin, HH Bui, V Huynh, D Phung International conference on machine learning, 1501-1509, 2017 | 147 | 2017 |
| Topic modelling meets deep neural networks: A survey H Zhao, D Phung, V Huynh, Y Jin, L Du, W Buntine arXiv preprint arXiv:2103.00498, 2021 | 122 | 2021 |
| Computing crowd consensus with partial agreement NQV Hung, HH Viet, NT Tam, M Weidlich, H Yin, X Zhou IEEE Transactions on Knowledge and Data Engineering 30 (1), 1-14, 2017 | 74 | 2017 |
| Neural topic model via optimal transport H Zhao, D Phung, V Huynh, T Le, W Buntine arXiv preprint arXiv:2008.13537, 2020 | 49 | 2020 |
| Otlda: A geometry-aware optimal transport approach for topic modeling V Huynh, H Zhao, D Phung Advances in Neural Information Processing Systems 33, 18573-18582, 2020 | 23 | 2020 |
| Probabilistic multilevel clustering via composite transportation distance N Ho, V Huynh, D Phung, MI Jordan AISTATS 2019, 2018 | 21 | 2018 |
| Streaming variational inference for dirichlet process mixtures V Huynh, D Phung, S Venkatesh Asian Conference on Machine Learning, 237-252, 2016 | 20 | 2016 |
| Scalable Nonparametric Bayesian Multilevel Clustering V Huynh, D Phung, V Svetha, N Long, M Hoffman, H Bui The 32th Conference on Uncertainty in Artificial Intelligence 45, 2887-2893, 2016 | 13 | 2016 |
| Streaming Clustering with Bayesian Nonparametric Models V Huynh, D Phung Neurocomputing, 2017 | 12 | 2017 |
| Optimal transport for deep generative models: State of the art and research challenges V Huynh, D Phung International Joint Conference on Artificial Intelligence 2021, 4450-4457, 2021 | 10 | 2021 |
| Supervised Restricted Boltzmann Machines TD Nguyen, D Phung, V Huynh, T Le The Conference on Uncertainty in Artificial Intelligence (UAI), 2017 | 9 | 2017 |
| Process mining and security: visualization in database intrusion detection VH Huynh, ANT Le Intelligence and Security Informatics: Pacific Asia Workshop, PAISI 2012 …, 2012 | 9 | 2012 |
| Text generation with deep variational GAN M Hossam, T Le, M Papasimeon, V Huynh, D Phung arXiv preprint arXiv:2104.13488, 2021 | 8 | 2021 |
| On scalable variant of Wasserstein barycenter T Le, V Huynh, N Ho, D Phung, M Yamada ArXiv Preprint 2019, 2, 1910 | 8 | 1910 |
| On efficient multilevel clustering via Wasserstein distances V Huynh, N Ho, N Dam, XL Nguyen, M Yurochkin, H Bui, D Phung Journal of Machine Learning Research 22 (145), 1-43, 2021 | 6 | 2021 |
| Learning conditional latent structures from multiple data sources V Huynh, D Phung, L Nguyen, S Venkatesh, HH Bui Advances in Knowledge Discovery and Data Mining: 19th Pacific-Asia …, 2015 | 6 | 2015 |
| Multi-label answer aggregation for crowdsourcing NT Tam, HH Viet, NQV Hung, M Weidlich, H Yin, X Zhou Technique report, 1-13, 2016 | 5 | 2016 |
| M-tree as an index structure for time series data HH Viet, DT Anh 2013 International Conference on Computing, Management and …, 2013 | 5 | 2013 |
| OptiGAN: Generative adversarial networks for goal optimized sequence generation M Hossam, T Le, V Huynh, M Papasimeon, D Phung 2020 International Joint Conference on Neural Networks (IJCNN), 1-8, 2020 | 4 | 2020 |
| Tree-wasserstein barycenter for large-scale multilevel clustering and scalable bayes T Le, V Huynh, N Ho, D Phung, M Yamada arXiv preprint arXiv:1910.04483, 2019 | 3 | 2019 |
