Phung, Dinh Q.

27 publications

MLJ 2022 Improving Kernel Online Learning with a Snapshot Memory Trung Le, Khanh Nguyen, Dinh Q. Phung

IJCAI 2021 Optimal Transport for Deep Generative Models: State of the Art and Research Challenges Viet Huynh, Dinh Q. Phung, He Zhao

IJCAI 2021 TIDOT: A Teacher Imitation Learning Approach for Domain Adaptation with Optimal Transport Tuan Nguyen, Trung Le, Nhan Dam, Quan Hung Tran, Truyen Nguyen, Dinh Q. Phung

IJCAI 2021 Topic Modelling Meets Deep Neural Networks: A Survey He Zhao, Dinh Q. Phung, Viet Huynh, Yuan Jin, Lan Du, Wray L. Buntine

IJCAI 2019 Learning Generative Adversarial Networks from Multiple Data Sources Trung Le, Quan Hoang, Hung Vu, Tu Dinh Nguyen, Hung Bui, Dinh Q. Phung

AAAI 2019 Robust Anomaly Detection in Videos Using Multilevel Representations Hung Vu, Tu Dinh Nguyen, Trung Le, Wei Luo, Dinh Q. Phung

IJCAI 2018 Geometric Enclosing Networks Trung Le, Hung Vu, Tu Dinh Nguyen, Dinh Q. Phung

ECML-PKDD 2018 Sqn2Vec: Learning Sequence Representation via Sequential Patterns with a Gap Constraint Dang Nguyen, Wei Luo, Tu Dinh Nguyen, Svetha Venkatesh, Dinh Q. Phung

AAAI 2017 Column Networks for Collective Classification Trang Pham, Truyen Tran, Dinh Q. Phung, Svetha Venkatesh

IJCAI 2017 Discriminative Bayesian Nonparametric Clustering Vu Nguyen, Dinh Q. Phung, Trung Le, Hung Bui

IJCAI 2017 Large-Scale Online Kernel Learning with Random Feature Reparameterization Tu Dinh Nguyen, Trung Le, Hung Bui, Dinh Q. Phung

UAI 2017 Supervised Restricted Boltzmann Machines Tu Dinh Nguyen, Dinh Q. Phung, Viet Huynh, Trung Le

UAI 2016 Budgeted Semi-Supervised Support Vector Machine Trung Le, Phuong Duong, Mi Dinh, Tu Dinh Nguyen, Vu Nguyen, Dinh Q. Phung

MLJ 2016 Introduction: Special Issue of Selected Papers from ACML 2014 Hang Li, Dinh Q. Phung, Tru H. Cao, Tu Bao Ho, Zhi-Hua Zhou

AISTATS 2016 Nonparametric Budgeted Stochastic Gradient Descent Trung Le, Vu Nguyen, Tu Dinh Nguyen, Dinh Q. Phung

UAI 2016 Scalable Nonparametric Bayesian Multilevel Clustering Viet Huynh, Dinh Q. Phung, Svetha Venkatesh, XuanLong Nguyen, Matthew D. Hoffman, Hung Hai Bui

AAAI 2015 Tensor-Variate Restricted Boltzmann Machines Tu Dinh Nguyen, Truyen Tran, Dinh Q. Phung, Svetha Venkatesh

WACV 2015 Visual Object Clustering via Mixed-Norm Regularization Xin Zhang, Duc-Son Pham, Dinh Q. Phung, Wanquan Liu, Budhaditya Saha, Svetha Venkatesh

AAAI 2012 A Sequential Decision Approach to Ordinal Preferences in Recommender Systems Truyen Tran, Dinh Q. Phung, Svetha Venkatesh

UAI 2012 A Slice Sampler for Restricted Hierarchical Beta Process with Applications to Shared Subspace Learning Sunil Kumar Gupta, Dinh Q. Phung, Svetha Venkatesh

CVPR 2012 Improved Subspace Clustering via Exploitation of Spatial Constraints Duc-Son Pham, Budhaditya Saha, Dinh Q. Phung, Svetha Venkatesh

UAI 2009 Ordinal Boltzmann Machines for Collaborative Filtering Tran The Truyen, Dinh Q. Phung, Svetha Venkatesh

AAAI 2008 The Hidden Permutation Model and Location-Based Activity Recognition Hung Hai Bui, Dinh Q. Phung, Svetha Venkatesh, Hai Phan

CVPR 2006 AdaBoost.MRF: Boosted Markov Random Forests and Application to Multilevel Activity Recognition Tran The Truyen, Dinh Q. Phung, Svetha Venkatesh, Hung Hai Bui

CVPR 2005 Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model Thi V. Duong, Hung Hai Bui, Dinh Q. Phung, Svetha Venkatesh

CVPR 2005 Learning and Detecting Activities from Movement Trajectories Using the Hierarchical Hidden Markov Models Nam Thanh Nguyen, Dinh Q. Phung, Svetha Venkatesh, Hung Bui

AAAI 2004 Learning Hierarchical Hidden Markov Models with General State Hierarchy Hung Hai Bui, Dinh Q. Phung, Svetha Venkatesh