Learning Parts-based Representations with Nonnegative Restricted Boltzmann Machine. - Tu Dinh Nguyen; Truyen Tran 001; Dinh Q. Phung; Svetha Venkatesh (ACML, 2013)

Learning sparse latent representation and distance metric for image retrieval. - Tu Dinh Nguyen; Truyen Tran 001; Dinh Q. Phung; Svetha Venkatesh (ICME, 2013)

Latent Patient Profile Modelling and Applications with Mixed-Variate Restricted Boltzmann Machine. - Tu Dinh Nguyen; Truyen Tran 001; Dinh Q. Phung; Svetha Venkatesh (PAKDD (1), 2013)

Tensor-Variate Restricted Boltzmann Machines. - Tu Dinh Nguyen; Truyen Tran 001; Dinh Q. Phung; Svetha Venkatesh (AAAI, 2015)

Stabilizing Sparse Cox Model Using Statistic and Semantic Structures in Electronic Medical Records. - Shivapratap Gopakumar; Tu Dinh Nguyen; Truyen Tran 001; Dinh Q. Phung; Svetha Venkatesh (PAKDD (2), 2015)

Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM). - Truyen Tran 001; Tu Dinh Nguyen; Dinh Q. Phung; Svetha Venkatesh (J. Biomed. Informatics, 2015)

Stabilizing High-Dimensional Prediction Models Using Feature Graphs. - Shivapratap Gopakumar; Truyen Tran 001; Tu Dinh Nguyen; Dinh Q. Phung; Svetha Venkatesh (IEEE J. Biomed. Health Informatics, 2015)

Multiple Kernel Learning with Data Augmentation. - Khanh Nguyen; Trung Le 001; Vu Nguyen 001; Tu Dinh Nguyen; Dinh Q. Phung (ACML, 2016)

Nonparametric Budgeted Stochastic Gradient Descent. - Trung Le 001; Vu Nguyen 001; Tu Dinh Nguyen; Dinh Q. Phung (AISTATS, 2016)

One-Pass Logistic Regression for Label-Drift and Large-Scale Classification on Distributed Systems. - Vu Nguyen 001; Tu Dinh Nguyen; Trung Le 001; Svetha Venkatesh; Dinh Q. Phung (ICDM, 2016)

Distributed data augmented support vector machine on Spark. - Tu Dinh Nguyen; Vu Nguyen 001; Trung Le 001; Dinh Q. Phung (ICPR, 2016)

Dual Space Gradient Descent for Online Learning. - Trung Le 001; Tu Dinh Nguyen; Vu Nguyen 001; Dinh Q. Phung (NIPS, 2016)

Budgeted Semi-supervised Support Vector Machine . - Trung Le 001; Phuong Duong; Mi Dinh; Tu Dinh Nguyen; Vu Nguyen 001; Dinh Q. Phung (UAI, 2016)

Graph-induced restricted Boltzmann machines for document modeling. - Tu Dinh Nguyen; Truyen Tran 001; Dinh Q. Phung; Svetha Venkatesh (Inf. Sci., 2016)

Animal Recognition and Identification with Deep Convolutional Neural Networks for Automated Wildlife Monitoring. - Hung Nguyen; Sarah J. Maclagan; Tu Dinh Nguyen; Thin Nguyen; Paul Flemons; Kylie Andrews; Euan G. Ritchie; Dinh Q. Phung (DSAA, 2017)

GoGP: Fast Online Regression with Gaussian Processes. - Trung Le 001; Khanh Nguyen; Vu Nguyen 001; Tu Dinh Nguyen; Dinh Q. Phung (ICDM, 2017)

Large-scale Online Kernel Learning with Random Feature Reparameterization. - Tu Dinh Nguyen; Trung Le 001; Hung Bui 001; Dinh Q. Phung (IJCAI, 2017)

Dual Discriminator Generative Adversarial Nets. - Tu Dinh Nguyen; Trung Le 001; Hung Vu; Dinh Q. Phung (NIPS, 2017)

Energy-Based Localized Anomaly Detection in Video Surveillance. - Hung Vu; Tu Dinh Nguyen; Anthony Travers; Svetha Venkatesh; Dinh Q. Phung (PAKDD (1), 2017)

Supervised Restricted Boltzmann Machines. - Tu Dinh Nguyen; Dinh Q. Phung; Viet Huynh; Trung Le 001 (UAI, 2017)

Multi-Generator Generative Adversarial Nets. - Quan Hoang; Tu Dinh Nguyen; Trung Le 001; Dinh Q. Phung (CoRR, 2017)

Energy-based Models for Video Anomaly Detection. - Hung Vu; Dinh Q. Phung; Tu Dinh Nguyen; Anthony Trevors; Svetha Venkatesh (CoRR, 2017)

Statistical Latent Space Approach for Mixed Data Modelling and Applications. - Tu Dinh Nguyen; Truyen Tran 001; Dinh Q. Phung; Svetha Venkatesh (CoRR, 2017)

Nonnegative Restricted Boltzmann Machines for Parts-based Representations Discovery and Predictive Model Stabilization. - Tu Dinh Nguyen; Truyen Tran 001; Dinh Q. Phung; Svetha Venkatesh (CoRR, 2017)

Analogical-based Bayesian Optimization. - Trung Le 001; Khanh Nguyen; Tu Dinh Nguyen; Dinh Q. Phung (CoRR, 2017)

KGAN: How to Break The Minimax Game in GAN. - Trung Le 001; Tu Dinh Nguyen; Dinh Q. Phung (CoRR, 2017)

Approximation Vector Machines for Large-scale Online Learning. - Trung Le 001; Tu Dinh Nguyen; Vu Nguyen 001; Dinh Q. Phung (J. Mach. Learn. Res., 2017)

Clustering Induced Kernel Learning. - Khanh Nguyen; Nhan Dam; Trung Le 001; Tu Dinh Nguyen; Dinh Q. Phung (ACML, 2018)

Batch Normalized Deep Boltzmann Machines. - Hung Vu; Tu Dinh Nguyen; Trung Le 001; Wei Luo 001; Dinh Q. Phung (ACML, 2018)

MGAN: Training Generative Adversarial Nets with Multiple Generators. - Quan Hoang; Tu Dinh Nguyen; Trung Le 001; Dinh Q. Phung (ICLR (Poster), 2018)

Bayesian Multi-Hyperplane Machine for Pattern Recognition. - Khanh Nguyen; Trung Le 001; Tu Dinh Nguyen; Dinh Q. Phung (ICPR, 2018)

Geometric Enclosing Networks. - Trung Le 001; Hung Vu; Tu Dinh Nguyen; Dinh Q. Phung (IJCAI, 2018)

Robust Bayesian Kernel Machine via Stein Variational Gradient Descent for Big Data. - Khanh Nguyen; Trung Le 001; Tu Dinh Nguyen; Dinh Q. Phung; Geoffrey I. Webb (KDD, 2018)

A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network. - Dai Quoc Nguyen; Tu Dinh Nguyen; Dat Quoc Nguyen; Dinh Q. Phung (NAACL-HLT (2), 2018)

Trans2Vec: Learning Transaction Embedding via Items and Frequent Itemsets. - Dang Nguyen 002; Tu Dinh Nguyen; Wei Luo 001; Svetha Venkatesh (PAKDD (3), 2018)

Sqn2Vec: Learning Sequence Representation via Sequential Patterns with a Gap Constraint. - Dang Nguyen 002; Wei Luo 001; Tu Dinh Nguyen; Svetha Venkatesh; Dinh Q. Phung (ECML/PKDD (2), 2018)

Learning Graph Representation via Frequent Subgraphs. - Dang Nguyen 002; Wei Luo 001; Tu Dinh Nguyen; Svetha Venkatesh; Dinh Q. Phung (SDM, 2018)

Detection of Unknown Anomalies in Streaming Videos with Generative Energy-based Boltzmann Models. - Hung Vu; Tu Dinh Nguyen; Dinh Q. Phung (CoRR, 2018)

Robust Anomaly Detection in Videos Using Multilevel Representations. - Hung Vu; Tu Dinh Nguyen; Trung Le 001; Wei Luo 001; Dinh Q. Phung (AAAI, 2019)

Three-Player Wasserstein GAN via Amortised Duality. - Nhan Dam; Quan Hoang; Trung Le 001; Tu Dinh Nguyen; Hung Bui 001; Dinh Phung 001 (IJCAI, 2019)

Learning Generative Adversarial Networks from Multiple Data Sources. - Trung Le 001; Quan Hoang; Hung Vu; Tu Dinh Nguyen; Hung Bui 001; Dinh Q. Phung (IJCAI, 2019)

A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization. - Dai Quoc Nguyen; Thanh Vu; Tu Dinh Nguyen; Dat Quoc Nguyen; Dinh Q. Phung (NAACL-HLT (1), 2019)

Relational Memory-based Knowledge Graph Embedding. - Dai Quoc Nguyen; Tu Dinh Nguyen; Dinh Q. Phung (CoRR, 2019)

Unsupervised Universal Self-Attention Network for Graph Classification. - Dai Quoc Nguyen; Tu Dinh Nguyen; Dinh Phung 001 (CoRR, 2019)

GoGP: scalable geometric-based Gaussian process for online regression. - Trung Le 001; Khanh Nguyen; Vu Nguyen 001; Tu Dinh Nguyen; Dinh Q. Phung (Knowl. Inf. Syst., 2019)

A convolutional neural network-based model for knowledge base completion and its application to search personalization. - Dai Quoc Nguyen; Dat Quoc Nguyen; Tu Dinh Nguyen; Dinh Phung 001 (Semantic Web, 2019)

A Capsule Network-based Model for Learning Node Embeddings. - Dai Quoc Nguyen; Tu Dinh Nguyen; Dat Quoc Nguyen; Dinh Phung 001 (CIKM, 2020)

A Self-attention Network Based Node Embedding Model. - Dai Quoc Nguyen; Tu Dinh Nguyen; Dinh Phung 001 (ECML/PKDD (3), 2020)

Quaternion Graph Neural Networks. - Dai Quoc Nguyen; Tu Dinh Nguyen; Dinh Q. Phung (ACML, 2021)

Universal Graph Transformer Self-Attention Networks. - Dai Quoc Nguyen; Tu Dinh Nguyen; Dinh Q. Phung (WWW (Companion Volume), 2022)

QuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings. - Dai Quoc Nguyen; Thanh Vu; Tu Dinh Nguyen; Dinh Q. Phung (WWW (Companion Volume), 2022)

Robust Variational Learning for Multiclass Kernel Models With Stein Refinement. - Khanh Nguyen; Trung Le 001; Tu Dinh Nguyen; Geoffrey I. Webb; Dinh Phung 001 (IEEE Trans. Knowl. Data Eng., 2022)