Tu Dinh Nguyen

According to our database, Tu Dinh Nguyen authored at least 52 papers between 2013 and 2022.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2022

Robust Variational Learning for Multiclass Kernel Models With Stein Refinement.

Khanh Nguyen, Trung Le, Tu Dinh Nguyen, Geoffrey I. Webb, Dinh Phung

IEEE Trans. Knowl. Data Eng., 2022

QuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings.

Dai Quoc Nguyen, Thanh Vu, Tu Dinh Nguyen, Dinh Q. Phung

Proceedings of the Companion of The Web Conference 2022, Virtual Event / Lyon, France, April 25, 2022

Universal Graph Transformer Self-Attention Networks.

Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Q. Phung

Proceedings of the Companion of The Web Conference 2022, Virtual Event / Lyon, France, April 25, 2022

2021

Quaternion Graph Neural Networks.

Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Q. Phung

Proceedings of the Asian Conference on Machine Learning, 2021

2020

A Self-attention Network Based Node Embedding Model.

Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

A Capsule Network-based Model for Learning Node Embeddings.

Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Phung

Proceedings of the CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, 2020

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

Semantic Web, 2019

GoGP: scalable geometric-based Gaussian process for online regression.

Trung Le, Khanh Nguyen, Vu Nguyen, Tu Dinh Nguyen, Dinh Q. Phung

Knowl. Inf. Syst., 2019

Unsupervised Universal Self-Attention Network for Graph Classification.

Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung

CoRR, 2019

Relational Memory-based Knowledge Graph Embedding.

Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Q. Phung

CoRR, 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

Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019

Learning Generative Adversarial Networks from Multiple Data Sources.

Trung Le, Quan Hoang, Hung Vu, Tu Dinh Nguyen, Hung Bui, Dinh Q. Phung

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Three-Player Wasserstein GAN via Amortised Duality.

Nhan Dam, Quan Hoang, Trung Le, Tu Dinh Nguyen, Hung Bui, Dinh Phung

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Robust Anomaly Detection in Videos Using Multilevel Representations.

Hung Vu, Tu Dinh Nguyen, Trung Le, Wei Luo, Dinh Q. Phung

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Detection of Unknown Anomalies in Streaming Videos with Generative Energy-based Boltzmann Models.

Hung Vu, Tu Dinh Nguyen, Dinh Q. Phung

CoRR, 2018

Learning Graph Representation via Frequent Subgraphs.

Dang Nguyen, Wei Luo, Tu Dinh Nguyen, Svetha Venkatesh, Dinh Q. Phung

Proceedings of the 2018 SIAM International Conference on Data Mining, 2018

Sqn2Vec: Learning Sequence Representation via Sequential Patterns with a Gap Constraint.

Dang Nguyen, Wei Luo, Tu Dinh Nguyen, Svetha Venkatesh, Dinh Q. Phung

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2018

Trans2Vec: Learning Transaction Embedding via Items and Frequent Itemsets.

Dang Nguyen, Tu Dinh Nguyen, Wei Luo, Svetha Venkatesh

Proceedings of the Advances in Knowledge Discovery and Data Mining, 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

Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2018

Robust Bayesian Kernel Machine via Stein Variational Gradient Descent for Big Data.

Khanh Nguyen, Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, Geoffrey I. Webb

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

Geometric Enclosing Networks.

Trung Le, Hung Vu, Tu Dinh Nguyen, Dinh Q. Phung

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Bayesian Multi-Hyperplane Machine for Pattern Recognition.

Khanh Nguyen, Trung Le, Tu Dinh Nguyen, Dinh Q. Phung

Proceedings of the 24th International Conference on Pattern Recognition, 2018

MGAN: Training Generative Adversarial Nets with Multiple Generators.

Quan Hoang, Tu Dinh Nguyen, Trung Le, Dinh Q. Phung

Proceedings of the 6th International Conference on Learning Representations, 2018

Batch Normalized Deep Boltzmann Machines.

Hung Vu, Tu Dinh Nguyen, Trung Le, Wei Luo, Dinh Q. Phung

Proceedings of The 10th Asian Conference on Machine Learning, 2018

Clustering Induced Kernel Learning.

Khanh Nguyen, Nhan Dam, Trung Le, Tu Dinh Nguyen, Dinh Q. Phung

Proceedings of The 10th Asian Conference on Machine Learning, 2018

2017

Approximation Vector Machines for Large-scale Online Learning.

Trung Le, Tu Dinh Nguyen, Vu Nguyen, Dinh Q. Phung

J. Mach. Learn. Res., 2017

KGAN: How to Break The Minimax Game in GAN.

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung

CoRR, 2017

Analogical-based Bayesian Optimization.

Trung Le, Khanh Nguyen, Tu Dinh Nguyen, Dinh Q. Phung

CoRR, 2017

Nonnegative Restricted Boltzmann Machines for Parts-based Representations Discovery and Predictive Model Stabilization.

Tu Dinh Nguyen, Truyen Tran, Dinh Q. Phung, Svetha Venkatesh

CoRR, 2017

Statistical Latent Space Approach for Mixed Data Modelling and Applications.

Tu Dinh Nguyen, Truyen Tran, Dinh Q. Phung, Svetha Venkatesh

CoRR, 2017

Energy-based Models for Video Anomaly Detection.

Hung Vu, Dinh Q. Phung, Tu Dinh Nguyen, Anthony Trevors, Svetha Venkatesh

CoRR, 2017

Multi-Generator Generative Adversarial Nets.

Quan Hoang, Tu Dinh Nguyen, Trung Le, Dinh Q. Phung

CoRR, 2017

Supervised Restricted Boltzmann Machines.

Tu Dinh Nguyen, Dinh Q. Phung, Viet Huynh, Trung Le

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Energy-Based Localized Anomaly Detection in Video Surveillance.

Hung Vu, Tu Dinh Nguyen, Anthony Travers, Svetha Venkatesh, Dinh Q. Phung

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2017

Dual Discriminator Generative Adversarial Nets.

Tu Dinh Nguyen, Trung Le, Hung Vu, Dinh Q. Phung

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Large-scale Online Kernel Learning with Random Feature Reparameterization.

Tu Dinh Nguyen, Trung Le, Hung Bui, Dinh Q. Phung

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

GoGP: Fast Online Regression with Gaussian Processes.

Trung Le, Khanh Nguyen, Vu Nguyen, Tu Dinh Nguyen, Dinh Q. Phung

Proceedings of the 2017 IEEE International Conference on Data Mining, 2017

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

Proceedings of the 2017 IEEE International Conference on Data Science and Advanced Analytics, 2017

2016

Graph-induced restricted Boltzmann machines for document modeling.

Tu Dinh Nguyen, Truyen Tran, Dinh Q. Phung, Svetha Venkatesh

Inf. Sci., 2016

Budgeted Semi-supervised Support Vector Machine .

Trung Le, Phuong Duong, Mi Dinh, Tu Dinh Nguyen, Vu Nguyen, Dinh Q. Phung

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Dual Space Gradient Descent for Online Learning.

Trung Le, Tu Dinh Nguyen, Vu Nguyen, Dinh Q. Phung

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Distributed data augmented support vector machine on Spark.

Tu Dinh Nguyen, Vu Nguyen, Trung Le, Dinh Q. Phung

Proceedings of the 23rd International Conference on Pattern Recognition, 2016

One-Pass Logistic Regression for Label-Drift and Large-Scale Classification on Distributed Systems.

Vu Nguyen, Tu Dinh Nguyen, Trung Le, Svetha Venkatesh, Dinh Q. Phung

Proceedings of the IEEE 16th International Conference on Data Mining, 2016

Nonparametric Budgeted Stochastic Gradient Descent.

Trung Le, Vu Nguyen, Tu Dinh Nguyen, Dinh Q. Phung

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Multiple Kernel Learning with Data Augmentation.

Khanh Nguyen, Trung Le, Vu Nguyen, Tu Dinh Nguyen, Dinh Q. Phung

Proceedings of The 8th Asian Conference on Machine Learning, 2016

2015

Stabilizing High-Dimensional Prediction Models Using Feature Graphs.

Shivapratap Gopakumar, Truyen Tran, Tu Dinh Nguyen, Dinh Q. Phung, Svetha Venkatesh

IEEE J. Biomed. Health Informatics, 2015

Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM).

Truyen Tran, Tu Dinh Nguyen, Dinh Q. Phung, Svetha Venkatesh

J. Biomed. Informatics, 2015

Stabilizing Sparse Cox Model Using Statistic and Semantic Structures in Electronic Medical Records.

Shivapratap Gopakumar, Tu Dinh Nguyen, Truyen Tran, Dinh Q. Phung, Svetha Venkatesh

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2015

Tensor-Variate Restricted Boltzmann Machines.

Tu Dinh Nguyen, Truyen Tran, Dinh Q. Phung, Svetha Venkatesh

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2013

Latent Patient Profile Modelling and Applications with Mixed-Variate Restricted Boltzmann Machine.

Tu Dinh Nguyen, Truyen Tran, Dinh Q. Phung, Svetha Venkatesh

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2013

Learning sparse latent representation and distance metric for image retrieval.

Tu Dinh Nguyen, Truyen Tran, Dinh Q. Phung, Svetha Venkatesh

Proceedings of the 2013 IEEE International Conference on Multimedia and Expo, 2013

Learning Parts-based Representations with Nonnegative Restricted Boltzmann Machine.

Tu Dinh Nguyen, Truyen Tran, Dinh Q. Phung, Svetha Venkatesh

Proceedings of the Asian Conference on Machine Learning, 2013