Dinh Q. Phung

发表

A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network pdf

Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, 2017, NAACL.

Multiple Instance Learning with the Optimal Sub-Pattern Assignment Metric pdf

Ba-Ngu Vo, Dinh Q. Phung, Ba-Tuong Vo, 2017, ArXiv.

Outlier Detection on Mixed-Type Data: An Energy-Based Approach

Svetha Venkatesh, Truyen Tran, Dinh Q. Phung, 2016, ADMA.

High level segmentation of instructional videos based on content density

Svetha Venkatesh, Chitra Dorai, Dinh Q. Phung, 2002, MULTIMEDIA '02.

Flickr hypergroups

Dinh Q. Phung, Daniel Gatica-Perez, Svetha Venkatesh, 2009, ACM Multimedia.

High accuracy context recovery using clustering mechanisms

Mohan Kumar, Svetha Venkatesh, Brett Adams, 2009, 2009 IEEE International Conference on Pervasive Computing and Communications.

Sparse Adaptive Multi-hyperplane Machine

Trung Le, Dinh Q. Phung, Khanh Nguyen, 2016, PAKDD.

Hierarchical semi-Markov conditional random fields for deep recursive sequential data

Truyen Tran, Dinh Q. Phung, Hung Hai Bui, 2008, Artif. Intell..

Preference Networks: Probabilistic Models for Recommendation Systems pdf

Svetha Venkatesh, Dinh Q. Phung, Tran The Truyen, 2007, AusDM.

Model-based learning for point pattern data

Nhan Dam, Ba-Ngu Vo, Dinh Q. Phung, 2017, Pattern Recognit..

A Simultaneous Extraction of Context and Community from pervasive signals using nested Dirichlet process

Flora D. Salim, Dinh Q. Phung, Duc V. Le, 2017, Pervasive Mob. Comput..

Split-Merge Augmented Gibbs Sampling for Hierarchical Dirichlet Processes

Svetha Venkatesh, Dinh Q. Phung, Santu Rana, 2013, PAKDD.

Multi-View Subspace Clustering for Face Images

Svetha Venkatesh, Duc-Son Pham, Wanquan Liu, 2015, 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA).

Hyper-community detection in the blogosphere

Svetha Venkatesh, Truyen Tran, Brett Adams, 2010, WSM@MM.

Learning deep representation of multityped objects and tasks pdf

Svetha Venkatesh, Truyen Tran, Dinh Q. Phung, 2016, ArXiv.

Learning Multi-faceted Activities from Heterogeneous Data with the Product Space Hierarchical Dirichlet Processes

Svetha Venkatesh, Dinh Q. Phung, Vu Nguyen, 2016, PAKDD Workshops.

Hierarchical Dirichlet Process for Tracking Complex Topical Structure Evolution and Its Application to Autism Research Literature

Dinh Q. Phung, Ognjen Arandjelovic, Svetha Venkatesh, 2015, PAKDD.

Latent sentiment topic modelling and nonparametric discovery of online mental health-related communities

Svetha Venkatesh, Dinh Q. Phung, Bo Dao, 2017, International Journal of Data Science and Analytics.

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

Wei Luo, Svetha Venkatesh, Tu Dinh Nguyen, 2018, ECML/PKDD.

Modelling human preferences for ranking and collaborative filtering: a probabilistic ordered partition approach

Svetha Venkatesh, Truyen Tran, Dinh Q. Phung, 2016, Knowledge and Information Systems.

Energy-Based Localized Anomaly Detection in Video Surveillance

Svetha Venkatesh, Tu Dinh Nguyen, Dinh Q. Phung, 2017, PAKDD.

A new transfer learning framework with application to model-agnostic multi-task learning

Svetha Venkatesh, Dinh Q. Phung, Santu Rana, 2016, Knowledge and Information Systems.

Academia versus social media: A psycho-linguistic analysis

Svetha Venkatesh, Dinh Q. Phung, Thin Nguyen, 2017, J. Comput. Sci..

Extraction of latent patterns and contexts from social honest signals using hierarchical Dirichlet processes

Svetha Venkatesh, Dinh Q. Phung, Nguyen Cong Thuong, 2013, 2013 IEEE International Conference on Pervasive Computing and Communications (PerCom).

Sparse Subspace Representation for Spectral Document Clustering

Svetha Venkatesh, Duc-Son Pham, Dinh Q. Phung, 2012, 2012 IEEE 12th International Conference on Data Mining.

Stabilized sparse ordinal regression for medical risk stratification

Wei Luo, Svetha Venkatesh, Truyen Tran, 2014, Knowledge and Information Systems.

Learning Hierarchical Hidden Markov Models with General State Hierarchy

Svetha Venkatesh, Dinh Q. Phung, Hung Hai Bui, 2004, AAAI.

Graph-induced restricted Boltzmann machines for document modeling

Svetha Venkatesh, Truyen Tran, Tu Dinh Nguyen, 2016, Inf. Sci..

Mood sensing from social media texts and its applications

Svetha Venkatesh, Brett Adams, Dinh Q. Phung, 2013, Knowledge and Information Systems.

Emotional Reactions to Real-World Events in Social Networks

Svetha Venkatesh, Brett Adams, Dinh Q. Phung, 2011, PAKDD Workshops.

Collaborative filtering via sparse Markov random fields pdf

Svetha Venkatesh, Truyen Tran, Dinh Q. Phung, 2016, Inf. Sci..

Multilevel Clustering via Wasserstein Means

Dinh Q. Phung, Nhat Ho, Hung Hai Bui, 2017, ICML.

Forward-Backward Smoothing for Hidden Markov Models of Point Pattern Data

Nhan Dam, Ba-Ngu Vo, Dinh Q. Phung, 2017, 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

Intervention-Driven Predictive Framework for Modeling Healthcare Data

Svetha Venkatesh, Dinh Q. Phung, Santu Rana, 2014, PAKDD.

Sparse Subspace Clustering via Group Sparse Coding

Svetha Venkatesh, Duc-Son Pham, Dinh Q. Phung, 2013, SDM.

Hierarchical topical segmentation in instructional films based on cinematic expressive functions

Svetha Venkatesh, Chitra Dorai, Dinh Q. Phung, 2003, MULTIMEDIA '03.

Preterm Birth Prediction: Stable Selection of Interpretable Rules from High Dimensional Data

Wei Luo, Svetha Venkatesh, Truyen Tran, 2016, MLHC.

Geometric Enclosing Networks

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2017, IJCAI.

GoGP: Fast Online Regression with Gaussian Processes

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2017, 2017 IEEE International Conference on Data Mining (ICDM).

Thurstonian Boltzmann Machines: Learning from Multiple Inequalities pdf

Svetha Venkatesh, Truyen Tran, Dinh Q. Phung, 2013, ICML.

On the extraction of thematic and dramatic functions of content in educational videos

Svetha Venkatesh, Chitra Dorai, Dinh Q. Phung, 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

Model-based classification and novelty detection for point pattern data

Dinh Q. Phung, Ba-Tuong Vo, Ba-Ngu Vo, 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

Detection of cross-channel anomalies

Svetha Venkatesh, Duc-Son Pham, Dinh Q. Phung, 2012, Knowledge and Information Systems.

Tensor-Variate Restricted Boltzmann Machines

Svetha Venkatesh, Truyen Tran, Tu Dinh Nguyen, 2015, AAAI.

Unsupervised context detection using wireless signals

Mohan Kumar, Svetha Venkatesh, Brett Adams, 2009, Pervasive Mob. Comput..

Sensing and using social context

Svetha Venkatesh, Brett Adams, Dinh Q. Phung, 2008, TOMCCAP.

A random finite set model for data clustering

Ba-Ngu Vo, Dinh Q. Phung, B. Vo, 2014, 17th International Conference on Information Fusion (FUSION).

Regularized nonnegative shared subspace learning

Svetha Venkatesh, Brett Adams, Dinh Q. Phung, 2011, Data Mining and Knowledge Discovery.

Approximation Vector Machines for Large-scale Online Learning

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2016, J. Mach. Learn. Res..

Hierarchical Bayesian nonparametric models for knowledge discovery from electronic medical records

Cheng Li, Svetha Venkatesh, Dinh Q. Phung, 2016, Knowl. Based Syst..

Human Behavior Recognition with Generic Exponential Family Duration Modeling in the Hidden Semi-Markov Model

Svetha Venkatesh, Dinh Q. Phung, Hung Hai Bui, 2006, 18th International Conference on Pattern Recognition (ICPR'06).

Estimation of the prevalence of adverse drug reactions from social media

Dinh Q. Phung, Svetha Venkatesh, Helen Christensen, 2017, Int. J. Medical Informatics.

Statistical Latent Space Approach for Mixed Data Modelling and Applications pdf

Svetha Venkatesh, Truyen Tran, Tu Dinh Nguyen, 2017, ArXiv.

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Svetha Venkatesh, Truyen Tran, Dinh Q. Phung, 2016, PAKDD.

Column Networks for Collective Classification

Svetha Venkatesh, Truyen Tran, Dinh Q. Phung, 2016, AAAI.

Exploiting side information in distance dependent Chinese restaurant processes for data clustering

Cheng Li, Svetha Venkatesh, Dinh Q. Phung, 2013, 2013 IEEE International Conference on Multimedia and Expo (ICME).

Computer assisted autism interventions for India

Svetha Venkatesh, Dinh Q. Phung, Stewart Greenhill, 2016, OZCHI.

Streaming Variational Inference for Dirichlet Process Mixtures

Svetha Venkatesh, Dinh Q. Phung, Viet Huynh, 2015, ACML.

Affective and Content Analysis of Online Depression Communities

Svetha Venkatesh, Dinh Q. Phung, Bo Dao, 2014, IEEE Transactions on Affective Computing.

Embedded Restricted Boltzmann Machines for fusion of mixed data types and applications in social measurements analysis

Svetha Venkatesh, Truyen Tran, Dinh Q. Phung, 2012, 2012 15th International Conference on Information Fusion.

Toxicity Prediction in Cancer Using Multiple Instance Learning in a Multi-task Framework

Wei Luo, Cheng Li, Svetha Venkatesh, 2016, PAKDD.

MGAN: Training Generative Adversarial Nets with Multiple Generators

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2018, ICLR.

A Bayesian Framework for Learning Shared and Individual Subspaces from Multiple Data Sources

Svetha Venkatesh, Brett Adams, Dinh Q. Phung, 2011, PAKDD.

Nonparametric Budgeted Stochastic Gradient Descent

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2016, AISTATS.

Fixed-lag particle filter for continuous context discovery using Indian Buffet Process

Svetha Venkatesh, Dinh Q. Phung, Nguyen Cong Thuong, 2014, 2014 IEEE International Conference on Pervasive Computing and Communications (PerCom).

Control Matching via Discharge Code Sequences pdf

Wei Luo, Svetha Venkatesh, Dinh Q. Phung, 2016, ArXiv.

Distributed data augmented support vector machine on Spark

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

Large-Scale Stylistic Analysis of Formality in Academia and Social Media

Svetha Venkatesh, Dinh Q. Phung, Thin Nguyen, 2016, WISE.

Transfer learning for rare cancer problems via Discriminative Sparse Gaussian Graphical model

Svetha Venkatesh, Dinh Q. Phung, Sunil Gupta, 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

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

Svetha Venkatesh, Truyen Tran, Tu Dinh Nguyen, 2015, PAKDD.

Small-Variance Asymptotics for Bayesian Nonparametric Models with Constraints

Cheng Li, Svetha Venkatesh, Dinh Q. Phung, 2015, PAKDD.

Analysing the History of Autism Spectrum Disorder Using Topic Models

Svetha Venkatesh, Dinh Q. Phung, Ognjen Arandjelovic, 2016, 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

Social reader: following social networks in the wilds of the blogosphere

Svetha Venkatesh, Brett Adams, Dinh Q. Phung, 2009, WSM '09.

An integrated framework for suicide risk prediction

Wei Luo, Svetha Venkatesh, Truyen Tran, 2013, KDD.

Budgeted Semi-supervised Support Vector Machine

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2016, UAI.

Detection of Cross-Channel Anomalies from Multiple Data Channels

Svetha Venkatesh, Duc-Son Pham, Dinh Q. Phung, 2011, 2011 IEEE 11th International Conference on Data Mining.

Topic transition detection using hierarchical hidden Markov and semi-Markov models

Svetha Venkatesh, Dinh Q. Phung, Hung Hai Bui, 2005, MULTIMEDIA '05.

A Capsule Network-based Embedding Model for Search Personalization pdf

Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Q. Phung, 2018, ArXiv.

Activity recognition and abnormality detection with the switching hidden semi-Markov model

Svetha Venkatesh, Dinh Q. Phung, Hung Hai Bui, 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

Unsupervised inference of significant locations from WiFi data for understanding human dynamics

Wei Luo, Svetha Venkatesh, Dinh Q. Phung, 2014, MUM.

A Bayesian Nonparametric Framework for Activity Recognition Using Accelerometer Data

Svetha Venkatesh, Dinh Q. Phung, Nguyen Cong Thuong, 2014, 2014 22nd International Conference on Pattern Recognition.

Multiple Kernel Learning with Data Augmentation

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2016, ACML.

Learning Conditional Latent Structures from Multiple Data Sources

Svetha Venkatesh, Dinh Q. Phung, Hung Hai Bui, 2015, PAKDD.

Extraction of social context and application to personal multimedia exploration

Svetha Venkatesh, Brett Adams, Dinh Q. Phung, 2006, MM '06.

Probabilistic Models over Ordered Partitions with Applications in Document Ranking and Collaborative Filtering

Svetha Venkatesh, Truyen Tran, Dinh Q. Phung, 2011, SDM.

Jointly Predicting Affective and Mental Health Scores Using Deep Neural Networks of Visual Cues on the Web

Duc Thanh Nguyen, Dinh Q. Phung, Svetha Venkatesh, 2018, WISE.

EEG-Based User Authentication in Multilevel Security Systems

Tien Pham, Wanli Ma, Dat Tran, 2013, ADMA.

Stable feature selection for clinical prediction: Exploiting ICD tree structure using Tree-Lasso

Svetha Venkatesh, Dinh Q. Phung, Sunil Kumar Gupta, 2015, J. Biomed. Informatics.

Using Twitter to learn about the autism community

Svetha Venkatesh, Terry Caelli, Dinh Q. Phung, 2015, Social Network Analysis and Mining.

Learning sparse latent representation and distance metric for image retrieval

Svetha Venkatesh, Truyen Tran, Tu Dinh Nguyen, 2013, 2013 IEEE International Conference on Multimedia and Expo (ICME).

EEG-Based Age and Gender Recognition Using Tensor Decomposition and Speech Features

Wanli Ma, Dat Tran, Xu Huang, 2013, ICONIP.

Data Mining of Intervention for Children with Autism Spectrum Disorder

Svetha Venkatesh, Dinh Q. Phung, Pratibha Vellanki, 2016, eHealth 360°.

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

Svetha Venkatesh, Truyen Tran, Tu Dinh Nguyen, 2017, ArXiv.

LTARM: A novel temporal association rule mining method to understand toxicities in a routine cancer treatment

Wei Luo, Svetha Venkatesh, Dinh Q. Phung, 2018, Knowl. Based Syst..

A Framework for Classifying Online Mental Health-Related Communities With an Interest in Depression

Svetha Venkatesh, Dinh Q. Phung, Thin Nguyen, 2016, IEEE Journal of Biomedical and Health Informatics.

Latent sentiment topic modelling and nonparametric discovery of online mental health-related communities

Svetha Venkatesh, Dinh Q. Phung, Bo Dao, 2017, International Journal of Data Science and Analytics.

Stabilizing Linear Prediction Models Using Autoencoder

Svetha Venkatesh, Truyen Tran, Dinh Q. Phung, 2016, ADMA.

Nonparametric Discovery of Learning Patterns and Autism Subgroups from Therapeutic Data

Svetha Venkatesh, Dinh Q. Phung, Pratibha Vellanki, 2014, 2014 22nd International Conference on Pattern Recognition.

The Hidden Permutation Model and Location-Based Activity Recognition

Svetha Venkatesh, Dinh Q. Phung, Hung Hai Bui, 2008, AAAI.

A Slice Sampler for Restricted Hierarchical Beta Process with Applications to Shared Subspace Learning pdf

Svetha Venkatesh, Dinh Q. Phung, Sunil Kumar Gupta, 2012, UAI.

Understanding Toxicities and Complications of Cancer Treatment: A Data Mining Approach

Wei Luo, Svetha Venkatesh, Dinh Q. Phung, 2015, Australasian Conference on Artificial Intelligence.

Data clustering using side information dependent Chinese restaurant processes

Cheng Li, Svetha Venkatesh, Dinh Q. Phung, 2015, Knowledge and Information Systems.

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

Svetha Venkatesh, Truyen Tran, Tu Dinh Nguyen, 2015, J. Biomed. Informatics.

Efficient duration and hierarchical modeling for human activity recognition

Svetha Venkatesh, Dinh Q. Phung, Hung Hai Bui, 2009, Artif. Intell..

Differentiating Sub-groups of Online Depression-Related Communities Using Textual Cues

Dinh Q. Phung, Svetha Venkatesh, Helen Christensen, 2015, WISE.

Effect of Mood, Social Connectivity and Age in Online Depression Community via Topic and Linguistic Analysis

Svetha Venkatesh, Dinh Q. Phung, Bo Dao, 2014, WISE.

iPoll: Automatic Polling Using Online Search

Wei Luo, Svetha Venkatesh, Truyen Tran, 2014, WISE.

Towards Discovery of Influence and Personality Traits through Social Link Prediction

Svetha Venkatesh, Brett Adams, Dinh Q. Phung, 2011, ICWSM.

Large-scale Online Kernel Learning with Random Feature Reparameterization

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2017, IJCAI.

Learning Boltzmann Distance Metric for Face Recognition

Svetha Venkatesh, Truyen Tran, Dinh Q. Phung, 2012, 2012 IEEE International Conference on Multimedia and Expo.

Analysis of psycholinguistic processes and topics in online autism communities

Svetha Venkatesh, Dinh Q. Phung, Thin Nguyen, 2013, 2013 IEEE International Conference on Multimedia and Expo (ICME).

Learning Multifaceted Latent Activities from Heterogeneous Mobile Data

Mohan Kumar, Svetha Venkatesh, Dinh Q. Phung, 2016, 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

Indoor Location Prediction Using Multiple Wireless Received Signal Strengths

Svetha Venkatesh, Brett Adams, Dinh Q. Phung, 2008, AusDM.

Prediction of Age, Sentiment, and Connectivity from Social Media Text

Svetha Venkatesh, Brett Adams, Dinh Q. Phung, 2011, WISE.

Narrative structure analysis with education and training videos for e-learning

Svetha Venkatesh, Chitra Dorai, Dinh Q. Phung, 2002, Object recognition supported by user interaction for service robots.

Modelling multilevel data in multimedia: A hierarchical factor analysis approach

Svetha Venkatesh, Dinh Q. Phung, Sunil Gupta, 2014, Multimedia Tools and Applications.

Stable clinical prediction using graph support vector machines

Cheng Li, Svetha Venkatesh, Dinh Q. Phung, 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

Investigating the impacts of epilepsy on EEG-based person identification systems

Tien Pham, Wanli Ma, Dat Tran, 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

When Can Neural Networks Learn Connected Decision Regions? pdf

Trung Le, Dinh Q. Phung, Trung Le, 2019, ArXiv.

Streaming clustering with Bayesian nonparametric models

Dinh Q. Phung, Viet Huynh, 2017, Neurocomputing.

A context-sensitive device to help people with autism cope with anxiety

Svetha Venkatesh, Brett Adams, Dinh Q. Phung, 2011, CHI Extended Abstracts.

Using Shannon Entropy as EEG Signal Feature for Fast Person Identification

Tien Pham, Wanli Ma, Dat Tran, 2014, ESANN.

Discovering latent affective dynamics among individuals in online mental health-related communities

Dinh Q. Phung, Bo Dao, Svetha Venkatesh, 2016, 2016 IEEE International Conference on Multimedia and Expo (ICME).

A nonparametric Bayesian Poisson gamma model for count data

Svetha Venkatesh, Dinh Q. Phung, Sunil Kumar Gupta, 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

Regularizing Topic Discovery in EMRs with Side Information by Using Hierarchical Bayesian Models

Cheng Li, Svetha Venkatesh, Dinh Q. Phung, 2014, 2014 22nd International Conference on Pattern Recognition.

Scalable Nonparametric Bayesian Multilevel Clustering

Svetha Venkatesh, Dinh Q. Phung, Matthew D. Hoffman, 2016, UAI.

A Bayesian Nonparametric Approach to Multilevel Regression

Svetha Venkatesh, Dinh Q. Phung, Hung Hai Bui, 2015, PAKDD.

Nonnegative shared subspace learning and its application to social media retrieval

Svetha Venkatesh, Truyen Tran, Brett Adams, 2010, KDD.

Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model

Svetha Venkatesh, Dinh Q. Phung, Hung Hai Bui, 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

Clustering Induced Kernel Learning

Nhan Dam, Trung Le, Tu Dinh Nguyen, 2018, ACML.

Learning Structured Outputs from Partial Labels using Forest Ensemble pdf

Svetha Venkatesh, Truyen Tran, Dinh Q. Phung, 2014, ArXiv.

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

Tu Dinh Nguyen, Dinh Q. Phung, Hung Vu, 2018, ArXiv.

A Bayesian Nonparametric Joint Factor Model for Learning Shared and Individual Subspaces from Multiple Data Sources

Svetha Venkatesh, Dinh Q. Phung, Sunil Kumar Gupta, 2012, SDM.

Interactive browsing system for anomaly video surveillance

Svetha Venkatesh, Dinh Q. Phung, Sunil Gupta, 2013, 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing.

Stabilizing Sparse Cox Model using Clinical Structures in Electronic Medical Records pdf

Svetha Venkatesh, Truyen Tran, Dinh Q. Phung, 2014, ArXiv.

A Framework for Mixed-Type Multioutcome Prediction With Applications in Healthcare

Svetha Venkatesh, Dinh Q. Phung, Sunil Gupta, 2017, IEEE Journal of Biomedical and Health Informatics.

Ordinal Boltzmann Machines for Collaborative Filtering

Svetha Venkatesh, Dinh Q. Phung, Tran The Truyen, 2009, UAI.

Theoretical Perspective of Deep Domain Adaptation pdf

Trung Le, Dinh Q. Phung, Khanh Nguyen, 2018, ArXiv.

Collaborating Differently on Different Topics: A Multi-Relational Approach to Multi-Task Learning

Svetha Venkatesh, Dinh Q. Phung, Santu Rana, 2015, PAKDD.

Stabilizing l1-norm prediction models by supervised feature grouping

Svetha Venkatesh, Dinh Q. Phung, Sunil Kumar Gupta, 2016, J. Biomed. Informatics.

Affective, Linguistic and Topic Patterns in Online Autism Communities

Svetha Venkatesh, Dinh Q. Phung, Thin Nguyen, 2014, WISE.

Stable Feature Selection with Support Vector Machines

Svetha Venkatesh, Dinh Q. Phung, Sunil Kumar Gupta, 2015, Australasian Conference on Artificial Intelligence.

Choice by Elimination via Deep Neural Networks pdf

Svetha Venkatesh, Truyen Tran, Dinh Q. Phung, 2016, ArXiv.

Learning Parts-based Representations with Nonnegative Restricted Boltzmann Machine

Svetha Venkatesh, Truyen Tran, Tu Dinh Nguyen, 2013, ACML.

A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization pdf

Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, 2018, NAACL.

Cumulative Restricted Boltzmann Machines for Ordinal Matrix Data Analysis pdf

Svetha Venkatesh, Truyen Tran, Dinh Q. Phung, 2014, ACML.

Data-mining twitter and the autism spectrum disorder: A Pilot study

Svetha Venkatesh, Terry Caelli, Dinh Q. Phung, 2014, 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014).

Energy-based Models for Video Anomaly Detection pdf

Svetha Venkatesh, Tu Dinh Nguyen, Dinh Q. Phung, 2017, ArXiv.

Event extraction using behaviors of sentiment signals and burst structure in social media

Svetha Venkatesh, Brett Adams, Dinh Q. Phung, 2013, Knowledge and Information Systems.

Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts pdf

Dinh Q. Phung, Hung Hai Bui, Svetha Venkatesh, 2014, ICML.

Learning Entry Profiles of Children with Autism from Multivariate Treatment Information Using Restricted Boltzmann Machines

Svetha Venkatesh, Dinh Q. Phung, Pratibha Vellanki, 2015, PAKDD Workshops.

Individualized arrhythmia detection with ECG signals from wearable devices

Wei Luo, Svetha Venkatesh, Terry Caelli, 2014, 2014 International Conference on Data Science and Advanced Analytics (DSAA).

Bayesian Multi-Hyperplane Machine for Pattern Recognition

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2018, 2018 24th International Conference on Pattern Recognition (ICPR).

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

Svetha Venkatesh, Truyen Tran, Tu Dinh Nguyen, 2013, PAKDD.

Tree-based iterated local search for Markov random fields with applications in image analysis

Svetha Venkatesh, Truyen Tran, Dinh Q. Phung, 2014, J. Heuristics.

Factorial Multi-Task Learning : A Bayesian Nonparametric Approach

Svetha Venkatesh, Dinh Q. Phung, Sunil Kumar Gupta, 2013, ICML.

Analogical-based Bayesian Optimization pdf

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2017, ArXiv.

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

Trung Le, Svetha Venkatesh, Tu Dinh Nguyen, 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).

KGAN: How to Break The Minimax Game in GAN pdf

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2017, ArXiv.

Dual Space Gradient Descent for Online Learning

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2016, NIPS.

Dual Discriminator Generative Adversarial Nets

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2017, NIPS.

A Sequential Decision Approach to Ordinal Preferences in Recommender Systems

Svetha Venkatesh, Truyen Tran, Dinh Q. Phung, 2012, AAAI.

Scalable Support Vector Machine for Semi-supervised Learning pdf

Trung Le, Van Nguyen, Dinh Q. Phung, 2016, ArXiv.

Conditional Entropy Approach to Multichannel EEG-Based Person Identification

Tien Pham, Wanli Ma, Dat Tran, 2015, CISIS-ICEUTE.

Mixed-norm sparse representation for multi view face recognition

Svetha Venkatesh, Duc-Son Pham, Wanquan Liu, 2015, Pattern Recognit..

Cognitive intervention in autism using multimedia stimulus

Svetha Venkatesh, Brett Adams, Dinh Q. Phung, 2011, MM '11.

Forecasting Patient Outflow from Wards having No Real-Time Clinical Data

Wei Luo, Svetha Venkatesh, Truyen Tran, 2016, 2016 IEEE International Conference on Healthcare Informatics (ICHI).

Visual Object Clustering via Mixed-Norm Regularization

Svetha Venkatesh, Duc-Son Pham, Wanquan Liu, 2015, 2015 IEEE Winter Conference on Applications of Computer Vision.

Neural Choice by Elimination via Highway Networks

Svetha Venkatesh, Truyen Tran, Dinh Q. Phung, 2016, PAKDD Workshops.

On Efficient Multilevel Clustering via Wasserstein Distances pdf

Nhan Dam, Dinh Q. Phung, XuanLong Nguyen, 2019, ArXiv.

Learning Generative Adversarial Networks from Multiple Data Sources

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2019, IJCAI.

A Sentiment-Aware Approach to Community Formation in Social Media

Svetha Venkatesh, Brett Adams, Dinh Q. Phung, 2021, ICWSM.

Discriminative Bayesian Nonparametric Clustering

Trung Le, Dinh Q. Phung, Vu Nguyen, 2017, IJCAI.

Introduction: special issue of selected papers from ACML 2014

Zhi-Hua Zhou, Hang Li, Tu Bao Ho, 2016, Machine Learning.

Classification and Pattern Discovery of Mood in Weblogs

Svetha Venkatesh, Truyen Tran, Brett Adams, 2010, PAKDD.

Faster training of very deep networks via p-norm gates

Svetha Venkatesh, Truyen Tran, Dinh Q. Phung, 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

Effective sparse imputation of patient conditions in electronic medical records for emergency risk predictions

Svetha Venkatesh, Dinh Q. Phung, Sunil Gupta, 2017, Knowledge and Information Systems.

Probabilistic Multilevel Clustering via Composite Transportation Distance

Michael I. Jordan, Dinh Q. Phung, Nhat Ho, 2018, AISTATS.

Using linguistic and topic analysis to classify sub-groups of online depression communities

Dinh Q. Phung, Svetha Venkatesh, Helen Christensen, 2015, Multimedia Tools and Applications.

Investigating the Impacts of Brain Conditions on EEG-Based Person Identification

Tien Pham, Wanli Ma, Dat Tran, 2015, CISIS-ICEUTE.

A Study on the Feasibility of Using EEG Signals for Authentication Purpose

Tien Pham, Wanli Ma, Dat Tran, 2013, ICONIP.

Computable social patterns from sparse sensor data

Svetha Venkatesh, Brett Adams, Dinh Q. Phung, 2008, LocWeb.

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

Geoffrey I. Webb, Trung Le, Tu Dinh Nguyen, 2018, KDD.

Mixed-Variate Restricted Boltzmann Machines pdf

Svetha Venkatesh, Truyen Tran, Dinh Q. Phung, 2014, ACML.

MCMC for Hierarchical Semi-Markov Conditional Random Fields pdf

Svetha Venkatesh, Truyen Tran, Dinh Q. Phung, 2009, NIPS 2009.

Clustering for point pattern data

Dinh Q. Phung, Ba-Tuong Vo, Ba-Ngu Vo, 2017, 2016 23rd International Conference on Pattern Recognition (ICPR).

Fast One-Class Support Vector Machine for Novelty Detection

Trung Le, Svetha Venkatesh, Dinh Q. Phung, 2015, PAKDD.

Funniest thing i've seen since [href=//flic.kr/p/KGEGB]: shifting perspectives from multimedia artefacts to utterances

Svetha Venkatesh, Brett Adams, Dinh Q. Phung, 2012, SAM '12.

Autism Blogs: Expressed Emotion, Language Styles and Concerns in Personal and Community Settings

Svetha Venkatesh, Dinh Q. Phung, Thin Nguyen, 2015, IEEE Transactions on Affective Computing.

Content Structure Discovery in Educational Videos Using Shared Structures in the Hierarchical Hidden Markov Models

Svetha Venkatesh, Dinh Q. Phung, Hung Hai Bui, 2004, SSPR/SPR.

Connectivity, Online Social Capital, and Mood: A Bayesian Nonparametric Analysis

Svetha Venkatesh, Dinh Q. Phung, Sunil Kumar Gupta, 2013, IEEE Transactions on Multimedia.

Online Social Capital: Mood, Topical and Psycholinguistic Analysis

Svetha Venkatesh, Dinh Q. Phung, Bo Dao, 2021, ICWSM.

Automatically learning structural units in educational videos with the hierarchical hidden Markov models

Svetha Venkatesh, Dinh Q. Phung, Hung Hai Bui, 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

Analysis of circadian rhythms from online communities of individuals with affective disorders

Svetha Venkatesh, Dinh Q. Phung, Bo Dao, 2014, 2014 International Conference on Data Science and Advanced Analytics (DSAA).

Multiple task transfer learning with small sample sizes

Svetha Venkatesh, Dinh Q. Phung, Sunil Gupta, 2015, Knowledge and Information Systems.

A probabilistic model with parsinomious representation for sensor fusion in recognizing activity in pervasive environment

Dinh Q. Phung, Dung T. Tran, 2006, 18th International Conference on Pattern Recognition (ICPR'06).

Animal Recognition and Identification with Deep Convolutional Neural Networks for Automated Wildlife Monitoring

Tu Dinh Nguyen, Dinh Q. Phung, Hung Nguyen, 2017, 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

Effective Identification of Similar Patients Through Sequential Matching over ICD Code Embedding

Wei Luo, Svetha Venkatesh, Dinh Q. Phung, 2018, Journal of Medical Systems.

EEG-Based User Authentication Using Artifacts

Tien Pham, Wanli Ma, Dat Tran, 2014, SOCO-CISIS-ICEUTE.

Multi-factor EEG-based user authentication

Tien Pham, Wanli Ma, Dat Tran, 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

TOBY: early intervention in autism through technology

Svetha Venkatesh, Brett Adams, Dinh Q. Phung, 2013, CHI.

SECC: Simultaneous extraction of context and community from pervasive signals

Flora D. Salim, Dinh Q. Phung, Nguyen Cong Thuong, 2016, 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom).

Supervised Restricted Boltzmann Machines

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2017, UAI.

Prediction of Population Health Indices from Social Media using Kernel-based Textual and Temporal Features

John Yearwood, Duc Thanh Nguyen, Dinh Q. Phung, 2017, WWW.

Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter

Ba-Ngu Vo, Dinh Q. Phung, Ba-Tuong Vo, 2013, IEEE Transactions on Signal Processing.

Learning From Ordered Sets and Applications in Collaborative Ranking pdf

Svetha Venkatesh, Truyen Tran, Dinh Q. Phung, 2014, ACML.

Pervasive multimedia for autism intervention

Svetha Venkatesh, Brett Adams, Dinh Q. Phung, 2012, Pervasive Mob. Comput..

Batch Normalized Deep Boltzmann Machines

Wei Luo, Trung Le, Tu Dinh Nguyen, 2018, ACML.

Estimating Support Scores of Autism Communities in Large-Scale Web Information Systems

Svetha Venkatesh, Dinh Q. Phung, Thin Nguyen, 2017, WISE.

Multi-modal abnormality detection in video with unknown data segmentation

Svetha Venkatesh, Duc-Son Pham, Dinh Q. Phung, 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

Discovering topic structures of a temporally evolving document corpus

Svetha Venkatesh, Dinh Q. Phung, Ognjen Arandjelovic, 2015, 1512.08008.

Topic Model Kernel: An Empirical Study towards Probabilistically Reduced Features for Classification

Svetha Venkatesh, Dinh Q. Phung, Tien-Vu Nguyen, 2013, ICONIP.

Eventscapes: visualizing events over time with emotive facets

Svetha Venkatesh, Brett Adams, Dinh Q. Phung, 2011, ACM Multimedia.

Kernel-based features for predicting population health indices from geocoded social media data

John Yearwood, Duc Thanh Nguyen, Dinh Q. Phung, 2017, Decis. Support Syst..

Learning Discriminative Sequence Models from Partially Labelled Data for Activity Recognition

Svetha Venkatesh, Dinh Q. Phung, Hung Hai Bui, 2008, PRICAI.

Nonparametric discovery and analysis of learning patterns and autism subgroups from therapeutic data

Svetha Venkatesh, Dinh Q. Phung, Pratibha Vellanki, 2017, Knowledge and Information Systems.

Exploiting feature relationships towards stable feature selection

Svetha Venkatesh, Dinh Q. Phung, Sunil Kumar Gupta, 2015, 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

What shall I share and with Whom? - A Multi-Task Learning Formulation using Multi-Faceted Task Relationships

Svetha Venkatesh, Dinh Q. Phung, Santu Rana, 2015, SDM.

A predictive framework for modeling healthcare data with evolving clinical interventions

Svetha Venkatesh, Dinh Q. Phung, Santu Rana, 2015, Stat. Anal. Data Min..

Discovery of latent subcommunities in a blog's readership

Svetha Venkatesh, Brett Adams, Dinh Q. Phung, 2010, TWEB.

Keeping up with Innovation: A Predictive Framework for Modeling Healthcare Data with Evolving Clinical Interventions

Svetha Venkatesh, Dinh Q. Phung, Santu Rana, 2014, SDM.

Nonparametric discovery of movement patterns from accelerometer signals

Dinh Q. Phung, Nguyen Cong Thuong, Sunil Gupta, 2016, Pattern Recognit. Lett..

An evaluation of randomized machine learning methods for redundant data: Predicting short and medium-term suicide risk from administrative records and risk assessments pdf

Svetha Venkatesh, Truyen Tran, Dinh Q. Phung, 2016, ArXiv.

Social reader: towards browsing the social web

Svetha Venkatesh, Brett Adams, Dinh Q. Phung, 2014, Multimedia Tools and Applications.

Multi-Generator Generative Adversarial Nets pdf

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2017, ArXiv.

Improved subspace clustering via exploitation of spatial constraints

Svetha Venkatesh, Duc-Son Pham, Dinh Q. Phung, 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

Exceptional Contrast Set Mining: Moving Beyond the Deluge of the Obvious

Wei Luo, Svetha Venkatesh, Dinh Q. Phung, 2016, Australasian Conference on Artificial Intelligence.

Probabilistic Models over Ordered Partitions with Application in Learning to Rank pdf

Svetha Venkatesh, Dinh Q. Phung, Tran The Truyen, 2010, ArXiv.

Large-scale statistical modeling of motion patterns: a Bayesian nonparametric approach

Svetha Venkatesh, Dinh Q. Phung, Santu Rana, 2012, ICVGIP '12.

Overcoming data scarcity of Twitter: Using tweets as bootstrap with application to autism-related topic content analysis

Svetha Venkatesh, Dinh Q. Phung, Ognjen Arandjelovic, 2015, 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

Constrained Sequence Classification for Lexical Disambiguation

Svetha Venkatesh, Dinh Q. Phung, Tran The Truyen, 2008, PRICAI.

AdaBoost.MRF: Boosted Markov Random Forests and Application to Multilevel Activity Recognition

Svetha Venkatesh, Dinh Q. Phung, Hung Hai Bui, 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

A framework for feature extraction from hospital medical data with applications in risk prediction

Wei Luo, Svetha Venkatesh, Truyen Tran, 2014, BMC Bioinformatics.

Effect of social capital on emotion, language style and latent topics in online depression community

Svetha Venkatesh, Dinh Q. Phung, Bo Dao, 2016, 2016 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future (RIVF).

MCNC: Multi-Channel Nonparametric Clustering from heterogeneous data

Svetha Venkatesh, Dinh Q. Phung, Vu Nguyen, 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

Learning Graph Representation via Frequent Subgraphs

Wei Luo, Svetha Venkatesh, Tu Dinh Nguyen, 2018, SDM.

Clustering Patient Medical Records via Sparse Subspace Representation

Svetha Venkatesh, Duc-Son Pham, Dinh Q. Phung, 2013, PAKDD.

Discriminative Cues for Different Stages of Smoking Cessation in Online Community

John Yearwood, Svetha Venkatesh, Dinh Q. Phung, 2016, WISE.

Textual Cues for Online Depression in Community and Personal Settings

Svetha Venkatesh, Dinh Q. Phung, Thin Nguyen, 2016, ADMA.

On Scalable Variant of Wasserstein Barycenter pdf

Makoto Yamada, Dinh Q. Phung, Nhat Ho, 2019, ArXiv.

Conditionally Dependent Dirichlet Processes for Modelling Naturally Correlated Data Sources

Dinh Q. Phung, X. Nguyen, S. Venkatesh, 2013 .

Predicting healthcare trajectories from medical records: A deep learning approach

Truyen Tran, Dinh Q. Phung, Svetha Venkatesh, 2017, J. Biomed. Informatics.

Graph-based Kernel Machine for Scalable Semi-supervised Learning

Dinh Q. Phung, Vu Nguyen, Khanh-Duy Nguyen, 2016 .

Learning Rank Functionals: An Empirical Study

Dinh Q. Phung, S. Venkatesh, T. Tran, 2014, ArXiv.

A study on the stability of EEG signals for user authentication

Wanli Ma, Dinh Q. Phung, Dinh Phung, 2015, 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER).

A Universal Self-Attention Graph Neural Network.

Dinh Q. Phung, D. Q. Nguyen, T. Nguyen, 2019 .

Bayesian Learning with Unbounded Capacity from Heterogeneous and Set-Valued Data

Dinh Q. Phung, 2018 .

Dirichlet Process Mixture Models with Pairwise Constraints for Data Clustering

Dinh Q. Phung, S. Rana, S. Venkatesh, 2016 .

Sparkling Vector Machines

Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, 2015 .

Supplementary Material for Bayesian Nonparametric Multilevel Clustering with Contexts

Dinh Q. Phung, X. Nguyen, S. Venkatesh, 2014 .

Effect of COVID-19 pandemic lockdowns on planned cancer surgery for 15 tumour types in 61 countries: an international, prospective, cohort study

Dinh Q. Phung, C. A. Shaw, M. Kumar, 2021, The Lancet Oncology.

Supplementary material for “Scalable Nonparametric Bayesian Multilevel Clustering”

Dinh Q. Phung, X. Nguyen, S. Venkatesh, 2016 .

The Monash Autism-ADHD genetics and neurodevelopment (MAGNET) project design and methodologies: a dimensional approach to understanding neurobiological and genetic aetiology

Dinh Q. Phung, J. Buitelaar, M. Bellgrove, 2021, Molecular Autism.

Tree-based iterated local search for Markov random fields with applications in image analysis

Dinh Q. Phung, T. Tran, S. Venkatesh, 2014, Journal of Heuristics.

Mood sensing from social media texts and its applications

Dinh Q. Phung, S. Venkatesh, Thin Nguyen, 2014, Knowledge and Information Systems.

Thurstonian Boltzmann Machines : Learning from Multiple Inequalities Supplementary Material

Dinh Q. Phung, S. Venkatesh, T. Tran, 2013 .

Learning How to Active Learn by Dreaming

Gholamreza Haffari, Dinh Q. Phung, Dinh Phung, 2019, ACL.

BYou Tube and I Find(VPersonalizing Multimedia Content Access To help users find material of interest in large multimedia collections, researchers are exploring techniques to allow authors to easily capture effective descriptive data, and methods for modeling user preferences.

Svetha Venkatesh, Chitra Dorai, Brett Adams, 2008 .

Learning Latent Activities from Social Signals with Hierarchical Dirichlet Processes

Dinh Q. Phung, Sunil Gupta, Svetha Venkatesh, 2014 .

Pair-based Uncertainty and Diversity Promoting Early Active Learning for Person Re-identification

Xiaoqin Zhang, Xiaojun Chang, Yi Yang, 2020, ACM Trans. Intell. Syst. Technol..

Dirichlet Process Mixture Models with Pairwise Constraints for Data Clustering

Dinh Q. Phung, S. Rana, Cheng Li, 2016, Annals of Data Science.

Continuous discovery of co-location contexts from Bluetooth data

Svetha Venkatesh, Dinh Q. Phung, Dinh Phung, 2015 .

A Relational Memory-based Embedding Model for Triple Classification and Search Personalization

Dinh Q. Phung, D. Q. Nguyen, T. Nguyen, 2019, ACL.

Maximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection

Tuan Nguyen, Trung Le, Lizhen Qu, 2019, ICLR.

An effective spatial-temporal attention based neural network for traffic flow prediction

Hai Le Vu, Zhiyuan Liu, Dinh Q. Phung, 2019, Transportation Research Part C: Emerging Technologies.

Topic Modelling Meets Deep Neural Networks: A Survey pdf

Dinh Q. Phung, Lan Du, Dinh Phung, 2021, IJCAI.

Robust Anomaly Detection in Videos Using Multilevel Representations

Wei Luo, Trung Le, Dinh Q. Phung, 2019, AAAI.

Deep Domain Adaptation for Vulnerable Code Function Identification

Lizhen Qu, Dinh Q. Phung, Paul Montague, 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

Understanding and Achieving Efficient Robustness with Adversarial Contrastive Learning

Trung Le, Dinh Q. Phung, Seyit Camtepe, 2021, ArXiv.

Model-Based Multiple Instance Learning

Dinh Q. Phung, B. Vo, B. Vo, 2017, ArXiv.

Tree-Wasserstein Barycenter for Large-Scale Multilevel Clustering and Scalable Bayes.

Makoto Yamada, Dinh Q. Phung, Nhat Ho, 2019, 1910.04483.

Multiple task transfer learning with small sample sizes

Dinh Q. Phung, S. Gupta, S. Venkatesh, 2016, Knowledge and Information Systems.

Latent sentiment topic modelling and nonparametric discovery of online mental health-related communities

Dinh Q. Phung, S. Venkatesh, Thin Nguyen, 2017, International Journal of Data Science and Analytics.

A convolutional neural network-based model for knowledge base completion and its application to search personalization

Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, 2019, Semantic Web.

Social reader: towards browsing the social web

Dinh Q. Phung, S. Venkatesh, Brett Adams, 2014, Multimedia tools and applications.

On Efficient Multilevel Clustering via Wasserstein Distances

Dinh Q. Phung, Nhat Ho, X. Nguyen, 2019, J. Mach. Learn. Res..

Parameterized Rate-Distortion Stochastic Encoder

Trung Le, Dinh Q. Phung, Dinh Phung, 2020, ICML.

TOBY play-pad application to teach children with ASD – A pilot trial

Svetha Venkatesh, Dinh Q. Phung, Thi Duong, 2013, Developmental neurorehabilitation.

OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation

Trung Le, Dinh Q. Phung, Michael Papasimeon, 2020, 2020 International Joint Conference on Neural Networks (IJCNN).

Relational Memory-based Knowledge Graph Embedding pdf

Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Q. Phung, 2019, ArXiv.

Unsupervised Universal Self-Attention Network for Graph Classification pdf

Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Q. Phung, 2019, ArXiv.

A Capsule Network-based Model for Learning Node Embeddings

Dat Quoc Nguyen, Dinh Q. Phung, D. Q. Nguyen, 2019, CIKM.

Improving Adversarial Robustness by Enforcing Local and Global Compactness

Trung Le, Dinh Q. Phung, Tamas Abraham, 2020, ECCV.

Under Consideration for Publication in Knowledge and Information Systems Discovering Topic Structures of a Temporally Evolving Document Corpus

Dinh Q. Phung, S. Venkatesh, Ognjen Arandjelovic, 2022 .

Universal Self-Attention Network for Graph Classification

Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Q. Phung, 2019 .

Data clustering using side information dependent Chinese restaurant processes

Dinh Q. Phung, S. Venkatesh, Cheng Li, 2016, Knowledge and Information Systems.

Dual-Component Deep Domain Adaptation: A New Approach for Cross Project Software Vulnerability Detection

Dinh Q. Phung, Dinh Phung, Trung Thanh Le, 2020, PAKDD.

Under Consideration for Publication in Knowledge and Information Systems Discovering Topic Structures of a Temporally Evolving Document Corpus

Dinh Q. Phung, S. Venkatesh, Ognjen Arandjelovic, 2022 .

Model-Based Multiple Instance Learning

Dinh Q. Phung, B. Vo, B. Vo, 2017, ArXiv.

Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions pdf

He Zhao, Dinh Q. Phung, Tamas Abraham, 2019, ArXiv.

A Self-Attention Network based Node Embedding Model pdf

Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Q. Phung, 2020, ECML/PKDD.

Detection of cross-channel anomalies

Dinh Q. Phung, S. Venkatesh, Duc-Son Pham, 2013, Knowledge and Information Systems.

Predicting healthcare trajectories from medical records: A deep learning approach

Dinh Q. Phung, S. Venkatesh, T. Tran, 2017, J. Biomed. Informatics.

A matrix factorization framework for jointly analyzing multiple nonnegative data sources

Svetha Venkatesh, Brett Adams, Dinh Q. Phung, 2011, SDM 2011.

BoMb-OT: On Batch of Mini-batches Optimal Transport

Dinh Q. Phung, Nhat Ho, H. Bui, 2021, ArXiv.

Advances in Knowledge Discovery and Data Mining

Geoffrey I. Webb, Vincent S. Tseng, Dinh Q. Phung, 2018, Lecture Notes in Computer Science.

Regularized nonnegative shared subspace learning

Dinh Q. Phung, S. Gupta, S. Venkatesh, 2011, Data Mining and Knowledge Discovery.

Text Generation with Deep Variational GAN pdf

Trung Le, Dinh Q. Phung, Michael Papasimeon, 2021, ArXiv.