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ORCIDDinh Q. Phung
Dinh Quoc Phung – Dinh Phung 0001
Person information
- affiliation: Monash University, Melbourne, Victoria, Australia
- affiliation: Deakin University, Center of Pattern Recognition and Data Analytics, Australia
Other persons with the same name
- Dinh Phung — disambiguation page
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2020 – today
- 2021
[i67]Anh Bui, Trung Le, He Zhao, Paul Montague, Seyit Camtepe, Dinh Phung:
Understanding and Achieving Efficient Robustness with Adversarial Contrastive Learning. CoRR abs/2101.10027 (2021)- [i66]Khai Nguyen, Quoc Nguyen, Nhat Ho, Tung Pham, Hung Bui, Dinh Phung, Trung Le:
BoMb-OT: On Batch of Mini-batches Optimal Transport. CoRR abs/2102.05912 (2021) - [i65]Dai Quoc Nguyen, Vinh Tong, Dinh Phung, Dat Quoc Nguyen:
Node Co-occurrence based Graph Neural Networks for Knowledge Graph Link Prediction. CoRR abs/2104.07396 (2021) - 2020
- [j57]Thin Nguyen
, Mark E. Larsen, Bridianne O'Dea, Hung Nguyen, Duc Thanh Nguyen, John Yearwood, Dinh Quoc Phung, Svetha Venkatesh, Helen Christensen:
Using spatiotemporal distribution of geocoded Twitter data to predict US county-level health indices. Future Gener. Comput. Syst. 110: 620-628 (2020) - [j56]Wenhe Liu, Xiaojun Chang, Ling Chen, Dinh Phung, Xiaoqin Zhang, Yi Yang, Alexander G. Hauptmann:
Pair-based Uncertainty and Diversity Promoting Early Active Learning for Person Re-identification. ACM Trans. Intell. Syst. Technol. 11(2): 21:1-21:15 (2020) - [c188]Dai Quoc Nguyen, Tuan Nguyen, Dinh Phung:
A Relational Memory-based Embedding Model for Triple Classification and Search Personalization. ACL 2020: 3429-3435 - [c187]Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Phung:
A Capsule Network-based Model for Learning Node Embeddings. CIKM 2020: 3313-3316 - [c186]Quan Hung Tran, Nhan Dam, Tuan Manh Lai, Franck Dernoncourt, Trung Le, Nham Le, Dinh Phung:
Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering. COLING 2020: 5205-5210 - [c185]Anh Bui, Trung Le, He Zhao, Paul Montague, Olivier Y. DeVel, Tamas Abraham, Dinh Q. Phung:
Improving Adversarial Robustness by Enforcing Local and Global Compactness. ECCV (27) 2020: 209-223 - [c184]Quan Hoang, Trung Le, Dinh Phung:
Parameterized Rate-Distortion Stochastic Encoder. ICML 2020: 4293-4303 - [c183]Nhan Dam, Trung Le, Viet Huynh, Dinh Phung:
Stein Variational Gradient Descent with Variance Reduction. IJCNN 2020: 1-8 - [c182]Mahmoud Hossam, Trung Le, Viet Huynh, Michael Papasimeon, Dinh Phung:
OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation. IJCNN 2020: 1-8 - [c181]Van Nguyen, Trung Le, Tue Le, Khanh Nguyen, Olivier Y. de Vel, Paul Montague, Dinh Phung:
Code Pointer Network for Binary Function Scope Identification. IJCNN 2020: 1-7 - [c180]Viet Huynh, He Zhao, Dinh Phung:
OTLDA: A Geometry-aware Optimal Transport Approach for Topic Modeling. NeurIPS 2020 - [c179]Tuan Nguyen, Trung Le, Khanh Nguyen, Olivier Y. de Vel, Paul Montague, John C. Grundy, Dinh Phung:
Deep Cost-Sensitive Kernel Machine for Binary Software Vulnerability Detection. PAKDD (2) 2020: 164-177 - [c178]Van Nguyen, Trung Le, Olivier Y. de Vel, Paul Montague, John C. Grundy, Dinh Phung:
Dual-Component Deep Domain Adaptation: A New Approach for Cross Project Software Vulnerability Detection. PAKDD (1) 2020: 699-711 - [c177]Van Nguyen, Trung Le, Tue Le, Khanh Nguyen, Olivier Y. de Vel, Paul Montague, John C. Grundy, Dinh Phung:
Code Action Network for Binary Function Scope Identification. PAKDD (1) 2020: 712-725 - [c176]Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung:
A Self-attention Network Based Node Embedding Model. ECML/PKDD (3) 2020: 364-377 - [i64]Mahmoud Hossam, Trung Le, Viet Huynh, Michael Papasimeon, Dinh Phung:
OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation. CoRR abs/2004.07534 (2020) - [i63]Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung:
A Self-Attention Network based Node Embedding Model. CoRR abs/2006.12100 (2020) - [i62]Anh Bui, Trung Le, He Zhao, Paul Montague, Olivier Y. DeVel, Tamas Abraham, Dinh Phung:
Improving Adversarial Robustness by Enforcing Local and Global Compactness. CoRR abs/2007.05123 (2020) - [i61]Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung:
Quaternion Graph Neural Networks. CoRR abs/2008.05089 (2020) - [i60]He Zhao, Dinh Phung, Viet Huynh, Trung Le, Wray L. Buntine:
Neural Sinkhorn Topic Model. CoRR abs/2008.13537 (2020) - [i59]Anh Bui, Trung Le, He Zhao, Paul Montague, Olivier Y. DeVel, Tamas Abraham, Dinh Phung:
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness. CoRR abs/2009.09612 (2020) - [i58]Dai Quoc Nguyen, Thanh Vu, Tu Dinh Nguyen, Dinh Phung:
QuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings. CoRR abs/2009.12517 (2020) - [i57]He Zhao, Trung Le, Paul Montague, Olivier Y. de Vel, Tamas Abraham, Dinh Phung:
Towards Understanding Pixel Vulnerability under Adversarial Attacks for Images. CoRR abs/2010.06131 (2020) - [i56]Mahmoud Hossam, Trung Le, He Zhao, Dinh Phung:
Explain2Attack: Text Adversarial Attacks via Cross-Domain Interpretability. CoRR abs/2010.06812 (2020) - [i55]Quan Hung Tran, Nhan Dam, Tuan Manh Lai, Franck Dernoncourt, Trung Le, Nham Le, Dinh Phung:
Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering. CoRR abs/2011.03096 (2020)
2010 – 2019
- 2019
- [j55]Trung Le, Khanh Nguyen, Vu Nguyen, Tu Dinh Nguyen, Dinh Q. Phung:
GoGP: scalable geometric-based Gaussian process for online regression. Knowl. Inf. Syst. 60(1): 197-226 (2019) - [j54]Dai Quoc Nguyen, Dat Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung:
A convolutional neural network-based model for knowledge base completion and its application to search personalization. Semantic Web 10(5): 947-960 (2019) - [c175]Hung Vu, Tu Dinh Nguyen, Trung Le, Wei Luo, Dinh Q. Phung:
Robust Anomaly Detection in Videos Using Multilevel Representations. AAAI 2019: 5216-5223 - [c174]Nhat Ho, Viet Huynh, Dinh Q. Phung, Michael I. Jordan:
Probabilistic Multilevel Clustering via Composite Transportation Distance. AISTATS 2019: 3149-3157 - [c173]Tue Le, Tuan Nguyen, Trung Le, Dinh Q. Phung, Paul Montague, Olivier Y. de Vel, Lizhen Qu:
Maximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection. ICLR (Poster) 2019 - [c172]Nhan Dam, Quan Hoang, Trung Le, Tu Dinh Nguyen, Hung Bui, Dinh Phung:
Three-Player Wasserstein GAN via Amortised Duality. IJCAI 2019: 2202-2208 - [c171]Trung Le, Quan Hoang, Hung Vu, Tu Dinh Nguyen, Hung Bui, Dinh Q. Phung:
Learning Generative Adversarial Networks from Multiple Data Sources. IJCAI 2019: 2823-2829 - [c170]Van Nguyen, Trung Le, Tue Le, Khanh Nguyen, Olivier Y. DeVel, Paul Montague, Lizhen Qu, Dinh Q. Phung:
Deep Domain Adaptation for Vulnerable Code Function Identification. IJCNN 2019: 1-8 - [c169]Dai Quoc Nguyen, Thanh Vu, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Q. Phung:
A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization. NAACL-HLT (1) 2019: 2180-2189 - [i54]Trung Le, Dinh Q. Phung:
When Can Neural Networks Learn Connected Decision Regions? CoRR abs/1901.08710 (2019) - [i53]Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Q. Phung:
Relational Memory-based Knowledge Graph Embedding. CoRR abs/1907.06080 (2019) - [i52]Viet Huynh, Nhat Ho, Nhan Dam, XuanLong Nguyen, Mikhail Yurochkin, Hung Bui, Dinh Q. Phung:
On Efficient Multilevel Clustering via Wasserstein Distances. CoRR abs/1909.08787 (2019) - [i51]Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung:
Unsupervised Universal Self-Attention Network for Graph Classification. CoRR abs/1909.11855 (2019) - [i50]He Zhao, Trung Le, Paul Montague, Olivier Y. de Vel, Tamas Abraham, Dinh Phung:
Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions. CoRR abs/1910.01329 (2019) - [i49]Tam Le, Viet Huynh, Nhat Ho, Dinh Q. Phung, Makoto Yamada:
On Scalable Variant of Wasserstein Barycenter. CoRR abs/1910.04483 (2019) - [i48]Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Phung:
A Capsule Network-based Model for Learning Node Embeddings. CoRR abs/1911.04822 (2019) - 2018
- [j53]Dang Nguyen, Wei Luo, Svetha Venkatesh, Dinh Q. Phung:
Effective Identification of Similar Patients Through Sequential Matching over ICD Code Embedding. J. Medical Syst. 42(5): 94:1-94:13 (2018) - [j52]Thin Nguyen, Svetha Venkatesh, Dinh Q. Phung:
Academia versus social media: A psycho-linguistic analysis. J. Comput. Sci. 25: 228-237 (2018) - [j51]Adham Beykikhoshk, Ognjen Arandjelovic, Dinh Q. Phung, Svetha Venkatesh:
Discovering topic structures of a temporally evolving document corpus. Knowl. Inf. Syst. 55(3): 599-632 (2018) - [j50]Dang Nguyen, Wei Luo, Dinh Q. Phung, Svetha Venkatesh:
LTARM: A novel temporal association rule mining method to understand toxicities in a routine cancer treatment. Knowl. Based Syst. 161: 313-328 (2018) - [j49]Ba-Ngu Vo, Nhan Dam, Dinh Q. Phung, Quang N. Tran, Ba-Tuong Vo:
Model-based learning for point pattern data. Pattern Recognit. 84: 136-151 (2018) - [c168]Khanh Nguyen, Nhan Dam, Trung Le, Tu Dinh Nguyen, Dinh Q. Phung:
Clustering Induced Kernel Learning. ACML 2018: 129-144 - [c167]Hung Vu, Tu Dinh Nguyen, Trung Le, Wei Luo, Dinh Q. Phung:
Batch Normalized Deep Boltzmann Machines. ACML 2018: 359-374 - [c166]Quan Hoang, Tu Dinh Nguyen, Trung Le, Dinh Q. Phung:
MGAN: Training Generative Adversarial Nets with Multiple Generators. ICLR (Poster) 2018 - [c165]Khanh Nguyen, Trung Le, Tu Dinh Nguyen, Dinh Q. Phung:
Bayesian Multi-Hyperplane Machine for Pattern Recognition. ICPR 2018: 609-614 - [c164]Trung Le, Hung Vu, Tu Dinh Nguyen, Dinh Q. Phung:
Geometric Enclosing Networks. IJCAI 2018: 2355-2361 - [c163]Khanh Nguyen, Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, Geoffrey I. Webb:
Robust Bayesian Kernel Machine via Stein Variational Gradient Descent for Big Data. KDD 2018: 2003-2011 - [c162]Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Q. Phung:
A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network. NAACL-HLT (2) 2018: 327-333 - [c161]Dang Nguyen, Wei Luo, Tu Dinh Nguyen, Svetha Venkatesh, Dinh Q. Phung:
Sqn2Vec: Learning Sequence Representation via Sequential Patterns with a Gap Constraint. ECML/PKDD (2) 2018: 569-584 - [c160]Dang Nguyen, Wei Luo, Tu Dinh Nguyen, Svetha Venkatesh, Dinh Q. Phung:
Learning Graph Representation via Frequent Subgraphs. SDM 2018: 306-314 - [c159]Hung Nguyen, Van Nguyen, Thin Nguyen, Mark E. Larsen, Bridianne O'Dea, Duc Thanh Nguyen, Trung Le, Dinh Q. Phung, Svetha Venkatesh, Helen Christensen:
Jointly Predicting Affective and Mental Health Scores Using Deep Neural Networks of Visual Cues on the Web. WISE (2) 2018: 100-110 - [e4]Dinh Q. Phung, Vincent S. Tseng, Geoffrey I. Webb, Bao Ho, Mohadeseh Ganji, Lida Rashidi:
Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part I. Lecture Notes in Computer Science 10937, Springer 2018, ISBN 978-3-319-93033-6 [contents] - [e3]Dinh Q. Phung, Vincent S. Tseng, Geoffrey I. Webb, Bao Ho, Mohadeseh Ganji, Lida Rashidi:
Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part II. Lecture Notes in Computer Science 10938, Springer 2018, ISBN 978-3-319-93036-7 [contents] - [e2]Dinh Q. Phung, Vincent S. Tseng, Geoffrey I. Webb, Bao Ho, Mohadeseh Ganji, Lida Rashidi:
Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part III. Lecture Notes in Computer Science 10939, Springer 2018, ISBN 978-3-319-93039-8 [contents] - [i47]Hung Vu, Tu Dinh Nguyen, Dinh Q. Phung:
Detection of Unknown Anomalies in Streaming Videos with Generative Energy-based Boltzmann Models. CoRR abs/1805.01090 (2018) - [i46]Dai Quoc Nguyen, Thanh Vu, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Q. Phung:
A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization. CoRR abs/1808.04122 (2018) - [i45]Nhat Ho, Viet Huynh, Dinh Q. Phung, Michael I. Jordan:
Probabilistic Multilevel Clustering via Composite Transportation Distance. CoRR abs/1810.11911 (2018) - [i44]Trung Le, Khanh Nguyen, Dinh Q. Phung:
Theoretical Perspective of Deep Domain Adaptation. CoRR abs/1811.06199 (2018) - 2017
- [j48]Truyen Tran, Dinh Quoc Phung, Hung Hai Bui, Svetha Venkatesh:
Hierarchical semi-Markov conditional random fields for deep recursive sequential data. Artif. Intell. 246: 53-85 (2017) - [j47]Thin Nguyen, Mark E. Larsen, Bridianne O'Dea, Duc Thanh Nguyen, John Yearwood, Dinh Q. Phung, Svetha Venkatesh, Helen Christensen:
Kernel-based features for predicting population health indices from geocoded social media data. Decis. Support Syst. 102: 22-31 (2017) - [j46]Bo Dao, Thin Nguyen, Svetha Venkatesh, Dinh Q. Phung:
Latent sentiment topic modelling and nonparametric discovery of online mental health-related communities. Int. J. Data Sci. Anal. 4(3): 209-231 (2017) - [j45]Thin Nguyen, Mark E. Larsen, Bridianne O'Dea, Dinh Q. Phung, Svetha Venkatesh, Helen Christensen:
Estimation of the prevalence of adverse drug reactions from social media. Int. J. Medical Informatics 102: 130-137 (2017) - [j44]Viet Huynh, Dinh Q. Phung:
Streaming clustering with Bayesian nonparametric models. Neurocomputing 258: 52-62 (2017) - [j43]Trang Pham, Truyen Tran, Dinh Q. Phung, Svetha Venkatesh:
Predicting healthcare trajectories from medical records: A deep learning approach. J. Biomed. Informatics 69: 218-229 (2017) - [j42]Trung Le, Tu Dinh Nguyen, Vu Nguyen, Dinh Q. Phung:
Approximation Vector Machines for Large-scale Online Learning. J. Mach. Learn. Res. 18: 111:1-111:55 (2017) - [j41]Pratibha Vellanki, Thi V. Duong, Sunil Gupta, Svetha Venkatesh, Dinh Q. Phung:
Nonparametric discovery and analysis of learning patterns and autism subgroups from therapeutic data. Knowl. Inf. Syst. 51(1): 127-157 (2017) - [j40]Budhaditya Saha, Sunil Gupta, Dinh Q. Phung, Svetha Venkatesh:
Effective sparse imputation of patient conditions in electronic medical records for emergency risk predictions. Knowl. Inf. Syst. 53(1): 179-206 (2017) - [j39]Thin Nguyen, Bridianne O'Dea, Mark E. Larsen, Dinh Q. Phung, Svetha Venkatesh, Helen Christensen:
Using linguistic and topic analysis to classify sub-groups of online depression communities. Multim. Tools Appl. 76(8): 10653-10676 (2017) - [j38]Nguyen Cong Thuong, Vu Nguyen, Flora D. Salim, Duc V. Le, Dinh Q. Phung:
A Simultaneous Extraction of Context and Community from pervasive signals using nested Dirichlet process. Pervasive Mob. Comput. 38: 396-417 (2017) - [j37]Budhaditya Saha, Sunil Gupta, Dinh Q. Phung, Svetha Venkatesh:
A Framework for Mixed-Type Multioutcome Prediction With Applications in Healthcare. IEEE J. Biomed. Health Informatics 21(4): 1182-1191 (2017) - [c158]Trang Pham, Truyen Tran, Dinh Q. Phung, Svetha Venkatesh:
Column Networks for Collective Classification. AAAI 2017: 2485-2491 - [c157]Hung Nguyen, Sarah J. Maclagan, Tu Dinh Nguyen, Thin Nguyen, Paul Flemons, Kylie Andrews, Euan G. Ritchie, Dinh Q. Phung:
Animal Recognition and Identification with Deep Convolutional Neural Networks for Automated Wildlife Monitoring. DSAA 2017: 40-49 - [c156]Nhan Dam, Dinh Q. Phung, Ba-Ngu Vo, Viet Huynh:
Forward-Backward Smoothing for Hidden Markov Models of Point Pattern Data. DSAA 2017: 252-261 - [c155]Trung Le, Khanh Nguyen, Vu Nguyen, Tu Dinh Nguyen, Dinh Q. Phung:
GoGP: Fast Online Regression with Gaussian Processes. ICDM 2017: 257-266 - [c154]Nhat Ho, XuanLong Nguyen, Mikhail Yurochkin, Hung Hai Bui, Viet Huynh, Dinh Q. Phung:
Multilevel Clustering via Wasserstein Means. ICML 2017: 1501-1509 - [c153]Tu Dinh Nguyen, Trung Le, Hung Bui, Dinh Q. Phung:
Large-scale Online Kernel Learning with Random Feature Reparameterization. IJCAI 2017: 2543-2549 - [c152]Vu Nguyen, Dinh Q. Phung, Trung Le, Hung Bui:
Discriminative Bayesian Nonparametric Clustering. IJCAI 2017: 2550-2556 - [c151]Tu Dinh Nguyen, Trung Le, Hung Vu, Dinh Q. Phung:
Dual Discriminator Generative Adversarial Nets. NIPS 2017: 2670-2680 - [c150]Hung Vu, Tu Dinh Nguyen, Anthony Travers, Svetha Venkatesh, Dinh Q. Phung:
Energy-Based Localized Anomaly Detection in Video Surveillance. PAKDD (1) 2017: 641-653 - [c149]Tu Dinh Nguyen, Dinh Q. Phung, Viet Huynh, Trung Le:
Supervised Restricted Boltzmann Machines. UAI 2017 - [c148]Thin Nguyen, Nguyen Hung, Svetha Venkatesh, Dinh Q. Phung:
Estimating Support Scores of Autism Communities in Large-Scale Web Information Systems. WISE (1) 2017: 347-355 - [c147]Thin Nguyen, Duc Thanh Nguyen, Mark E. Larsen, Bridianne O'Dea, John Yearwood, Dinh Q. Phung, Svetha Venkatesh, Helen Christensen:
Prediction of Population Health Indices from Social Media using Kernel-based Textual and Temporal Features. WWW (Companion Volume) 2017: 99-107 - [i43]Ba-Ngu Vo, Quang N. Tran, Dinh Q. Phung, Ba-Tuong Vo:
Model-based Classification and Novelty Detection For Point Pattern Data. CoRR abs/1701.08473 (2017) - [i42]Quang N. Tran, Ba-Ngu Vo, Dinh Q. Phung, Ba-Tuong Vo:
Clustering For Point Pattern Data. CoRR abs/1702.02262 (2017) - [i41]Ba-Ngu Vo, Dinh Q. Phung, Quang N. Tran, Ba-Tuong Vo:
Model-Based Multiple Instance Learning. CoRR abs/1703.02155 (2017) - [i40]Quang N. Tran, Ba-Ngu Vo, Dinh Q. Phung, Ba-Tuong Vo, Nguyen Cong Thuong:
Multiple Instance Learning with the Optimal Sub-Pattern Assignment Metric. CoRR abs/1703.08933 (2017) - [i39]Quan Hoang, Tu Dinh Nguyen, Trung Le, Dinh Q. Phung:
Multi-Generator Generative Adversarial Nets. CoRR abs/1708.02556 (2017) - [i38]Trung Le, Hung Vu, Tu Dinh Nguyen, Dinh Q. Phung:
Geometric Enclosing Networks. CoRR abs/1708.04733 (2017) - [i37]Hung Vu, Dinh Q. Phung, Tu Dinh Nguyen, Anthony Trevors, Svetha Venkatesh:
Energy-based Models for Video Anomaly Detection. CoRR abs/1708.05211 (2017) - [i36]Tu Dinh Nguyen, Truyen Tran, Dinh Q. Phung, Svetha Venkatesh:
Statistical Latent Space Approach for Mixed Data Modelling and Applications. CoRR abs/1708.05594 (2017) - [i35]Tu Dinh Nguyen, Truyen Tran, Dinh Q. Phung, Svetha Venkatesh:
Nonnegative Restricted Boltzmann Machines for Parts-based Representations Discovery and Predictive Model Stabilization. CoRR abs/1708.05603 (2017) - [i34]Tu Dinh Nguyen, Trung Le, Hung Vu, Dinh Q. Phung:
Dual Discriminator Generative Adversarial Nets. CoRR abs/1709.03831 (2017) - [i33]Trung Le, Khanh Nguyen, Tu Dinh Nguyen, Dinh Q. Phung:
Analogical-based Bayesian Optimization. CoRR abs/1709.06390 (2017) - [i32]Trung Le, Tu Dinh Nguyen, Dinh Q. Phung:
KGAN: How to Break The Minimax Game in GAN. CoRR abs/1711.01744 (2017) - [i31]Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Q. Phung:
A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network. CoRR abs/1712.02121 (2017) - 2016
- [j36]Tu Dinh Nguyen, Truyen Tran, Dinh Q. Phung, Svetha Venkatesh:
Graph-induced restricted Boltzmann machines for document modeling. Inf. Sci. 328: 60-75 (2016) - [j35]Truyen Tran, Dinh Q. Phung, Svetha Venkatesh:
Collaborative filtering via sparse Markov random fields. Inf. Sci. 369: 221-237 (2016) - [j34]Iman Kamkar, Sunil Kumar Gupta, Dinh Q. Phung, Svetha Venkatesh:
Stabilizing l1-norm prediction models by supervised feature grouping. J. Biomed. Informatics 59: 149-168 (2016) - [j33]Budhaditya Saha, Sunil Gupta, Dinh Q. Phung, Svetha Venkatesh:
Multiple task transfer learning with small sample sizes. Knowl. Inf. Syst. 46(2): 315-342 (2016) - [j32]Truyen Tran, Dinh Q. Phung, Svetha Venkatesh:
Modelling human preferences for ranking and collaborative filtering: a probabilistic ordered partition approach. Knowl. Inf. Syst. 47(1): 157-188 (2016) - [j31]Cheng Li, Santu Rana, Dinh Q. Phung, Svetha Venkatesh:
Data clustering using side information dependent Chinese restaurant processes. Knowl. Inf. Syst. 47(2): 463-488 (2016) - [j30]Sunil Gupta, Santu Rana, Budhaditya Saha, Dinh Q. Phung, Svetha Venkatesh:
A new transfer learning framework with application to model-agnostic multi-task learning. Knowl. Inf. Syst. 49(3): 933-973 (2016) - [j29]Cheng Li, Santu Rana, Dinh Q. Phung, Svetha Venkatesh:
Hierarchical Bayesian nonparametric models for knowledge discovery from electronic medical records. Knowl. Based Syst. 99: 168-182 (2016) - [j28]Hang Li, Dinh Q. Phung, Tru Cao, Tu Bao Ho, Zhi-Hua Zhou:
Introduction: special issue of selected papers from ACML 2014. Mach. Learn. 103(2): 137-139 (2016) - [j27]Sunil Gupta, Dinh Q. Phung, Svetha Venkatesh:
Modelling multilevel data in multimedia: A hierarchical factor analysis approach. Multim. Tools Appl. 75(9): 4933-4955 (2016) - [j26]Nguyen Cong Thuong, Sunil Gupta, Svetha Venkatesh, Dinh Q. Phung:
Nonparametric discovery of movement patterns from accelerometer signals. Pattern Recognit. Lett. 70: 52-58 (2016) - [j25]Budhaditya Saha, Thin Nguyen, Dinh Q. Phung, Svetha Venkatesh:
A Framework for Classifying Online Mental Health-Related Communities With an Interest in Depression. IEEE J. Biomed. Health Informatics 20(4): 1008-1015 (2016) - [c146]Khanh Nguyen, Trung Le, Vu Nguyen, Tu Dinh Nguyen, Dinh Q. Phung:
Multiple Kernel Learning with Data Augmentation. ACML 2016: 49-64 - [c145]Thin Nguyen, Svetha Venkatesh, Dinh Q. Phung:
Textual Cues for Online Depression in Community and Personal Settings. ADMA 2016: 19-34 - [c144]Kien Do, Truyen Tran, Dinh Q. Phung, Svetha Venkatesh:
Outlier Detection on Mixed-Type Data: An Energy-Based Approach. ADMA 2016: 111-125 - [c143]Shivapratap Gopakumar, Truyen Tran, Dinh Q. Phung, Svetha Venkatesh:
Stabilizing Linear Prediction Models Using Autoencoder. ADMA 2016: 651-663 - [c142]