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ORCIDDinh Q. Phung
Dinh Quoc Phung – Dinh Phung 0001
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- affiliation: Monash University, Melbourne, Victoria, Australia
- affiliation: Deakin University, Center of Pattern Recognition and Data Analytics, Australia
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2020 – today
- 2022
[j59]Trung Le, Khanh Nguyen, Dinh Q. Phung:
Improving kernel online learning with a snapshot memory. Mach. Learn. 111(3): 997-1018 (2022)- [c214]Thuy-Trang Vu, Shahram Khadivi, Dinh Q. Phung, Gholamreza Haffari:
Domain Generalisation of NMT: Fusing Adapters with Leave-One-Domain-Out Training. ACL (Findings) 2022: 582-588 - [c213]Thanh Nguyen-Duc, Trung Le, He Zhao, Jianfei Cai, Dinh Q. Phung:
Particle-based Adversarial Local Distribution Regularization. AISTATS 2022: 5212-5224 - [c212]Tam Le, Truyen Nguyen, Dinh Phung, Viet Anh Nguyen:
Sobolev Transport: A Scalable Metric for Probability Measures with Graph Metrics. AISTATS 2022: 9844-9868 - [c211]Trung Le, Anh Tuan Bui, Le Minh Tri Tue, He Zhao, Paul Montague, Quan Hung Tran, Dinh Q. Phung:
On Global-view Based Defense via Adversarial Attack and Defense Risk Guaranteed Bounds. AISTATS 2022: 11438-11460 - [c210]Khai Nguyen, Dang Nguyen, Quoc Dinh Nguyen, Tung Pham, Hung Bui, Dinh Phung, Trung Le, Nhat Ho:
On Transportation of Mini-batches: A Hierarchical Approach. ICML 2022: 16622-16655 - [c209]Van-Anh Nguyen, Dai Quoc Nguyen, Van Nguyen, Trung Le, Quan Hung Tran, Dinh Phung:
ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection. ICSE-Companion 2022: 178-182 - [c208]Thanh Nguyen-Duc, He Zhao, Jianfei Cai, Dinh Phung:
MED-TEX: Transfer and Explain Knowledge with Less Data from Pretrained Medical Imaging Models. ISBI 2022: 1-4 - [c207]Tin Duy Vo, Manh Luong, Duong Minh Le, Hieu Tran, Nhan Do, Tuan-Duy H. Nguyen, Thien Nguyen, Hung Bui, Dat Quoc Nguyen, Dinh Q. Phung:
Vietnamese Speech-based Question Answering over Car Manuals. IUI Companion 2022: 117-119 - [c206]Dai Quoc Nguyen, Vinh Tong, Dinh Q. Phung, Dat Quoc Nguyen:
Node Co-occurrence based Graph Neural Networks for Knowledge Graph Link Prediction. WSDM 2022: 1589-1592 - [i87]Tam Le, Truyen Nguyen, Dinh Q. Phung, Viet Anh Nguyen:
Sobolev Transport: A Scalable Metric for Probability Measures with Graph Metrics. CoRR abs/2202.10723 (2022) - [i86]Tuan Anh Bui, Trung Le, Quan Hung Tran, He Zhao, Dinh Q. Phung:
A Unified Wasserstein Distributional Robustness Framework for Adversarial Training. CoRR abs/2202.13437 (2022) - [i85]Hoang Phan, Trung Le, Trung Phung, Tuan Anh Bui, Nhat Ho, Dinh Q. Phung:
Global-Local Regularization Via Distributional Robustness. CoRR abs/2203.00553 (2022) - [i84]Chuanxia Zheng, Guoxian Song, Tat-Jen Cham, Jianfei Cai, Dinh Q. Phung, Linjie Luo:
High-Quality Pluralistic Image Completion via Code Shared VQGAN. CoRR abs/2204.01931 (2022) - [i83]Hoang Phan, Ngoc Tran, Trung Le, Toan Tran, Nhat Ho, Dinh Phung:
Stochastic Multiple Target Sampling Gradient Descent. CoRR abs/2206.01934 (2022) - [i82]Vy Vo, Van Nguyen, Trung Le, Quan Hung Tran, Gholamreza Haffari, Seyit Camtepe, Dinh Q. Phung:
An Additive Instance-Wise Approach to Multi-class Model Interpretation. CoRR abs/2207.03113 (2022) - 2021
- [j58]Viet Huynh, Nhat Ho, Nhan Dam, XuanLong Nguyen, Mikhail Yurochkin, Hung Bui, Dinh Q. Phung:
On efficient multilevel Clustering via Wasserstein distances. J. Mach. Learn. Res. 22: 145:1-145:43 (2021) - [c205]Tuan-Anh Bui, Trung Le, He Zhao, Paul Montague, Olivier Y. de Vel, Tamas Abraham, Dinh Phung:
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness. AAAI 2021: 6831-6839 - [c204]Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Q. Phung:
Quaternion Graph Neural Networks. ACML 2021: 236-251 - [c203]Thuy-Trang Vu, Xuanli He, Dinh Q. Phung, Gholamreza Haffari:
Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selection. EMNLP (1) 2021: 3335-3346 - [c202]Van-Anh Nguyen, Tuan Nguyen, Trung Le, Quan Hung Tran, Dinh Phung:
STEM: An approach to Multi-source Domain Adaptation with Guarantees. ICCV 2021: 9332-9343 - [c201]He Zhao, Dinh Phung, Viet Huynh, Trung Le, Wray L. Buntine
:
Neural Topic Model via Optimal Transport. ICLR 2021 - [c200]Trung Le, Tuan Nguyen, Nhat Ho, Hung Bui, Dinh Phung:
LAMDA: Label Matching Deep Domain Adaptation. ICML 2021: 6043-6054 - [c199]Tuan Nguyen, Trung Le, Nhan Dam, Quan Hung Tran, Truyen Nguyen, Dinh Q. Phung:
TIDOT: A Teacher Imitation Learning Approach for Domain Adaptation with Optimal Transport. IJCAI 2021: 2862-2868 - [c198]Viet Huynh, Dinh Q. Phung, He Zhao:
Optimal Transport for Deep Generative Models: State of the Art and Research Challenges. IJCAI 2021: 4450-4457 - [c197]He Zhao, Dinh Q. Phung, Viet Huynh, Yuan Jin, Lan Du, Wray L. Buntine:
Topic Modelling Meets Deep Neural Networks: A Survey. IJCAI 2021: 4713-4720 - [c196]Van Nguyen, Trung Le, Olivier Y. de Vel, Paul Montague, John Grundy, Dinh Phung:
Information-theoretic Source Code Vulnerability Highlighting. IJCNN 2021: 1-8 - [c195]Manh-Ha Bui, Toan Tran, Anh Tran, Dinh Q. Phung:
Exploiting Domain-Specific Features to Enhance Domain Generalization. NeurIPS 2021: 21189-21201 - [c194]Trung Phung, Trung Le, Long Vuong, Toan Tran, Anh Tran, Hung Bui, Dinh Q. Phung:
On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources. NeurIPS 2021: 27720-27733 - [c193]Tuan Nguyen, Trung Le, He Zhao, Quan Hung Tran, Truyen Nguyen, Dinh Q. Phung:
Most: multi-source domain adaptation via optimal transport for student-teacher learning. UAI 2021: 225-235 - [i81]Tuan-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) - [i80]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) - [i79]He Zhao, Dinh Phung, Viet Huynh, Yuan Jin, Lan Du, Wray L. Buntine:
Topic Modelling Meets Deep Neural Networks: A Survey. CoRR abs/2103.00498 (2021) - [i78]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) - [i77]Mahmoud Hossam, Trung Le, He Zhao, Viet Huynh, Dinh Phung:
Improved and Efficient Text Adversarial Attacks using Target Information. CoRR abs/2104.13484 (2021) - [i76]Mahmoud Hossam, Trung Le, Michael Papasimeon, Viet Huynh, Dinh Phung:
Text Generation with Deep Variational GAN. CoRR abs/2104.13488 (2021) - [i75]Son D. Dao, Ethan Zhao, Dinh Phung, Jianfei Cai:
Multi-Label Image Classification with Contrastive Learning. CoRR abs/2107.11626 (2021) - [i74]Jing Liu, Bohan Zhuang, Mingkui Tan, Xu Liu, Dinh Phung, Yuanqing Li, Jianfei Cai:
Elastic Architecture Search for Diverse Tasks with Different Resources. CoRR abs/2108.01224 (2021) - [i73]Thuy-Trang Vu, Xuanli He, Dinh Q. Phung, Gholamreza Haffari:
Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selection. CoRR abs/2109.04292 (2021) - [i72]Van-Anh Nguyen, Dai Quoc Nguyen, Van Nguyen, Trung Le, Quan Hung Tran, Dinh Q. Phung:
ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection. CoRR abs/2110.07317 (2021) - [i71]Manh-Ha Bui, Toan Tran, Anh Tuan Tran, Dinh Phung:
Exploiting Domain-Specific Features to Enhance Domain Generalization. CoRR abs/2110.09410 (2021) - [i70]Trung Le, Dat Do, Tuan Nguyen, Huy Nguyen, Hung Bui, Nhat Ho, Dinh Q. Phung:
On Label Shift in Domain Adaptation via Wasserstein Distance. CoRR abs/2110.15520 (2021) - [i69]Dang Nguyen, Khai Nguyen, Dinh Phung, Hung Bui, Nhat Ho:
Model Fusion of Heterogeneous Neural Networks via Cross-Layer Alignment. CoRR abs/2110.15538 (2021) - [i68]Trung Phung, Trung Le, Long Vuong, Toan Tran, Anh Tran, Hung Bui, Dinh Phung:
On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources. CoRR abs/2111.13822 (2021) - [i67]Vinh Tong, Dai Quoc Nguyen, Dinh Q. Phung, Dat Quoc Nguyen:
Two-view Graph Neural Networks for Knowledge Graph Completion. CoRR abs/2112.09231 (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) - [c192]Dai Quoc Nguyen, Tuan Nguyen, Dinh Phung:
A Relational Memory-based Embedding Model for Triple Classification and Search Personalization. ACL 2020: 3429-3435 - [c191]He Zhao, Piyush Rai, Lan Du, Wray L. Buntine, Dinh Phung, Mingyuan Zhou:
Variational Autoencoders for Sparse and Overdispersed Discrete Data. AISTATS 2020: 1684-1694 - [c190]Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Phung:
A Capsule Network-based Model for Learning Node Embeddings. CIKM 2020: 3313-3316 - [c189]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 - [c188]Tuan-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 - [c187]Thuy-Trang Vu, Dinh Phung, Gholamreza Haffari:
Effective Unsupervised Domain Adaptation with Adversarially Trained Language Models. EMNLP (1) 2020: 6163-6173 - [c186]Quan Hoang, Trung Le, Dinh Phung:
Parameterized Rate-Distortion Stochastic Encoder. ICML 2020: 4293-4303 - [c185]Mahmoud Hossam, Trung Le, He Zhao, Dinh Phung:
Explain2Attack: Text Adversarial Attacks via Cross-Domain Interpretability. ICPR 2020: 8922-8928 - [c184]Nhan Dam, Trung Le, Viet Huynh, Dinh Phung:
Stein Variational Gradient Descent with Variance Reduction. IJCNN 2020: 1-8 - [c183]Mahmoud Hossam, Trung Le, Viet Huynh, Michael Papasimeon, Dinh Phung:
OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation. IJCNN 2020: 1-8 - [c182]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 - [c181]Viet Huynh, He Zhao, Dinh Phung:
OTLDA: A Geometry-aware Optimal Transport Approach for Topic Modeling. NeurIPS 2020 - [c180]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 - [c179]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 - [c178]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 - [c177]Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung:
A Self-attention Network Based Node Embedding Model. ECML/PKDD (3) 2020: 364-377 - [i66]Mahmoud Hossam, Trung Le, Viet Huynh, Michael Papasimeon, Dinh Phung:
OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation. CoRR abs/2004.07534 (2020) - [i65]Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung:
A Self-Attention Network based Node Embedding Model. CoRR abs/2006.12100 (2020) - [i64]Tuan-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) - [i63]Thanh Nguyen-Duc, He Zhao, Jianfei Cai, Dinh Phung:
MED-TEX: Transferring and Explaining Knowledge with Less Data from Pretrained Medical Imaging Models. CoRR abs/2008.02593 (2020) - [i62]Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung:
Quaternion Graph Neural Networks. CoRR abs/2008.05089 (2020) - [i61]He Zhao, Dinh Phung, Viet Huynh, Trung Le, Wray L. Buntine:
Neural Sinkhorn Topic Model. CoRR abs/2008.13537 (2020) - [i60]Tuan-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) - [i59]Dai Quoc Nguyen, Thanh Vu, Tu Dinh Nguyen, Dinh Phung:
QuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings. CoRR abs/2009.12517 (2020) - [i58]Thuy-Trang Vu, Dinh Phung, Gholamreza Haffari:
Effective Unsupervised Domain Adaptation with Adversarially Trained Language Models. CoRR abs/2010.01739 (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) - [c176]Hung Vu, Tu Dinh Nguyen, Trung Le, Wei Luo, Dinh Q. Phung:
Robust Anomaly Detection in Videos Using Multilevel Representations. AAAI 2019: 5216-5223 - [c175]Thuy-Trang Vu, Ming Liu, Dinh Phung, Gholamreza Haffari:
Learning How to Active Learn by Dreaming. ACL (1) 2019: 4091-4101 - [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]