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Results for: Dinh Phung

Bayesian Multi-Hyperplane Machine for Pattern Recognition

K. Nguyen, T. Le, T. Dinh, D. Phung

2018 / pp. 609 - 614 / 2018 24th International Conference on Pattern Recognition (ICPR)

Robust Bayesian kernel machine via stein variational gradient descent for big data

K. Nguyen, T. Le, T. Nguyen, D. Phung, G. I. Webb

2018 / pp. 2003 - 2011 / Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Approximation vector machines for large-scale online learning

T. Le, T. Nguyen, V. Nguyen, D. Phung

2018 / Vol. 18, pp. 3962 - 4016 / The Journal of Machine Learning Research

MGAN: Training generative adversarial nets with multiple generators

Q. Hoang, T. Nguyen, T. Le, D. Phung

2018 / In International Conference on Learning Representations

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

D. Nguyen, W. Luo, S. Venkatesh, D. Phung

2018 / Vol. 42, pp. 94 / Journal of Medical Systems

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

D. Nguyen, W. Luo, D. Phung, S. Venkatesh

2018 / Vol. 161, pp. 313 - 328 / Knowledge-Based Systems

Jointly predicting affective and mental health scores using deep neural networks of visual cues on the Web

H. Nguyen, V. Nguyen, T. Nguyen, M. Larsen, B. O'Dea, D. Thanh, T. Le, D. Phung, S. Venkatesh, H. Christensen

2018 / Proceedings of the International Conference on Web Information Systems Engineering (WISE)

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

D. Nguyen, W. Luo, T. Nguyen, S. Venkatesh, D. Phung

2018 / pp. 569 - 584 / The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)

Discovering topic structures of a temporally evolving document corpus

A. Beykikhoshk, O. Arandjelovic, D. Phung, S. Venkatesh

2018 / Vol. 55, pp. 599 - 632 / Knowledge and Information Systems

Learning Graph Representation via Frequent Subgraphs

D. Nguyen, W. Luo, T. Nguyen, S. Venkatesh, D. Phung

2018 / pp. 306 - 314 / Proceedings of the 2018 SIAM International Conference on Data Mining (SDM)

GoGP: scalable geometric-based Gaussian process for online regression

T. Le, K. Nguyen, V. Nguyen, T. Nguyen, D. Phung

2018 / pp. 1 - 30 / Knowledge and Information Systems

GoGP: Fast online regression with gaussian processes

T. Le, K. Nguyen, V. Nguyen, T. Nguyen, D. Phung

2017 / pp. 257 - 266 / IEEE International Conference on Data Mining (ICDM)

Data mining of intervention for children with autism spectrum disorder

P. Vellanki, T. Duong, D. Phung, S. Venkatesh

2017 / pp. 376 - 383 / eHealth 360°

A framework for mixed-type multioutcome prediction with applications in healthcare

B. Saha, S. Gupta, D. Phung, S. Venkatesh

2017 / Vol. 21, pp. 1182 - 1191 / IEEE journal of biomedical and health informatics

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

T. Tran, D. Phung, H. Bui, S. Venkatesh

2017 / Vol. 246, pp. 53 - 85 / Artificial Intelligence

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

T. Nguyen, V. Nguyen, F. D. Salim, D. V. Le, D. Phung

2017 / Vol. 28, pp. 396 - 417 / Pervasive and Mobile Computing