Results for: Truyen Tran

Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view

W. Luo, D. Phung, T. Tran, S. Gupta, S. Rana, C. Karmakar, A. Shilton, J. Yearwood, N. Dimitrova, T. Ho, O.

2016 / Vol. 18, pp. e323 / Journal of medical Internet research

A deep language model for software code

H. Dam, T. Tran, T. Pham

2016 / pp. 1 - 4 / FSE 2016: Proceedings of the Foundations Software Engineering International Symposium; Workshop on Naturalness of Software (NL+SE)

Preference Relation-based Markov Random Fields

S. Liu, G. Li, T. Tran, J. Yuan

2015 / Proc. of 7th Asian Conference on Machine Learning (ACML)

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

T. Tran, D. Phung, S. Venkatesh

2015 / Vol. 21, pp. 25 - 45 / Journal of Heuristics

Characterization and prediction of issue-related risks in software projects

M. Choetkiertikul, H. Dam, T. Tran, A. Ghose

2015 / MSR

Predicting delays in software projects using networked classification

M. Choetkiertikul, H. Dam, T. Tran, A. Ghose

2015 / 30th IEEE/ACM International Conference on Automated Software Engineering

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

T. Tran, T. Nguyen, D. Phung, S. Venkatesh

2015 / Vol. 54, pp. 96 - 105 / Journal of Biomedical Informatics

Machine-learning prediction of cancer survival: a prospective study examining the impact of combining clinical and genomic data

D. M. Ashley, S. Gupta, T. Tran, W. Luo, P. K. Lorgelly, D. Thomas, S. B. Fox, S. Venkatesh

2015 / ASCO 2015: Proceedings of the 2015 Annual Meeting of American Society of Clinical Oncology

Who will answer my question on Stack Overflow?

M. Choetkiertikul, D. Avery, H. Dam, T. Tran, A. Ghose

2015 / 24th Australasian Software Engineering Conference (ASWEC 2015)

Ordinal Random Fields for Recommender Systems

S. Liu, T. Tran, G. Li, J. Yuan

2014 / Proc. of 6th Asian Conference on Machine Learning (ACML)