Truyen Tran
Truyen Tran
Associate Professor, Applied AI Institute, Deakin University
Verified email at deakin.edu.au - Homepage
Title
Cited by
Cited by
Year
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, ...
Journal of medical Internet research 18 (12), 2016
2542016
Deepr: a convolutional net for medical records
P Nguyen, T Tran, N Wickramasinghe, S Venkatesh
IEEE journal of biomedical and health informatics 21 (1), 22-30, 2017
251*2017
Predicting healthcare trajectories from medical records: A deep learning approach
T Pham, T Tran, D Phung, S Venkatesh
Journal of Biomedical Informatics 69, 218--229, 2017
2292017
DeepCare: A deep dynamic memory model for predictive medicine
T Pham, T Tran, D Phung, S Venkatesh
PAKDD 6, 26094, 2016
2272016
Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM)
T Tran, TD Nguyen, D Phung, S Venkatesh
Journal of Biomedical Informatics, 2015
1302015
Column networks for collective classification
T Pham, T Tran, D Phung, S Venkatesh
AAAI'17, 2017
1172017
Risk stratification using data from electronic medical records better predicts suicide risks than clinician assessments
T Tran, W Luo, D Phung, R Harvey, M Berk, RL Kennedy, S Venkatesh
BMC Psychiatry (ECR Best paper awarded by CRESP), 2014
1072014
Automatic feature learning for predicting vulnerable software components
HK Dam, T Tran, T Pham, SW Ng, J Grundy, A Ghose
IEEE Transactions on Software Engineering, 2018
95*2018
Nonnegative shared subspace learning and its application to social media retrieval
SK Gupta, D Phung, B Adams, T Tran, S Venkatesh
KDD'10, 1169-1178, 2010
862010
Lessons learned from using a deep tree-based model for software defect prediction in practice
HK Dam, T Pham, SW Ng, T Tran, J Grundy, A Ghose, T Kim, CJ Kim
MSR'19, 2019
84*2019
A deep learning model for estimating story points
M Choetkiertikul, HK Dam, T Tran, T Pham, A Ghose, T Menzies
IEEE Transactions on Software Engineering, DOI:10.1109/TSE.2018.2792473, 2018
822018
Ordinal Boltzmann machines for collaborative filtering
TT Truyen, DQ Phung, S Venkatesh
UAI'09 (Best paper runner up), 548-556, 2009
722009
Improving generalization and stability of Generative Adversarial Networks
H Thanh-Tung, T Tran, S Venkatesh
ICLR'19, 2019
702019
Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry
S Gupta, T Tran, W Luo, D Phung, RL Kennedy, A Broad, D Campbell, ...
BMJ Open, 2014
692014
Explainable software analytics
HK Dam, T Tran, A Ghose
ICSE'18, 2018
682018
A deep language model for software code
HK Dam, T Tran, T Pham
FSE'16 Workshop on Naturalness of Software (NL+SE), 2016
662016
Learning regularity in skeleton trajectories for anomaly detection in videos
R Morais, V Le, B Saha, T Tran, MR Mansour, S Venkatesh
CVPR'19, 2019
652019
Stabilized sparse ordinal regression for medical risk stratification
T Tran, D Phung, W Luo, S Venkatesh
Knowledge and Information Systems, 2015
532015
Graph transformation policy network for chemical reaction prediction
K Do, T Tran, S Venkatesh
KDD'19, 2019
472019
Mixed-variate restricted Boltzmann machines
T Tran, D Phung, S Venkatesh
ACML'11, 2011
442011
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