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Truyen Tran
Truyen Tran
Associate Professor | Head of AI, Health and Science @ 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
5042016
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
3912017
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
341*2017
DeepCare: A deep dynamic memory model for predictive medicine
T Pham, T Tran, D Phung, S Venkatesh
PAKDD, 30-41, 2016
3072016
Learning regularity in skeleton trajectories for anomaly detection in videos
R Morais, V Le, T Tran, B Saha, M Mansour, S Venkatesh
In 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR …, 2019
1982019
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
182*2018
Column networks for collective classification
T Pham, T Tran, D Phung, S Venkatesh
AAAI'17, 2017
1702017
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
166*2019
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
1632015
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
1432018
Hierarchical conditional relation networks for video question answering
TM Le, V Le, S Venkatesh, T Tran
CVPR'20, 2020
1332020
Explainable software analytics
HK Dam, T Tran, A Ghose
ICSE'18, 2018
1272018
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
1252014
Graph transformation policy network for chemical reaction prediction
K Do, T Tran, S Venkatesh
Proceedings of the 25th ACM SIGKDD International Conference 609, 750-760, 2019
1202019
Improving generalization and stability of Generative Adversarial Networks
H Thanh-Tung, T Tran, S Venkatesh
ICLR'19, 2019
1202019
Catastrophic forgetting and mode collapse in GANs
H Thanh-Tung, T Tran
2020 International Joint Conference on Neural Networks (IJCNN), 1-10, 2020
1172020
A deep language model for software code
HK Dam, T Tran, T Pham
FSE'16 Workshop on Naturalness of Software (NL+SE), 2016
1042016
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
912010
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
882014
Ordinal Boltzmann machines for collaborative filtering
TT Truyen, DQ Phung, S Venkatesh
UAI'09 (Best paper runner up), 548-556, 2009
822009
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