| 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 | 1041 | 2016 |
| 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 | 616 | 2017 |
| 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 | 513* | 2017 |
| Catastrophic forgetting and mode collapse in GANs H Thanh-Tung, T Tran IJCNN'20, 1-10, 2020 | 458 | 2020 |
| DeepCare: A deep dynamic memory model for predictive medicine T Pham, T Tran, D Phung, S Venkatesh PAKDD, 30-41, 2016 | 436 | 2016 |
| Learning regularity in skeleton trajectories for anomaly detection in videos R Morais, V Le, T Tran, B Saha, M Mansour, S Venkatesh CVPR19, 11988-11996, 2019 | 425 | 2019 |
| 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 | 407* | 2018 |
| Hierarchical conditional relation networks for video question answering TM Le, V Le, S Venkatesh, T Tran CVPR'20, 2020 | 361 | 2020 |
| 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 | 330* | 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 | 290 | 2018 |
| Explainable software analytics HK Dam, T Tran, A Ghose ICSE'18, 2018 | 236 | 2018 |
| 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 | 231 | 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 | 222 | 2015 |
| Column networks for collective classification T Pham, T Tran, D Phung, S Venkatesh AAAI'17, 2017 | 198 | 2017 |
| Towards effective AI-powered agile project management HK Dam, T Tran, J Grundy, A Ghose, Y Kamei ICSE'19, 2019 | 195* | 2019 |
| Improving generalization and stability of Generative Adversarial Networks H Thanh-Tung, T Tran, S Venkatesh ICLR'19, 2019 | 187 | 2019 |
| 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 | 173 | 2014 |
| A deep language model for software code HK Dam, T Tran, T Pham FSE'16 Workshop on Naturalness of Software (NL+SE), 2016 | 158 | 2016 |
| Dual memory neural computer for asynchronous two-view sequential learning H Le, T Tran, S Venkatesh KDD'18, 2018 | 142 | 2018 |
| Theory and evaluation metrics for learning disentangled representations K Do, T Tran ICLR'20, 2020 | 124 | 2020 |
Svetha VenkateshDeakin Distinguished Professor, Deakin UniversityVerified email at deakin.edu.au
