Projects per year
Personal profile
Biography
My research involves both theoretical and practical aspects. More specifically, this focuses on deep generative models, kernel methods, optimization in machine learning and Bayesian inference whose gained theories can be applied to supervised learning, semi-supervised learning, adversarial learning, online learning, anomaly detection, and cyber security. I have published in the top-notch conferences and high quality journals in machine learning, artificial intelligence, and data mining including NIPS, ICLR, AISTATS, UAI, IJCAI, ICDM and Journal of Machine Learning Research (JMLR).
Research area keywords
- Deep Generative Models
- Optimisation for Machine Learning
- Kernel Methods
- Online Learning
- Deep Learning for Cyber Security
- Anomaly Detection
Network
Recent external collaboration on country level. Dive into details by clicking on the dots.
Projects 2018 2020
- 3 Active
Towards Robust Learning Systems via Amortized Optimization and Domain Adaptation
Le, T., Phung, D., Xiang, Y., Zhang, J., Erfani, S., Rubinstein, B., Leckie, C., Nock, R. & Knight, K.
15/04/19 → 14/04/20
Project: Research
Cardiorespiratory function as a non-invasive biomarker for detecting seizures with artificial intelligence: Utilising data from long-term patient recordings from an epilepsy monitoring unit
O'Brien, T., Kwan, P., Sivathamboo, S., Perucca, P., Ge, Z., Le, T., Phung, D., Bonnington, P. & Piccenna, L.
8/04/19 → 8/04/20
Project: Research
Deep Learning for Cyber (Data61 CRP 38)
Phung, D., Le, T., Xiang, Y., Zhang, J., Wen, S., Murray, T. & Nock, R.
15/06/18 → 30/06/20
Project: Research
Research Output 2009 2019
Deep domain adaptation for vulnerable code function identification
Nguyen, V., Le, T., Le, T., Nguyen, K., Devel, O., Montague, P., Qu, L. & Phung, DI., 2019, International Joint Conference on Neural Networks (IJCNN) 2019. Angelov, P. & Roveri, M. (eds.). IEEE, Institute of Electrical and Electronics Engineers, 8 p. 8851923Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
GoGP: scalable geometric-based Gaussian process for online regression
Le, T., Nguyen, K., Nguyen, V., Nguyen, T. D. & Phung, D., 20 Jul 2019, In : Knowledge and Information Systems. p. 197-226 30 p.Research output: Contribution to journal › Article › Research › peer-review
1
Citation
(Scopus)
Learning Generative Adversarial Networks from multiple data sources
Le, T., Hoang, Q., Vu, H., Nguyen, T. D., Bui, H. & Phung, D., 2019, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. Kraus, S. (ed.). California USA: International Joint Conferences on Artificial Intelligence, p. 2823-2829 7 p.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
Open Access
File
Maximal divergence sequential auto-encoder for binary software vulnerability detection*
Le, T., Nguyen, T., Le, T., Phung, D., Montague, P., De Vel, O. & Qu, L., 2019, International Conference on Learning Representations 2019. Rush, A. (ed.). La Jolla CA USA: International Conference on Learning Representations (ICLR), 15 p.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
Open Access
File
Robust anomaly detection in videos using multilevel representations
Vu, H., Nguyen, T. D., Le, T., Luo, W. & Phung, D., 2019, Proceedings of AAAI19-Thirty-Third AAAI conference on Artificial Intelligence. Van Hentenryck, P. & Zhou, Z-H. (eds.). Palo Alto CA USA: Association for the Advancement of Artificial Intelligence (AAAI), p. 5216-5223 8 p. 2579. (Proceedings of the AAAI Conference on Artificial Intelligence; vol. 33, no. 1).Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
Open Access
File
Prizes
The first prize of Imagine Cup 2010 Australia round
Trung Le (Recipient), 2010
Prize: Prize (including medals and awards)