Developing AI-based solutions that enhance industry performance
Under the theme of AI and machine learning, we are developing theories that explain the generalisation ability of (deep) machine learning models that will enable research applicable to a range of different technologies and applications, particularly for industry engagement. Our team of researchers will:
- Find principled designs for AI and machine learning algorithms to enhance the trustworthiness of AI and machine learning techniques and tools
- Develop algorithms that deal with weakly supervised information, causally responsible representations, heterogeneous information fusion, visual plausible data generation, energy cost efficient computation, and model robustness and scalability in the wide
This research will have broader applications in eCommerce, health, cybersecurity, logistics and supply chain, and streamlined manufacturing.
Explore our research
Current projects
Deep neural architectures
Since the development of the first real deep neural network AlexNet in 2012, deep learning has made great progress in computer vision and natural language processing. Lots of these breakthroughs […]
Generative adversarial networks
Generative Adversarial Networks (GANs) were called as the most interesting idea in the last 10 years in machine learning by Turing award recipient Yann LeCun. Their most significant impact has […]
Label-noise learning
Learning with noisy labels becomes a more and more important topic recently. The reason is that, in the era of big data, datasets are becoming larger and larger. Often, large-scale […]
Robust/adversarial learning
We are also interested in how to reduce the side effect of noise on the instance, which may be caused by the failure of sensors or even malicious attacks. We […]
Statistical (deep) learning theory
Deep learning algorithms have given exciting performances, e.g., painting pictures, beating Go champions, and autonomously driving cars, among others, showing that they have very good generalisation abilities (small differences between […]
Transfer learning
Just like human, machine can also find the common knowledge between tasks and transfer the knowledge from one task to another one. In machine learning, we can exploit training examples […]
Dr Tongliang Liu
Phone
+61 2 8627 5966
Email
tongliang.liu@sydney.edu.au
Dr Chang Xu
Phone
+61 2 8627 6015
Email
c.xu@sydney.edu.au
Core Research Team
Dr Tongliang Liu
