2024 ARC Discovery Project DP240102050
Data Complexity and Uncertainty-Resilient Deep Variational Learning
Professor Longbing Cao and Professor Joao Gama (Partner Investigator).

Enterprise data present increasingly significant characteristics and complexities, such as multi-aspect, heterogeneous and hierarchical features and interactions, and evolving dependencies and multi-distributions. They continue to significantly challenge the state-of-the-art probabilistic and neural learning systems with limited to insufficient capabilities and capacity. This research aims to develop a theory of flexible deep variational learning transforming new deep probabilistic models with flexible variational neural mechanisms for analytically explainable, complexity-resilient analytics of real-life data. The outcomes are expected to fill important knowledge gaps and lift critical innovation competencies in wide domains.

Access the relevant information on at the ARC grant outcome announcement webpage.