Deep Interaction Learning in Unlabelled Big Data and Complex Systems

该项目旨在有效地模拟深嵌在未标记的大数据和复杂系统中的复杂交互，这些系统通常是分层的、异构的、上下文的、动态的甚至是对比的。学习这种交互是强大的数据科学和实现大数据价值的基石，但它对现有的数据分析和学习系统提出了重大挑战和知识缺口。预期成果包括新一代无监督学习现实大数据复杂交互的理论和方法，有望实现大数据复杂性的内在处理，并大幅提升澳大利亚在前沿数据科学研究和应用方面的领导地位。

This project aims to effectively model intricate interactions deeply embedded in unlabelled big data and complex systems, which are often hierarchical, heterogeneous, contextual, dynamic or even contrastive. Learning such interactions is the keystone of robust data science and for realizing the value of big data but it poses significant challenges and knowledge gaps to existing data analytics and learning systems. The expected outcomes include new-generation theories and methods for the unsupervised learning of complex interactions in real-life big data, which are anticipated to enable the intrinsic processing of big data complexities and substantially enhance Australia’s leadership in frontier data science research and applications.