Deep Interaction Learning in Unlabelled Big Data and Complex Systems

无标签大数据和复杂系统中的深度交互学习

• 批准号: FT190100734
• 负责人: Prof Longbing Cao
• 金额: $ 71.32万
• 依托单位: Macquarie University
• 依托单位国家: 澳大利亚
• 项目类别: ARC Future Fellowships
• 财政年份: 2020
• 资助国家: 澳大利亚
• 起止时间: 2020-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/FT190100734
• 关键词: Deep; Interaction; Learning; Unlabelled; Big

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.

该项目旨在有效地建模深入嵌入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构成了重大挑战和知识缺口。预期成果包括新一代理论和方法，用于对现实生活中的大数据中的复杂交互进行无监督学习，预计这些理论和方法将实现对大数据复杂性的内在处理，并大大提高澳大利亚在前沿数据科学研究和应用方面的领导地位。