Towards Explainable Multi-source Multivariate Time-series Analysis

迈向可解释的多源多元时间序列分析

• 批准号: DE200101610
• 负责人: Dr Sen Wang
• 金额: $ 28.32万
• 依托单位: The University of Queensland
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Early Career Researcher Award
• 财政年份: 2020
• 资助国家: 澳大利亚
• 起止时间: 2020-01-01 至 2023-12-01
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DE200101610
• 关键词: Towards; Explainable; Multi; source; Multivariate

The aim of this project is to build deep learning models with transparent reasoning behind the results that can be easily interpreted by humans. The research rests on translating pertinent knowledge from multiple sources of complex data containing event sequences into graph form and embedding those knowledge graphs into a sophisticated deep learning model. Such an accomplishment represents the next great advance in machine intelligence and will lay the theoretical foundations for building intelligent analysis tools that truly work in tandem with people. The potential benefits to science, society, and the Australian economy, particularly in finance, sensor technologies, and emergency health services would be appreciable.

该项目的目的是建立深度学习模型，其结果背后的推理是透明的，可以很容易地被人类解释。该研究基于将包含事件序列的多个复杂数据源中的相关知识转换为图形形式，并将这些知识图嵌入到复杂的深度学习模型中。这样的成就代表了机器智能的下一个重大进步，并将为构建真正与人协同工作的智能分析工具奠定理论基础。对科学、社会和澳大利亚经济的潜在好处，特别是在金融、传感器技术和紧急医疗服务方面，将是可观的。