Towards Explainable Multi-source Multivariate Time-series Analysis

这个项目的目的是建立深度学习模型，在结果背后有透明的推理，可以很容易地被人类解释。该研究的基础是将包含事件序列的多个复杂数据来源的相关知识转化为图的形式，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巨大进步，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、传感器技术和紧急卫生服务方面，将是可观的。

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.