Quantification, optimisation, and application of deep uncertainty

深度不确定性的量化、优化和应用

• 批准号: DP190102181
• 负责人: Prof Saeid Nahavandi
• 金额: $ 30.33万
• 依托单位: Deakin University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2019
• 资助国家: 澳大利亚
• 起止时间: 2019-03-14 至 2023-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP190102181
• 关键词: Quantification; optimisation; application; deep; uncertainty

This project aims to develop a framework for deep uncertainty quantification. There is currently a fundamental gap between deep learning research and the methods required to quantify and manage uncertainties. The research will propose a novel distribution-free methodology to generate deep predictive uncertainty estimates to avoid the assumptions of existing methods. The quality of estimates will be enhanced by applying an interval-based adversarial training step. The project is expected to help data-driven Australian organisations and industries to better quantify and manage forecasting uncertainties. This project will provide them with significant cost savings through better decision making and more robust planning.

该项目旨在开发一个深度不确定性量化的框架。目前，深度学习研究与量化和管理不确定性所需的方法之间存在根本差距。这项研究将提出一种新的无分布的方法来生成深度预测不确定性估计，以避免现有方法的假设。通过采用基于间隔的对抗性训练步骤，估计的质量将得到提高。该项目预计将帮助数据驱动型澳大利亚组织和行业更好地量化和管理预测不确定性。该项目将通过更好的决策和更健全的规划为他们节省大量成本。