Data Complexity and Uncertainty-Resilient Deep Variational Learning

数据复杂性和不确定性弹性深度变分​​学习

• 批准号: DP240102050
• 负责人: Prof Longbing Cao
• 金额: $ 31.25万
• 依托单位: Macquarie University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2024
• 资助国家: 澳大利亚
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP240102050
• 关键词: Data; Complexity; Uncertainty; Resilient; Deep

Enterprise data present increasingly significant characteristics and complexities, such as multi-aspect, heterogeneous and hierarchical features and interactions, and evolving dependencies and multi-distributions. They continue to significantly challenge the state-of-the-art probabilistic and neural learning systems with limited to insufficient capabilities and capacity. This research aims to develop a theory of flexible deep variational learning transforming new deep probabilistic models with flexible variational neural mechanisms for analytically explainable, complexity-resilient analytics of real-life data. The outcomes are expected to fill important knowledge gaps and lift critical innovation competencies in wide domains.

企业数据呈现出越来越显著的特征和复杂性，如多方面、异构和层次化的特征和交互，以及不断发展的依赖性和多分布性。它们继续对最先进的概率和神经学习系统提出重大挑战，这些系统的能力和容量有限。本研究旨在开发一种灵活的深度变分学习理论，通过灵活的变分神经机制转换新的深度概率模型，以实现对现实数据的分析可解释，复杂性弹性分析。预计这些成果将填补重要的知识空白，并提高广泛领域的关键创新能力。