Declarative Networks: Towards Robust and Explainable Deep Learning

声明式网络：迈向稳健且可解释的深度学习

• 批准号: FT200100421
• 负责人: Prof Stephen Gould
• 金额: $ 74.41万
• 依托单位: The Australian National University
• 依托单位国家: 澳大利亚
• 项目类别: ARC Future Fellowships
• 财政年份: 2021
• 资助国家: 澳大利亚
• 起止时间: 2021-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/FT200100421
• 关键词: Declarative; Networks; Towards; Robust; Explainable

The aim of this project is to develop declarative machine learning techniques that exploit inherent structure and models of the world. Deep learning has become the dominant approach for machine learning with many products and promises built on this technology. But deep learning is expensive, opaque, brittle and relies solely on human labelled data. This project intends to make deep learning more reliable by establishing theory and algorithms that allow physical and mathematical models to be embedded within a deep learning framework, providing performance guarantees and interpretability. This would likely benefit machine learning based products that can understand the world and interact with humans naturally through vision and language.

该项目的目的是开发利用世界固有结构和模型的声明性机器学习技术。深度学习已成为机器学习的主要方法，许多产品和承诺都基于该技术。但深度学习成本高昂、不透明、脆弱，并且仅依赖于人类标记数据。该项目旨在通过建立理论和算法，将物理和数学模型嵌入到深度学习框架中，从而提供性能保证和可解释性，从而使深度学习更加可靠。这可能有利于基于机器学习的产品，这些产品可以理解世界并通过视觉和语言自然地与人类互动。