Declarative Networks: Towards Robust and Explainable Deep Learning

这个项目的目的是开发声明式机器学习技术，利用世界的内在结构和模型。深度学习已经成为机器学习的主要方法，许多产品和承诺都建立在这项技术之上。但深度学习昂贵、不透明、脆弱，而且完全依赖于人类标记的数据。该项目旨在通过建立理论和算法，允许将物理和数学模型嵌入深度学习框架，提供性能保证和可解释性，从而使深度学习更加可靠。这可能会使基于机器学习的产品受益，这些产品可以理解世界，并通过视觉和语言与人类自然互动。

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.