Bridging the Generalization and Interpretation Gaps in Deep Neural Networks

弥合深度神经网络的泛化和解释差距

• 批准号: 2324389
• 负责人: Yuan Ke
• 金额: $ 18万
• 依托单位: University of Georgia Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2324389&HistoricalAwards=false
• 关键词: Bridging; Generalization; Interpretation; Gaps; Deep

This project focuses on enhancing the reliability and understandability of advanced artificial intelligence (AI) systems, specifically deep neural networks (DNNs) - a type of AI that uses layered structures of interconnected elements, or "neurons," to process and interpret data in sophisticated ways. Currently, these AI systems face two main challenges. Firstly, they may struggle to apply what they've learned in one situation to a different one. This issue, known as the "generalization gap," is similar to a student who has crammed for an exam but struggles to apply the knowledge in a real-world scenario. Secondly, DNNs, like many AI systems, work in ways that can be difficult for humans to understand. This "interpretation gap" is like using a complicated machine without a user manual, which can make it hard to correct mistakes or explain why specific decisions were made. These challenges could have implications for any sector where AI is used, from healthcare to autopilot. If AI makes mistakes because of the generalization gap or if it's not clear why a decision was made due to the interpretation gap, it could lead to significant errors, lack of trust, or even potential harm. This project aims to study these issues, enhancing the reliability and transparency of AI systems, enabling us to apply these technologies more confidently and effectively. By doing so, it will advance our scientific understanding of AI, support education in this vital field, and benefit society by ensuring AI technologies are more dependable and understandable. The project also provides research opportunities for graduate students. This project aims to develop a new framework to address the generalization and interpretation gaps in DNNs by investigating a series of well-defined research problems. The work includes the development of novel statistical theories for a better understanding of generalization errors in DNNs, the creation of robust and computationally efficient algorithms, and the promotion of innovative approaches for out-of-distribution generalizations. This project will advance our understanding of DNNs, develop new methods and algorithms, and provide insights into practical applications in diverse fields. The principal investigators will incorporate the research findings of this project into their educational endeavors.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目的重点是增强高级人工智能（AI）系统的可靠性和可理解性，特别是深度神经网络（DNN）--一种使用互连元件或“神经元”的分层结构来处理和解释数据的人工智能。复杂的方式。目前，这些人工智能系统面临两个主要挑战。首先，他们可能很难将他们在一种情况下学到的东西应用到另一种情况下。这个问题被称为“概括差距”，类似于一个学生为了考试而死记硬背，但却很难将知识应用到现实世界中。其次，DNN和许多人工智能系统一样，其工作方式可能很难让人类理解。这种“解释差距”就像使用一台没有用户手册的复杂机器，这可能会导致很难纠正错误或解释为什么会做出特定的决定。这些挑战可能会对任何使用人工智能的行业产生影响，从医疗保健到自动驾驶。如果人工智能因为泛化差距而犯错误，或者如果由于解释差距而不清楚为什么做出决定，那么它可能会导致重大错误，缺乏信任，甚至潜在的伤害。该项目旨在研究这些问题，提高人工智能系统的可靠性和透明度，使我们能够更自信，更有效地应用这些技术。通过这样做，它将促进我们对人工智能的科学理解，支持这一重要领域的教育，并通过确保人工智能技术更可靠和更容易理解来造福社会。该项目还为研究生提供了研究机会。该项目旨在开发一个新的框架，通过调查一系列定义明确的研究问题来解决DNN中的泛化和解释差距。这项工作包括开发新的统计理论，以更好地理解DNN中的泛化错误，创建强大且计算效率高的算法，以及推广用于分布外泛化的创新方法。该项目将促进我们对DNN的理解，开发新的方法和算法，并为不同领域的实际应用提供见解。主要研究人员将把本项目的研究成果纳入他们的教育工作中。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。