NSF-BSF: Collaborative Research: CIF: Small: Neural Estimation of Statistical Divergences: Theoretical Foundations and Applications to Communication Systems

NSF-BSF：协作研究：CIF：小型：统计差异的神经估计：通信系统的理论基础和应用

• 批准号: 2308445
• 负责人: Henry Pfister
• 金额: $ 40万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2308445&HistoricalAwards=false
• 关键词: NSF; BSF; Collaborative; Research; CIF

An abundance of data and recent advances in computation have dramatically increased the reliability and performance of information processing systems. In many applications, these systems provide approximate answers to statistical questions about the distributions that generate the data. Estimators based on neural networks have become the method of choice when dealing with large and complicated datasets. Their popularity is due to their excellent performance in practice and their computational efficiency. But knowledge of the reasons behind their outstanding performance remains quite limited. The goal of this project is to improve the understanding of why neural estimation works and to provide formal performance guarantees that help guide practical applications in fields such as wireless communications. In addition to technological developments, the project features graduate student mentoring, undergraduate inclusion, outreach to high school via a summer camp, international collaboration, and the development of tutorial videos.This project models questions about data using statistical divergences that measure the discrepancy between probability distributions. While there are many approaches to estimating statistical divergences from data, neural estimators have now become the method of choice when dealing with large, high-dimensional datasets. The project will develop a comprehensive statistical and computational theory of neural estimation and apply it to novel applications to communications. It has two main thrusts. The first targets a non-asymptotic neural estimation theory that accounts for all known sources of error: functional approximation, empirical estimation, and optimization. The second will devise a flexible, efficiently computable, and provably accurate methodology for neural capacity estimation and data-driven polar coding.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.

大量数据和计算的最新进展显着提高了信息处理系统的可靠性和性能。在许多应用程序中，这些系统提供了有关生成数据的分布的统计问题的近似答案。在处理大型且复杂的数据集时，基于神经网络的估计器已成为选择方法。它们的受欢迎程度是由于它们在实践中的出色表现和计算效率。但是了解其出色表现背后的原因仍然非常有限。该项目的目的是提高对神经估计为何有效的理解，并提供正式的绩效保证，以帮助指导无线通信等领域的实际应用。除了技术发展外，该项目还具有研究生指导，本科生的包容性，通过夏令营，国际合作以及教程视频的开发来开发高中。此项目模型使用统计差异来衡量概率分布之间差异的数据问题。尽管有许多方法可以估算数据的统计差异，但在处理大型高维数据集时，神经估计器现在已成为选择方法。该项目将开发出神经估计的全面统计和计算理论，并将其应用于新颖的应用程序中。它有两个主要推力。第一个目标是一种非质合神经估计理论，该理论说明了所有已知的错误来源：功能近似，经验估计和优化。第二个将对神经容量估计和数据驱动的极性编码进行灵活，有效的计算和准确的方法。该奖项反映了NSF的法定任务，并通过基金会的智力优点和更广泛的影响来评估NSF的法定任务。