CIF:Small:Toward a Modern Theory of Compression: Manifold Sources and Learned Compressors

CIF：小：迈向现代压缩理论：流形源和学习压缩机

• 批准号: 2306278
• 负责人: Aaron Wagner
• 金额: $ 60万
• 依托单位: Cornell 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: 2023-06-15 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2306278&HistoricalAwards=false
• 关键词: CIF; Small; Toward; Modern; Theory

Past improvements in our communications infrastructure have been fueled primarily by increasing our ability to move bits around the world. In contrast, there has been less attention paid to how data compression can be used to reduce the number of bits that must be communicated to begin with. Recent advances in machine learning suggest that the number of bits that we must send to sustain a teleconference or stream a video is significantly lower than previously thought. Reducing the number of bits that must be transmitted would enable more realistic and immersive telepresence. This, in turn, may lead to a reduced need for travel, including reduced commuting. The resulting benefits would be manifold, including reduced carbon emissions, reduced spread of infectious disease, and more flexible arrangements for those balancing work with child- or elder-care. This project will contribute to the grand challenge of achieving transparent, secure, inexpensive communications with a full range of telepresence capabilities.The impetus for this project is the exemplary performance obtained by Artificial Neural Network-based compressors on multimedia sources. Although these compressors have attractive empirical performance, there is little theory to explain their performance or point the way toward future improvements. The project will develop a modern theory of data compression that will help explain why these compressors perform as well as they do, estimate their degree of suboptimality, and point the way toward how they can be improved. New theory will be developed focusing on the two most salient aspects of these compressors, namely their ability to apply nonlinear transforms to the data and the fact that they are learned from source samples. The project will also increase the external visibility of researchers and results in information theory and lead to more diverse workforce through the support of female and veteran Ph.D. students.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.

我们的通信基础设施的过去改进主要是通过提高我们在全球范围内移动碎屑的能力来推动的。 相比之下，人们对如何使用数据压缩来减少必须传达的位数的关注较少。机器学习的最新进展表明，我们必须发送的维持电话会议或流式传输视频的位数明显低于以前想象的。减少必须传输的位数量将使更现实和身临其境的触觉呈现。反过来，这可能导致对旅行的需求减少，包括减少通勤。由此产生的好处将是多种多样的，包括减少碳排放，传染病的传播量减少以及与儿童或老年人护理平衡工作的人更加灵活的安排。该项目将带来巨大的挑战，即通过全方位的远程悬念能力实现透明，安全，廉价的通信。该项目的动力是由基于人造神经网络的压缩机在多媒体源上获得的示例性能。尽管这些压缩机具有有吸引力的经验表现，但几乎没有理论可以解释其表现或指向未来改进的道路。该项目将开发一种现代的数据压缩理论，这将有助于解释为什么这些压缩机的性能和估计次级次要程度的程度，并为如何改善它们的道路。 将开发新的理论，重点关注这些压缩机的两个最突出的方面，即它们将非线性转换应用于数据的能力以及从源样本中学到的事实。 该项目还将提高研究人员的外部知名度，并导致信息理论，并通过支持女性和经验丰富的博士学位，从而导致更多样化的劳动力。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响标准来评估值得支持。