TensorLABE - Robust Characterization of Data Tensors and Synthetic Data Generation

TensorLABE - 数据张量的稳健表征和合成数据生成

• 批准号: 2223932
• 负责人: Tim Andersen
• 金额: $ 15.65万
• 依托单位: Boise State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2223932&HistoricalAwards=false
• 关键词: TensorLABE; Robust; Characterization; Data; Tensors

Modern computational methods, such as Machine Learning (ML) based approaches, have produced impressive gains in efficiency and performance but are increasingly dependent on massive amounts of data. These data-driven approaches transition from the classical techniques of human-engineered source code to algorithms trained on a dataset to produce the desired solution, placing the data in the driver's seat. The proliferation of these data-driven technologies is being enabled and hastened by new hardware and software systems specifically designed to support the complex data-driven computation associated with these algorithms and the massive volumes of data accompanying them. But despite the impressive performance gains of these new hardware and software systems, understanding their design's data component has languished in favor of performance-driven advances in software and hardware-based solutions. The lack of data understanding has led to a number of undesirable outcomes such as unwanted bias in the data-driven solution, an inability to determine the actual suitability of a data set to solving a given problem ahead of time, an inability to determine if a data set has been manipulated or corrupted, and an inability to produce accurate synthetic data that can be used to train and test the performance of these software and hardware systems. This project aims to provide a robust framework for the characterization of large-scale tensor-based datasets to improve understanding of the data itself and enable the production of synthetic data that more accurately replicates real-world data for use in system design testing and validation.Specifically, this project proposes to advance knowledge in the fields of multilinear algebra, large-scale data analytics, machine learning, and artificial intelligence by incorporating a variety of tensor methods for statistical, structural, and performative data analyses to achieve more robust data characterization. A more holistic set of data characterizations will enable better assessment of data for bias and evaluation of datasets for suitability for a particular task. It will also allow the comparison of datasets to understand their differences and assess data for corruption or manipulation. A proof of concept will be established by incorporating the data characterization methods developed in the project into generating synthetic data with higher degrees of realism than conventional methods. The approach will be validated by testing the ability of the synthetic data to characterize software/hardware system performance more accurately.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.

现代计算方法，例如基于机器学习（ML）的方法，在效率和性能方面产生了令人印象深刻的提高，但越来越多地取决于大量数据。这些数据驱动的方法从人工设计的源代码的经典技术过渡到在数据集中训练的算法以生成所需的解决方案，并将数据放置在驾驶员座椅中。这些数据驱动的技术的扩散是通过专门设计的新硬件和软件系统启用和加速的，该系统专门为支持与这些算法相关的复杂数据驱动的计算以及随附的数据量相关的大量数据。但是，尽管这些新的硬件和软件系统的性能令人印象深刻，但了解其设计的数据组件仍然倾向于以软件和基于硬件的解决方案的性能驱动的进步。缺乏数据理解导致了许多不良结果，例如数据驱动解决方案中不需要的偏见，无法确定数据集以提前解决给定问题的实际适用性，无法确定数据集已被操纵或损坏，并且是否可以使用准确的合成数据来培训和测试这些软件和这些软件和硬件和硬件和硬件和难以训练的系统。该项目旨在为表征基于大规模张量的数据集提供一个强大的框架，以提高对数据本身的了解并启用合成数据的生产，从而更准确地复制现实世界数据，以在系统设计测试和验证中使用。特别是在系统设计测试和验证中。统计，结构和性能数据分析的方法，以实现更强大的数据表征。一组更整体的数据特征将使数据集以适合特定任务的数据集评估数据和数据集评估。它还将允许数据集比较数据集的差异并评估数据是否损坏或操纵。将通过将项目中开发的数据表征方法纳入比传统方法更高的合成数据来确定概念证明。该方法将通过测试合成数据更准确地表征软件/硬件系统性能的能力来验证该方法。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审查标准，被认为值得通过评估来获得支持。