AF: Medium: Collaborative Research: Beyond Sparsity: Refined Measures of Complexity for Linear Algebra
AF:媒介:协作研究:超越稀疏性:线性代数复杂性的精确度量
基本信息
- 批准号:1763315
- 负责人:
- 金额:$ 55.21万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2018
- 资助国家:美国
- 起止时间:2018-03-15 至 2022-02-28
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Modern data science applications exploit structure in real life data using machine learning (including deep learning) algorithms. At the core of most of these systems are algorithms for a branch of mathematics called linear algebra. In particular, a large portion of these algorithms utilize the fact that real life data has properties that can be captured using certain parsimonious linear algebraic structures. This project studies new, more powerful linear algebraic structures and algorithms that exploit these new structures. Given the fundamental importance of these algorithms, ideas generated from this project are expected to be implemented in widely deployed machine learning systems. The outreach component of this project involves (1) a technical workshop for researchers from diverse areas and (2) outreach events for K-12 students.A variety of problems in modern data science have been successfully characterized using a width. For example, one of the most common widths, the rank of a matrix, has a near-ubiquitous use across many applications. This project significantly expands the understanding of several recently proposed widths and extracts their full potential for positive practical outcomes. Furthermore, it contributes to the recently growing work on beyond worst-case analysis in linear algebra, machine learning and coding theory.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.
现代数据科学应用程序使用机器学习(包括深度学习)算法来开发现实生活数据中的结构。大多数这些系统的核心是一个被称为线性代数的数学分支的算法。特别是,这些算法中的很大一部分利用了这样一个事实,即现实生活中的数据具有可以使用某些简约线性代数结构来捕获的属性。这个项目研究新的、更强大的线性代数结构和利用这些新结构的算法。鉴于这些算法的根本重要性,该项目产生的想法有望在广泛部署的机器学习系统中实现。该项目的外展部分包括(1)为不同领域的研究人员举办的技术研讨会和(2)为K-12学生举办的外展活动。使用宽度成功地描述了现代数据科学中的各种问题。例如,矩阵的秩是最常见的宽度之一,在许多应用程序中几乎无处不在地使用。该项目极大地扩大了对最近提出的几个宽度的理解,并充分挖掘了它们取得积极实际成果的潜力。此外,它还有助于最近线性代数、机器学习和编码理论中超越最坏情况分析的工作。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Christopher Re其他文献
Do Multimodal Foundation Models Understand Enterprise Workflows? A Benchmark for Business Process Management Tasks
多模式基础模型理解企业工作流程吗?
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Michael Wornow;A. Narayan;Ben T Viggiano;Ishan S. Khare;Tathagat Verma;Tibor Thompson;Miguel Angel Fuentes Hernandez;Sudharsan Sundar;Chloe Trujillo;Krrish Chawla;Rongfei Lu;Justin Shen;Divya Nagaraj;Joshua Martinez;Vardhan Agrawal;Althea Hudson;Nigam H. Shah;Christopher Re - 通讯作者:
Christopher Re
Christopher Re的其他文献
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{{ truncateString('Christopher Re', 18)}}的其他基金
Collaborative Research: Hardware-Aware Matrix Computations for Deep Learning Applications
协作研究:深度学习应用的硬件感知矩阵计算
- 批准号:
2247015 - 财政年份:2023
- 资助金额:
$ 55.21万 - 项目类别:
Standard Grant
AF:III:Small:Collaborative Research: New Frontiers in Join Algorithms: Optimality, Noise, and Richer Languages
AF:III:Small:协作研究:连接算法的新领域:最优性、噪声和更丰富的语言
- 批准号:
1318205 - 财政年份:2013
- 资助金额:
$ 55.21万 - 项目类别:
Standard Grant
AF:III:Small:Collaborative Research: New Frontiers in Join Algorithms: Optimality, Noise, and Richer Languages
AF:III:Small:协作研究:连接算法的新领域:最优性、噪声和更丰富的语言
- 批准号:
1356918 - 财政年份:2013
- 资助金额:
$ 55.21万 - 项目类别:
Standard Grant
CAREER: A Scalable, Declarative, Imprecise Database Management System
职业:可扩展、声明式、不精确的数据库管理系统
- 批准号:
1353606 - 财政年份:2013
- 资助金额:
$ 55.21万 - 项目类别:
Continuing Grant
EAGER Collaborative: Bringing Together Computational and Linguistic Methods to Extract 'Dark' Geosciences Data for the EarthCube Framework
EAGER Collaborative:结合计算和语言方法为 EarthCube 框架提取“暗”地球科学数据
- 批准号:
1242902 - 财政年份:2012
- 资助金额:
$ 55.21万 - 项目类别:
Standard Grant
CAREER: A Scalable, Declarative, Imprecise Database Management System
职业:可扩展、声明式、不精确的数据库管理系统
- 批准号:
1054009 - 财政年份:2011
- 资助金额:
$ 55.21万 - 项目类别:
Continuing Grant
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