Collaborative Research: Randomized Feature Methods for Modeling and Dynamics: Theory and Algorithms
协作研究:建模和动力学的随机特征方法:理论和算法
基本信息
- 批准号:2208340
- 负责人:
- 金额:$ 21.46万
- 依托单位:
- 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 2025-08-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
The objective of this research program is to develop consistent and theoretically validated machine learning algorithms for high-stakes decisions. The project will study randomized feature networks as a simpler but equally powerful alternative to fully-trainable neural networks for high-dimensional function approximation. The long-term goal is to develop methods that integrate machine learning and dynamical systems, a challenging new frontier in data science for scientific problems. This project also provides research training opportunities for undergraduate students, graduate students, and postdoctoral fellows.The main goal of this project is to develop new algorithms for data-driven function approximation, with the goal of using learning techniques for scientific modeling and dynamics. The focus is on the construction of randomized algorithms with complexity, accuracy, and/or stability guarantees. Rigorous algorithmic design and modeling is at the core of this scientific computing project, where we leverage advances in machine learning to augment simulations and extract better features for approximating dynamical systems. This project introduces a family of new algorithms based on randomized features with adaptive thresholding procedures to improve accuracy without overfitting. By incorporating various structural information, this has the potential to avoid the curse-of-dimensionality for several physical problems of interest. The main test problems focus on scientific models, high-dimensional systems, and high-dimensional dynamical systems. In addition, by understanding random feature models, we provide one avenue toward a better understanding of neural network models.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.
该研究项目的目标是为高风险决策开发一致且理论上经过验证的机器学习算法。 该项目将研究随机特征网络作为一种更简单但同样强大的替代完全可训练神经网络的高维函数逼近。长期目标是开发集成机器学习和动态系统的方法,这是数据科学中具有挑战性的新前沿科学问题。该项目还为本科生、研究生和博士后研究员提供研究培训机会。该项目的主要目标是开发数据驱动函数近似的新算法,目标是将学习技术用于科学建模和动力学。重点是构建具有复杂性,准确性和/或稳定性保证的随机算法。严格的算法设计和建模是这个科学计算项目的核心,在这个项目中,我们利用机器学习的进步来增强模拟,并提取更好的特征来近似动态系统。这个项目介绍了一个家庭的新算法的基础上随机功能与自适应阈值程序,以提高准确性,而不会过度拟合。 通过结合各种结构信息,这有可能避免几个感兴趣的物理问题的维数灾难。主要的测试问题集中在科学模型,高维系统和高维动力系统。此外,通过理解随机特征模型,我们提供了一个更好地理解神经网络模型的途径。该奖项反映了NSF的法定使命,并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。
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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Rachel Ward其他文献
Patient-Specific Heart Constraint: A Tool for Optimization and Evaluation of Mean Heart Dose in Breast Cancer Patients
- DOI:
10.1016/j.prro.2020.10.005 - 发表时间:
2021-03-01 - 期刊:
- 影响因子:
- 作者:
Rachel Ward;Katrina West;Drew Latty;Rachael Beldham-Collins;Dan Jia;Wei Wang;Shamira Cross;Val Gebski;Verity Ahern;Kirsty Stuart - 通讯作者:
Kirsty Stuart
Police peer supporters in a domestic abuse-specific peer support initiative: Motivations, experiences and challenges
警察同伴支持者参与针对家庭虐待的同伴支持倡议:动机、经验和挑战
- DOI:
10.1177/14613557241244607 - 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Rachel Ward;Liliana Belkin - 通讯作者:
Liliana Belkin
Prenatal fruit juice exposure enhances memory consolidation in male post-weanling Sprague-Dawley rats
产前果汁暴露增强雄性断奶后斯普拉格-道利大鼠的记忆巩固
- DOI:
- 发表时间:
2020 - 期刊:
- 影响因子:3.7
- 作者:
Rachel Ward;C. Scavuzzo;P. Mandhane;F. Bolduc;C. Dickson - 通讯作者:
C. Dickson
The Benefit of Virtual Outpatient Rehabilitative Care: a Pilot Study
- DOI:
10.1016/j.apmr.2022.08.879 - 发表时间:
2022-12-01 - 期刊:
- 影响因子:
- 作者:
Rebekah Harris;Emma Fitzelle-Jones;Rachel Ward;Elisa Ogawa;Catherine Kelly;Mariam Abutaleb;Thomas Travison;Jonathan Bean - 通讯作者:
Jonathan Bean
Status epilepticus alert reduces time to administration of second-line antiseizure medications
癫痫持续状态警报缩短了二线抗癫痫药物的给药时间
- DOI:
10.1212/cpj.0000000000000544 - 发表时间:
2018 - 期刊:
- 影响因子:0
- 作者:
M. Villamar;A. Cook;Chenlu Ke;Yan Xu;Jordan L. Clay;K. Dolbec;Rachel Ward;L. Goldstein;Meriem Bensalem - 通讯作者:
Meriem Bensalem
Rachel Ward的其他文献
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{{ truncateString('Rachel Ward', 18)}}的其他基金
CAREER: Sparsity-aware Sampling Theorems and Applications
职业:稀疏感知采样定理和应用
- 批准号:
1255631 - 财政年份:2013
- 资助金额:
$ 21.46万 - 项目类别:
Continuing Grant
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Cell Research
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Cell Research
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- 批准号:30824808
- 批准年份:2008
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- 批准号:10774081
- 批准年份:2007
- 资助金额:45.0 万元
- 项目类别:面上项目
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