I/UCRC: University of Florida Planning Grant: I/UCRC for Big Learning
I/UCRC:佛罗里达大学规划补助金:I/UCRC 大学习
基本信息
- 批准号:1624782
- 负责人:
- 金额:$ 1.5万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2016
- 资助国家:美国
- 起止时间:2016-07-01 至 2017-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
This project proposes to establish the NSF I/UCR Center for Big Learning (CBL). The mission of CBL is to pioneer in large-scale deep learning algorithms, systems, and applications through unified and coordinated efforts in the CBL consortium. The vision of CBL is to create intelligence enablers towards intelligence-driven society. With the explosive big data generated from natural systems, engineered systems, and human activities, we need intelligent algorithms and systems to facilitate our decision making with distilled insights automatically at scale. The proposed CBL center is a timely initiative as our society moves towards intelligence-enabled world of opportunities. The CBL consortium is expected to become the magnet of deep learning research and applications and attract leading researchers, enthusiastic entrepreneurs, IT and industry giants working together on accomplishing the promising mission and vision. This planning grant will lead to a successful proposal for the establishment of the NSF I/UCR Center for Big Learning with a solid consortium across multiple campuses and a large number of industry partners. CBL has the following broader impacts. (1) Making significant contributions and impacts to the deep learning community on pioneering research and applications to address a broad spectrum of real-world challenges. (2) Making significant contributions and impacts to promote products and services of industry in general and our members in particular. (3) Making significant contributions and impacts to the urgently-needed education of our next-generation talents with real-world settings and world-class mentors from both academia and industry. (4) Our meetings, forums, conferences, and planned training sessions will greatly promote and broaden the research and materialization of DL. The proposed project aims to establish the NSF I/UCR Center for Big Learning (CBL). With dramatic breakthroughs in multiple modalities of challenges (e.g., image, video, speech, text, and Q&A), the renaissance of machine intelligence is looming.The mission of CBL is to pioneer in large-scale deep learning (DL) algorithms, systems, and applications through unified and coordinated efforts in the CBL consortium via fusion of broad expertise from our large number of faculty members, students, and industry partners. The vision of CBL is to create intelligence enablers towards intelligence-driven society. CBL possesses the pioneering intellectual merit in the following key research themes. (1) Novel algorithms. This theme focuses on novel DL algorithms and architectures, such as deep architecture, complex deep neural networks, brain-inspired components, optimization, deep reinforcement learning, and unsupervised learning. (2) Novel systems. We propose novel architectures, resource management, and software frameworks for enabling large-scale DL platforms and applications on desktops, mobiles, clusters, and clouds. (3) Novel applications in health, mobile/IoT, and surveillance. During the planning phase, we will establish a solid center strategic plan, marketing plan, and the CBL consortium that consists of four academic sites and a large number of industrial members. CBL has the following broader impacts. (1) Making significant contributions and impacts to the deep learning community on pioneering research and applications to address a broad spectrum of real-world challenges. (2) Making significant contributions and impacts to promote products and services of industry in general and our members in particular. (3) Making significant contributions and impacts to the urgently-needed education of our next-generation talents with real-world settings and world-class mentors from both academia and industry. (4) Our meetings, forums, conferences, and planned training sessions will greatly promote and broaden the research and materialization of DL.
该项目建议建立NSF I/UCR大学习中心(CBL)。CBL的使命是通过CBL联盟的统一和协调努力,在大规模深度学习算法,系统和应用程序方面发挥先锋作用。CBL的愿景是为智能驱动的社会创造智能使能器。随着自然系统、工程系统和人类活动产生的爆炸性大数据,我们需要智能算法和系统来帮助我们进行大规模的决策。拟议中的CBL中心是一个及时的举措,因为我们的社会走向智能化的机会世界。CBL联盟有望成为深度学习研究和应用的磁石,吸引领先的研究人员、热情的企业家、IT和行业巨头共同努力,实现充满希望的使命和愿景。这项规划拨款将导致建立NSF I/UCR大学习中心的成功提案,该中心拥有多个校区和大量行业合作伙伴的坚实联盟。CBL具有以下更广泛的影响。(1)在开拓性研究和应用方面为深度学习社区做出重大贡献和影响,以应对广泛的现实挑战。(2)为推广行业产品和服务,特别是我们的会员做出重大贡献和影响。(3)通过现实世界的环境和来自学术界和工业界的世界级导师,为我们迫切需要的下一代人才的教育做出重大贡献和影响。(4)我们的会议,论坛,研讨会和计划的培训课程将大大促进和扩大DL的研究和实现。拟议项目旨在建立NSF I/UCR大学习中心(CBL)。随着多种挑战模式的重大突破(例如,CBL的使命是通过CBL联盟的统一和协调努力,通过融合我们大量教职员工、学生和行业合作伙伴的广泛专业知识,在大规模深度学习(DL)算法、系统和应用方面发挥先锋作用。CBL的愿景是为智能驱动的社会创造智能使能器。CBL在以下关键研究主题中具有开创性的智力价值。(1)新颖的算法。本主题侧重于新颖的深度学习算法和架构,如深度架构、复杂的深度神经网络、受大脑启发的组件、优化、深度强化学习和无监督学习。(2)新颖的系统。我们提出了新的架构,资源管理和软件框架,使大规模的DL平台和应用程序的桌面,移动,集群和云。(3)健康、移动的/物联网和监控领域的新应用。在规划阶段,我们将建立一个坚实的中心战略计划,营销计划,和CBL联盟,包括四个学术网站和大量的工业成员。CBL具有以下更广泛的影响。(1)在开拓性研究和应用方面为深度学习社区做出重大贡献和影响,以应对广泛的现实挑战。(2)为推广行业产品和服务,特别是我们的会员做出重大贡献和影响。(3)通过现实世界的环境和来自学术界和工业界的世界级导师,为我们迫切需要的下一代人才的教育做出重大贡献和影响。(4)我们的会议,论坛,研讨会和计划的培训课程将大大促进和扩大DL的研究和实现。
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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Xiaolin Li其他文献
Firm positionality and strategic communication: Analyzing the value of informativeness for managers
公司定位和战略沟通:分析信息对管理者的价值
- DOI:
10.1177/03063070231188809 - 发表时间:
2023 - 期刊:
- 影响因子:2.1
- 作者:
Xiaolin Li;T. Awan;Farooq Mughal - 通讯作者:
Farooq Mughal
DeepSky: Identifying Absorption Bumps via Deep Learning
DeepSky:通过深度学习识别吸收颠簸
- DOI:
10.1109/bigdatacongress.2016.34 - 发表时间:
2016 - 期刊:
- 影响因子:0
- 作者:
Xiaoyong Yuan;Min Li;Sudeep Gaddam;Xiaolin Li;Yinan Zhao;Jingzhe Ma;J. Ge - 通讯作者:
J. Ge
Effect of ignition, initial pressure and temperature on the lower flammability limit of hydrogen/air mixture
点火、初始压力和温度对氢气/空气混合物可燃下限的影响
- DOI:
- 发表时间:
2022 - 期刊:
- 影响因子:7.2
- 作者:
Kai Zhang;Tianpei Luo;Yanchao Li;Tianjiao Zhang;Xiaolin Li;Zongling Zhang;Sheng Shang;Yonghao Zhou;Changshuai Zhang;Xiangfeng Chen;Wei Gao - 通讯作者:
Wei Gao
Pc 4 photodynamic therapy of U87‐derived human glioma in the nude rat
PC 4光动力疗法对裸鼠U87来源的人神经胶质瘤的治疗
- DOI:
- 发表时间:
2005 - 期刊:
- 影响因子:2.4
- 作者:
John E. George;Y. Ahmad;D. Varghai;Xiaolin Li;J. Berlin;David J Jackowe;M. Jungermann;M. S. Wolfe;L. Lilge;A. Totonchi;R. Morris;Allyn Peterson;W. Lust;M. E. Kenney;C. Hoppel;Jiayang Sun;N. Oleinick;D. Dean - 通讯作者:
D. Dean
Bi-Functional N-Doped Tungsten Trioxide Microspheres as Electrode Material for Lithium Ion Battery and Direct Methanol Fuel Cell
双功能氮掺杂三氧化钨微球作为锂离子电池和直接甲醇燃料电池电极材料
- DOI:
10.1021/acs.jpcc.0c05249 - 发表时间:
2020 - 期刊:
- 影响因子:3.7
- 作者:
Xiaolin Li;Shengda Guo;Xianchao Hu;Dong Li;Zhiliang Liu;Wenli Yao;Yang Zhou - 通讯作者:
Yang Zhou
Xiaolin Li的其他文献
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{{ truncateString('Xiaolin Li', 18)}}的其他基金
I-Corps: iLooc-Commercialization Feasibility Research and Implementation of Indoor Smartphone Localization via Indoor Positioning Satellites and Opportunistic Sensing
I-Corps:iLooc-通过室内定位卫星和机会传感实现室内智能手机定位的商业化可行性研究和实现
- 批准号:
1506361 - 财政年份:2014
- 资助金额:
$ 1.5万 - 项目类别:
Standard Grant
MRI: Acquisition of GatorCloud: Enabling High-Impact Scientific Research and Collaboration via Software Defined Networks and Cloud Services
MRI:收购 GatorCloud:通过软件定义网络和云服务实现高影响力的科学研究和协作
- 批准号:
1229576 - 财政年份:2012
- 资助金额:
$ 1.5万 - 项目类别:
Standard Grant
CAREER: SMART: Scalable Adaptive Runtime Management Algorithms and Toolkit for Large-Scale Dynamic Scientific Applications
职业:SMART:用于大规模动态科学应用的可扩展自适应运行时管理算法和工具包
- 批准号:
0953371 - 财政年份:2010
- 资助金额:
$ 1.5万 - 项目类别:
Continuing Grant
CAREER: SMART: Scalable Adaptive Runtime Management Algorithms and Toolkit for Large-Scale Dynamic Scientific Applications
职业:SMART:用于大规模动态科学应用的可扩展自适应运行时管理算法和工具包
- 批准号:
1128805 - 财政年份:2010
- 资助金额:
$ 1.5万 - 项目类别:
Continuing Grant
Conference on Analysis, Modeling and Computation of PDE and Multiphase Flow; Stony Brook, NY
偏微分方程和多相流分析、建模和计算会议;
- 批准号:
0422640 - 财政年份:2004
- 资助金额:
$ 1.5万 - 项目类别:
Standard Grant
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