Phase I IUCRC University of Florida: Center for Big Learning

第一阶段 IUCRC 佛罗里达大学:大学习中心

基本信息

  • 批准号:
    1747783
  • 负责人:
  • 金额:
    $ 75万
  • 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:
    2018-02-01 至 2025-01-31
  • 项目状态:
    未结题

项目摘要

This project establishes the NSF Industry/University Collaborative Research Center for Big Learning (CBL). The vision is to create intelligence towards intelligence-driven society. Through catalyzing the fusion of diverse expertise from the consortium of faculty members, students, industry partners, and federal agencies, CBL seeks to create state-of-the-art deep learning methodologies and technologies and enable intelligent applications, transforming broad domains, such as business, healthcare, Internet-of-Things, and cybersecurity. This timely initiative creates a unique platform for empowering our next-generation talents with cutting-edge technologies of societal relevance and significance. This project establishes the NSF Industry/University Collaborative Research Center for Big Learning (CBL) at University of Florida (UF). With substantial breakthroughs in multiple modalities of challenges, such as computer vision, speech recognition, and natural language understanding, the renaissance of machine intelligence is dawning. The CBL vision is to create intelligence towards intelligence-driven society. The mission is to pioneer novel deep learning algorithms, systems, and applications through unified and coordinated efforts in the CBL consortium. The UF Site will focus on intelligent platforms and applications and closely collaborate with other sites on deep learning algorithms, systems, and applications.The CBL will have broad transformative impacts in technologies, education, and society. CBL aims to create pioneering research and applications to address a broad spectrum of real-world challenges, making significant contributions and impacts to the deep learning community. The discoveries from CBL will make significant contributions to promote products and services of industry in general and CBL industry partners in particular. As the magnet of deep learning research and applications, CBL offers an ideal platform to nurture next-generation talents through world-class mentors from both academia and industry, disseminates the cutting-edge technologies, and facilitates industry/university collaboration and technology transfer.The center repository will be hosted at http://nsfcbl.org. The data, code, documents will be well organized and maintained on the CBL servers for the duration of the center for more than five years and beyond. The internal code repository will be managed by GitLab. After the software packages are well documented and tested, they will be released and managed by popular public code hosting services, such as GitHub and Bitbucket.
该项目建立了NSF行业/大学大学习合作研究中心(CBL)。我们的愿景是创造智能,走向智能驱动的社会。通过促进来自教职员工、学生、行业合作伙伴和联邦机构的不同专业知识的融合,CBL寻求创建最先进的深度学习方法和技术,并实现智能应用,从而改变商业、医疗保健、物联网和网络安全等广泛领域。这一及时的倡议为我们的下一代人才创造了一个独特的平台,使其能够利用具有社会意义和重要意义的尖端技术。该项目在佛罗里达大学(UF)建立了NSF行业/大学大学习协作研究中心(CBL)。随着计算机视觉、语音识别和自然语言理解等多种挑战形式的实质性突破,机器智能的复兴正在到来。CBL的愿景是创造智能,走向智能驱动的社会。其使命是通过CBL联盟的统一和协调努力,开创新型深度学习算法、系统和应用程序的先河。UF网站将专注于智能平台和应用程序,并在深度学习算法、系统和应用程序方面与其他网站密切合作。CBL将在技术、教育和社会方面产生广泛的变革影响。CBL的目标是创建开创性的研究和应用程序,以应对广泛的现实世界挑战,为深度学习社区做出重大贡献和影响。CBL的发现将为促进整个行业,特别是CBL行业合作伙伴的产品和服务做出重大贡献。作为深度学习研究和应用的磁石,CBL提供了一个理想的平台,通过来自学术界和产业界的世界级导师来培养下一代人才,传播尖端技术,促进产学研合作和技术转移。中心知识库将设在http://nsfcbl.org.在中心五年以上的时间里,数据、代码、文件将在CBL服务器上得到很好的组织和维护。内部代码库将由GitLab管理。在软件包被很好地记录和测试之后,它们将由流行的公共代码托管服务发布和管理,如GitHub和BitBucket。

项目成果

期刊论文数量(10)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Shared Manifold Learning Using a Triplet Network for Multiple Sensor Translation and Fusion With Missing Data
Wave Physics-Informed Matrix Factorizations
  • DOI:
    10.1109/tsp.2023.3348948
  • 发表时间:
    2024-01-01
  • 期刊:
  • 影响因子:
    5.4
  • 作者:
    Tetali,Harsha Vardhan;Harley,Joel B.;Haeffele,Benjamin D.
  • 通讯作者:
    Haeffele,Benjamin D.
Improving Question Generation with Sentence-level Semantic Matching and Answer Position Inferring
  • DOI:
    10.1609/aaai.v34i05.6366
  • 发表时间:
    2019-12
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Xiyao Ma;Qile Zhu;Yanlin Zhou;Xiaolin Li;D. Wu
  • 通讯作者:
    Xiyao Ma;Qile Zhu;Yanlin Zhou;Xiaolin Li;D. Wu
Learning Tensor Representations to Improve Quality of Wavefield Data
学习张量表示以提高波场数据的质量
GraphBTM: Graph Enhanced Autoencoded Variational Inference for Biterm Topic Model
  • DOI:
    10.18653/v1/d18-1495
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Qile Zhu;Zheng Feng;Xiaolin Li
  • 通讯作者:
    Qile Zhu;Zheng Feng;Xiaolin Li
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Joel Harley其他文献

Joel Harley的其他文献

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{{ truncateString('Joel Harley', 18)}}的其他基金

EAGER: Real-Time: Ultrasonic Reconstruction and Localization with Deep Helmholtz Networks
EAGER:实时:利用深亥姆霍兹网络进行超声重建和定位
  • 批准号:
    1839704
  • 财政年份:
    2018
  • 资助金额:
    $ 75万
  • 项目类别:
    Standard Grant

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