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I/UCRC: University of Florida Planning Grant: I/UCRC for Big Learning

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
批准号:
1624782
负责人:
Xiaolin Li
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
项目状态:
已结题
起止时间:
2016-07-01 至 2017-12-31

项目摘要

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中文摘要
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英文摘要
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.
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