Carnegie Mellon University Planning Grant: I/UCRC for Big Learning

卡内基梅隆大学规划补助金：I/UCRC for Big Learning

• 批准号: 1650485
• 负责人: Ruslan Salakhutdinov
• 金额: $ 1.5万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-02-15 至 2018-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1650485&HistoricalAwards=false
• 关键词: Carnegie; Mellon; University; Planning; Grant

This project will study the feasibility of establishing the Center for Big Learning (CBL), as an NSF IUCRC. The mission of CBL is to develop novel large-scale deep learning algorithms, systems, and applications through unified and coordinated efforts in the CBL consortium. The vision of CBL is to develop intelligence algorithms towards intelligence-driven society. With the explosion of big data generated from natural systems, scientific experiments, engineered systems, and human activities, we need to develop intelligent algorithms and systems to facilitate our decision making with distilled insights automatically at scale. The proposed CBL center is a timely initiative as we move 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, entrepreneurs, IT and industry giants working together on accomplishing our mission and vision. This planning grant will lead to a successful Phase I proposal for the establishment of the Center for Big Learning at CMU 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 Deep Learning.Recent dramatic breakthroughs in deep learning (DL) and multi-model learning (e.g., image, video, speech, and text), hold great promise for making a big impact on many research areas, including computational biology, neuroscience, medical diagnosis, computer vision, data mining, and robotics. The key mission of CBL at CMU 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 at CMU is to develop intelligent algorithm 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 neural networks, complex recurrent neural networks, brain-inspired components, optimization, deep reinforcement learning, and unsupervised learning.(2) Novel systems. We propose to develop 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.

该项目将研究建立大学习中心（CBL）作为NSF IUCRC的可行性。CBL的使命是通过CBL联盟的统一和协调努力，开发新的大规模深度学习算法，系统和应用程序。CBL的愿景是开发智能算法，实现智能驱动的社会。随着自然系统、科学实验、工程系统和人类活动产生的大数据的爆炸式增长，我们需要开发智能算法和系统，以便于我们在大规模自动进行决策。拟议中的CBL中心是一个及时的举措，因为我们正在走向充满机遇的智能世界。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、企业家、IT和行业巨头共同努力，实现我们的使命和愿景。这项规划拨款将导致成功的第一阶段提案，在CMU建立一个跨多个校区和大量行业合作伙伴的坚实联盟。CBL具有以下广泛的影响。(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、研讨会和计划中的培训课程将极大地促进和拓宽深度学习的研究和实现。最近在深度学习（DL）和多模型学习（例如，图像、视频、语音和文本），对许多研究领域产生重大影响，包括计算生物学、神经科学、医学诊断、计算机视觉、数据挖掘和机器人技术。CBL在CMU的关键使命是通过CBL联盟中的统一和协调努力，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，系统和应用程序方面开拓进取。CMU CBL的愿景是开发智能算法，迈向智能驱动的社会。CBL在以下几个关键研究领域具有开创性的学术价值：（1）新颖的算法。本主题侧重于新型深度学习算法和架构，如深度神经网络、复杂递归神经网络、大脑启发组件、优化、深度强化学习和无监督学习。(2)新颖的系统。我们建议开发新的架构，资源管理和软件框架，以支持桌面，移动设备，集群和云上的大规模DL平台和应用程序。(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。