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

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的愿景是向智能驱动的社会开发智能算法。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爆炸式增长，我们需要开发智能算法和系统，以帮助我们进行大规模自动提取洞察力的决策。在我们迈向充满机会的智能世界之际，拟议的CBL中心是一个及时的举措。CBL联盟有望成为深度学习研究和应用的磁石，并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。这笔规划拨款将导致在CMU建立大型学习中心的第一阶段提案获得成功，该中心拥有跨多个校区和大量行业合作伙伴的坚实财团。(1)在开创性研究和应用方面对深度学习社区做出重大贡献和影响，以应对广泛的现实世界挑战。(2)对推广工业界的产品和服务，特别是我们会员的产品和服务作出重大贡献和影响。(3)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，这些人才来自真实世界的背景和来自学术界和产业界的世界级导师。(4)我们的会议、论坛、会议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体化。最近深度学习(DL)和多模型学习(如图像、视频、语音和文本)的戏剧性突破，有望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。CBL在CMU的主要使命是通过CBL联盟的统一和协调努力，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、系统和应用方面开创先河。CBL在CMU的愿景是向智能驱动的社会开发智能算法。CBL在以下关键研究主题方面具有开创性的智慧价值：(1)新颖的算法。本文重点介绍了深度神经网络、复杂递归神经网络、脑启发组件、优化、深度强化学习、无监督学习等新的动态学习算法和体系结构。(2)新系统。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、集群和云上的大规模数字图书馆平台和应用。(3)健康、移动/物联网和监控方面的新应用。在规划阶段，我们将建立坚实的中心战略规划、营销计划，以及由四个学术站点和大量行业成员组成的CBL联合体。

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.