University of Missouri-Kansas City Planning Grant: I/UCRC for Big Learning

NSF I/UCRC大学习中心（CBL）的使命是探索新兴的大规模深度学习（DL）的研究前沿，以实现有效和高效的计算智能，为大数据和大系统时代的智能研究和应用设计新颖的学习算法和系统机制。通过多个学术站点的大型学习联盟（与佛罗里达，CMU和俄勒冈州合作）和大量行业合作伙伴，该中心寻求催化学术界，政府，行业利益相关者的智慧融合，算法，系统和教育的快速创新，以及技术转移到具有现实世界相关性和意义的尖端产品和服务中。拟议中心的更广泛影响：随着自然系统、工程系统和人类/生命活动产生的数据的爆炸式增长，我们需要智能软件和硬件，以自动大规模地提取见解来促进我们的决策。随着我们的社会走向充满机遇的智能世界，拟议中的I/UCRC大学习中心是一个及时的倡议。大学习联盟有望成为深度学习研究和应用的磁石，吸引顶尖的研究人员、热情的企业家、IT和行业巨头共同努力，实现CBL的美好使命和愿景。具体而言，CBL具有以下更广泛的影响。(1)在开创性研究和应用方面为深度学习社区做出重大贡献和影响，以解决广泛的现实挑战。(2)为促进整个行业，特别是我们会员的产品和服务作出重大贡献和影响。(3)通过现实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教育做出重大贡献和影响。(4)我们的会议、论坛、会议和计划的培训课程将极大地促进和拓宽DL的研究和实体化。随着信号压缩、分类和识别在多种挑战模式（如图像、视频、语音、文本以及生命、健康和科学数据）中的重大突破，计算智能的复兴迫在眉睫。CBL的使命是通过团结和协调的努力，深入整合和融合我们众多教职员工、学生和行业合作伙伴的广泛专业知识，引领这一新兴趋势。CBL的愿景是创造智能驱动社会的智能推动者。CBL在以下关键研究主题上具有开创性的智力价值。(1)算法新颖。本主题侧重于新颖的深度学习算法和架构，如深度架构、复杂深度神经网络、大脑启发组件、深度学习的优化和加速、神经机器和传统机器学习算法的适应。(2)系统新颖。我们为嵌入式设备、移动设备、桌面设备、集群和云提出了新的资源管理策略、异构架构和软件工具包。(3)在商业、健康、成像和智能领域的新应用，包括新图像/视频建模和压缩中的深度残差网络、熵编码中大规模上下文模型的RNN、大规模视觉对象再识别以及成像靶向药物递送。在计划阶段，我们将建立一个坚实的中心战略计划，营销计划，以及由五个学术站点和几十个行业成员组成的大学习联盟。

The mission of the proposed NSF I/UCRC Center for Big Learning (CBL) is to explore research frontiers in emerging large-scale deep learning (DL) to realize effective and efficient computational intelligence, design novel learning algorithms and system mechanisms for intelligence research and applications in the era of big data and big systems. Through the big learning consortium of multiple academic sites (in collaboration with Florida, CMU, and Oregon) and a large number of industry partners, the center seeks to catalyze the fusion of wisdom from academia, government, industry stakeholders, the rapid innovation in algorithms, systems, and education, and technology transfer into cutting-edge products and services with real-world relevance and significance. Broader Impacts of the proposed center: with the explosive growth of data generated from natural systems, engineered systems, and human/life activities, we need intelligent software and hardware to facilitate our decision making with distilled insights automatically at scale. The proposed I/UCRC Center for Big Learning is a timely initiative as our society moves towards intelligence-enabled world of opportunities. The Big Learning 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 missions and visions of CBL. In particular, 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.With dramatic breakthroughs in signal compression, classification and identification in multiple modalities of challenges (e.g., image, video, speech, text, and life, health & science data), the renaissance of computational intelligence is looming. The mission of the CBL is to pioneer in this emerging trend through united and coordinated efforts and deep integration and 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 architectures, complex deep neural networks, brain-inspired components, optimization and acceleration of the deep learning, neural machines, and adaptation of conventional machine learning algorithms. (2) Novel systems. We propose novel resource management strategies, heterogeneous architectures, and software tool kits for embedded devices, mobiles, desktops, clusters, and clouds. (3) Novel applications in business, health, imaging, and smart things, including deep residual networks in new image/video modeling and compression, RNN for large scale context models in entropy coding, large scale visual object re-identification, and targeted drug delivery with imaging. During the planning phase, we will establish a solid center strategic plan, marketing plan, and the consortium of big learning that consists of five academic sites and several dozens of industrial members.