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

NSF I/UCRC Center for Big Learning（CBL）的使命是探索新兴的大规模深度学习（DL）领域的研究前沿，实现有效、高效的计算智能，为大数据、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）和大量的行业合作伙伴，该中心旨在促进学术界，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意义和意义的尖端产品和服务。 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性增长，我们需要智能软件和硬件来帮助我们进行大规模的决策。拟议的I/UCRC大学习中心是一个及时的举措，因为我们的社会走向智能化的机会世界。大学习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、热情的企业家、IT和行业巨头共同努力，实现CBL充满希望的使命和愿景。特别是，CBL具有以下更广泛的影响。(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，会议和计划中的培训课程将极大地促进和拓宽DL的研究和实现。随着信号压缩，分类和识别在多种挑战模式（例如，图像、视频、语音、文本和生命、健康科学数据），计算智能的复兴正在逼近。CBL的使命是通过团结协调的努力，以及我们大量教职员工，学生和行业合作伙伴的广泛专业知识的深度整合和融合，引领这一新兴趋势。CBL的愿景是为智能驱动的社会创造智能使能器。CBL在以下关键研究主题中具有开创性的智力价值。(1)新颖的算法。本主题重点关注新型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、神经机器以及传统机器学习算法的适应。(2)新颖的系统。我们提出了新的资源管理策略，异构体系结构，和软件工具包的嵌入式设备，手机，台式机，集群和云。(3)商业、健康、成像和智能事物中的新应用，包括新图像/视频建模和压缩中的深度残差网络，熵编码中的大规模上下文模型的RNN，大规模视觉对象重新识别，以及成像的靶向药物递送。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，以及由五个学术网站和几十个工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.