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

点击翻译按钮获取中文摘要

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.