报 告 人：黄广斌  副教授 主 持 人：王熙照  副所长 日      期：2015年12月7日 时      间：早上 10：00 地      点：计软学院938会议室 This talk will introduce a new generation of learning theory (the resultant biologically inspired learning technique referred to as Extreme Learning Machine (ELM)) and its wide type of applications. ELM outperforms many popular learning methods (e.g., conventional neural networks and support vector machines) with faster learning speed (up to tens of thousands times) and higher accuracies. Preliminary studies shows that ELM also outperforms deep learning in some applications. ELM theories may have explained the reasons why the brain are globally ordered but may be locally random. This talk wishes to share with audiences the trends of machine learning: 1) turning point from machine learning engineering to machine learning science; 2) convergence of machine learning and biological learning; 3) from human and (living) thing intelligence to machine intelligence; 4) from Internet of Things (IoT) to Internet of Intelligent Things and Society of Intelligent Things. BIOGRAPHY黄广斌 (Guang-Bin Huang)  是IEEE Transactions on Cybernetics, Neurocomputing，Cognitive Computation和Neural Networks编辑。 他被Thomson Reuters 评为“Highly Cited Researcher”， 以及列 为“2014 The World's Most Influential Scientific Minds”之一。他也 是IEEE Transactions on Neural Networks and Learning Systems (2013)最佳论文获得者。 他是新加坡南洋理工大学电子电气工程学院副教授 (tenured) ；德 国宝马集团和南洋理工大学联合研究实验室人机交互，脑机交互以 及汽车辅助驾驶项目负责人；劳斯莱斯和南洋理工大学联合研究实 验室导航决策辅助系统项目负责人；新科工程和南洋理工大学联合 研究实验室场景识别和机器学习项目负责人；台达电子和南洋理工 大学联合研究实验室数据分析和视频项目负责人。 他的一个主要学术贡献是提出一套全新的学习理论和方法： Extreme Learning Machines （ELM，超限学习机）。ELM突破了 前30年流行的前馈神经网络以及近20年广泛应用的支撑向量机 （SVM）的理论和技术瓶颈，弥补了机器学习和脑学习机制之间的 空白，解决了计算机之父冯·诺依曼（John von Neumann）60年前 的关于人脑和计算机结构和能力的困惑。 
 


 Title: Extreme Learning Machines (ELM) - Filling the Gap between Frank Rosenblatt's Dream and John von Neumann's Puzzle?     Reporter: Associate Prof. Guangbin Huang   Host: Associate director Xizhao Wang   Date: December 7, 2015   Time: 10:00am   Address: 938 Meeting Room of College of Computer Science &Software Engineering, South School of Shenzhen University   This talk will introduce a new generation of learning theory (the resultant biologically inspired learning technique referred to as Extreme Learning Machine (ELM)) and its wide type of applications. ELM outperforms many popular learning methods (e.g., conventional neural networks and support vector machines) with faster learning speed (up to tens of thousands times) and higher accuracies. Preliminary studies shows that ELM also outperforms deep learning in some applications. ELM theories may have explained the reasons why the brain are globally ordered but may be locally random. This talk wishes to share with audiences the trends of machine learning: 1) turning point from machine learning engineering to machine learning science; 2) convergence of machine learning and biological learning; 3) from human and (living) thing intelligence to machine intelligence; 4) from Internet of Things (IoT) to Internet of Intelligent Things and Society of Intelligent Things.  BIOGRAPHY 黄广斌 (Guang-Bin Huang)  是IEEE Transactions on Cybernetics, Neurocomputing，Cognitive Computation和Neural Networks编辑。 他被Thomson Reuters 评为“Highly Cited Researcher”， 以及列 为“2014 The World's Most Influential Scientific Minds”之一。他也 是IEEE Transactions on Neural Networks and Learning Systems (2013)最佳论文获得者。  他是新加坡南洋理工大学电子电气工程学院副教授 (tenured) ；德 国宝马集团和南洋理工大学联合研究实验室人机交互，脑机交互以 及汽车辅助驾驶项目负责人；劳斯莱斯和南洋理工大学联合研究实 验室导航决策辅助系统项目负责人；新科工程和南洋理工大学联合 研究实验室场景识别和机器学习项目负责人；台达电子和南洋理工 大学联合研究实验室数据分析和视频项目负责人。  他的一个主要学术贡献是提出一套全新的学习理论和方法： Extreme Learning Machines （ELM，超限学习机）。ELM突破了 前30年流行的前馈神经网络以及近20年广泛应用的支撑向量机 （SVM）的理论和技术瓶颈，弥补了机器学习和脑学习机制之间的 空白，解决了计算机之父冯·诺依曼（John von Neumann）60年前 的关于人脑和计算机结构和能力的困惑。