特邀报告
时间: 2017-01-03 | 访问: 311


          李德毅院士
   
李德毅,指挥自动化和人工智能专家。1944年11月出生于江苏省泰县(现江苏省泰州市姜堰区)。中国工程院院士、欧亚科学院院士,指挥自动化和人工智能专家。1944年出生于江苏省。1967年毕业于南京工学院(现东南大学),1983年获英国爱丁堡海里奥特·瓦特大学博士学位。现任总参第61研究所研究员,中国指挥和控制学会名誉理事长,中国人工智能学会理事长。李德毅参加了多项电子信息系统重大工程的研制和开发;最早提出“控制流—数据流”图对理论和一整套用逻辑语言实现的方法,等。获国家和省部级二等奖以上奖励9项,获得10项发明专利,发表论文130多篇,出版中文著作5本、英文专著3本。现为北京邮电大学计算机学院院长。现任中国电子系统工程研究所副所 长,曾任国家自然科学基金委员会信息科学部主任。
报告题目:无人驾驶的图灵测试
报告摘要:自从图灵测试提出以来,脑认知的度量和测试一直是人工智能的热点话题。图灵测试根本上是一个不确定性人工智能测试。测试的问题域,无论是机器对话、机器写诗、机器为图配题、还是机器自动驾驶,图灵测试都允许测试者现场介入,尽管判定结果存在整体客观性,但都会带有近似性和主观性的成分。我们认为,和对话、诗词写作、图配题等智能活动相比,驾驶图灵测试可通过驾驶行为大数据进行更为精确、更为客观的评测机器人的认知。无人驾驶的根本问题不在于车而在于人,其核心是物化驾驶员在开放条件下对不确定性驾驶环境的认知,它是在长期的驾驶实践中从环境感知到决策控制的经验积累形成的。回顾十年来我们参加的一次次无人车比赛和里程碑试验,智能车各种感知和认知手段,相互依存,彼此缠绕。在各类比赛场、测试场,无人车表现千奇百怪、反反复复,我们困惑过,迷茫过,试来试去,换车、换平台、加电源、换模块等,通过多车交叉验证和常态试验,终于理出了头绪,重点是物化驾驶员认知,解耦出类脑的功能模块,研发机器驾驶脑,和汽车一起构成轮式机器人。当前,国家正在建立多个智能驾驶专用试验场与评估环境,它们很可能发展成为人与轮式机器人比赛驾驶智能的试验场,发展成为赛车手和赛车机器人角逐冠军的比赛场,发展成为后图灵时代的图灵测试场。这样一来,基于驾驶的图灵测试可大大推动我国类脑研究和无人车的产业化发展。

       C. L. Philip CHEN (陈俊龙教授)
      
Chair Professor  Dean of Faculty of Science and Technology
       IEEE Fellow, AAAS Fellow, CAA Fellow, HKIE Fellow, IASCYS Academician
       Sr. Past President, IEEE Systems, Man, and Cybernetics Society
       Editor-in-Chief, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2014 -

  
  Dr. Chen was a visiting research scientist at the Materials Directorate, Wright Laboratory, Wright-Patterson Air Force Base. He was a senior research fellow sponsored by the National Research Council, National Academy of Sciences (U.S.A) and was a research faculty fellow at NASA Glenn Research Center for several years. Dr. Chen hase leven paperslisted in“Highly Cited Papers”, 2 of which are in top 0.1%, by the ISI Web of Knowledge,Essential Science Indicatorsfrom Jan 2006 to Jan 2016.For more information, please refer to professor Chen's personal web pages :http://www.fst.umac.mo/en/staff/pchen.html
报告题目: A Fuzzy Restricted Boltzmann Machine: An approach to enhance deep learning
报告摘要: In recent years, deep learning caves out a research wave in machine learning. With outstanding performance, more and more applications of deep learning in pattern recognition, image recognition, speech recognition, and video processing have been developed. Restricted Boltzmann machine (RBM) plays an important role in current deep learning techniques, as most of existing deep networks are based on or related to it. In particular, deep belief networks and deep Boltzmann machines are formed by stacking RBMs layer by layer and optionally fine-tuning the resulted deep networks with a backpropagation algorithm. RBMs have been widely applied in diverse fields, such as image recognition and classification, dimensionality reduction, feature learning, and collaborative filtering, etc. This talk will introduce a Fuzzy Restricted Boltzmann Machine (FRBM) that is established by replacing real-valued weights and bias terms with symmetric triangular fuzzy numbers (STFNs) or Gaussian fuzzy numbers and corresponding learning algorithms. A theorem is concluded that all FRBMs with symmetric fuzzy numbers will have identical learning algorithm to that of FRBMs with STFNs. Experiments results in MNIST handwriting recognition and Bar-and-Stripes benchmark indicate that the proposed FRBMs significantly outperform RBMs in learning accuracy and generalization ability, especially when encountering unlearned samples and recovering incomplete images.

          Professor Hui Xiong(熊晖教授)
 
      Dr. Hui Xiong is currently a Full Professor and Vice Chair in the Management Science and Information Systems Department, and the Director of Rutgers Center for Information Assurance at Rutgers, the State University of New Jersey, where he received a two-year early promotion/tenure (2009), the Rutgers University Board of Trustees Research Fellowship for Scholarly Excellence (2009), and the Best Research Paper Award at the 2011 IEEE International Conference on Data Mining (ICDM). He is also a Distinguished Visiting Professor (Grand Master Chair Professor) at the University of Science and Technology of China (USTC). For his outstanding contributions to data mining and mobile computing, he was elected an ACM Distinguished Scientist in 2014.
      Dr. Xiong is a prominent researcher in the areas of business intelligence, data mining, big data, and geographic information systems (GIS). He has a distinguished academic record that includes 200+ referred papers in conference proceedings and journals, and an authoritative Encyclopedia of GIS (Springer). He is serving on the editorial boards of IEEE Transactions on knowledge and Data Engineering (TKDE), ACM Transactions on Knowledge Discovery from Data (TKDD), ACM Transactions on Management Information Systems (TMIS), and IEEE Transactions on Big Data. Also, he served as a Program Co-Chair of the Industrial and Government Track for the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012), a Program Co-Chair for the IEEE 2013 International Conference on Data Mining (ICDM-2013), and a General Co-Chair for the IEEE 2015 International Conference on Data Mining (ICDM-2015).

报告题目:
Talent Analytics: Prospects and Opportunities
报告摘要:The big data trend has made its way to talent management. Indeed, the availability of large-scale human resources (HR) data provide unparalleled opportunities for business leaders to understand talent behaviors and generate useful talent knowledge, which in turn deliver intelligence for real-time decision making and effective people management at work. In this talk, I will introduce the state-of-the-art techniques used to evaluate the management performance, recruit and retain great people, enhance talent development, and demonstrate how these techniques are used at cutting-edge companies. In particular, I will explain how data analytic techniques can be used on people-related issues, such as recruiting, performance evaluation, talent retention, talent development, job matching, team management, and organizational stability analysis. Finally, I will present two case studies: 1) recruitment market trend analysis with sequential latent variable models, and 2) talent circle detection in job transition networks.

  宗成庆教授
   宗成庆,1998年3月毕业于中国科学院计算技术研究所,获博士学位。1998年5月至2000年4月在中国科学院自动化研究所从事博士后研究,2000年4月起在自动化所工作至今,现为模式识别国家重点实验室研究员、博士生导师。主要从事机器翻译和自然语言处理相关领域的研究,主持国家自然科学基金项目、国家“863”计划项目等10余项,在国际权威刊物和顶级会议上发表论文70余篇,出版专著和译著各一部。目前他是国际计算语言学委员会(ICCL)委员、亚洲自然语言处理联合会(AFNLP)副主席、中国中文信息学会副理事长、中国人工智能学会理事和中国计算机学会中文信息技术专委会副主任,担任期刊ACM TALLIP副主编、《自动化学报》副主编、IEEE Intelligent Systems、Machine Translation和JCST编委等职务。
报告题目:实体识别与语义角色标注
报告摘要:在自然语言文本中,人名、地名和组织机构名等命名实体占有相当大的比例,命名实体识别的准确率高低直接影响整个自然语言处理系统的性能,并对后续任务的完成产生重要影响。而语义角色标注则是直接针对句子的5个W(Who, What, Where, When, Why)问题寻找答案的自然语言理解核心技术。无论是命名实体识别,还是语义角色标注,对于知识库(知识图谱)构建都具有重要的用途。本报告首先介绍命名实体识别和语义角色标注研究的基本方法,然后介绍近年来这两项技术研究的主要进展,最后对相关研究所面临的问题和未来方向做简要分析和展望。

          孟德宇副教授
     
     孟德宇,西安交通大学数学与统计学院副教授,博导。曾赴香港理工大学,Essex大学与卡内基梅隆大学进行学术访问与合作。共接收/发表论文70余篇,其中包括TPAMI, TIP, TKDE, TNNLS等国际期刊与ICML、 NIPS、 CVPR、ICCV等国际会议论文。担任ICML,NIPS等会议程序委员会委员,AAAI2016高级程序委员会委员。曾获陕西省青年科技奖,陕西省优秀博士论文奖,入选首批西安交通大学青年拔尖人才计划。目前主要聚焦于自步学习、误差建模、张量稀疏性等机器学习相关方向的研究。
报告题目:误差建模原理
报告摘要:传统机器学习主要关注于确定性信息的建模,而在复杂场景下,机器学习方法容易出现对数据噪音的鲁棒性问题,而该鲁棒性问题与误差函数的选择紧密相关。本次报告聚焦于如何针对包含复杂噪音数据进行误差建模的鲁棒机器学习原理。这一理对在线视频处理、医学图像恢复等问题,已体现出个性化的应用优势,该原理亦有希望能够引导出更多有趣的机器学习相关应用与发现


 
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