大规模机器学习的实现设想
Making super large-scale machine learning possible
刘铁岩   
报告人照片   刘铁岩博士,现任微软亚洲研究院首席研究员/主任研究员,美国卡内基梅隆大学(CMU)客座教授、英国诺丁汉大学荣誉教授、中国科技大学、中山大学、南开大学兼职教授/博导。刘博士的研究兴趣包括:人工智能、机器学习、信息检索、数据挖掘等。他的先锋性工作促进了机器学习与信息检索之间的融合,被国际学术界公认为“排序学习”领域的代表人物, 他在该领域的学术论文已被引用近万次,并受斯普林格出版社之邀撰写了该领域的首部学术专著(并成为斯普林格计算机领域华人作者的十大畅销书之一)。
  The capability of learning super big models is becoming crucial in this big data era. For example, one may need to learn an LDA model with millions of topics, or a word embedding model with billions of parameters. However, it turns out that training such big models is very challenging: with the state-of-the-art machine learning technologies, one has to use a huge number of machines for this purpose, which is clearly beyond the capability of common machine learning practitioners. In this research, we want to answer the question whether it is possible to train super big machine learning models using just a modest computer cluster. To achieve this goal, we focus on two kind of innovations. First, make important modifications to the training procedure of existing machine learning algorithms, to make them much more cost-effective. Second, develop a new parameter server based distributed machine learning framework, which specifically targets the efficient training of super big models.
报告时间:2015年10月23日14时30分    报告地点:科大西区科技楼西楼18楼1810
报名截止日期:2015年10月22日    可选人数:30