基于结构化数据随机特征的非监督特征表示学习:理论、算法和应用
Unsupervised Feature Representation Learning via Random Features for Structured Data: Theory, Algorithm, and Applications
吴凌飞   Lingfei Wu
报告人照片   吴凌飞,IBM全球研究院总部(IBM T.J. Watson Research Center) 研究员,威廉玛丽大学计算机系博士,主要研究方向为机器学习,深度学习,表征学习,自然语言处理,大数据。已经发表20几篇顶尖杂志和会议,同时也是13项美国专利的发明人。吴博士长期担任多家国际顶尖杂志的评审,同时担任IEEE Big Data'18 Tutorial Co-Chair,并长期担任AI/ML/DL/DM国际顶会的TPC,如ICML'19, ICLR'19等。
  Most standard machine models are designed for inputs with a vector feature representation. For many structured inputs such as time-series, strings, histograms, and graphs, since there are no explicit features in data, much work has aimed to develop the effective representation of these complex inputs. Despite the great success of Deep Learning models have achieved, we aim to develop a generic methodology to learn feature representation directly from "unlabeled" data for structured inputs. In the first part of this talk, we will first talk about D2KE, a generic framework to learn a kernel and its embedding from any distance (ICLR'19 submission). In the second part, I will present how to apply D2KE for learning embeddings of multivariate time-series (AIStats'18, oral paper). In the third part, I will discuss how to apply D2KE for learning universal text embedding from pre-trained word embedding (EMNLP'18).
报告时间:2018年11月09日14时30分    报告地点:西区科技实验西二楼多功能报告厅
报名截止日期:2018年11月09日    可选人数:100