应我所的邀请，美国加州大学伯克利分校John Paisley博士后于2013年3月14日在我所进行学术交流与访问。2013年3月14日上午，John Paisley学者在雷达信号处理重点实验室报告厅新科技楼1602作了有关Bayesian Nonparametric Topic Models and "Big Data"学术报告。报告会由雷达信号处理重点实验室杜兰教授主持。报告详细介绍了Bayesian nonparametrics is an area in machine learning in which models grow in size and complexity as data accrue. As such, they are particularly relevant to the world of "Big Data", where it may be difficult or even counterproductive to fix the number of parameters a priori. A stumbling block for Bayesian nonparametrics has been that their algorithms for posterior inference generally show poor scalability. In this talk, we tackle this issue in the domain of large-scale text collections. Our model is a novel tree-structured model in which documents are represented by collections of paths in an infinite-dimensional tree. We develop a general and efficient variational inference strategy for learning such models based on stochastic optimization, and show that with this combination of modeling and inference approach, we are able to learn high-quality models using millions of documents.
John Paisley做报告
报告结束后，John Paisley博士后与雷达信号处理重点实验室的师生们进行了座谈交流。双方分别介绍各自的科研经历和主要研究方向，并对当前热点问题进行深入的讨论。在讨论过程中学生们也踊跃提出了自己在科研中所遇到的问题，John Paisley博士后和老师们结合自己的研究经验给出了建设性的意见，给予同学们极大的启发和帮助。此次座谈交流会，增强了学术研究氛围，激发了广大学生的科研热情。
