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Representing knowledge through graphical models

This will be presented in English.

Ruiwen Zhang (SAS Institute)
13:1013:50 Thursday, April 12, 2018
Secondary topics:  AI应用的硬件、软件栈(Hardware and Software stack for AI applications)
Tags: wl

您将学到什么 (What you'll learn)

Understand theories and methods for graphical models and their applications

描述 (Description)

本议题将用英文进行,并且不提供同声传译, 但讲师会在结束时用中文答疑。

Graphical models have enjoyed a surge of interest in many areas of machine learning recently due to their ability to learn and perform inference in large networks. Prominent applications of graphical models include studying functional connectivity among brain regions, exploring the regulatory relationships between genes, and recommending the most related information from millions of items.

Drawing on several real-world cases, Ruiwen Zhang demonstrates how to visualize the structure of a probabilistic model and provide better insights into the model’s properties, which can be further used to design and motivate new models. She also explains how to reduce the computational complexity required to perform inference and learning in sophisticated models using graphical models.


借助多个实际案例,Ruiwen Zhang将演示如何将概率模型的结构可视化,从而能更好地洞察模型的特征,这可以进一步用于设计和创新模型。她还将解释如何降低图模型在复杂模型中执行推断和学习所需的计算的复杂性。

Photo of Ruiwen Zhang

Ruiwen Zhang

SAS Institute

Ruiwen Zhang is a senior research statistician at SAS, where she focuses on machine learning and data mining. She holds a PhD from the Department of Statistics and Operation Research at the University of North Carolina at Chapel Hill.