报告人：Xiao Luo (Peking University)
时间：2020-10-23 12:00-13:30
地点：Room 1303, Sciences Building No. 1
各位数院研究生同学：
研究生学术午餐会是在公司领导的大力支持下，由研究生会负责组织的系列学术交流活动。
午餐会每次邀请一位同学作为主讲人，面向全院各专业背景的研究生介绍自己科研方向的基本问题、概念和方法，并汇报近期的研究成果和进展，是研究生展示自我、促进交流的学术平台。
研究生会已经举办了四十三期活动，我们将于2020年10月23日周五举办第四十四期学术午餐会活动，欢迎感兴趣的老师和同学积极报名参加。
报告人简介：kaiyun体育登录网页入口数学科学公司2017级直博研究生，导师为邓明华教授。主要研究方向为深度学习，生物信息学。曾获2019-2020国家奖学金，2019-2020（kaiyun体育登录网页入口）校长奖学金。
Abstract: Convolutional neural networks (CNNs) have outperformed conventional methods in modeling the sequence specificity of DNA–protein binding. While previous studies have built a connection between CNNs and probabilistic models, simple models of CNNs cannot achieve sufficient accuracy on this problem. Recently, some methods of neural networks have increased performance using complex neural networks whose results cannot be directly interpreted. However, it is difficult to combine probabilistic models and CNNs effectively to improve DNA–protein binding predictions. In this article, we present a novel global pooling method: expectation pooling for predicting DNA–protein binding. Our pooling method stems naturally from the expectation maximization algorithm, and its benefits can be interpreted both statistically and via deep learning theory. Through experiments, we demonstrate that our pooling method improves the prediction performance DNA–protein binding. Our interpretable pooling method combines probabilistic ideas with global pooling by taking the expectations of inputs without increasing the number of parameters. We also analyze the hyperparameters in our method and propose optional structures to help fit different datasets. We explore how to effectively utilize these novel pooling methods and show that combining statistical methods with deep learning is highly beneficial, which is promising and meaningful for future studies in this field.
This is a joint work with Xinming Tu.
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