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【学术报告】 DeepPINK: Reproducible Feature Selection in Deep Neural Networks

发布日期:2018-07-06 14:53:43   来源:十大网赌网址   点击量:
主题:  DeepPINK: Reproducible Feature Selection in Deep Neural Networks

内容摘要: Deep learning has become increasingly popular in both supervised and unsupervised machine learning thanks to its outstanding empirical performance. However, because of their intrinsic complexity, most deep learning methods are largely treated as black box tools with little interpretability. Even though recent attempts have been made to facilitate the interpretability of deep neural networks (DNNs), existing methods are susceptible to noise and lack of robustness. Therefore, scientists are justifiably cautious about the reproducibility of the discoveries, which is often related to the interpretability of the underlying statistical models. In this paper, we describe a method to increase the interpretability and reproducibility of DNNs by incorporating the idea of feature selection with controlled error rate. By designing a new DNN architecture and integrating it with the recently proposed knockoffs framework, we perform feature selection with a controlled error rate, while maintaining high power. This new method, DeepPINK (Deep feature selection using Paired-Input Nonlinear Knockoffs), is applied to both simulated and real data sets to demonstrate its empirical utility. This is a joint work with Yingying Fan, Yang Lu and William Noble.

 

主讲人:Jinchi Lvhttp://www-bcf.usc.edu/~jinchilv/

时间:201879日下午3

地点:1308

主讲人简历:

南加州大学助理教授,普林斯顿大学博士,范剑青的学生;在Journal of the American Statistical AssociationThe Annals of Statistics等顶级统计期刊发表多篇研究论文;曾获得多个统计和大数据方面的奖项;他的研究兴趣包括统计机器学习,深度学习,高维统计和大规模推理,大数据,可扩展贝叶斯推理,经济计量学等。

 

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