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Nov, 2020
稀疏深度学习的高效变分推断及理论保证
Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee
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Jincheng Bai, Qifan Song, Guang Cheng
TL;DR
本文旨在通过完全贝叶斯处理下的尖峰-平板先验训练稀疏深度神经网络,通过连续放松伯努利分布开发一组计算有效的变分推断方法。实证结果表明,这种变分程序不仅提供了关于贝叶斯预测分布的不确定性量化,而且还能通过训练稀疏多层神经网络实现一致的变量选择。
Abstract
sparse deep learning
aims to address the challenge of huge storage consumption by deep neural networks, and to recover the sparse structure of target functions. Although tremendous empirical successes have been achieved, most
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