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	Learning Deep Architectures via Generalized Whitened Neural Networks
	Ping Luo
	2017
会议地点	澳大利亚
英文摘要	Whitened Neural Network (WNN) is a recent advanced deep architecture, which improves convergence and generalization of canonical neural networks by whitening their internal hidden representation. However, the whitening transformation increases computation time. Unlike WNN that reduced runtime by performing whitening every thousand iterations, which degenerates convergence due to the ill conditioning, we present generalized WNN (GWNN), which has three appealing properties. First, GWNN is able to learn compact representation to reduce computations. Second, it enables whitening transformation to be performed in a short period, preserving good conditioning. Third, we propose a data-independent estimation of the covariance matrix to further improve computational efficiency. Extensive experiments on various datasets demonstrate the benefits of GWNN.
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/11771]
专题	深圳先进技术研究院_集成所
作者单位	2017
推荐引用方式 GB/T 7714	Ping Luo. Learning Deep Architectures via Generalized Whitened Neural Networks[C]. 见:. 澳大利亚.

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