Learning Sparse Structured Ensembles with SG-MCMC and Network Pruning

ICLR 2018 Yichi ZhangZhijian Ou

An ensemble of neural networks is known to be more robust and accurate than an individual network, however usually with linearly-increased cost in both training and testing. In this work, we propose a two-stage method to learn Sparse Structured Ensembles (SSEs) for neural networks... (read more)

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