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Meta-Learning with Hessian-Free Approach in Deep Neural Nets Training
[article]
2018
arXiv
pre-print
Meta-learning is a promising method to achieve efficient training method towards deep neural net and has been attracting increases interests in recent years. But most of the current methods are still not capable to train complex neuron net model with long-time training process. In this paper, a novel second-order meta-optimizer, named Meta-learning with Hessian-Free(MLHF) approach, is proposed based on the Hessian-Free approach. Two recurrent neural networks are established to generate the
arXiv:1805.08462v2
fatcat:jtji5tikjfdknlla3y4rk3lxem