Meta Learning Backpropagation And Improving It

12/29/2020 ∙ by Louis Kirsch, et al. ∙ 91

Many concepts have been proposed for meta learning with neural networks (NNs), e.g., NNs that learn to control fast weights, hyper networks, learned learning rules, and meta recurrent neural networks (Meta RNNs). Our Variable Shared Meta Learning (VS-ML) unifies the above and demonstrates that simple weight-sharing and sparsity in an NN is sufficient to express powerful learning algorithms. A simple implementation of VS-ML called Variable Shared Meta RNN allows for implementing the backpropagation learning algorithm solely by running an RNN in forward-mode. It can even meta-learn new learning algorithms that improve upon backpropagation, generalizing to different datasets without explicit gradient calculation.

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