Title: Lookahead-LSTM Optimizer: A Meta-Learning K-steps Method
Primary area: optimization
Keywords: Optimizer, LSTM, gereralization, optimization, gradient

Abstract: All of the parametric machine learning models need to be optimized. At present, there are various optimization algorithms base on gradient, and they all have one thing in common---hand-designed. The purpose of Meta-learning is learning to learn, this thought can be used to do optimization automatically, without algorithm selection and hyper-parameters tuning. Previous works have trained a model to optimize other models using gradient information, but there still have some issues. We want to further improve the previous works in generalization and data transferability. then we propose k-steps Lookahead-LSTM optimization algorithm for this