Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training neural networks using learned optimizers. One of method is for training a neural network layer comprising a plurality of network parameters having a plurality of dimensions each having a plurality of indices, the method comprising: maintaining a set of values corresponding to respective sets of indices of each dimension, each value representing a measure of central tendency of past gradients of the network parameters having an index in the dimension that is in the set of indices; performing a training step to obtain a new gradient for each network parameter; updating each set of values using the new gradients; and for each network parameter: generating an input from the updated sets of values; processing the input using an optimizer neural network to generate an output defining an update for the network parameter; and applying the update.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training neural networks using learned optimizers. One of method is for training a neural network layer comprising a plurality of network parameters having a plurality of dimensions each having a plurality of indices, the method comprising: maintaining a set of values corresponding to respective sets of indices of each dimension, each value representing a measure of central tendency of past gradients of the network parameters having an index in the dimension that is in the set of indices; performing a training step to obtain a new gradient for each network parameter; updating each set of values using the new gradients; and for each network parameter: generating an input from the updated sets of values; processing the input using an optimizer neural network to generate an output defining an update for the network parameter; and applying the update.