  Meta-learning consists in learning learning algorithms. We use a Long Short
Term Memory (LSTM) based network to learn to compute on-line updates of the
parameters of another neural network. These parameters are stored in the cell
state of the LSTM. Our framework allows to compare learned algorithms to
hand-made algorithms within the traditional train and test methodology. In an
experiment, we learn a learning algorithm for a one-hidden layer Multi-Layer
Perceptron (MLP) on non-linearly separable datasets. The learned algorithm is
able to update parameters of both layers and generalise well on similar
datasets.
