Neural Architecture Search With Reinforcement Learning,
ICLR 2017, by Barret Zoph, Quoc V. Le (Google Brain)
This paper given a approach to find a better structure of network by reinforcement learning.
They use a recurrent network (RNN) as the controller to generate the model descriptions of neural networks, then train this RNN to maximize the expected accuracy of the generated architectures with reinforcement learning.
This could save lots of test by human to change every possible parameters of a network one by one.
However, as the first paper to slove this problem by reinforcement learning, strong computational resources will be required to implement this idea.
The main contribution in this paper is the idea to build the controller by a RNN:
Our work is based on the observation that the structure and connectivity of a neural network can be typically specified by a variable-length string. It is therefore possible to use a recurrent network – the controller – to generate such string.
This limited the search space of networks into a variable-length string.