First published: 2016/11/05 (6 years ago) Abstract: Neural networks are powerful and flexible models that work well for many
difficult learning tasks in image, speech and natural language understanding.
Despite their success, neural networks are still hard to design. In this paper,
we use a recurrent network to generate the model descriptions of neural
networks and train this RNN with reinforcement learning to maximize the
expected accuracy of the generated architectures on a validation set. On the
CIFAR-10 dataset, our method, starting from scratch, can design a novel network
architecture that rivals the best human-invented architecture in terms of test
set accuracy. Our CIFAR-10 model achieves a test error rate of 3.84, which is
only 0.1 percent worse and 1.2x faster than the current state-of-the-art model.
On the Penn Treebank dataset, our model can compose a novel recurrent cell that
outperforms the widely-used LSTM cell, and other state-of-the-art baselines.
Our cell achieves a test set perplexity of 62.4 on the Penn Treebank, which is
3.6 perplexity better than the previous state-of-the-art.