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Chapter 14
On the CIFAR-10 dataset, this method, starting from scratch, designed a novel
network architecture that rivals the best human-invented architecture in terms of
test set accuracy. The CIFAR-10 model achieves a test error rate of 3.65, which is
0.09 percent better and 1.05x faster than the previous state-of-the-art model that
used a similar architectural scheme. On the Penn Treebank dataset, the model can
compose a novel recurrent cell that outperforms the widely used an LSTM cell (see
Chapter 9, Autoencoders), and other state-of-the-art baselines. The cell achieves a test
set perplexity of 62.4 on the Penn Treebank, which is 3.6 better than the previous
state-of-the-art model.
The key outcome of the paper is shown in Figure 2. A controller network based
on RNNs produces a sample architecture A with probability p. This candidate
architecture A is trained by a child network to get a candidate accuracy R.
Then a gradient of p is computed and scaled by R to update the controller. This
reinforcement learning operation is computed in a cycle a number of times. The
process of generating an architecture stops if the number of layers exceeds a
certain value. The details of how a RL-based policy gradient method is used by the
controller RNN to generate better architectures are in [1]. Here we emphasize the
fact that NAS uses a meta-modeling algorithm based on Q-learning with εε − gggggggggggg
exploration strategy and with experience replay (see Chapter 11, Reinforcement
Learning) to explore the model search space:
Figure 2: NAS with Recurrent Neural Networks
Since the original paper in late 2016, a Cambrian explosion of model generation
techniques has been observed. Initially, the goal was to generate the entire model in
one single step. Later, a cell-based approach has been proposed where the generation
is divided into two macro-steps: first a cell structure is automatically built and then a
predefined number of discovered cells are stacked together to generate an entire endto-end
architecture [2].
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