First published: 2016/11/05 (4 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.
Find a topology by reinforcement learning. They use REINFORCE from [Williams 1992](http://www.shortscience.org/paper?bibtexKey=Williams:92).
* Structure and connectivity of a Neural Network can be represented by a variable-length string.
* The RNN controller in Neural Architecture Search is auto-regressive, which means it predicts hyperparameters one a time, conditioned on previous predictions
* policy gradient method to maximize the expected accuracy of the sampled architectures
* In our experiments, the process of generating an architecture stops if the number of layers exceeds
a certain value.
* Computer Vision - **CIFAR-10**: 3.65% error (State of the art are Dense-Nets with 3.46% error)
* Language - **Penn Treebank**: a test set perplexity of 62.4 (3.6 perplexity better than the previous state-of-the-art)
They had a Control Experiment "Comparison against Random Search" in which they showed that they are much better than a random exploration of the data. However, the paper lacks details how exactly the random search was implemented.
## Related Work
* [Designing Neural Network Architectures using Reinforcement Learning](https://arxiv.org/pdf/1611.02167.pdf) ([summary](http://www.shortscience.org/paper?bibtexKey=journals/corr/1611.02167))