Neural Architecture Search with Reinforcement Learning

Understand

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.