  Neural Architecture Search (NAS) has shown great success in automating the
design of neural networks, but the prohibitive amount of computations behind
current NAS methods requires further investigations in improving the sample
efficiency and the network evaluation cost to get better results in a shorter
time. In this paper, we present a novel scalable Monte Carlo Tree Search (MCTS)
based NAS agent, named AlphaX, to tackle these two aspects. AlphaX improves the
search efficiency by adaptively balancing the exploration and exploitation at
the state level, and by a Meta-Deep Neural Network (DNN) to predict network
accuracies for biasing the search toward a promising region. To amortize the
network evaluation cost, AlphaX accelerates MCTS rollouts with a distributed
design and reduces the number of epochs in evaluating a network by transfer
learning, which is guided with the tree structure in MCTS. In 12 GPU days and
1000 samples, AlphaX found an architecture that reaches 97.84\% top-1 accuracy
on CIFAR-10, and 75.5\% top-1 accuracy on ImageNet, exceeding SOTA NAS methods
in both the accuracy and sampling efficiency. Particularly, we also evaluate
AlphaX on NASBench-101, a large scale NAS dataset; AlphaX is 3x and 2.8x more
sample efficient than Random Search and Regularized Evolution in finding the
global optimum. Finally, we show the searched architecture improves a variety
of vision applications from Neural Style Transfer, to Image Captioning and
Object Detection.
