Iterative Monte Carlo Tree Search for Neural Architecture Search

Mehraveh Javan Roshtkhari, Matthew Toews, Marco Pedersoli

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TL;DR: We propose an iterative MCTS for NAS to learn optimal order of nodes in search tree.

Abstract:

Recent work has shown Monte-Carlo Tree Search (MCTS) as an effective approach for
Neural Architecture Search (NAS) in producing competitive architectures. However, the
performance of the tree search is highly sensitive to the node visiting order. If the initial
nodes are highly discriminative, good configurations can be efficiently found with minimal
sampling. In contrast, non-discriminative initial nodes require exploring an exponential
number of nodes before finding good solutions. In this paper, we present an iterative NAS
approach to jointly train the recognition model with MCTS and learn the optimal node
ordering of the tree. With our approach, the order of node visits in the tree is iteratively
refined based on the estimated performance of the nodes on the validation set. With this
approach, good architectures are more likely to naturally emerge at the beginning of the
tree, improving the search process. Experiments on two classification benchmarks and a
segmentation task show that the proposed method can improve the performance of MCTS,
compared to state-of-the-art MCTS approaches for NAS.

Submission Number: 42