AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search

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... (read more)

PDF Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Neural Architecture Search CIFAR-10 AlphaX-1 (cutout NASNet) Top-1 Error Rate 2.82% # 10
Search Time (GPU days) 224 # 8
Neural Architecture Search CIFAR-10 Image Classification AlphaX-1 (cutout NASNet) Params 3.59M # 4
Neural Architecture Search ImageNet AlphaX-1 Top-1 Error Rate 24.5 # 28
Params 5.4M # 13

Methods used in the Paper


METHOD TYPE
Random Search
Hyperparameter Search