Neural Architecture Search Using 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. 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.

[1]  Dale Schuurmans,et al.  Improving Policy Gradient by Exploring Under-appreciated Rewards , 2016, ICLR.

[2]  Jürgen Schmidhuber,et al.  LSTM: A Search Space Odyssey , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[3]  Geoffrey J. Gordon,et al.  DeepArchitect: Automatically Designing and Training Deep Architectures , 2017, ArXiv.

[4]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[5]  Quoc V. Le,et al.  Efficient Neural Architecture Search via Parameter Sharing , 2018, ICML.

[6]  Yuandong Tian,et al.  ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero , 2019, ICML.

[7]  Quoc V. Le,et al.  Neural Architecture Search with Reinforcement Learning , 2016, ICLR.

[8]  Leon A. Gatys,et al.  A Neural Algorithm of Artistic Style , 2015, ArXiv.

[9]  Tie-Yan Liu,et al.  Neural Architecture Optimization , 2018, NeurIPS.

[10]  Martin Jaggi,et al.  Evaluating the Search Phase of Neural Architecture Search , 2019, ICLR.

[11]  Ramesh Raskar,et al.  Designing Neural Network Architectures using Reinforcement Learning , 2016, ICLR.

[12]  Yoshua Bengio,et al.  Algorithms for Hyper-Parameter Optimization , 2011, NIPS.

[13]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[14]  Yiming Yang,et al.  DARTS: Differentiable Architecture Search , 2018, ICLR.

[15]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Frank Hutter,et al.  SGDR: Stochastic Gradient Descent with Warm Restarts , 2016, ICLR.

[17]  Vijay Vasudevan,et al.  Learning Transferable Architectures for Scalable Image Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[18]  Alok Aggarwal,et al.  Regularized Evolution for Image Classifier Architecture Search , 2018, AAAI.

[19]  Li Fei-Fei,et al.  Progressive Neural Architecture Search , 2017, ECCV.

[20]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[21]  Csaba Szepesvári,et al.  Bandit Based Monte-Carlo Planning , 2006, ECML.

[22]  Frank Hutter,et al.  Simple And Efficient Architecture Search for Convolutional Neural Networks , 2017, ICLR.

[23]  Oriol Vinyals,et al.  Hierarchical Representations for Efficient Architecture Search , 2017, ICLR.

[24]  Kirthevasan Kandasamy,et al.  High Dimensional Bayesian Optimisation and Bandits via Additive Models , 2015, ICML.

[25]  Peter Auer,et al.  Finite-time Analysis of the Multiarmed Bandit Problem , 2002, Machine Learning.

[26]  Yoshua Bengio,et al.  Show, Attend and Tell: Neural Image Caption Generation with Visual Attention , 2015, ICML.

[27]  Aaron Klein,et al.  NAS-Bench-101: Towards Reproducible Neural Architecture Search , 2019, ICML.

[28]  Gaofeng Meng,et al.  RENAS: Reinforced Evolutionary Neural Architecture Search , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).