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

arXiv: https://arxiv.org/abs/1903.11059 · HTML: https://mlsanity.com/paper/1903.11059/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2019.

## Built on by (most cited first, 30 of 33)

- [Compute Trends Across Three Eras of Machine Learning](https://mlsanity.com/paper/2202.05924/) (2022)
- [Neural Architecture Search: Insights from 1000 Papers](https://mlsanity.com/paper/2301.08727/) (2023)
- [How Powerful are Performance Predictors in Neural Architecture Search?](https://mlsanity.com/paper/2104.01177/) (2021)
- [Stronger NAS with Weaker Predictors](https://mlsanity.com/paper/2102.10490/) (2021)
- [CATE: Computation-aware Neural Architecture Encoding with Transformers](https://mlsanity.com/paper/2102.07108/) (2021)
- [GradSign: Model Performance Inference with Theoretical Insights](https://mlsanity.com/paper/2110.08616/) (2021)
- [Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2106.11890/) (2021)
- [AgentSwift: Efficient LLM Agent Design via Value-guided Hierarchical Search](https://mlsanity.com/paper/2506.06017/) (2025)
- [NAR-Former: Neural Architecture Representation Learning towards Holistic Attributes Prediction](https://mlsanity.com/paper/2211.08024/) (2022)
- [FlowerFormer: Empowering Neural Architecture Encoding using a Flow-aware Graph Transformer](https://mlsanity.com/paper/2403.12821/) (2024)
- [RANK-NOSH: Efficient Predictor-Based Architecture Search via Non-Uniform Successive Halving](https://mlsanity.com/paper/2108.08019/) (2021)
- [NAS-HPO-Bench-II: A Benchmark Dataset on Joint Optimization of Convolutional Neural Network Architecture and Training Hyperparameters](https://mlsanity.com/paper/2110.10165/) (2021)
- [Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search](https://mlsanity.com/paper/2104.05309/) (2021)
- [GPUNet: Searching the Deployable Convolution Neural Networks for GPUs](https://mlsanity.com/paper/2205.00841/) (2022)
- [Monte Carlo Tree Search based Space Transfer for Black-box Optimization](https://mlsanity.com/paper/2412.07186/) (2024)
- [Part-X: A Family of Stochastic Algorithms for Search-Based Test Generation with Probabilistic Guarantees](https://mlsanity.com/paper/2110.10729/) (2021)
- [Neural Architecture Search via Combinatorial Multi-Armed Bandit](https://mlsanity.com/paper/2101.00336/) (2021)
- [CE-NAS: An End-to-End Carbon-Efficient Neural Architecture Search Framework](https://mlsanity.com/paper/2406.01414/) (2024)
- [Automated Dominative Subspace Mining for Efficient Neural Architecture Search](https://mlsanity.com/paper/2210.17180/) (2022)
- [Procrastinated Tree Search: Black-box Optimization with Delayed, Noisy, and Multi-Fidelity Feedback](https://mlsanity.com/paper/2110.07232/) (2021)
- [Training BatchNorm Only in Neural Architecture Search and Beyond](https://mlsanity.com/paper/2112.00265/) (2021)
- [On the Evolution of the MCTS Upper Confidence Bounds for Trees by Means of Evolutionary Algorithms in the Game of Carcassonne](https://mlsanity.com/paper/2112.09697/) (2021)
- [Carbon-Efficient Neural Architecture Search](https://mlsanity.com/paper/2307.04131/) (2023)
- [Cascade Bagging for Accuracy Prediction with Few Training Samples](https://mlsanity.com/paper/2108.05613/) (2021)
- [Extensible Proxy for Efficient NAS](https://mlsanity.com/paper/2210.09459/) (2022)
- [Multi-Objective Neural Architecture Search by Learning Search Space Partitions](https://mlsanity.com/paper/2406.00291/) (2024)
- [AutoScout: Structured Optimization for Automating ML System Configuration](https://mlsanity.com/paper/2603.11603/) (2026)
- [Machine Learning for CUDA+MPI Design Rules](https://mlsanity.com/paper/2203.02530/) (2022)
- [Towards Understanding the Effects of Evolving the MCTS UCT Selection Policy](https://mlsanity.com/paper/2302.03352/) (2023)
- [Maneuver Decision-Making Through Proximal Policy Optimization And Monte Carlo Tree Search](https://mlsanity.com/paper/2309.08611/) (2023)

## Abstract (arXiv)

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 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.