# GP-NAS-ensemble: a model for NAS Performance Prediction

arXiv: https://arxiv.org/abs/2301.09231 · HTML: https://mlsanity.com/paper/2301.09231/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 50 references that are in the map.
- First posted on arXiv 23 Jan 2023.

## Builds on (most cited first, 28 of 28)

- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017)
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018)
- [Single Path One-Shot Neural Architecture Search with Uniform Sampling](https://mlsanity.com/paper/1904.00420/) (2019)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [Large-Scale Evolution of Image Classifiers](https://mlsanity.com/paper/1703.01041/) (2017)
- [Neural Architecture Optimization](https://mlsanity.com/paper/1808.07233/) (2018)
- [Genetic CNN](https://mlsanity.com/paper/1703.01513/) (2017)
- [Neural Predictor for Neural Architecture Search](https://mlsanity.com/paper/1912.00848/) (2019)
- [BRP-NAS: Prediction-based NAS using GCNs](https://mlsanity.com/paper/2007.08668/) (2020)
- [One-Shot Neural Architecture Search via Self-Evaluated Template Network](https://mlsanity.com/paper/1910.05733/) (2019)
- [AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling](https://mlsanity.com/paper/2011.09011/) (2020)
- [TransNAS-Bench-101: Improving Transferability and Generalizability of Cross-Task Neural Architecture Search](https://mlsanity.com/paper/2105.11871/) (2021)
- [A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS](https://mlsanity.com/paper/2004.01899/) (2020)
- [ReNAS:Relativistic Evaluation of Neural Architecture Search](https://mlsanity.com/paper/1910.01523/) (2019)
- [Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets](https://mlsanity.com/paper/2107.00860/) (2021)
- [Accurate, Data-Efficient, Unconstrained Text Recognition with Convolutional Neural Networks](https://mlsanity.com/paper/1812.11894/) (2018)
- [Stronger NAS with Weaker Predictors](https://mlsanity.com/paper/2102.10490/) (2021)
- [You Only Search Once: Single Shot Neural Architecture Search via Direct Sparse Optimization](https://mlsanity.com/paper/1811.01567/) (2018)
- [Continual and Multi-Task Architecture Search](https://mlsanity.com/paper/1906.05226/) (2019)
- [Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search](https://mlsanity.com/paper/1803.00370/) (2018)
- [NSGA-Net: Neural Architecture Search using Multi-Objective Genetic Algorithm](https://mlsanity.com/paper/1810.03522/) (2018)
- [Prior-Guided One-shot Neural Architecture Search](https://mlsanity.com/paper/2206.13329/) (2022)
- [Arch-Graph: Acyclic Architecture Relation Predictor for Task-Transferable Neural Architecture Search](https://mlsanity.com/paper/2204.05941/) (2022)
- [Cascade Bagging for Accuracy Prediction with Few Training Samples](https://mlsanity.com/paper/2108.05613/) (2021)

## Built on by (most cited first, 7 of 7)

- [DisWOT: Student Architecture Search for Distillation WithOut Training](https://mlsanity.com/paper/2303.15678/) (2023)
- [RD-NAS: Enhancing One-shot Supernet Ranking Ability via Ranking Distillation from Zero-cost Proxies](https://mlsanity.com/paper/2301.09850/) (2023)
- [Pruning Large Language Models via Accuracy Predictor](https://mlsanity.com/paper/2309.09507/) (2023)
- [TVT: Training-Free Vision Transformer Search on Tiny Datasets](https://mlsanity.com/paper/2311.14337/) (2023)
- [MiCo: End-to-End Mixed Precision Neural Network Co-Exploration Framework for Edge AI](https://mlsanity.com/paper/2508.09500/) (2025)
- [A Survey on Recent Teacher-student Learning Studies](https://mlsanity.com/paper/2304.04615/) (2023)
- [MiCoPro: End-to-End Mixed Precision HW/SW Co-design with HW-aware Proxy Model](https://mlsanity.com/paper/2608.06916/) (2026)

## Abstract (arXiv)

It is of great significance to estimate the performance of a given model architecture without training in the application of Neural Architecture Search (NAS) as it may take a lot of time to evaluate the performance of an architecture. In this paper, a novel NAS framework called GP-NAS-ensemble is proposed to predict the performance of a neural network architecture with a small training dataset. We make several improvements on the GP-NAS model to make it share the advantage of ensemble learning methods. Our method ranks second in the CVPR2022 second lightweight NAS challenge performance prediction track.