# Efficient Neural Architecture Search with Performance Prediction

arXiv: https://arxiv.org/abs/2108.01854 · HTML: https://mlsanity.com/paper/2108.01854/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 18 references that are in the map.
- First posted on arXiv 4 Aug 2021.

## Builds on (most cited first, 13 of 13)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Efficient Neural Architecture Search via Parameter Sharing](https://mlsanity.com/paper/1802.03268/) (2018)
- [AutoAugment: Learning Augmentation Policies from Data](https://mlsanity.com/paper/1805.09501/) (2018)
- [NAS-Bench-101: Towards Reproducible Neural Architecture Search](https://mlsanity.com/paper/1902.09635/) (2019)
- [Evolving Deep Convolutional Neural Networks for Image Classification](https://mlsanity.com/paper/1710.10741/) (2017)
- [Data Dropout: Optimizing Training Data for Convolutional Neural Networks](https://mlsanity.com/paper/1809.00193/) (2018)
- [Understanding Neural Architecture Search Techniques](https://mlsanity.com/paper/1904.00438/) (2019)
- [EENA: Efficient Evolution of Neural Architecture](https://mlsanity.com/paper/1905.07320/) (2019)
- [Reinforced Evolutionary Neural Architecture Search](https://mlsanity.com/paper/1808.00193/) (2018)
- [Neural Architecture Refinement: A Practical Way for Avoiding Overfitting in NAS](https://mlsanity.com/paper/1905.02341/) (2019)
- [A Study of the Learning Progress in Neural Architecture Search Techniques](https://mlsanity.com/paper/1906.07590/) (2019)
- [Regularized Evolutionary Algorithm for Dynamic Neural Topology Search](https://mlsanity.com/paper/1905.06252/) (2019)

## Built on by (most cited first, 2 of 2)

- [Don't Complete It! Preventing Unhelpful Code Completion for Productive and Sustainable Neural Code Completion Systems](https://mlsanity.com/paper/2209.05948/) (2022)
- [Learning Computational Efficient Bots with Costly Features](https://mlsanity.com/paper/2308.09629/) (2023)

## Abstract (arXiv)

Neural networks are powerful models that have a remarkable ability to extract patterns that are too complex to be noticed by humans or other machine learning models. Neural networks are the first class of models that can train end-to-end systems with large learning capacities. However, we still have the difficult challenge of designing the neural network, which requires human experience and a long process of trial and error. As a solution, we can use a neural architecture search to find the best network architecture for the task at hand. Existing NAS algorithms generally evaluate the fitness of a new architecture by fully training from scratch, resulting in the prohibitive computational cost, even if operated on high-performance computers. In this paper, an end-to-end offline performance predictor is proposed to accelerate the evaluation of sampled architectures. Index Terms- Learning Curve Prediction, Neural Architecture Search, Reinforcement Learning.