# Low-Cost Recurrent Neural Network Expected Performance Evaluation

arXiv: https://arxiv.org/abs/1805.07159 · HTML: https://mlsanity.com/paper/1805.07159/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2018.

## Built on by (most cited first, 5 of 5)

- [A Data-driven Approach to Neural Architecture Search Initialization](https://mlsanity.com/paper/2111.03524/) (2021)
- [GreenMachine: Automatic Design of Zero-Cost Proxies for Energy-Efficient NAS](https://mlsanity.com/paper/2411.15290/) (2024)
- [GreenFactory: Ensembling Zero-Cost Proxies to Estimate Performance of Neural Networks](https://mlsanity.com/paper/2505.09344/) (2025)
- [Reliable and Fast Recurrent Neural Network Architecture Optimization](https://mlsanity.com/paper/2106.15295/) (2021)
- [Addressing Tactic Volatility in Self-Adaptive Systems Using Evolved Recurrent Neural Networks and Uncertainty Reduction Tactics](https://mlsanity.com/paper/2204.10308/) (2022)

## Abstract (arXiv)

Recurrent neural networks are a powerful tool, but they are very sensitive to their hyper-parameter configuration. Moreover, training properly a recurrent neural network is a tough task, therefore selecting an appropriate configuration is critical. Varied strategies have been proposed to tackle this issue. However, most of them are still impractical because of the time/resources needed. In this study, we propose a low computational cost model to evaluate the expected performance of a given architecture based on the distribution of the error of random samples of the weights. We empirically validate our proposal using three use cases. The results suggest that this is a promising alternative to reduce the cost of exploration for hyper-parameter optimization.