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arXiv · 2106.15295

Reliable and Fast Recurrent Neural Network Architecture Optimization

Andrés Camero·Jamal Toutouh·Enrique Alba
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Abstract

This article introduces Random Error Sampling-based Neuroevolution (RESN), a novel automatic method to optimize recurrent neural network architectures. RESN combines an evolutionary algorithm with a training-free evaluation approach. The results show that RESN achieves state-of-the-art error performance while reducing by half the computational time.

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Andrés Camero, Jamal Toutouh, Enrique Alba. 2021-06-29. Reliable and Fast Recurrent Neural Network Architecture Optimization. https://arxiv.org/abs/2106.15295

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