Reliable and Fast Recurrent Neural Network Architecture Optimization

• URL: http://arxiv.org/abs/2106.15295v1
• Date: Tue, 29 Jun 2021 12:16:19 GMT
• Title: Reliable and Fast Recurrent Neural Network Architecture Optimization
• Authors: Andr\'es Camero and Jamal Toutouh and Enrique Alba
• Abstract summary: This article introduces Random Error Sampling-based Neuroevolution (RESN), a novel automatic method to optimize recurrent neural network architectures. Results show that RESN achieves state-of-the-art error performance while reducing by half the computational time.
• Score: 7.287830861862003
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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