Reliable and Fast Recurrent Neural Network Architecture Optimization
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 14 references that are in the map. First posted on arXiv 29 Jun 2021.
Builds on
7 of 14 references.
References · 7
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
- 2018Evolutionary Architecture Search For Deep Multitask Networkscited by 23
- 2017Metaheuristic Design of Feedforward Neural Networks: A Review of Two Decades of Researchcited by 16
- 2019Investigating Recurrent Neural Network Memory Structures using Neuro-Evolutioncited by 9
- 2018Low-Cost Recurrent Neural Network Expected Performance Evaluationcited by 5
- 2018BIN-CT: Urban Waste Collection based in Predicting the Container Fill Levelcited by 1
Built on by
Not cited yet.