The content discusses the challenges of designing neural network architectures, particularly recurrent neural networks (RNNs), and introduces a multi-objective evolutionary algorithm-based method for RNN architecture search. The proposed method aims to optimize model accuracy and complexity objectives simultaneously. It explores the importance of considering multiple objectives in neural architecture search to find a balance between model performance and computational resources. The study includes experiments on word-level natural language processing tasks, sequence learning tasks, and sentiment analysis tasks to evaluate the effectiveness of the proposed approach.
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arxiv.org
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