论文标题
Meeso：一种自动构建深度学习模型的多目标端到端自我优化方法
MEESO: A Multi-objective End-to-End Self-Optimized Approach for Automatically Building Deep Learning Models
论文作者
论文摘要
深度学习已被广泛用于来自不同领域的各种应用，例如计算机视觉，自然语言处理等。但是，训练模型通常是通过许多昂贵的实验手动开发的。这项手动工作通常需要大量的计算资源，时间和经验。为了简化深度学习的使用并减轻人类的努力，自动化的深度学习已成为一种潜在的工具，可以为用户和研究人员释放负担。通常，自动方法应支持模型选择的多样性，评估应允许用户决定其需求。为此，我们提出了一种自动构建深度学习模型的多目标端到端自优化方法。 MNIST，时尚和CIFAR10等知名数据集的实验结果表明，我们的算法可以发现与最新方法相比，可以发现各种竞争模型。此外，我们的方法还引入了多目标权衡解决方案，以供用户做出更好的决策，以实现准确性和不确定性指标。
Deep learning has been widely used in various applications from different fields such as computer vision, natural language processing, etc. However, the training models are often manually developed via many costly experiments. This manual work usually requires substantial computing resources, time, and experience. To simplify the use of deep learning and alleviate human effort, automated deep learning has emerged as a potential tool that releases the burden for both users and researchers. Generally, an automatic approach should support the diversity of model selection and the evaluation should allow users to decide upon their demands. To that end, we propose a multi-objective end-to-end self-optimized approach for constructing deep learning models automatically. Experimental results on well-known datasets such as MNIST, Fashion, and Cifar10 show that our algorithm can discover various competitive models compared with the state-of-the-art approach. In addition, our approach also introduces multi-objective trade-off solutions for both accuracy and uncertainty metrics for users to make better decisions.
