论文标题
关于用于数值优化的深度学习的表现：蛋白质结构预测的应用
On the performance of deep learning for numerical optimization: an application to protein structure prediction
论文作者
论文摘要
深度神经网络最近引起了人们的关注，以建立和评估知觉任务的人工学习模型。在这里，我们介绍了有关处理全球优化问题的深度学习模型的性能的研究。提出的方法采用了神经体系结构搜索（NAS）的思想，以生成有效的神经网络来解决手头问题。网络体系结构的空间使用有向的无环图表示，目标是找到最佳的体系结构，以优化新的，以前未知的任务的目标函数。不同于提出具有GPU计算负担的非常大的网络和较长的培训时间，我们专注于寻找轻巧的实现以找到最佳的体系结构。首先通过在CEC 2017基准套件上进行经验实验对NAS的性能进行分析。此后，它应用于一组蛋白质结构预测（PSP）问题。实验表明，与手工设计的算法相比，生成的学习模型可以实现竞争成果。给定足够的计算预算
Deep neural networks have recently drawn considerable attention to build and evaluate artificial learning models for perceptual tasks. Here, we present a study on the performance of the deep learning models to deal with global optimization problems. The proposed approach adopts the idea of the neural architecture search (NAS) to generate efficient neural networks for solving the problem at hand. The space of network architectures is represented using a directed acyclic graph and the goal is to find the best architecture to optimize the objective function for a new, previously unknown task. Different from proposing very large networks with GPU computational burden and long training time, we focus on searching for lightweight implementations to find the best architecture. The performance of NAS is first analyzed through empirical experiments on CEC 2017 benchmark suite. Thereafter, it is applied to a set of protein structure prediction (PSP) problems. The experiments reveal that the generated learning models can achieve competitive results when compared to hand-designed algorithms; given enough computational budget
