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Architecture-Aware Bayesian Optimization for Neural Network Tuning

机译：神经网络调整的架构感知贝叶斯优化

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摘要

Hyperparameter optimization of a neural network is a non-trivial task. It is time-consuming to evaluate a hyperparameter setting, no analytical expression of the impact of the hyperparameters are available, and the evaluations are noisy in the sense that the result is dependent on the training process and weight initialization. Bayesian optimization is a powerful tool to handle these problems. However, hyperparameter optimization of neural networks poses additional challenges, since the hyperparameters can be integer-valued, categorical, and/or conditional, whereas Bayesian optimization often assumes variables to be real-valued. In this paper we present an architecture-aware transformation of neural networks applied in the kernel of a Gaussian process to boost the performance of hyperparameter optimization. The empirical experiment in this paper demonstrates that by introducing an architecture-aware transformation of the kernel, the performance of the Bayesian optimizer shows a clear improvement over a naive implementation and that the results are comparable to other state-of-the-art methods.
机译：神经网络的超参数优化是一项艰巨的任务。评估超参数设置非常耗时，无法获得对超参数影响的分析表达式，并且从结果取决于训练过程和权重初始化的角度来看，评估是嘈杂的。贝叶斯优化是处理这些问题的有力工具。但是，神经网络的超参数优化带来了额外的挑战，因为超参数可以是整数值，分类和/或条件值，而贝叶斯优化通常假定变量为实值。在本文中，我们提出了一种神经网络的体系结构感知转换，该转换应用于高斯过程的内核中，以提高超参数优化的性能。本文中的经验实验表明，通过引入内核的体系结构感知转换，贝叶斯优化器的性能相对于朴素的实现具有明显的改进，并且其结果可与其他最新方法相媲美。

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