论文标题
HMCNA：使用隐藏的马尔可夫链和贝叶斯优化的神经建筑搜索
HMCNAS: Neural Architecture Search using Hidden Markov Chains and Bayesian Optimization
论文作者
论文摘要
神经体系结构搜索已在各种任务中实现了最先进的表现，超越了人为设计的网络。但是，许多需要人类定义的假设，这些假设仍然需要解决问题或生成的模型：最终模型体系结构，要采样的层数，强迫操作，小搜索空间，最终有助于具有较高性能的模型以诱导系统诱导偏见的成本。在本文中，我们提出了HMCNA，该HMCNA由两个新的组成部分组成：i）一种利用人类设计的模型的信息来自主产生复杂的搜索空间，ii）ii）具有贝叶斯优化的进化算法，具有贝叶斯优化，能够从scratch中产生竞争性的cnn，而无需依靠人类定义的参数searpacts或小型搜索范围。实验结果表明，所提出的方法导致在很短的时间内获得的竞争架构。 HMCNA通过提供一种创建竞争模型的方法来概括NAS的步骤，而无需对特定任务的任何人类知识。
Neural Architecture Search has achieved state-of-the-art performance in a variety of tasks, out-performing human-designed networks. However, many assumptions, that require human definition, related with the problems being solved or the models generated are still needed: final model architectures, number of layers to be sampled, forced operations, small search spaces, which ultimately contributes to having models with higher performances at the cost of inducing bias into the system. In this paper, we propose HMCNAS, which is composed of two novel components: i) a method that leverages information about human-designed models to autonomously generate a complex search space, and ii) an Evolutionary Algorithm with Bayesian Optimization that is capable of generating competitive CNNs from scratch, without relying on human-defined parameters or small search spaces. The experimental results show that the proposed approach results in competitive architectures obtained in a very short time. HMCNAS provides a step towards generalizing NAS, by providing a way to create competitive models, without requiring any human knowledge about the specific task.
