The automated machine learning (AutoML) field has become increasingly
relevant in recent years. These algorithms can develop models without the need
for expert knowledge, facilitating the application of machine learning
techniques in the industry. Neural Architecture Search (NAS) exploits deep
learning techniques to autonomously produce neural network architectures whose
results rival the state-of-the-art models hand-crafted by AI experts. However,
this approach requires significant computational resources and hardware
investments, making it less appealing for real-usage applications. This article
presents the third version of Pareto-Optimal Progressive Neural Architecture
Search (POPNASv3), a new sequential model-based optimization NAS algorithm
targeting different hardware environments and multiple classification tasks.
Our method is able to find competitive architectures within large search
spaces, while keeping a flexible structure and data processing pipeline to
adapt to different tasks. The algorithm employs Pareto optimality to reduce the
number of architectures sampled during the search, drastically improving the
time efficiency without loss in accuracy. The experiments performed on images
and time series classification datasets provide evidence that POPNASv3 can
explore a large set of assorted operators and converge to optimal architectures
suited for the type of data provided under different scenarios.

本文介绍了 Pareto-Optimal Progressive Neural Architecture Search (POPNASv3) 的第三个版本，这是一个新的顺序模型优化 NAS 算法，旨在针对不同的硬件环境和多个分类任务。我们的方法能够在大型搜索空间中找到具有竞争力的结构，同时保持灵活的结构和数据处理管道以适应不同的任务。