Improving efficiency and sustainability of automated machine learning

提高自动化机器学习的效率和可持续性

• 批准号: 2786001
• 金额: --
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2786001
• 关键词: Improving; efficiency; sustainability; automated; machine

In the recent years, automated machine learning (AutoML) has been proven successful in discovering efficient neural networks, improving upon manually designed networks in a number of machine learning tasks - such as image classification, super-resolution, automatic speech recognition, and more. However, early AutoML techniques were extremely demanding in terms of computational resources, often necessitating orders of magnitude more GPU-hours than a normal training. Although significant improvements have been made in making AutoML more efficient since then, the time and resources required to automatically optimise a neural network's structure still remain one of the main factors limiting applicability of AutoML, especially when very large search spaces and datasets are considered. This project aims to investigate the scalability and related limitations of the existing approaches with a special focus on those that utilise zero-cost proxies in order to speed-up AutoML. It will study a number of techniques in order to navigate large search spaces more effectively and increase fidelity of the zero-cost proxies, resulting in a significant improvements in both searching time and quality of discovered models, compared to the current state-of-the-art, making AutoML more efficient and sustainable.

近年来，自动化机器学习 (AutoML) 已被证明可以成功地发现高效的神经网络，并在许多机器学习任务（例如图像分类、超分辨率、自动语音识别等）中改进手动设计的网络。然而，早期的 AutoML 技术对计算资源的要求极高，通常需要比正常训练多几个数量级的 GPU 小时。尽管自那时起 AutoML 在提高效率方面取得了重大改进，但自动优化神经网络结构所需的时间和资源仍然是限制 AutoML 适用性的主要因素之一，特别是在考虑非常大的搜索空间和数据集时。该项目旨在研究现有方法的可扩展性和相关限制，特别关注那些利用零成本代理来加速 AutoML 的方法。它将研究多种技术，以便更有效地导航大型搜索空间并提高零成本代理的保真度，与当前的状态相比，从而在搜索时间和发现模型的质量方面取得显着改进-艺术，使 AutoML 更加高效和可持续。