Efficient second-order optimisation algorithms for learning from big data

用于从大数据中学习的高效二阶优化算法

• 批准号: DE180100923
• 负责人: A/Prof Fred Roosta
• 金额: $ 24.39万
• 依托单位: The University of Queensland
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Early Career Researcher Award
• 财政年份: 2018
• 资助国家: 澳大利亚
• 起止时间: 2018-06-04 至 2023-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DE180100923
• 关键词: Efficient; second; order; optimisation; algorithms

This project aims to apply a diverse range of scientific computing techniques to design and implement new, second-order methods that can surpass first-order alternatives in the next generation of optimisation methods for large-scale machine learning (ML). Scalable optimisation methods are now an integral part ML in the presence of “big data”. While the development of efficient first-order methods has grown in the ML community, second-order alternatives have largely been ignored. The project expects to facilitate the development of more effective ML algorithms for extraction of knowledge from large data sets.

该项目旨在应用各种科学计算技术来设计和实施新的二阶方法，这些方法可以在下一代大规模机器学习（ML）优化方法中超越一阶替代方法。可扩展的优化方法现在是“大数据”存在下机器学习的一个组成部分。虽然高效的一阶方法在ML社区中得到了发展，但二阶替代方法在很大程度上被忽视了。该项目希望促进开发更有效的机器学习算法，用于从大型数据集中提取知识。