Computational Foundations of Machine Learning in the Era of Big Data

大数据时代机器学习的计算基础

• 批准号: RGPIN-2017-05032
• 负责人: Yu, Yaoliang
• 金额: $ 2.04万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635104
• 关键词: Computational; Foundations; Machine; Learning; Era

Machine learning (ML), a field that develops software that can improve itself through learning and experience, has been largely driven by the availability of historical data, and by the need to develop efficient and scalable algorithms and supporting theories. Conversely, the success of ML in science, engineering, and commerce, along with technological innovations, has led to an unprecedented growth and enthusiasm in big data collection, thereby redefining computational efficiency and inviting system solutions. For example, the recent AlphaGo system of Deepmind that beats top human Go players needed 1900 CPUs and 280 GPUs to carry out the computation. How to balance computation with communication in this vast distributed cluster, without compromising system throughput or correctness? On the other hand, a small startup developing a mobile app may not afford the same computational power as Google, hence often has to turn into primitive solutions. How to build an algorithmic framework for ML that provides ''knobs'' to adjust the computational load, with explicit, controllable loss on the accuracy? Meeting such diverse computational needs in the big data era has thus been a grand challenge for the ML field.

机器学习（ML）是一个开发软件的领域，可以通过学习和经验来改进自己，它在很大程度上受到历史数据可用性的驱动，并且需要开发高效和可扩展的算法和支持理论。相反，机器学习在科学、工程和商业领域的成功，沿着技术创新，导致了大数据收集的空前增长和热情，从而重新定义了计算效率并邀请了系统解决方案。例如，Deepmind最近击败顶级人类围棋选手的AlphaGo系统需要1900个CPU和280个GPU来执行计算。如何在这个庞大的分布式集群中平衡计算和通信，而不影响系统吞吐量或正确性？另一方面，开发移动的应用程序的小型初创公司可能无法负担与Google相同的计算能力，因此通常不得不转向原始的解决方案。如何为ML构建一个算法框架，提供“旋钮”来调整计算负载，并在准确性上有明确的可控损失？因此，在大数据时代满足如此多样化的计算需求是ML领域面临的巨大挑战。