Machine Learning for Tomorrow: Efficient, Flexible, Robust and Automated

面向未来的机器学习：高效、灵活、稳健和自动化

• 批准号: EP/T005637/1
• 负责人: Richard Turner
• 金额: $ 208.89万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FT005637%2F1
• 关键词: Machine; Learning; Tomorrow; Efficient; Flexible

Artificial intelligence systems have recently led to significant advances in the state-of-the-art in downstream fields including computer vision, speech and natural language processing, and game playing. Although impressive, these advances mask a set of fundamental limitations of the underlying machine learning technology that need to be addressed to unlock gains in a wide variety of applications relevant to industry and society. These limitations come in four main forms. First current approaches are data-inefficient requiring extremely large and painstakingly curated datasets. Second, they are inflexible solving single tasks that are fixed through time. Third, the current approaches are brittle as performance can degrade catastrophically in the face of noise, missing data or adversarially selected data points. Fourth, the approaches are only semi-automated requiring an expert to design and tune them. These limitations mean that many important application domains are currently out of reach. For example, in medicine we typically have only small and noisy datasets which requires data-efficient and robust machine learning. Providing machine learning as a service requires fully-automated machine learning. This Prosperity Partnership will develop machine learning that is data-efficient, robust, flexible and automated by leveraging recently developed technology from the University of Cambridge's Machine Learning Group and deep expertise from Microsoft Research Cambridge. This partnership has identified a unique testbed of impactful application domains: health, enterprise tools and games development. This research programme is central to realising Microsoft's vision to empower every developer, organization and individual to innovate and transform the world with AI. Moreover, this area of immediate and wide-ranging national importance, and provides pathways to impact by partnering with one of the world's largest technology companies.

人工智能系统最近导致了下游领域的最新进步，包括计算机视觉，语音和自然语言处理以及游戏玩法。尽管令人印象深刻，但这些进步掩盖了基础机器学习技术的一系列基本限制，这些局限性需要解决，以解锁与行业和社会有关的广泛应用中的收益。这些限制有四种主要形式。第一种当前方法是数据信息，需要非常大且精心策划的数据集。其次，它们是不灵活的解决单个任务，这些任务是通过时间固定的。第三，当前的方法是脆弱的，因为表现会在噪声，缺少数据或对手选择的数据点时灾难性地降解。第四，这些方法只是半自动化的，需要专家设计和调整它们。这些限制意味着目前许多重要的应用程序域都无法触及。例如，在医学中，我们通常只有小且嘈杂的数据集，需要数据效率和健壮的机器学习。提供机器学习作为服务需要完全自动化的机器学习。这种繁荣合作伙伴关系将通过利用剑桥大学的机器学习组和Microsoft Research Cambridge的深入专业知识来开发数据效率，健壮，灵活和自动化的机器学习。该合作伙伴关系确定了有影响力的应用领域的独特测试：健康，企业工具和游戏开发。该研究计划是实现微软愿景的核心，以增强每个开发人员，组织和个人的能力，以通过AI创新和改变世界。此外，这一领域具有直接和广泛的国家重要性，并通过与世界上最大的技术公司之一合作提供了影响途径。

项目成果

FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification

• DOI: 10.48550/arxiv.2206.08671
• 发表时间: 2022-06
• 期刊: ArXiv
• 作者: Aliaksandra Shysheya;J. Bronskill;Massimiliano Patacchiola;Sebastian Nowozin;Richard E. Turner

Multi-disciplinary fairness considerations in machine learning for clinical trials

• DOI: 10.1145/3531146.3533154
• 发表时间: 2022-01-01
• 期刊: FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency
• 作者: Chien, Isabel;Deliu, Nina;Kilbertus, Niki
• 通讯作者: Kilbertus, Niki

Memory Efficient Meta-Learning with Large Images

• 发表时间: 2021-07
• 作者: J. Bronskill;Daniela Massiceti;Massimiliano Patacchiola;Katja Hofmann;Sebastian Nowozin;Richard E. Turner

How Tight Can PAC-Bayes be in the Small Data Regime?

PAC-Bayes 在小数据制度中可以有多严格？

• 发表时间: 2021
• 期刊: Advances in Neural Information Processing Systems
• 作者: Foong A.Y.K.

Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification

• DOI: 10.48550/arxiv.2206.09843
• 发表时间: 2022-06
• 期刊: ArXiv
• 作者: Massimiliano Patacchiola;J. Bronskill;Aliaksandra Shysheya;Katja Hofmann;Sebastian Nowozin;Richard E. Turner