Machine Learning

机器学习

• 批准号: CRC-2021-00185
• 负责人: Guo, Yuhong
• 金额: $ 5.46万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Canada Research Chairs
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=755471
• 关键词: Machine; Learning

The widespread emergence of digital data has created new opportunities both to break the central bottleneck of machine learning for prediction systems caused by the reliance on human-annotated data and to address ethical and security concerns associated with information sharing. This research seeks to develop new information transfer methods that facilitate effective information sharing across diverse and changing real-world scenarios, aiming to reduce dependence on human annotation for supervised learning, while addressing privacy and fairness considerations. The key outcome will be a new generation of information transfer technologies that allow machine learning to be applied in diverse, socially beneficial and environmentally adaptive services.

数字数据的广泛出现已经创造了新的机会，既可以破坏因依赖人类批准数据而引起的预测系统的中央瓶颈，并解决与信息共享相关的道德和安全问题。这项研究旨在开发新的信息传输方法，以促进跨不同现实世界情景的有效信息共享，旨在减少对监督学习的人类注释的依赖，同时解决隐私和公平的考虑。关键结果将是新一代的信息传输技术，允许机器学习在多样化，对社会有益和环境自适应服务中应用。