Lifelong Machine Learning

终身机器学习

• 批准号: CRC-2021-00558
• 负责人: AnbilParthipan, SarathChandar
• 金额: $ 5.1万
• 依托单位: École Polytechnique de Montréal
• 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=755202
• 关键词: Lifelong; Machine; Learning

A key aspect of human intelligence is the ability to quickly adapt to solving new problems in a constantly evolving world. Humans do this by accumulating knowledge over their lifetimes, and composing and reusing this knowledge to perform new tasks. Despite significant progress in machine learning research, powered by advances in deep learning, adapting to changes in the world continues to be an important limitation. This research program proposes addressing this limitation by exploring the paradigm of lifelong learning. Lifelong learning systems learn multiple tasks continually over time and should have the ability to transfer previously gained knowledge to effectively and efficiently learn new tasks. Developing effective lifelong learning systems can have a significant impact on theoretical and practical aspects of machine learning. To this end, this research program will develop novel model architectures that leverage modularity in design and long-term memory to grow their knowledge over time. The program also proposes working on developing new training tools such as optimizers designed for lifelong learning, ways to learn new tasks from natural language instruction and interaction, and ways to leverage recent advances in pre-training toward building systems that can be continually adapted to new tasks. These complementary research directions of developing new architectures and new training tools can help overcome fundamental limitations of modern neural network-based learning systems such as catastrophic forgetting and capacity saturation, and enable a system to transfer the knowledge gained from learning a new task to improve on tasks it has seen before, and tasks it will see in the future. From a practical standpoint, developing lifelong learning systems can help any machine learning system stay up to date and adapt to changes in the world without having to undergo the expensive process of periodic retraining on newly collected data. Advances in lifelong learning systems can also enable practical applications such as dialogue systems that can become more personalized over the course of interaction with a user, and drug discovery systems that can accumulate knowledge over time and get better at finding drugs for new targets. Developing ways to continually adapt learning systems can also make large-scale pre-trained models more accessible and resource-efficient, and have a significant impact in any setting in which they are deployed.

人类智能的一个关键方面是能够快速适应不断发展的世界中解决新问题的能力。人类是通过在他们的一生中积累知识，并撰写和重复这些知识来执行新任务来做到这一点。尽管机器学习研究取得了重大进展，但由深度学习的进步提供了支持，但适应世界变化仍然是一个重要的限制。该研究计划提出通过探索终身学习范式来解决这一限制。终身学习系统会随着时间的流逝不断学习多个任务，并应具有传递先前获得的知识的能力，以有效，有效地学习新任务。开发有效的终身学习系统可以对机器学习的理论和实际方面产生重大影响。为此，该研究计划将开发新型的模型体系结构，以利用设计和长期记忆的模块化，以随着时间的推移而发展知识。该计划还建议开发新的培训工具，例如为终身学习而设计的优化器，从自然语言指导和互动中学习新任务的方法以及利用最新进展的方法来培养构建系统的最新进展，这些系统可以不断适应新任务。这些开发新体系结构和新培训工具的互补研究方向可以帮助克服基于灾难性的遗忘和容量饱和的现代神经网络学习系统的基本局限性，并使一个系统能够从学习新任务中转移知识以改进以前所看到的任务，以及将来会看到的任务。从实际的角度来看，开发终身学习系统可以帮助任何机器学习系统保持最新状态，并适应世界上的变化，而无需在新收集的数据上进行定期重新训练的昂贵过程。终身学习系统的进步还可以实现实用应用，例如对话系统，这些应用程序可以在与用户的互动过程中变得更加个性化，并且可以随着时间的推移积累知识并在寻找新目标的药物方面变得更好。开发不断调整学习系统的方法还可以使大规模的预训练模型更加易于访问和资源效率，并在部署的任何环境中都产生重大影响。