CAREER: Towards Biologically Inspired Lifelong Learning with Multimodal Association

职业生涯：通过多模式关联迈向受生物启发的终身学习

• 批准号: 2325863
• 负责人: Bo Tang
• 金额: $ 50万
• 依托单位: Worcester Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2325863&HistoricalAwards=false
• 关键词: CAREER; Towards; Biologically; Inspired; Lifelong

Humans have the ability to continuously learn, accumulate and fine-tune knowledge and skills from a sequence of tasks over their lifetimes. Such lifelong learning is also crucial for computational systems to achieve high levels of performance, flexibility, and adaptation when they interact with real world environments and process streaming sensory data. This project will develop biologically inspired lifelong learning architectures and methods that integrate functions and characteristics of a mammalian brain, which is arguably the best learning system the world has seen. The success of this research will advance fundamental knowledge in computational lifelong learning and will have the potential to transform how the field creates human-like artificial intelligence with lifelong learning capabilities. In addition, the outcome of this research will be integrated into a new curriculum, and opportunities will be provided to students from under-represented groups to participate in computational intelligence research.The project will explore a new machine learning paradigm to address many critical challenges facing current deep neural networks when learning is performed from sequential tasks and different sources. The proposed research will introduce a lifelong learning framework consisting of a feature learning network, a convertible short-term and long-term memory network, and a memory replay network. To achieve effective lifelong learning, this project will address the following three research challenges: (1) learning to memorize -- achieving the optimal balance between plasticity and stability of neural connections to improve the efficiency of both learning and memory networks; (2) learning to recall -- optimizing cue effectiveness in both learning and memory replay networks to address the problem of catastrophic forgetting; and (3) learning to associate -- enabling multimodality association at the memory level. Since the proposed models mimic the hierarchical architecture, short- and long-term memory mechanisms, and feedback function of the mammalian brain, they have the potential to contribute to an artificial general intelligence that better accumulates knowledge without interference, learns multimodality association, and even predicts the future.This project is jointly funded by Robust Intelligence and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人类有能力在一生中不断学习、积累和微调一系列任务中的知识和技能。这种终身学习对于计算系统在与真实的世界环境交互并处理流式传感数据时实现高水平的性能、灵活性和适应性也至关重要。该项目将开发受生物启发的终身学习架构和方法，整合哺乳动物大脑的功能和特征，这可以说是世界上最好的学习系统。这项研究的成功将推进计算终身学习的基础知识，并有可能改变该领域如何创造具有终身学习能力的类人人工智能。此外，该研究成果将被整合到新的课程中，并为来自代表性不足群体的学生提供参与计算智能研究的机会。该项目将探索一种新的机器学习范式，以解决当前深度神经网络在从顺序任务和不同来源进行学习时面临的许多关键挑战。这项研究将引入一个终身学习框架，包括一个特征学习网络，一个可转换的短期和长期记忆网络，和一个记忆重放网络。为了实现有效的终身学习，该项目将解决以下三个研究挑战：（1）学习记忆-实现神经连接的可塑性和稳定性之间的最佳平衡，以提高学习和记忆网络的效率;（2）学习回忆-优化学习和记忆重放网络中的线索有效性，以解决灾难性遗忘问题;以及（3）学习联想--在记忆水平上实现多模态联想。由于所提出的模型模仿了哺乳动物大脑的分层结构，短期和长期记忆机制以及反馈功能，因此它们有可能有助于人工通用智能，更好地积累知识而不受干扰，学习多模态关联，该项目由Robust Intelligence和刺激竞争研究的既定计划（EPSCoR）联合资助。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Generalized Federated Learning via Sharpness Aware Minimization

• DOI: 10.48550/arxiv.2206.02618
• 发表时间: 2022-06
• 作者: Zhe Qu;Xingyu Li;Rui Duan;Yaojiang Liu;Bo Tang;Zhuo Lu

FedLGA: Toward System-Heterogeneity of Federated Learning via Local Gradient Approximation

• DOI: 10.1109/tcyb.2023.3247365
• 发表时间: 2021-12
• 期刊: IEEE Transactions on Cybernetics
• 影响因子: 11.8
• 作者: Xingyu Li;Zhe Qu;Bo Tang;Zhuo Lu

On the Convergence of Multi-Server Federated Learning With Overlapping Area

• DOI: 10.1109/tmc.2022.3200016
• 发表时间: 2023-11-01
• 期刊: IEEE TRANSACTIONS ON MOBILE COMPUTING
• 影响因子: 7.9
• 作者: Qu, Zhe;Li, Xingyu;Liu, Yao
• 通讯作者: Liu, Yao