EFRI BRAID: Unsupervised Continual Learning with Hierarchical Timescales and Plasticity Mechanisms

EFRI BRAID：具有分层时间尺度和可塑性机制的无监督持续学习

• 批准号: 2223793
• 负责人: Gianfranco Doretto
• 金额: $ 200万
• 依托单位: West Virginia University Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2223793&HistoricalAwards=false
• 关键词: EFRI; BRAID; Unsupervised; Continual; Learning

Humans and animals can easily adapt to their environment with limited information. They sense the world around them and continuously adapt their behavior to the current situation by changing the “configuration” of their nervous system, a phenomenon called plasticity. Though this ability seems natural to humans, it is very difficult to achieve in software or hardware systems. In addition, current continuous learning methods are trained under unrealistic conditions and require supervision. This project aims to understand how to endow autonomous agents, such as robots, with the adaptability and resiliency of biology. Biological plasticity in weakly electric fish will guide engineering of new machine learning algorithms. These algorithms will enable autonomous agents to continuously sense and adapt to their environment without interrupting operations for manual training. This interdisciplinary project is integrated with a range of outreach activities involving local high schools and undergraduate students. Workshops and demonstrations on biology-inspired machine learning will be organized, aimed at spurring interest of rural students in coding and robotics.A grand challenge in artificial intelligence (AI) is how to achieve unsupervised continual learning in the open world. Current methods used in AI and machine learning operate with single-modality data, collected and consumed in controlled conditions, typically in a supervised manner. However, biological systems achieve lifelong learning by processing streams of multisensory data that continuously shape their neural networks (plasticity) while retaining previous knowledge (stability). This dynamic adaptation operates unsupervised, on a range of timescales and rules. The project will study those principles observed in the cerebellar feedback pathways of electric fish, which are responsible for driving plasticity, enabling adaptation of its function at different timescales and learning and forgetting at multiple speeds. This will enable the translational development of novel paradigms in continual learning that will support new levels of resiliency and lifelong learning in real-time autonomous systems in the open world. To achieve this goal the project will overcome some key technical hurdles, e.g., in enabling 1) data efficiency in processing inputs continuously as time-variant, potentially correlated, data streams in a fully unsupervised manner; 2) flexibility to learn and forget at different speeds; 3) generation of suitable internal representations from multiple modalities to improve autonomous resilience. This project is jointly funded by the Emerging Frontiers in Research and Innovation Brain-Inspired Dynamics for Engineering Energy-Efficient Circuits and Artificial Intelligence Program (BRAID) 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.

人类和动物可以通过有限的信息轻松适应其环境。他们感受到周围的世界，并通过改变神经系统的“配置”（一种称为可塑性的现象）来不断地使其行为适应当前情况。尽管这种能力对人类来说似乎很自然，但在软件或硬件系统中很难实现。此外，当前的持续学习方法是在不切实际的条件下进行培训，需要监督。该项目旨在了解如何赋予生物学的适应性和弹性等自主代理（例如机器人）。弱电鱼中的生物可塑性将指导新机器学习算法的工程。这些算法将使自主代理能够继续感知并适应其环境，而无需中断手动培训的操作。这个跨学科项目与涉及当地高中和本科生的一系列外展活动融合在一起。将组织有关生物学启发的机器学习的研讨会和演示，旨在激发农村学生对编码和机器人的兴趣。人工智能（AI）的巨大挑战是如何实现开放世界中无人监督的连续学习。 AI和机器学习操作中使用的当前方法具有单模式数据，通常以受控的方式收集和消费。但是，生物系统通过处理多感觉数据的流来实现终身学习，这些数据可以不断地塑造其神经网络（可塑性），同时保留先前的知识（稳定性）。这种动态适应性在一系列时间尺度和规则上无监督。该项目将研究电动鱼的小脑反馈途径中观察到的那些原理，这些原理负责驱动可塑性，使其在不同的时间表上适应其功能，并以多种速度学习和忘记。这将使新型范式在连续学习中的转化发展，这将支持开放世界中实时自主系统中新的弹性和终身学习水平。为了实现这一目标，该项目将克服一些关键的技术障碍，例如，在启用1）数据效率以随着时间变化的，潜在相关的数据流以完全无处不在的方式连续处理输入； 2）灵活地学习和忘记以不同的速度； 3）从多种方式产生合适的内部表示，以提高自主弹性。该项目由研究和创新的脑力启发动力学的新兴领域共同资助，用于工程能节能电路和人工智能计划（Braid）和既定计划，以刺激竞争性研究（EPSCOR）。本奖颁奖典礼反映了NSF的法定任务，并通过使用基金会的智力效果来评估诚实地支持了NSF的法定任务。

项目成果

DNA: Deformable Neural Articulations Network for Template-free Dynamic 3D Human Reconstruction from Monocular RGB-D Video

DNA：可变形神经关节网络，用于从单目 RGB-D 视频进行无模板动态 3D 人体重建

• DOI: 10.1109/cvprw59228.2023.00375
• 发表时间: 2023
• 期刊: IEEE
• 作者: Vo, Khoa;Pham, Trong-Thang;Yamazaki, Kashu;Tran, Minh;Le, Ngan
• 通讯作者: Le, Ngan

A perspective on the neuromorphic control of legged locomotion in past, present, and future insect-like robots

• DOI: 10.1088/2634-4386/acc04f
• 发表时间: 2023-03
• 期刊: Neuromorphic Computing and Engineering
• 作者: N. Szczecinski;C. Goldsmith;W. Nourse;R. Quinn

More Synergy, Less Redundancy: Exploiting Joint Mutual Information for Self-Supervised Learning

• DOI: 10.1109/icip49359.2023.10222547
• 发表时间: 2023-07
• 期刊: 2023 IEEE International Conference on Image Processing (ICIP)
• 作者: S. Mohamadi;Gianfranco Doretto;Don Adjeroh

A Robust Likelihood Model for Novelty Detection

• DOI: 10.48550/arxiv.2306.03331
• 发表时间: 2023-06
• 期刊: ArXiv
• 作者: Ranya Almohsen;Shivang Patel;Don Adjeroh;Gianfranco Doretto

CLIP-TSA: Clip-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly Detection

• DOI: 10.1109/icip49359.2023.10222289
• 发表时间: 2022-12
• 期刊: 2023 IEEE International Conference on Image Processing (ICIP)
• 作者: Hyekang Joo;Khoa T. Vo;Kashu Yamazaki;Ngan T. H. Le