CML-IOT 2020: the second workshop on continual and multimodal learning for internet of things

Susu Xu, Shijia Pan, Tong Yu
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引用次数: 1

Abstract

With the deployment of Internet of Things (IoT), large amount of sensors are connected into the Internet, providing large-amount, streaming, and multimodal data. These data have distinct statistical characteristics over time and sensing modalities, which are hardly captured by traditional learning methods. Continual and multimodal learning allows integration, adaptation, and generalization of the knowledge learned from experiential data collected with heterogeneity to new situations. Therefore, continual and multimodal learning is an important step to enable efficient ubiquitous computing on IoT devices. The major challenges to combine continual learning and multimodal learning with real-world data include 1) how to fuse and transfer knowledge between the multimodal data under constrained computational resources, 2) how to learn continually despite the missing, imbalanced or noisy data under constrained computational resources, 3) how to effectively reserve privacy and retain security when learning knowledge from streaming and multimodal data collected by multiple stakeholders, and 4) how to develop large-scale distributed learning systems to efficiently learn from continual and multimodal data. We organize this workshop to bring people working on different disciplines together to tackle these challenges in this topic. This workshop aims to explore the intersection and combination of continual machine learning and multimodal modeling with applications in the Internet of Things. The workshop welcomes works addressing these issues in different applications/domains as well as algorithmic and systematic approaches to leverage continual learning on multimodal data. We further seek to develop a community that systematically handles the streaming multimodal data widely available in real-world ubiquitous computing systems. In 2019, we held the First Workshop on Continual and Multimodal Learning for Internet of Things (https://cmliot2019.github.io/) with Ubicomp 2019, London, UK. The First workshop accepted 12 papers from 17 submissions. The one-day agenda included 3 sessions and attracted around 20 attendees from academia and industries to discuss and share visions.
CML-IOT 2020:第二次关于物联网持续和多模式学习的研讨会
随着物联网的部署,大量传感器接入互联网,提供海量、流化、多模态的数据。这些数据随时间和感知方式的变化具有明显的统计特征,而传统的学习方法很难捕捉到这些特征。持续和多模式的学习允许整合、适应和概括从异质性收集的经验数据中学到的知识到新的情况。因此,持续和多模式学习是在物联网设备上实现高效泛在计算的重要一步。将持续学习和多模态学习与现实数据相结合面临的主要挑战包括:1)如何在计算资源受限的多模态数据之间融合和传递知识;2)如何在计算资源受限的情况下,在数据缺失、不平衡或有噪声的情况下持续学习;3)如何在从多个利益相关者收集的流数据和多模态数据中学习知识时,有效地保留隐私和安全。4)如何开发大规模分布式学习系统,从连续的多模态数据中高效学习。我们组织这次研讨会是为了把不同学科的人聚集在一起,共同应对这一主题的挑战。本次研讨会旨在探讨持续机器学习和多模态建模与物联网应用的交叉和结合。研讨会欢迎在不同应用/领域中解决这些问题的工作,以及利用对多模态数据的持续学习的算法和系统方法。我们进一步寻求开发一个社区,系统地处理在现实世界的普适计算系统中广泛可用的流多模态数据。2019年,我们与英国伦敦Ubicomp 2019联合举办了首届物联网持续多模式学习研讨会(https://cmliot2019.github.io/)。第一届研讨会共收到17篇论文中的12篇。为期一天的会议议程包括3场会议,吸引了来自学术界和工业界的约20名与会者讨论和分享愿景。
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