CAREER: Enabling Continual Multi-view Representation Learning: An Adversarial Perspective

职业：实现持续的多视图表示学习：对抗性视角

• 批准号: 2144772
• 负责人: Ming Shao
• 金额: $ 49.9万
• 依托单位: University of Massachusetts, Dartmouth
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-15 至 2027-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144772&HistoricalAwards=false
• 关键词: CAREER; Enabling; Continual; Multi; view

Representation learning techniques attempt to extract and abstract key information (i.e., the features) from raw data to be used in analyses in a wide range of applications, such as cybersecurity, industry, finance, economics, and scientific discovery. As a critical step in machine learning systems, representation learning is meant to be robust in its capacity, regardless of the mutation of raw data due to noises or the variations of raw data caused by capture devices. In the era of big data, representation learning techniques are confronted with new challenges. Massive data collected from different sensors (e.g., the multi-view camera system) or presented in different modalities (e.g., audio-visual-text) have overloaded existing representation learning techniques. In addition, streaming data received from the Internet and sensitive data accumulated over time, such as personal albums and electronic health records, require the established representation learning model to adapt and account for incoming data. This project will develop a robust continual representation learning model to address these challenges. In real-world scenarios where data access is restricted (e.g., sensitive data) or the processing power of devices is limited (e.g., edge and mobile devices), stakeholders will benefit from the adaptive representation learning techniques to enable continual data analyses.This project seeks to advance the fundamental understanding of continual multi-view robust representation learning by integrating machine intelligence and human knowledge in AI-enabled security contexts. There are three unique contributions. First, the project will investigate multi-view consistency pursuit to fuse knowledge and generate a view-invariant representation robust to domain shifts frequently encountered in real-world data. Second, this research will revisit and explore adversarial learning in multi-view contexts to enable new attack modes, including iterative, cross-view, and induced modes. Generated adversarial samples and training procedures will benefit and empower the acquired multi-view representation learning models to mitigate various forms of artificial noise. Third, new continual learning models will be created through a novel Memory Bounded Search Tree to enable the evolution of multi-view representation learning despite continual streams of data. Furthermore, to reduce the search space and uncertainty related to the data, this research will leverage human knowledge to acquire critical annotations and empirical strategies for the proposed continual learning models.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.

表示学习技术试图从原始数据中提取和抽象关键信息（即功能），这些数据可用于在广泛的应用中，例如网络安全，行业，金融，经济学和科学发现。作为机器学习系统的关键步骤，无论是由于噪声或捕获设备引起的原始数据的变化而导致的原始数据突变，代表性学习的能力是可靠的。在大数据时代，表示学习技术面临着新的挑战。从不同传感器（例如，多视图摄像头系统）收集的大量数据或以不同方式呈现（例如Audio-Visual-Text）已经超载了现有表示的学习技术。此外，从Internet接收到的流数据以及随着时间的推移积累的敏感数据，例如个人相册和电子健康记录，需要既定的表示模型，以适应和说明传入的数据。该项目将开发出强大的持续表示学习模型来应对这些挑战。在实际情况下，数据访问受到限制（例如敏感数据）或设备的处理能力有限（例如，边缘和移动设备），利益相关者将从自适应表示的学习技术中受益，以启用持续数据分析。试图通过在AI支持的安全环境中整合机器智能和人类知识来促进对持续多视图强大表示学习的基本理解。有三个独特的贡献。首先，该项目将研究多视图的一致性追求，以融合知识，并生成对现实世界中经常遇到的域移动的观看不变表示。其次，这项研究将在多视图上下文中重新访问和探索对抗性学习，以实现新的攻击模式，包括迭代，跨视图和诱导模式。生成的对抗样本和培训程序将使获得的多视图表示模型受益并授权，以减轻各种形式的人造噪声。第三，将通过新颖的内存有限的搜索树创建新的持续学习模型，以使多视图表示学习的演变具有连续的数据流。此外，为了减少与数据相关的搜索空间和不确定性，本研究将利用人类知识来获得拟议的持续学习模型的关键注释和经验策略。该奖项反映了NSF的法定任务，并被认为是值得通过使用评估的支持。基金会的智力优点和更广泛的影响审查标准。

项目成果

Critic-over-Actor-Critic Modeling: Finding Optimal Strategy in ICU Environments

• DOI: 10.1109/bigdata55660.2022.10021125
• 发表时间: 2022-12
• 期刊: 2022 IEEE International Conference on Big Data (Big Data)
• 作者: Riazat Ryan;Ming Shao