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（即特征），以用于网络安全、工业、金融、经济和科学发现等广泛应用的分析。作为机器学习系统中的关键步骤，表示学习的能力应该是稳健的，无论原始数据因噪声而发生突变，还是捕获设备导致原始数据发生变化。大数据时代，表征学习技术面临新的挑战。从不同传感器（例如，多视图相机系统）收集或以不同方式（例如，视听文本）呈现的海量数据已经使现有的表示学习技术超载。此外，从互联网接收的流数据和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，需要建立的表示学习模型来适应和解释传入的数据。该项目将开发一个强大的持续表示学习模型来应对这些挑战。在数据访问受到限制（例如敏感数据）或设备处理能力有限（例如边缘和移动设备）的现实场景中，利益相关者将受益于自适应表示学习技术以实现持续的数据分析。该项目旨在通过在人工智能支持的安全环境中集成机器智能和人类知识，增进对持续多视图鲁棒表示学习的基本理解。共有三项独特的贡献。首先，该项目将研究多视图一致性追求，以融合知识并生成对现实世界数据中经常遇到的域转移稳健的视图不变表示。其次，本研究将重新审视和探索多视图环境中的对抗性学习，以实现新的攻击模式，包括迭代、跨视图和诱导模式。生成的对抗性样本和训练程序将有益于并增强所获得的多视图表示学习模型，以减轻各种形式的人工噪声。第三，将通过新颖的内存有限搜索树创建新的持续学习模型，以实现多视图表示学习的发展，尽管有连续的数据流。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取所提出的持续学习模型的关键注释和实证策略。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值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

Critic-over-Actor-Critic 建模：在 ICU 环境中寻找最佳策略

• DOI: 10.1109/bigdata55660.2022.10021125
• 发表时间: 2022-12-17
• 期刊: 2022 IEEE International Conference on Big Data (Big Data)
• 作者: Riazat Ryan;Ming Shao
• 通讯作者: Ming Shao