A Survey on Graph Structure Learning: Progress and Opportunities
Center for Research on Intelligent Perception and Computing（智能感知与计算研究中心）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Department of Computer Science, George Mason University（乔治梅森大学计算机科学系）；The Paul G. Allen School of Computer Science and Engineering, University of Washington（华盛顿大学保罗·G·艾伦计算机科学与工程学院）；Department of Computer Science, Emory University（埃默里大学计算机科学系）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Yanqiao Zhu, Weizhi Xu, Jinghao Zhang, Yuanqi Du, Jieyu Zhang, Qiang Liu, Carl Yang, Shu Wu
备注：
Work in progress, in submission to IJCAI 2022 (Survey Track)
Graphs are widely used to describe real-world objects and their interactions. Graph Neural Networks (GNNs) as a de facto model for analyzing graphstructured data, are highly sensitive to the quality of the given graph structures. Therefore, noisy or incomplete graphs often lead to unsatisfactory representations and prevent us from fully understanding the mechanism underlying the system. In pursuit of an optimal graph structure for downstream tasks, recent studies have sparked an effort around the central theme of Graph Structure Learning (GSL), which aims to jointly learn an optimized graph structure and corresponding graph representations. In the presented survey, we broadly review recent progress in GSL methods. Specifically, we first formulate a general pipeline of GSL and review state-of-the-art methods classified by the way of modeling graph structures, followed by applications of GSL across domains. Finally, we point out some issues in current studies and discuss future directions.
Macquarie University（麦考瑞大学）；CSIRO’s Data61（澳大利亚联邦科学与工业研究组织数据61研究所）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；School of Computer Science, Wuhan University（武汉大学计算机学院）；Department of Computer Science, University of Illinois at Chicago（伊利诺伊大学芝加哥分校计算机科学系）
As communities represent similar opinions, similar functions, similar purposes, etc., community detection is an important and extremely useful tool in both scientific inquiry and data analytics. However, the classic methods of community detection, such as spectral clustering and statistical inference, are falling by the wayside as deep learning techniques demonstrate an increasing capacity to handle high-dimensional graph data with impressive performance. Thus, a survey of current progress in community detection through deep learning is timely. Structured into three broad research streams in t…
George Mason University（乔治梅森大学）；Microsoft（微软）；University of Pittsburgh（匹兹堡大学）；University of Maryland, Baltimore County（马里兰大学巴尔的摩郡分校）
Deep Learning (DL) models have achieved superior performance in many application domains, including vision, language, medical, commercial ads, entertainment, etc. With the fast development, both DL applications and the underlying serving hardware have demonstrated strong scaling trends, i.e., Model Scaling and Compute Scaling, for example, the recent pre-trained model with hundreds of billions of parameters with ~TB level memory consumption, as well as the newest GPU accelerators providing hundreds of TFLOPS. With both scaling trends, new problems and challenges emerge in DL inference serving …
In the Internet of Things (IoT) era, billions of sensors and devices collect and process data from the environment, transmit them to cloud centers, and receive feedback via the internet for connectivity and perception. However, transmitting massive amounts of heterogeneous data, perceiving complex environments from these data, and then making smart decisions in a timely manner are difficult. Artificial intelligence (AI), especially deep learning, is now a proven success in various areas including computer vision, speech recognition, and natural language processing. AI introduced into the IoT h…
East China Normal University（华东师范大学）；Macau University of Science and Technology（澳门科技大学）；Institute of Technical Education and Research, Siksha ‘O’ Anusandhan University（锡克沙·奥·阿努桑丹大学技术教育与研究所）
Few-shot learning (FSL) has emerged as an effective learning method and shows great potential. Despite the recent creative works in tackling FSL tasks, learning valid information rapidly from just a few or even zero samples still remains a serious challenge. In this context, we extensively investigated 200+ latest papers on FSL published in the past three years, aiming to present a timely and comprehensive overview of the most recent advances in FSL along with impartial comparisons of the strengths and weaknesses of the existing works. For the sake of avoiding conceptual confusion, we first el…
University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Lehigh University（理海大学）
With recent advances in data collection from multiple sources, multi-view data has received significant attention. In multi-view data, each view represents a different perspective of data. Since label information is often expensive to acquire, multi-view clustering has gained growing interest, which aims to obtain better clustering solution by exploiting complementary and consistent information across all views rather than only using an individual view. Due to inevitable sensor failures, data in each view may contain error. Error often exhibits as noise or feature-specific corruptions or outli…
Center for Artificial Intelligence Research（人工智能研究中心）；University of Agder（阿格德大学）
The past decade has seen significant progress in artificial intelligence (AI), which has resulted in algorithms being adopted for resolving a variety of problems. However, this success has been met by increasing model complexity and employing black-box AI models that lack transparency. In response to this need, Explainable AI (XAI) has been proposed to make AI more transparent and thus advance the adoption of AI in critical domains. Although there are several reviews of XAI topics in the literature that identified challenges and potential research directions in XAI, these challenges and resear…
Alzheimer's Disease (AD), as the most devastating neurodegenerative disease worldwide, has reached nearly 10 million new cases annually. Current technology provides unprecedented opportunities to study the progression and etiology of this disease with the advanced in imaging techniques. With the recent emergence of a society driven by big data and machine learning (ML), researchers have exerted considerable effort to summarize recent advances in ML-based AD diagnosis. Here, we outline some of the most prevalent and recent ML models for assessing the progression of AD and provide insights on th…
A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Ahead
eBrain Lab, Division of Engineering, New York University Abu Dhabi（eBrain实验室，工程系，纽约大学阿布扎克分校）；Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute, New York University Abu Dhabi（量子与拓扑系统中心（CQTS），NYUAD研究机构，纽约大学阿布扎克分校）
As we seek to deploy machine learning models beyond virtual and controlled domains, it is critical to analyze not only the accuracy or the fact that it works most of the time, but if such a model is truly robust and reliable. This paper studies strategies to implement adversary robustly trained algorithms towards guaranteeing safety in machine learning algorithms. We provide a taxonomy to classify adversarial attacks and defenses, formulate the Robust Optimization problem in a min-max setting and divide it into 3 subcategories, namely: Adversarial (re)Training, Regularization Approach, and Cer…
Recent advances in Internet of Things (IoT) technologies and the reduction in the cost of sensors have encouraged the development of smart environments, such as smart homes. Smart homes can offer home assistance services to improve the quality of life, autonomy and health of their residents, especially for the elderly and dependent. To provide such services, a smart home must be able to understand the daily activities of its residents. Techniques for recognizing human activity in smart homes are advancing daily. But new challenges are emerging every day. In this paper, we present recent algori…
Cardiovascular disorders account for nearly 1 in 3 deaths in the United States. Care for these disorders are often determined during visits to acute care facilities, such as hospitals. While the length of stay in these settings represents just a small proportion of patients' lives, they account for a disproportionately large amount of decision making. To overcome this bias towards data from acute care settings, there is a need for longitudinal monitoring in patients with cardiovascular disorders. Longitudinal monitoring can provide a more comprehensive picture of patient health, allowing for m…
Nowadays, deep neural networks are widely used in mission critical systems such as healthcare, self-driving vehicles, and military which have direct impact on human lives. However, the black-box nature of deep neural networks challenges its use in mission critical applications, raising ethical and judicial concerns inducing lack of trust. Explainable Artificial Intelligence (XAI) is a field of Artificial Intelligence (AI) that promotes a set of tools, techniques, and algorithms that can generate high-quality interpretable, intuitive, human-understandable explanations of AI decisions. In additi…
This survey article has grown out of the RL4ED workshop organized by the authors at the Educational Data Mining (EDM) 2021 conference. We organized this workshop as part of a community-building effort to bring together researchers and practitioners interested in the broad areas of reinforcement learning (RL) and education (ED). This article aims to provide an overview of the workshop activities and summarize the main research directions in the area of RL for ED.
When Federated Learning Meets Quantum Computing: Survey and Research Opportunities
Department of Computer Science and Engineering, BITS Pilani Dubai Campus（计算机科学与工程系，比斯帕尼迪拜校区）；Department of Computer Science, Missouri University of Science and Technology（计算机科学系，密苏里科技大学）
Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities
School of Computer Science, McGill University（麦吉尔大学计算机科学学院）；School of Electrical and Computer Engineering, McGill University（麦吉尔大学电气与计算机工程学院）；Samsung Research America（三星美国研究院）；Department of Electrical and Computer Engineering, Western University（西安大略大学电气与计算机工程系）；School of Computing Science, Simon Fraser University（西蒙菲莎大学计算科学学院）
Inverse reinforcement learning (IRL) is the problem of inferring the reward function of an agent, given its policy or observed behavior. Analogous to RL, IRL is perceived both as a problem and as a class of methods. By categorically surveying the current literature in IRL, this article serves as a reference for researchers and practitioners of machine learning and beyond to understand the challenges of IRL and select the approaches best suited for the problem on hand. The survey formally introduces the IRL problem along with its central challenges such as the difficulty in performing accurate …
