There has been a surge of recent interest in learning representations for graph-structured data. Graph representation learning methods have generally fallen into three main categories, based on the availability of labeled data. The first, network embedding (such as shallow graph embedding or graph auto-encoders), focuses on learning unsupervised representations of relational structure. The second, graph regularized neural networks, leverages graphs to augment neural network losses with a regularization objective for semi-supervised learning. The third, graph neural networks, aims to learn differentiable functions over discrete topologies with arbitrary structure. However, despite the popularity of these areas there has been surprisingly little work on unifying the three paradigms. Here, we aim to bridge the gap between graph neural networks, network embedding and graph regularization models. We propose a comprehensive taxonomy of representation learning methods for graph-structured data, aiming to unify several disparate bodies of work. Specifically, we propose a Graph Encoder Decoder Model (GRAPHEDM), which generalizes popular algorithms for semi-supervised learning on graphs (e.g. GraphSage, Graph Convolutional Networks, Graph Attention Networks), and unsupervised learning of graph representations (e.g. DeepWalk, node2vec, etc) into a single consistent approach. To illustrate the generality of this approach, we fit over thirty existing methods into this framework. We believe that this unifying view both provides a solid foundation for understanding the intuition behind these methods, and enables future research in the area.
Fraunhofer Center for Machine Learning（弗劳恩霍夫机器学习中心）；Fraunhofer IAIS, Institute for Intelligent Analysis and Information Systems（弗劳恩霍夫智能分析与信息系统研究所）；Fraunhofer SCAI, Institute for Algorithms and Scientific Computing（弗劳恩霍夫算法与科学计算研究所）；Fraunhofer ITWM, Institute for Industrial Mathematics（弗劳恩霍夫工业数学研究所）；Fraunhofer IOSB, Institute for Optronics, System Technologies and Image Exploitation（弗劳恩霍夫光电子学、系统技术与图像开发研究所）
Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process which leads to the notion of informed machine learning. In this paper, we present a structured overview of various approaches in this field. We provide a definition and propose a concept for informed machine learning which illustrates its building blocks and distinguishes it from conventional machine learning. We introduce a taxonomy that serves as a classification framework for informed mac…
National Institute of Advanced Industrial Science and Technology (AIST)（日本产业技术综合研究所）；Sumitomo Electric Industries, Ltd.（住友电气工业株式会社）
In this article, we propose the Artificial Intelligence Security Taxonomy to systematize the knowledge of threats, vulnerabilities, and security controls of machine-learning-based (ML-based) systems. We first classify the damage caused by attacks against ML-based systems, define ML-specific security, and discuss its characteristics. Next, we enumerate all relevant assets and stakeholders and provide a general taxonomy for ML-specific threats. Then, we collect a wide range of security controls against ML-specific threats through an extensive review of recent literature. Finally, we classify the…
Southeast University（东南大学）；Purple Mountain Laboratories（紫金山实验室）；Ocean University of China（中国海洋大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Anhui University（安徽大学）；The University of Sydney（悉尼大学）；JD Explore Academy（京东探索研究院）
With the development of convolutional neural networks, hundreds of deep learning based dehazing methods have been proposed. In this paper, we provide a comprehensive survey on supervised, semi-supervised, and unsupervised single image dehazing. We first discuss the physical model, datasets, network modules, loss functions, and evaluation metrics that are commonly used. Then, the main contributions of various dehazing algorithms are categorized and summarized. Further, quantitative and qualitative experiments of various baseline methods are carried out. Finally, the unsolved issues and challeng…
University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；Tennessee Tech University（田纳西理工大学）；North Carolina A&T State University（北卡罗来纳农工州立大学）
An increasing body of work has recognized the importance of exploiting machine learning (ML) advancements to address the need for efficient automation in extracting access control attributes, policy mining, policy verification, access decisions, etc. In this work, we survey and summarize various ML approaches to solve different access control problems. We propose a novel taxonomy of the ML model's application in the access control domain. We highlight current limitations and open challenges such as lack of public real-world datasets, administration of ML-based access control systems, understan…
NVIDIA（英伟达）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
The open-world deployment of Machine Learning (ML) algorithms in safety-critical applications such as autonomous vehicles needs to address a variety of ML vulnerabilities such as interpretability, verifiability, and performance limitations. Research explores different approaches to improve ML dependability by proposing new models and training techniques to reduce generalization error, achieve domain adaptation, and detect outlier examples and adversarial attacks. However, there is a missing connection between ongoing ML research and well-established safety principles. In this paper, we present…
Massachusetts Institute of Technology（麻省理工学院）；Columbia University（哥伦比亚大学）；The London School of Economics and Political Science（伦敦政治经济学院）；University of Kentucky（肯塔基大学）
Probabilistic machine learning increasingly informs critical decisions in medicine, economics, politics, and beyond. We need evidence to support that the resulting decisions are well-founded. To aid development of trust in these decisions, we develop a taxonomy delineating where trust in an analysis can break down: (1) in the translation of real-world goals to goals on a particular set of available training data, (2) in the translation of abstract goals on the training data to a concrete mathematical problem, (3) in the use of an algorithm to solve the stated mathematical problem, and (4) in t…
In the meantime, a wide variety of terminologies, motivations, approaches, and evaluation criteria have been developed within the research field of explainable artificial intelligence (XAI). With the amount of XAI methods vastly growing, a taxonomy of methods is needed by researchers as well as practitioners: To grasp the breadth of the topic, compare methods, and to select the right XAI method based on traits required by a specific use-case context. Many taxonomies for XAI methods of varying level of detail and depth can be found in the literature. While they often have a different focus, the…
Orchestrating the Development Lifecycle of Machine Learning-Based IoT Applications: A Taxonomy and Survey
Newcastle University（纽卡斯尔大学）；University of Leeds（利兹大学）；The University of Sydney（悉尼大学）；Cardiff University（卡迪夫大学）；China University of Geoscience (Wuhan)（中国地质大学（武汉））
Large Language Models for EEG: A Comprehensive Survey and Taxonomy
Department of Computer Science and Engineering, Indian Institute of Technology Patna（计算机科学与工程系，印度理工学院帕纳布分校）；Infocomm Technology Cluster, Singapore Institute of Technology（信息通信技术集群，新加坡理工学院）
Clustering is a fundamental machine learning task which has been widely studied in the literature. Classic clustering methods follow the assumption that data are represented as features in a vectorized form through various representation learning techniques. As the data become increasingly complicated and complex, the shallow (traditional) clustering methods can no longer handle the high-dimensional data type. With the huge success of deep learning, especially the deep unsupervised learning, many representation learning techniques with deep architectures have been proposed in the past decade. …
Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects
Nanjing University of Science and Technology（南京理工大学）；Xidian University（西安电子科技大学）；CSIRO（联邦科学与工业研究组织）；University of Western Australia（西澳大学）；University of Technology Sydney（悉尼科技大学）
The University of Melbourne（墨尔本大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；The University of Macau（澳门大学）
Containerization is a lightweight application virtualization technology, providing high environmental consistency, operating system distribution portability, and resource isolation. Existing mainstream cloud service providers have prevalently adopted container technologies in their distributed system infrastructures for automated application management. To handle the automation of deployment, maintenance, autoscaling, and networking of containerized applications, container orchestration is proposed as an essential research problem. However, the highly dynamic and diverse feature of cloud workl…
Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy
University of Southern Queensland（南昆士兰大学）；Lingnan University（岭南大学）；Wuhan University of Technology（武汉理工大学）；University of Electronic Science and Technology of China（电子科技大学）；Hong Kong Polytechnic University（香港理工大学）
本文系统综述机器遗忘的最新方法、指标、数据集与挑战，并指出其在隐私、安全、公平及可信 AI 中的未来方向。
Modern high throughput sequencing technologies like metagenomic sequencing generate millions of sequences which have to be classified based on their taxonomic rank. Modern approaches either apply local alignment and comparison to existing data sets like MMseqs2 or use deep neural networks as it is done in DeepMicrobes and BERTax. Alignment-based approaches are costly in terms of runtime, especially since databases get larger and larger. For the deep learning-based approaches, specialized hardware is necessary for a computation, which consumes large amounts of energy. In this paper, we propose …
Carnegie Mellon University（卡内基梅隆大学）；University of Washington（华盛顿大学）；Determined AI
Despite increasing interest in the field of Interpretable Machine Learning (IML), a significant gap persists between the technical objectives targeted by researchers' methods and the high-level goals of consumers' use cases. In this work, we synthesize foundational work on IML methods and evaluation into an actionable taxonomy. This taxonomy serves as a tool to conceptualize the gap between researchers and consumers, illustrated by the lack of connections between its methods and use cases components. It also provides the foundation from which we describe a three-step workflow to better enable …
This paper investigates the existing resource management approaches in Cloud Data Centres for energy and thermal efficiency. It identifies the need for integrated computing and cooling systems management and learning-based solutions in resource management systems. A taxonomy on energy and thermal efficient resource management in data centres is proposed based on an in-depth analysis of the literature. Furthermore, a detailed survey on existing approaches is conducted according to the taxonomy and recent advancements including machine learning-based resource management approaches and cooling ma…
From Rule-Based Models to Deep Learning Transformers Architectures for Natural Language Processing and Sign Language Translation Systems: Survey, Taxonomy and Performance Evaluation
United Arab Emirates University（阿联酋大学）；Emirates Center for Mobility Research（阿联酋移动性研究中心）
A Survey and Taxonomy of Distributed Data Mining Research Studies: A Systematic Literature Review
Dian Nuswantoro University（迪安努斯万托罗大学）
内容：数据采掘（Data Mining，DM）方法已经在逐年发展，并且到今天为止，还存在DM技术的增强，其运行速度可以比传统方法快几倍，称为分布式数据采掘（Distributed Data Mining，DDM）。它实际上并不是数据处理中的一个新领域，但近年来这方面的研究受到了越来越多的关注。问题：关于DDM在高声誉期刊和会议上的出版物数量显着增加。这使得研究者很难对DDM有一个全面的了解，需要进一步的研究。解决方案：我们进行了系统的文献回顾，以映射以前在DDM领域的研究。我们的目标是通过识别DDM领域的差距以及热点领域本身来为新的研究提供动力。结果：通过对文献综述中提出的7个研究问题的回答，得出了一些结论。此外，本文还对DDM的研究领域进行了分类。最后，本文对2000年至2015年DDM的发展进行了系统的文献综述，以帮助未来的研究者对DDM的发展现状有一个全面的了解。
Lenovo Machine Intelligence Center（联想机器智能中心）；Delft University of Technology（代尔夫特理工大学）；The Chinese University of Hong Kong（香港中文大学）
Meta-learning has gained wide popularity as a training framework that is more data-efficient than traditional machine learning methods. However, its generalization ability in complex task distributions, such as multimodal tasks, has not been thoroughly studied. Recently, some studies on multimodality-based meta-learning have emerged. This survey provides a comprehensive overview of the multimodality-based meta-learning landscape in terms of the methodologies and applications. We first formalize the definition of meta-learning in multimodality, along with the research challenges in this growing…
Goldsmiths, University of London（伦敦大学金史密斯学院）；University of The Arts London（伦敦艺术大学）；Queen Mary University of London（伦敦玛丽女王大学）；Monash University（莫纳什大学）
Generative deep learning systems offer powerful tools for artefact generation, given their ability to model distributions of data and generate high-fidelity results. In the context of computational creativity, however, a major shortcoming is that they are unable to explicitly diverge from the training data in creative ways and are limited to fitting the target data distribution. To address these limitations, there have been a growing number of approaches for optimising, hacking and rewriting these models in order to actively diverge from the training data. We present a taxonomy and comprehensi…
ENIB（法国国家电子与计算机工程学院）；Addis Ababa University（亚的斯亚贝巴大学）；University of Miami（迈阿密大学）；IRL CROSSING（IRL CROSSING实验室）
Machine learning has proved useful in many software disciplines, including computer vision, speech and audio processing, natural language processing, robotics and some other fields. However, its applicability has been significantly hampered due its black-box nature and significant resource consumption. Performance is achieved at the expense of enormous computational resource and usually compromising the robustness and trustworthiness of the model. Recent researches have been identifying a lack of interactivity as the prime source of these machine learning problems. Consequently, interactive ma…
Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design computer agents with intelligent capabilities such as understanding, reasoning, and learning through integrating multiple communicative modalities, including linguistic, acoustic, visual, tactile, and physiological messages. With the recent interest in video understanding, embodied autonomous agents, text-to-image generation, and multisensor fusion in application domains such as healthcare and robotics, multimodal machine learning has brought unique computational and theoretical challenges to the mac…
