开放式杂货店是一家杂货店,客户不必排队等待。开发这样的系统并不是微不足道的,因为它面临着认识到人的动态和巨大流动的挑战。特别是,可以有效地将每个快照分配给相应客户的聚类方法对于系统至关重要。为了解决无公开结帐杂货店中的独特挑战,我们提出了一种有效的人群聚类方法。具体而言,我们首先提出一个拥挤的子图(CSG),以将大规模和连续数据流之间的关系定位。 CSG由拟议的选择链接 - 重量(plw)策略构建,\ textbf {picks}基于时间空间信息的节点,\ textbf {links}通过轨迹信息和\ textbf {comute} links}链接由拟议的von mises-fisher(VMF)相似性度量。然后,为了确保该方法适应动态和看不见的人的流程,我们提出了图形卷积网络(GCN),采用简单的最近邻居(NN)策略,以准确地聚集CSG的实例。 GCN被采用以将功能投射到低维可分离空间中,而NN能够快速在动态人流动下为此空间产生结果。实验结果表明,在这种情况下,提出的方法优于其他替代算法。实际上,整个系统已被实施并部署在几个现实的开放式杂货中。
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Recent years witnessed the breakthrough of face recognition with deep convolutional neural networks. Dozens of papers in the field of FR are published every year. Some of them were applied in the industrial community and played an important role in human life such as device unlock, mobile payment, and so on. This paper provides an introduction to face recognition, including its history, pipeline, algorithms based on conventional manually designed features or deep learning, mainstream training, evaluation datasets, and related applications. We have analyzed and compared state-of-the-art works as many as possible, and also carefully designed a set of experiments to find the effect of backbone size and data distribution. This survey is a material of the tutorial named The Practical Face Recognition Technology in the Industrial World in the FG2023.
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Graph convolutional network (GCN) has been successfully applied to many graph-based applications; however, training a large-scale GCN remains challenging. Current SGD-based algorithms suffer from either a high computational cost that exponentially grows with number of GCN layers, or a large space requirement for keeping the entire graph and the embedding of each node in memory. In this paper, we propose Cluster-GCN, a novel GCN algorithm that is suitable for SGD-based training by exploiting the graph clustering structure. Cluster-GCN works as the following: at each step, it samples a block of nodes that associate with a dense subgraph identified by a graph clustering algorithm, and restricts the neighborhood search within this subgraph. This simple but effective strategy leads to significantly improved memory and computational efficiency while being able to achieve comparable test accuracy with previous algorithms. To test the scalability of our algorithm, we create a new Amazon2M data with 2 million nodes and 61 million edges which is more than 5 times larger than the previous largest publicly available dataset (Reddit). For training a 3-layer GCN on this data, Cluster-GCN is faster than the previous state-of-the-art VR-GCN (1523 seconds vs 1961 seconds) and using much less memory (2.2GB vs 11.2GB). Furthermore, for training 4 layer GCN on this data, our algorithm can finish in around 36 minutes while all the existing GCN training algorithms fail to train due to the out-of-memory issue. Furthermore, Cluster-GCN allows us to train much deeper GCN without much time and memory overhead, which leads to improved prediction accuracy-using a 5-layer Cluster-GCN, we achieve state-of-the-art test F1 score 99.36 on the PPI dataset, while the previous best result was 98.71 by [16]. Our codes are publicly available at https://github.com/google-research/google-research/ tree/master/cluster_gcn.
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Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges on existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this survey, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose a new taxonomy to divide the state-of-the-art graph neural networks into four categories, namely recurrent graph neural networks, convolutional graph neural networks, graph autoencoders, and spatial-temporal graph neural networks. We further discuss the applications of graph neural networks across various domains and summarize the open source codes, benchmark data sets, and model evaluation of graph neural networks. Finally, we propose potential research directions in this rapidly growing field.
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深度聚类最近引起了极大的关注。尽管取得了显着的进展,但以前的大多数深度聚类作品仍有两个局限性。首先,其中许多集中在某些基于分布的聚类损失上,缺乏通过对比度学习来利用样本(或增强)关系的能力。其次,他们经常忽略了间接样本结构信息,从而忽略了多尺度邻里结构学习的丰富可能性。鉴于这一点,本文提出了一种新的深聚类方法,称为图像聚类,其中包括对比度学习和多尺度图卷积网络(IcicleGCN),该网络(ICICELGCN)也弥合了卷积神经网络(CNN)和图形卷积网络(GCN)之间的差距。作为对比度学习与图像聚类任务的多尺度邻域结构学习之间的差距。所提出的IcicleGCN框架由四个主要模块组成,即基于CNN的主链,实例相似性模块(ISM),关节群集结构学习和实例重建模块(JC-SLIM)和多尺度GCN模块(M -GCN)。具体而言,在每个图像上执行了两个随机增强,使用两个重量共享视图的骨干网络用于学习增强样品的表示形式,然后将其馈送到ISM和JC-SLIM以进行实例级别和集群级别的对比度分别学习。此外,为了实施多尺度的邻域结构学习,通过(i)通过(i)层次融合的层相互作用和(ii)共同自适应学习确保他们的最后一层,同时对两个GCN和自动编码器进行了同时培训。层输出分布保持一致。多个图像数据集上的实验证明了IcicleGCN优于最先进的群集性能。
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情绪预测在心理健康和情绪感知计算中起着至关重要的作用。情绪的复杂性质是由于其对一个人的生理健康,精神状态和周围环境的依赖而产生的,这使其预测一项艰巨的任务。在这项工作中,我们利用移动传感数据来预测幸福和压力。除了一个人的生理特征外,我们还通过天气和社交网络纳入了环境的影响。为此,我们利用电话数据来构建社交网络并开发机器学习体系结构,该架构从图形网络的多个用户中汇总信息,并将其与数据的时间动态集成在一起,以预测所有用户的情感。社交网络的构建不会在用户的EMA或数据收集方面产生额外的成本,也不会引起隐私问题。我们提出了一种自动化用户社交网络影响预测的架构,能够处理现实生活中社交网络的动态分布,从而使其可扩展到大规模网络。我们广泛的评估突出了社交网络集成提供的改进。我们进一步研究了图形拓扑对模型性能的影响。
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基于图的异常检测已被广泛用于检测现实世界应用中的恶意活动。迄今为止,现有的解决此问题的尝试集中在二进制分类制度中的结构特征工程或学习上。在这项工作中,我们建议利用图形对比编码,并提出监督的GCCAD模型,以将异常节点与正常节点的距离与全球环境(例如所有节点的平均值)相比。为了使用稀缺标签处理场景,我们通过设计用于生成合成节点标签的图形损坏策略,进一步使GCCAD成为一个自制的框架。为了实现对比目标,我们设计了一个图形神经网络编码器,该编码器可以在消息传递过程中推断并进一步删除可疑链接,并了解输入图的全局上下文。我们在四个公共数据集上进行了广泛的实验,表明1)GCCAD显着且始终如一地超过各种高级基线,2)其自我监督版本没有微调可以通过其完全监督的版本来实现可比性的性能。
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行人轨迹预测是自动驾驶的重要技术,近年来已成为研究热点。以前的方法主要依靠行人的位置关系来模型社交互动,这显然不足以代表实际情况中的复杂病例。此外,大多数现有工作通常通常将场景交互模块作为独立分支介绍,并在轨迹生成过程中嵌入社交交互功能,而不是同时执行社交交互和场景交互,这可能破坏轨迹预测的合理性。在本文中,我们提出了一个名为社会软关注图卷积网络(SSAGCN)的一个新的预测模型,旨在同时处理行人和环境之间的行人和场景相互作用之间的社交互动。详细说明,在建模社交互动时,我们提出了一种新的\ EMPH {社会软关注功能},其充分考虑了行人之间的各种交互因素。并且它可以基于各种情况下的不同因素来区分行人周围的人行力的影响。对于物理互动,我们提出了一个新的\ emph {顺序场景共享机制}。每个时刻在每个时刻对一个代理的影响可以通过社会柔和关注与其他邻居共享,因此场景的影响在空间和时间尺寸中都是扩展。在这些改进的帮助下,我们成功地获得了社会和身体上可接受的预测轨迹。公共可用数据集的实验证明了SSAGCN的有效性,并取得了最先进的结果。
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图形神经网络(GNN)是通过学习通用节点表示形式来建模和处理图形结构数据的主要范例。传统的培训方式GNNS取决于许多标记的数据,这导致了成本和时间的高需求。在某个特殊场景中,它甚至不可用。可以通过图形结构数据本身生成标签的自我监督表示学习是解决此问题的潜在方法。并且要研究对异质图的自学学习问题的研究比处理同质图更具挑战性,对此,研究也更少。在本文中,我们通过基于Metapath(SESIM)的结构信息提出了一种用于异质图的自我监督学习方法。提出的模型可以通过预测每个Metapath中节点之间的跳跃数来构建借口任务,以提高主任务的表示能力。为了预测跳跃数量,Sesim使用数据本身来生成标签,避免了耗时的手动标签。此外,预测每个Metapath中的跳跃数量可以有效地利用图形结构信息,这是节点之间的重要属性。因此,Sesim加深对图形结构模型的理解。最后,我们共同培训主要任务和借口任务,并使用元学习来平衡借口任务对主要任务的贡献。经验结果验证了SESIM方法的性能,并证明该方法可以提高传统神经网络在链接预测任务和节点分类任务上的表示能力。
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Point cloud learning has lately attracted increasing attention due to its wide applications in many areas, such as computer vision, autonomous driving, and robotics. As a dominating technique in AI, deep learning has been successfully used to solve various 2D vision problems. However, deep learning on point clouds is still in its infancy due to the unique challenges faced by the processing of point clouds with deep neural networks. Recently, deep learning on point clouds has become even thriving, with numerous methods being proposed to address different problems in this area. To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds. It covers three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation. It also presents comparative results on several publicly available datasets, together with insightful observations and inspiring future research directions.
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疾病预测是医学应用中的知名分类问题。 GCNS提供了一个强大的工具,用于分析患者相对于彼此的特征。这可以通过将问题建模作为图形节点分类任务来实现,其中每个节点是患者。由于这种医学数据集的性质,类别不平衡是疾病预测领域的普遍存在问题,其中类的分布是歪曲的。当数据中存在类别不平衡时,现有的基于图形的分类器倾向于偏向于主要类别并忽略小类中的样本。另一方面,所有患者中罕见阳性病例的正确诊断在医疗保健系统中至关重要。在传统方法中,通过将适当的权重分配给丢失函数中的类别来解决这种不平衡,这仍然依赖于对异常值敏感的权重的相对值,并且在某些情况下偏向于小类(ES)。在本文中,我们提出了一种重加权的对抗性图形卷积网络(RA-GCN),以防止基于图形的分类器强调任何特定类的样本。这是通过将基于图形的神经网络与每个类相关联来完成的,这负责加权类样本并改变分类器的每个样本的重要性。因此,分类器自身调节并确定类之间的边界,更加关注重要样本。分类器和加权网络的参数受到侵犯方法训练。我们在合成和三个公共医疗数据集上显示实验。与最近的方法相比,ra-gcn展示了与最近的方法在所有三个数据集上识别患者状态的方法相比。详细分析作为合成数据集的定量和定性实验提供。
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Graph is an important data representation which appears in a wide diversity of real-world scenarios. Effective graph analytics provides users a deeper understanding of what is behind the data, and thus can benefit a lot of useful applications such as node classification, node recommendation, link prediction, etc. However, most graph analytics methods suffer the high computation and space cost. Graph embedding is an effective yet efficient way to solve the graph analytics problem. It converts the graph data into a low dimensional space in which the graph structural information and graph properties are maximumly preserved. In this survey, we conduct a comprehensive review of the literature in graph embedding. We first introduce the formal definition of graph embedding as well as the related concepts. After that, we propose two taxonomies of graph embedding which correspond to what challenges exist in different graph embedding problem settings and how the existing work address these challenges in their solutions. Finally, we summarize the applications that graph embedding enables and suggest four promising future research directions in terms of computation efficiency, problem settings, techniques and application scenarios.
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人类每天产生的exabytes数据,导致越来越需要对大数据带来的多标签学习的大挑战的新努力。例如,极端多标签分类是一个有效且快速增长的研究区域,可以处理具有极大数量的类或标签的分类任务;利用具有有限监督的大规模数据构建一个多标签分类模型对实际应用变得有价值。除此之外,如何收获深度学习的强大学习能力,有巨大努力,以更好地捕获多标签的标签依赖性学习,这是深入学习解决现实世界分类任务的关键。然而,有人指出,缺乏缺乏系统性研究,明确关注分析大数据时代的多标签学习的新兴趋势和新挑战。呼吁综合调查旨在满足这项任务和描绘未来的研究方向和新应用。
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Data-efficient learning on graphs (GEL) is essential in real-world applications. Existing GEL methods focus on learning useful representations for nodes, edges, or entire graphs with ``small'' labeled data. But the problem of data-efficient learning for subgraph prediction has not been explored. The challenges of this problem lie in the following aspects: 1) It is crucial for subgraphs to learn positional features to acquire structural information in the base graph in which they exist. Although the existing subgraph neural network method is capable of learning disentangled position encodings, the overall computational complexity is very high. 2) Prevailing graph augmentation methods for GEL, including rule-based, sample-based, adaptive, and automated methods, are not suitable for augmenting subgraphs because a subgraph contains fewer nodes but richer information such as position, neighbor, and structure. Subgraph augmentation is more susceptible to undesirable perturbations. 3) Only a small number of nodes in the base graph are contained in subgraphs, which leads to a potential ``bias'' problem that the subgraph representation learning is dominated by these ``hot'' nodes. By contrast, the remaining nodes fail to be fully learned, which reduces the generalization ability of subgraph representation learning. In this paper, we aim to address the challenges above and propose a Position-Aware Data-Efficient Learning framework for subgraph neural networks called PADEL. Specifically, we propose a novel node position encoding method that is anchor-free, and design a new generative subgraph augmentation method based on a diffused variational subgraph autoencoder, and we propose exploratory and exploitable views for subgraph contrastive learning. Extensive experiment results on three real-world datasets show the superiority of our proposed method over state-of-the-art baselines.
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在计算机视觉中长期以来一直研究了时间行动定位。现有的最先进的动作定位方法将每个视频划分为多个动作单位(即,在一级方法中的两级方法和段中的提案),然后单独地对每个视频进行操作,而不明确利用他们在学习期间的关系。在本文中,我们声称,动作单位之间的关系在行动定位中发挥着重要作用,并且更强大的动作探测器不仅应捕获每个动作单元的本地内容,还应允许更广泛的视野与相关的上下文它。为此,我们提出了一般图表卷积模块(GCM),可以轻松插入现有的动作本地化方法,包括两阶段和单级范式。具体而言,我们首先构造一个图形,其中每个动作单元被表示为节点,并且两个动作单元之间作为边缘之间的关系。在这里,我们使用两种类型的关系,一个类型的关系,用于捕获不同动作单位之间的时间连接,另一类是用于表征其语义关系的另一个关系。特别是对于两级方法中的时间连接,我们进一步探索了两种不同的边缘,一个连接重叠动作单元和连接周围但脱节的单元的另一个。在我们构建的图表上,我们将图形卷积网络(GCNS)应用于模拟不同动作单位之间的关系,这能够了解更有信息的表示来增强动作本地化。实验结果表明,我们的GCM始终如一地提高了现有行动定位方法的性能,包括两阶段方法(例如,CBR和R-C3D)和一级方法(例如,D-SSAD),验证我们的一般性和有效性GCM。
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Clustering is a fundamental problem in network analysis that finds closely connected groups of nodes and separates them from other nodes in the graph, while link prediction is to predict whether two nodes in a network are likely to have a link. The definition of both naturally determines that clustering must play a positive role in obtaining accurate link prediction tasks. Yet researchers have long ignored or used inappropriate ways to undermine this positive relationship. In this article, We construct a simple but efficient clustering-driven link prediction framework(ClusterLP), with the goal of directly exploiting the cluster structures to obtain connections between nodes as accurately as possible in both undirected graphs and directed graphs. Specifically, we propose that it is easier to establish links between nodes with similar representation vectors and cluster tendencies in undirected graphs, while nodes in a directed graphs can more easily point to nodes similar to their representation vectors and have greater influence in their own cluster. We customized the implementation of ClusterLP for undirected and directed graphs, respectively, and the experimental results using multiple real-world networks on the link prediction task showed that our models is highly competitive with existing baseline models. The code implementation of ClusterLP and baselines we use are available at https://github.com/ZINUX1998/ClusterLP.
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作为图理论中最基本的任务之一,子图匹配是许多领域的关键任务,范围从信息检索,计算机视觉,生物学,化学和自然语言处理。然而,子图匹配问题仍然是NP完整问题。这项研究提出了一种基于端到端学习的近似近似方法,用于匹配任务,称为子图匹配网络(子GMN)。所提出的子-GMN首先使用图表表示学习将节点映射到节点级嵌入。然后,它结合了度量学习和注意机制,以模拟数据图和查询图中匹配节点之间的关系。为了测试所提出方法的性能,我们将方法应用于两个数据库。我们使用了两种现有方法,即GNN和FGNN作为基线进行比较。我们的实验表明,在数据集1上,平均而言,亚GMN的准确性分别比GNN和FGNN高12.21 \%和3.2 \%。平均运行时间次-GMN的运行速度比FGNN快20-40倍。此外,所有数据集2的实验中sub-gmn的平均F1得分达到0.95,这表明sub-gmn输出更正确的节点到节点匹配。与以前的基于GNNS的子图匹配任务相比,我们提出的子GMN允许在测试/应用程序阶段进行改变的查询和数据图,而大多数以前基于GNN的方法只能在数据图中在数据图中找到匹配的子图片,在训练阶段使用的相同查询图的测试/应用。我们提出的子-GMN的另一个优点是,它可以输出节点到节点匹配的列表,而大多数现有的基于端GNN的方法无法提供匹配的节点对。
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分层群集的主要挑战之一是如何适当地识别群集树较低级别的代表点,这些点将被用作群集树的较高级别的根源以进行进一步的聚合。然而,传统的分层聚类方法采用了一些简单的技巧来选择可能不像代表的“代表”点。因此,构造的簇树在其稳健性和可靠性较弱的方面不太吸引。针对这个问题,我们提出了一种新的分层聚类算法,其中,在构建聚类树形图的同时,我们可以有效地检测基于对每个子最小跨越树中的互易读数的互动最近数据点进行评分的代表点。 UCI数据集的广泛实验表明,所提出的算法比其他基准更准确。同时,在我们的分析下,所提出的算法具有O(nlogn)时间复杂度和O(logn)空间复杂度,表明它具有在处理具有更少时间和存储消​​耗的大规模数据方面具有可扩展性。
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Recent research in clustering face embeddings has found that unsupervised, shallow, heuristic-based methods -- including $k$-means and hierarchical agglomerative clustering -- underperform supervised, deep, inductive methods. While the reported improvements are indeed impressive, experiments are mostly limited to face datasets, where the clustered embeddings are highly discriminative or well-separated by class (Recall@1 above 90% and often nearing ceiling), and the experimental methodology seemingly favors the deep methods. We conduct a large-scale empirical study of 17 clustering methods across three datasets and obtain several robust findings. Notably, deep methods are surprisingly fragile for embeddings with more uncertainty, where they match or even perform worse than shallow, heuristic-based methods. When embeddings are highly discriminative, deep methods do outperform the baselines, consistent with past results, but the margin between methods is much smaller than previously reported. We believe our benchmarks broaden the scope of supervised clustering methods beyond the face domain and can serve as a foundation on which these methods could be improved. To enable reproducibility, we include all necessary details in the appendices, and plan to release the code.
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社交机器人被称为社交网络上的自动帐户,这些帐户试图像人类一样行事。尽管图形神经网络(GNNS)已大量应用于社会机器人检测领域,但大量的领域专业知识和先验知识大量参与了最先进的方法,以设计专门的神经网络体系结构,以设计特定的神经网络体系结构。分类任务。但是,在模型设计中涉及超大的节点和网络层,通常会导致过度平滑的问题和缺乏嵌入歧视。在本文中,我们提出了罗斯加斯(Rosgas),这是一种新颖的加强和自我监督的GNN Architecture搜索框架,以适应性地指出了最合适的多跳跃社区和GNN体系结构中的层数。更具体地说,我们将社交机器人检测问题视为以用户为中心的子图嵌入和分类任务。我们利用异构信息网络来通过利用帐户元数据,关系,行为特征和内容功能来展示用户连接。 Rosgas使用多代理的深钢筋学习(RL)机制来导航最佳邻域和网络层的搜索,以分别学习每个目标用户的子图嵌入。开发了一种用于加速RL训练过程的最接近的邻居机制,Rosgas可以借助自我监督的学习来学习更多的判别子图。 5个Twitter数据集的实验表明,Rosgas在准确性,训练效率和稳定性方面优于最先进的方法,并且在处理看不见的样本时具有更好的概括。
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