Rosales Henson (bottomrelish5)

Moreover, other significant advantages can be pointed out by the use of the proposed approach, such as a better network generalization ability, a limited computational burden, and the robustness with respect to the number of training samples. Please find the source code and pretrained models from https//liangjiandeng.github.io/Projects_Res/HSRnet_2021tnnls.html.Multi-dimensional classification (MDC) assumes heterogeneous class spaces for each example, where class variables from different class spaces characterize semantics of the example along different dimensions. The heterogeneity of class spaces leads to incomparability of the modeling outputs from different class spaces, which is the major difficulty in designing MDC approaches. In this article, we make a first attempt toward adapting maximum margin techniques for MDC problem and a novel approach named M³MDC is proposed. Specifically, M³MDC maximizes the margins between each pair of class labels with respect to individual class variable while modeling relationship across class variables (as well as class labels within individual class variable) via covariance regularization. The resulting formulation admits convex objective function with nonlinear constraints, which can be solved via alternating optimization with quadratic programming (QP) or closed-form solution in either alternating step. Comparative studies on the most comprehensive real-world MDC datasets to date are conducted and it is shown that M³MDC achieves highly competitive performance against state-of-the-art MDC approaches.As a unified framework for graph neural networks, message passing-based neural network (MPNN) has attracted a lot of research interest and has been shown successfully in a number of domains in recent years. However, because of over-smoothing and vanishing gradients, deep MPNNs are still difficult to train. To alleviate these issues, we first introduce a deep hierarchical layer aggregation (DHLA) strategy, which utilizes a block-based layer aggregation to aggregate representations from different layers and transfers the output of the previous block to the subsequent block, so that deeper MPNNs can be easily trained. Additionally, to stabilize the training process, we also develop a novel normalization strategy, neighbor normalization (NeighborNorm), which normalizes the neighbor of each node to further address the training issue in deep MPNNs. Our analysis reveals that NeighborNorm can smooth the gradient of the loss function, i.e., adding NeighborNorm makes the optimization landscape much easier to navigate. Selleck CPI-1205 Experimental results on two typical graph pattern-recognition tasks, including node classification and graph classification, demonstrate the necessity and effectiveness of the proposed strategies for graph message-passing neural networks.The consensus problem of general linear multiagent systems (MASs) is studied under switching topologies by using observer-based event-triggered control method in this article. On the basis of the output information of agents, two kinds of novel event-triggered adaptive control schemes are designed to achieve the leaderless and leader-follower consensus problems, which do not need to utilize the global information of the communication networks. Finally, two simulation examples are introduced to show that the consensus error converges to zero and Zeno behavior is eliminated in MASs. Compared with the existing output feedback control research, one of the significant advantages of our methods is that the controller protocols and triggering mechanisms do not rely on any global information, are independent of the network scale, and are fully distributed ways.It is very challenging for machine learning methods to reach the goal of general-purpose learning since there are so many complicated situations in different tasks. The learning methods need to generate flexible internal representations for all scenarios met before. The hierarchical in