Titlebook: Artificial Intelligence; Second CAAI Internat Lu Fang,Daniel Povey,Ruiping Wang Conference proceedings 2022 The Editor(s) (if applicable) a

某人

User Management and Database Securityfeatures of the same node (positive samples) of the shallow GCN while alienating the features of other nodes (negative samples) , so that the deep GCN can learn the performance of the shallow GCN. Experiments show that our method can effectively alleviate the over-smoothing phenomenon. At the same t

AUGER

Linguistic Interval-Valued Spherical Fuzzy Sets and Related Properties, we give the concept of the linguistic interval-valued spherical fuzzy number, and various operational laws, then the measure formula, score and accuracy functions of the linguistic interval-valued spherical fuzzy number are defined with a brief study of their related properties. At last, an admiss

變形

A Genetic Algorithm for?Causal Discovery Based on?Structural Causal Modelnd causal graph cyclicity, which effectively ensures the accuracy of causal discovery. In the search phase, an efficient random search is designed based on genetic algorithm, which greatly improves the causal discovery efficiency. This paper implements the corresponding algorithm, namely SCM-GA (Str

畢業(yè)典禮

Stochastic and?Dual Adversarial GAN-Boosted Zero-Shot Knowledge Graphc generator and an additional classifier to improve the model’s ability of approximating features and classifying unseen tasks. The experiments on NELL-ZS and Wiki-ZS datasets show that the proposed SDA outperforms the classic methods in zero-shot KG completion task. In particular, the proposed SDA

SEED

Dictionary Learning-Based Reinforcement Learning with Non-convex Sparsity Regularizery, we employ the proximal splitting method to update the multivariate optimization problem. Hence, the non-convex sparsity regularized dictionary learning-based RL is developed and validated in different benchmark RL environments. The proposed algorithm can obtain the best control performances among

FAWN

Deep Twin Support Vector Networks. In the numerical experiments, our proposed DTSVN and MDTSVN are compared with the other four methods on MNIST, FASHION MNIST and CIFAR10 datasets. The results demonstrate that our DTSVN achieves the best prediction accuracy for the binary problem, and our MDTSVN significantly outperforms other exi

Hyperplasia

Dynamic Clustering Federated Learning for Non-IID Data make them much closer to IID and concentrating on the training the models in each cluster. Then we analyze the changing trend of model validity named model quality and define one suitable function to describe expiration dynamics. As a solution, we propose .ynamic .lustering .ederated .earning (DCFL

新鮮

Dynamic Network Embedding by Using Sparse Deep Autoencoderdata. Experimental results on simulated benchmark networks and real-world networks prove that, compared with existing network embedding methods utilizing dense structures, our method is able to greatly reduce the number of training weights, while minimally affecting or sometimes even improving the e

Lice692

Deep Graph Convolutional Networks Based on Contrastive Learning: Alleviating Over-smoothing Phenomenfeatures of the same node (positive samples) of the shallow GCN while alienating the features of other nodes (negative samples) , so that the deep GCN can learn the performance of the shallow GCN. Experiments show that our method can effectively alleviate the over-smoothing phenomenon. At the same t

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