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A local density optimization method based on a graph convolutional network

Frontiers of Information Technology & Electronic engineering 2020年第12期21卷 1795-1803页

作者： Hao WANG Li-yan DONG Tie-hu FAN Ming-hui SUN College of Computer Science and Technology Jilin UniversityChangchun 130012China MOE Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin UniversityChangchun 130012China College of Instrumentation&Electrical Engineering Jilin UniversityChangchun 130012China

Success has been obtained using a semi-supervised graph analysis method based on a graph convolutional network(GCN).However,GCN ignores some local information at each node in the graph,so that data preprocessing is incomplete and the model generated is not accurate ***,in the case of numerous unsupervised models based on graph embedding technology,local node information is *** this paper,we apply a local analysis method based on the similar neighbor hypothesis to a GCN,and propose a local density definition;we call this method *** LDGCN algorithm processes the input data of GCN in two methods,i.e.,the unbalanced and balanced ***,the optimized input data contains detailed local node information,and then the model generated is accurate after *** also introduce the implementation of the LDGCN algorithm through the principle of GCN,and use three mainstream datasets to verify the effectiveness of the LDGCN algorithm(i.e.,the Cora,Citeseer,and Pubmed datasets).Finally,we compare the performances of several mainstream graph analysis algorithms with that of the LDGCN *** results show that the LDGCN algorithm has better performance in node classification tasks.

关键词： Semi-supervised learning Graph convolutional network Graph embedding Local density

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Event Prediction Model Combining Ordinary Differential Equation and Hypernetworks

SSRN

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SSRN 2023年

作者： Zhang, Junhe Ma, Fuyuan Wang, Xin Yang, Bo Wang, Ying College of Computer Science and Technology Jilin University Jilin Changchun130012 China College of Artificial Intelligence Jilin University Jilin Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Jilin Changchun130012 China

Modeling the temporal evolution patterns of network events is crucial for enhancing user experience and creating commercial value. Existing methods combine graph neural networks and time-series models to capture spatial and temporal features, but they assume non-interaction among nodes when the network structure is stable, implying that node states do not undergo updates. Moreover, utilizing a single model to fit all events in these methods may lead to an excessive emphasis on global evolutionary patterns, potentially neglecting the individual heterogeneity among events. In this paper, we propose an event prediction model combining Ordinary Differential Equations and Hypernetworks, named ODEH. Firstly, to model the continuous evolution process of network structures under stability, we utilize ordinary differential equations based on graph neural networks to capture the continuous information dissemination among nodes and the interaction between node representation channels. Then, we use a message passing mechanism to model the direct impact of network topology changes on network entities. Finally, a hypernetwork is used to capture the individual characteristics of events and fine-tune the prediction model. Experimental results on multiple datasets demonstrate ODEH's superiority over baseline methods, highlighting its effectiveness in event prediction. © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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AutoSTL: Automated Spatio-Temporal Multi-Task Learning

arXiv

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arXiv 2023年

作者： Zhang, Zijian Zhao, Xiangyu Miao, Hao Zhang, Chunxu Zhao, Hongwei Zhang, Junbo College of Computer Science and Technology Jilin University China School of Data Science City University of Hong Kong Hong Kong Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University China Department of Computer Science Aalborg University Denmark JD Intelligent Cities Research China JD iCity JD Technology China Hong Kong Institute for Data Science City University of Hong Kong Hong Kong

Spatio-temporal prediction plays a critical role in smart city construction. Jointly modeling multiple spatio-temporal tasks can further promote an intelligent city life by integrating their inseparable relationship. However, existing studies fail to address this joint learning problem well, which generally solve tasks individually or a fixed task combination. The challenges lie in the tangled relation between different properties, the demand for supporting flexible combinations of tasks and the complex spatio-temporal dependency. To cope with the problems above, we propose an Automated Spatio-Temporal multi-task Learning (AutoSTL) method to handle multiple spatio-temporal tasks jointly. Firstly, we propose a scalable architecture consisting of advanced spatio-temporal operations to exploit the complicated dependency. Shared modules and feature fusion mechanism are incorporated to further capture the intrinsic relationship between tasks. Furthermore, our model automatically allocates the operations and fusion weight. Extensive experiments on benchmark datasets verified that our model achieves state-of-the-art performance. As we can know, AutoSTL is the first automated spatio-temporal multi-task learning method. Copyright © 2023, The Authors. All rights reserved.

关键词： Smart city

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Msa-Net: A Medical Image Segmentation Network Based on Spatial Pyramid and Attention Mechanism

SSRN

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SSRN 2023年

作者： Feng, Yuncong Zhu, Xiaoyan Li, Yang Zhang, Xiaoli Lu, Huimin College of Computer Science and Engineering Changchun University of Technology Jilin Changchun130012 China Artificial Intelligence Research Institute Changchun University of Technology Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China Academy for Advanced Interdisciplinary Studies Northeast Normal University Jilin Changchun130024 China Jilin Provincial Smart Health Joint Innovation Laboratory for the New Generation of AI 3000 North Yuanda Avenue Gaoxin North District Changchun130102 China

Medical image segmentation is an important way to assist doctors to accurately diagnose diseases. However, semantic features are difficult to be fully extracted due to the complexity of medical image lesion tissue features (e.g., size, location, and morphology) and the high similarity between the background and the target region. To tackle these problems, we propose a novel medical image segmentation network MSA-Net based on the spatial pyramid and attention mechanism. This technique facilitates the extraction of multi-scale spatial information and the establishment of channel dependencies within images. By incorporating efficient pyramid attention and channel-spatial dual attention modules, the network autonomously focuses on lesion regions while suppressing background interference. Additionally, the SPCA (Spatial Pyramid-Coordinate Attention) module is integrated at the bottleneck layer, enabling the assimilation of larger receptive field information and prioritizing relevant features. This augmentation improves the network's ability to gather both location and channel-based insights. We proceed to evaluate our model qualitatively and quantitatively across four public datasets: ISIC2018, Lung segmentation, Kvasir-SEG, and ISLES2022. The results substantiate the superior performance of our proposed model. © 2023, The Authors. All rights reserved.

关键词： Deep learning

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A Survey of Sum-Product Networks Structural Learning

SSRN

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SSRN 2022年

作者： Xia, Riting Zhang, Yan Liu, Xueyan Yang, Bo School of Artificial Intelligence Jilin University Jilin Changchun130012 China School of Computer Science and Technology Jilin University Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineer Jilin University Ministry of Education Jilin Changchun130012 China College of Communication Engineering Jilin University Jilin Changchun130012 China

Sum-product networks (SPNs) in deep probabilistic models have made great progress in computer vision, robotics, neuro-symbolic artificial intelligence, natural language processing, probabilistic programming languages, and other fields. Compared with probabilistic graphical models and deep probabilistic models, SPNs can balance the tractability and expressive efficiency. In addition, SPNs remain more interpretable than deep neural models. The expressiveness and complexity of SPNs depend on its own structure. Thus, how to design an effective SPN structure learning algorithm that can balance expressiveness and complexity has become a hot research topic in recent years. In this paper, we review SPN structure learning comprehensively, including the motivation of SPN structure learning, a systematic review of related theories, the proper categorization of different SPN structure learning algorithms, several evaluation approaches and some helpful online resources. Moreover, we discuss some open issues and research directions for SPN structure learning. To our knowledge, this is the first survey to focus specifically on SPN structure learning, and we hope to provide useful references for researchers in related fields. © 2022, The Authors. All rights reserved.

关键词： Efficiency

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Bi-classifier with neighborhood aggregation for unsupervised domain adaptation

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Information Sciences 2025年 718卷

作者： Shuai Lü Xinyu Zhang Zongze Li Jingyao Li Meng Kang Key Laboratory of Symbolic Computation and Knowledge Engineering (Jilin University) Ministry of Education Changchun 130012 China College of Computer Science and Technology Jilin University Changchun 130012 China College of Software Jilin University Changchun 130012 China

Recently, adversarial learning with bi-classifier has been confirmed to be effective in unsupervised domain adaptation, which can effectively promote the alignment of different distributions across domains. Most previous methods with bi-classifier exploit the output disagreement of the bi-classifier to learn transferable representations. However, only considering the disagreement between two classifiers cannot guarantee that the model performs well in the target domain. The main reason is that the model does not consider the local structure of the target domain data. To address this problem, we propose a Bi-Classifier with Neighborhood Aggregation (BCNA). Specifically, we provide two pseudo-labels for a target sample based on the nearest neighbor aggregation strategy. Then, we propose a self-supervised loss with two pseudo-labels based on confidence weight. In addition, we also propose a novel consistency regularization loss for bi-classifier. Unlike previous methods with bi-classifier, we achieve the goal of minimizing the disagreement between two classifiers from the perspective of different views of the sample. Our method can increase the discriminability of the model while ensuring the feature transferability. Extensive experiments show that our method has achieved state-of-the-art performance on three datasets that are widely used in unsupervised domain adaptation.

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Spirit distillation: Precise real-time semantic segmentation of road scenes with insufficient data

arXiv

引用

arXiv 2021年

作者： Wu, Zhiyuan Jiang, Yu Cui, Chupeng Yang, Zongmin Xue, Xinhui Qi, Hong Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China With College of Computer Science and Technology Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China

Semantic segmentation of road scenes is one of the key technologies for realizing autonomous driving scene perception, and the effectiveness of deep Convolutional Neural Networks(CNNs) for this task has been demonstrated. Stateof-art CNNs for semantic segmentation suffer from excessive computations as well as large-scale training data requirement. Inspired by the ideas of Fine-tuning-based Transfer Learning (FTT) and feature-based knowledge distillation, we propose a new knowledge distillation method for cross-domain knowledge transference and efficient data-insufficient network training, named Spirit Distillation(SD), which allow the student network to mimic the teacher network to extract general features, so that a compact and accurate student network can be trained for real-time semantic segmentation of road scenes. Then, in order to further alleviate the trouble of insufficient data and improve the robustness of the student, an Enhanced Spirit Distillation (ESD) method is proposed, which commits to exploit a more comprehensive general features extraction capability by considering images from both the target and the proximity domains as input. To our knowledge, this paper is a pioneering work on the application of knowledge distillation to few-shot learning. Persuasive experiments conducted on Cityscapes semantic segmentation with the prior knowledge transferred from COCO2017 and KITTI demonstrate that our methods can train a better student network (mIOU and high-precision accuracy boost by 1.4% and 8.2% respectively, with 78.2% segmentation variance) with only 41.8% FLOPs (see Fig. 1). Copyright © 2021, The Authors. All rights reserved.

关键词： Autonomous vehicles

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ESAE: Evolutionary Strategy-Based Architecture Evolution 14th

ESAE: Evolutionary Strategy-Based Architecture Evolution

引用

14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Gu, Xue Meng, Ziyao Liang, Yanchun Xu, Dong Huang, Han Han, Xiaosong Wu, Chunguo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education School of Computer Zhuhai College of Jilin University Zhuhai519041 China Department of Electrical Engineering and Computer Science Informatics Institute and Christopher S. Bond Life Sciences Center University of Missouri ColumbiaMO65211 United States School of Software Engineering South China University of Technology GuangZhou510006 China

ISBN: (纸本)9789811534249

Although deep neural networks (DNNs) play important roles in many fields, the architecture design of DNNs can be challenging due to the difficulty of input data representation, the huge number of parameters and the complex layer relationships. To overcome the obstacles of architecture design, we developed a new method to generate the optimal structure of DNNs, named Evolutionary Strategy-based Architecture Evolution (ESAE), consisting of a bi-level representation and a probability distribution learning approach. The bi-level representation encodes architectures in the gene and parameter levels. The probability distribution learning approach ensures the efficient convergence of the architecture searching process. By using Fashion-MNIST and CIFAR-10, the effectiveness of the proposed ESAS is verified. The evolved DNNs, starting from a trivial initial architecture with one single convolutional layer, achieved the accuracies of 94.48% and 93.49% on Fashion-MNIST and CIFAR-10, respectively, and require remarkably less hardware costs in terms of GPUs and running time, compared with the existing state-of-the-art manual screwed architectures. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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A Novel Distributed Differential Privacy Preserving Based on Random Forest in Data Centers

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Procedia Computer Science 2022年 214卷 1531-1540页

作者： Xi Wang Weibei Fan Jing He Chi-Hung Chi Suzhou Institute of Industrial Technology Suzhou 215006 China Key laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 P.R. China College of Computer Nanjing University of Posts and Telecommunications Nanjing 210003 China Department of Neuroscience University of Oxford Oxford OX1 3QR England School of Computer Nanyang Technological University Nanyang Avenue 639798 Singapore

Traditional privacy-preserving technologies have been unable to provide adequate protection and are vulnerable to background knowledge. MapReduce is parallel distributed computing model which has the advantages of good scalability and high fault tolerance. In this paper, we design a differential privacy protection based on MapReduce for the security problems faced in the distributed environment. Firstly, we propose a random forest algorithm DPMRRF that satisfies differential privacy under the MapReduce framework. Secondly, we use the MapReduce framework in the Hadoop platform to make the classification results satisfy differential privacy by adding random noise values to the leaf nodes. Finally, through the design of the feature selection scheme combined with the index mechanism, three different privacy budget allocation methods are adopted, namely, equal distribution, equal distribution and equal distribution. Experiments show that the algorithm has better classification accuracy while ensuring data availability. The proposed algorithm effectively improves the classification accuracy and operating efficiency, and greatly reduces the amount of computation.

关键词： MapReduce Differential privacy protection Random forest Privacy budget

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Enhanced Doubly Robust Learning for Debiasing Post-Click Conversion Rate Estimation

arXiv

引用

arXiv 2021年

作者： Guo, Siyuan Zou, Lixin Liu, Yiding Ye, Wenwen Cheng, Suqi Wang, Shuaiqiang Chen, Hechang Yin, Dawei Chang, Yi School of Artificial Intelligence Jilin University China Baidu Inc. China International Center of Future Science Jilin University China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University China

Post-click conversion, as a strong signal indicating the user preference, is salutary for building recommender systems. However, accurately estimating the post-click conversion rate (CVR) is challenging due to the selection bias, i.e., the observed clicked events usually happen on users’ preferred items. Currently, most existing methods utilize counterfactual learning to debias recommender systems. Among them, the doubly robust (DR) estimator has achieved competitive performance by combining the error imputation based (EIB) estimator and the inverse propensity score (IPS) estimator in a doubly robust way. However, inaccurate error imputation may result in its higher variance than the IPS estimator. Worse still, existing methods typically use simple model-agnostic methods to estimate the imputation error, which are not sufficient to approximate the dynamically changing model-correlated target (i.e., the gradient direction of the prediction model). To solve these problems, we first derive the bias and variance of the DR estimator. Based on it, a more robust doubly robust (MRDR) estimator has been proposed to further reduce its variance while retaining its double robustness. Moreover, we propose a novel double learning approach for the MRDR estimator, which can convert the error imputation into the general CVR estimation. Besides, we empirically verify that the proposed learning scheme can further eliminate the high variance problem of the imputation learning. To evaluate its effectiveness, extensive experiments are conducted on a semi-synthetic dataset and two real-world datasets. The results demonstrate the superiority of the proposed approach over the state-of-the-art methods. The code is available at https://***/guosyjlu/MRDR-DL. Copyright © 2021, The Authors. All rights reserved.

关键词： Recommender systems

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