2021 International Joint Conference on Neural Networks (IJCNN)最新文献_第5页

Federated Variational Autoencoder for Collaborative Filtering 协同滤波的联邦变分自编码器

2021 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2021-07-18 DOI: 10.1109/IJCNN52387.2021.9533358

Mirko Palato

{"title":"Federated Variational Autoencoder for Collaborative Filtering","authors":"Mirko Palato","doi":"10.1109/IJCNN52387.2021.9533358","DOIUrl":"https://doi.org/10.1109/IJCNN52387.2021.9533358","url":null,"abstract":"Recommender Systems (RSs) are valuable technologies that help users in their decision-making process. Generally, RSs are designed with the assumption that a central server stores and manages historical users' behaviors. However, users are nowadays more aware of privacy issues leading to a higher demand for privacy-preserving technologies. To cope with this issue, the Federated Learning (FL) paradigm can provide good performance without harming the users' privacy. Some efforts have been devoted to adapt standard collaborative filtering methods (e.g., matrix factorization) into the FL framework in recent years. In this paper, we present a Federated Variational Autoencoder for Collaborative Filtering (FedVAE), which extends the state-of-the-art MultVAE model. Additionally, we propose an adaptive learning rate schedule to accelerate learning. We also discuss the potential privacy-preserving capabilities of FedVAE. An extensive experimental evaluation on five benchmark data sets shows that our proposal can achieve performance close to MultVAE in a reasonable number of iterations. We also empirically demonstrate that the adaptive learning rate guarantees both accelerated learning and good stability.","PeriodicalId":396583,"journal":{"name":"2021 International Joint Conference on Neural Networks (IJCNN)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122061046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

A Dynamic Multi-criteria Multi-engine Approach for Text Simplification 一种动态多准则多引擎文本简化方法

2021 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2021-07-18 DOI: 10.1109/IJCNN52387.2021.9533365

Victor Henrique Alves Ribeiro, P. Cavalin, E. Morais

引用次数: 0

CcGL-GAN: Criss-Cross Attention and Global-Local Discriminator Generative Adversarial Networks for text-to-image synthesis CcGL-GAN:用于文本到图像合成的交叉注意和全局-局部鉴别器生成对抗网络

2021 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2021-07-18 DOI: 10.1109/IJCNN52387.2021.9533396

Xihong Ye, Luanhao Lu

引用次数: 0

Multi-Label Streaming Feature Selection via Class-Imbalance Aware Rough Set 基于类不平衡感知粗糙集的多标签流特征选择

2021 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2021-07-18 DOI: 10.1109/IJCNN52387.2021.9533614

Yizhang Zou, Xuegang Hu, Peipei Li, Junlong Li

{"title":"Multi-Label Streaming Feature Selection via Class-Imbalance Aware Rough Set","authors":"Yizhang Zou, Xuegang Hu, Peipei Li, Junlong Li","doi":"10.1109/IJCNN52387.2021.9533614","DOIUrl":"https://doi.org/10.1109/IJCNN52387.2021.9533614","url":null,"abstract":"Multi-label feature selection aims to select discriminative attributes in multi-label scenario, but most of existing multi-label feature selection methods fail to consider streaming features, i.e. features gradually flow one by one, which is more common in real-world applications. In addition, though there are already some representative works on multi-label streaming feature selection, they fail to tackle the class-imbalance problem, which exists widely in multi-label learning. In fact, class-imbalance will lead to the performance degradation of multi-label learning models. Thus considering class-imbalance problem in multi-label scenario is beneficial to multi-label feature selection because more precise feature evaluation is achieved. Motivated by this, we propose a new rough set named as class-imbalance aware rough set model which can fit class-imbalance problem well. To address streaming features, we construct a novel streaming feature selection framework called SFSCI(Streaming Feature Selection via Class-Imbalance aware rough set), which contains online irrelevancy discarding and online redundancy reduction. Finally, an empirical study on a series of benchmark data sets demonstrates that the proposed method is superior to other state-of-the-art multi-label feature selection methods, including several multi-label streaming feature selection methods.","PeriodicalId":396583,"journal":{"name":"2021 International Joint Conference on Neural Networks (IJCNN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129864187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Link Prediction with Multiple Structural Attentions in Multiplex Networks 多路网络中多结构关注的链路预测

2021 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2021-07-18 DOI: 10.1109/IJCNN52387.2021.9533609

Shangrong Huang, Quanyu Ma, Chao Yang, Yazhou Yao

{"title":"Link Prediction with Multiple Structural Attentions in Multiplex Networks","authors":"Shangrong Huang, Quanyu Ma, Chao Yang, Yazhou Yao","doi":"10.1109/IJCNN52387.2021.9533609","DOIUrl":"https://doi.org/10.1109/IJCNN52387.2021.9533609","url":null,"abstract":"Many real networks can be viewed as multiplex networks with more than one layers. As different layers are usually not independent from each other, they can provide complementary information in the task of link prediction. In this paper, with the help of attention mechanism, we dig the structural correlations among different layers of the multiplex network as well as the network structural information of the target layer to make more precise link predictions. Specifically, we introduce three different attentions, namely the intra-layer distance/degree attention, the intra-layer neighbourhood attention, and the interlayer structural attention, to calculate both the influence among nodes in the same layer and the link correlations in different layers. Compared with other state-of-the art methods which usually require the information of node attributes or edge types, we only utilize the topological information of the network and thus provide a more general link prediction solution for multiplex network. We conduct comprehensive experiments on several real-world datatsets of different scales. By comparing with the state-of-the-art link prediction algorithms, we show the advantages of our algorithm, and the effectiveness of different attentions. Also, through visual case studies we uncover some intuitions about the relationship between the graph structure and the existence of a link. We make our source code anonymously available at: (will be released after review)","PeriodicalId":396583,"journal":{"name":"2021 International Joint Conference on Neural Networks (IJCNN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128779823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Spatial and Temporal Aware Graph Convolutional Network for Flood Forecasting 洪水预测的时空感知图卷积网络

2021 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2021-07-18 DOI: 10.1109/IJCNN52387.2021.9533694

Jun Feng, Zhongyi Wang, Yirui Wu, Yuqi Xi

引用次数: 7

A Quantum Activation Function for Neural Networks: Proposal and Implementation 神经网络的量子激活函数:提议与实现

2021 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2021-07-18 DOI: 10.1109/IJCNN52387.2021.9533362

Saurabh Kumar, Siddharth Dangwal, Soumik Adhikary, D. Bhowmik

{"title":"A Quantum Activation Function for Neural Networks: Proposal and Implementation","authors":"Saurabh Kumar, Siddharth Dangwal, Soumik Adhikary, D. Bhowmik","doi":"10.1109/IJCNN52387.2021.9533362","DOIUrl":"https://doi.org/10.1109/IJCNN52387.2021.9533362","url":null,"abstract":"A non-linear activation function is an integral component of neural network algorithms used for various tasks such as data classification and pattern recognition. In neu-romorphic/emerging-hardware-based implementations of neural network algorithms, the non-linear activation function is often implemented through dedicated analog electronics. This enables faster execution of the activation function during training and inference of neural networks compared to conventional digital implementation. Here, with a similar motivation, we propose a novel non-linear activation function that can be used in a neural network for data classification. Our activation function can be implemented by taking advantage of the inherent nonlinearity in qubit preparation and SU(2) operation in quantum mechanics. These operations are directly implementable on quantum hardware through single-qubit quantum gates as we show here. In addition, the SU(2) parameters are adjustable here making the activation function adaptable; we adjust the parameters through classical feedback like in a variational algorithm in quantum machine learning. Using our proposed quantum activation function, we report accurate classification using popular machine learning data sets like Fisher's Iris, Wisconsin's Breast Cancer (WBC), Abalone, and MNIST on three different platforms: simulations on a classical computer, simulations on a quantum simulation framework like Qiskit, and experimental implementation on quantum hardware (IBM-Q). Then we use a Bloch-sphere-based approach to intuitively explain how our proposed quantum activation function, with its adaptability, helps in data classification.","PeriodicalId":396583,"journal":{"name":"2021 International Joint Conference on Neural Networks (IJCNN)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128586212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Relation-Guided Attention Mechanism for Relational Triple Extraction 一种关系导向的关系三重抽取注意机制

2021 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2021-07-18 DOI: 10.1109/IJCNN52387.2021.9533950

Yi Yang, Xueming Li, Xu Li

{"title":"A Relation-Guided Attention Mechanism for Relational Triple Extraction","authors":"Yi Yang, Xueming Li, Xu Li","doi":"10.1109/IJCNN52387.2021.9533950","DOIUrl":"https://doi.org/10.1109/IJCNN52387.2021.9533950","url":null,"abstract":"Relational triples are the essential parts of knowledge graphs, which can be usually found in natural language sentences. Relational triple extraction aims to extract all entity pairs with semantic relations from sentences. Recent studies on triple extraction focus on the triple overlap problem where multiple relational triples share single entities or entity pairs in a sentence. Besides, we find sentences may contain implicit relations, and it is challenging for most existing methods to extract implicit relational triples whose relations are implicit in the sentence. In this paper, we propose a relation-guided attention mechanism (RGAM) for relational triple extraction. Firstly, we extract subjects of all possible triples from the sentence, and then identify the corresponding objects under target relations with relation guidance. We utilize relations as prior knowledge instead of regarding relations as classification labels, and apply attention mechanism to obtain fine-grained relation representations, which guide extracted subjects to find the corresponding objects. Our approach (RGAM) can not only learn multiple dependencies in each triple, but also be suitable for extracting implicit relational triples and handling the overlapping triple problem. Extensive experiments show that our model achieves state-of-the-art performance on two public datasets NYT and WebNLG, which demonstrates the effectiveness of our approach.","PeriodicalId":396583,"journal":{"name":"2021 International Joint Conference on Neural Networks (IJCNN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128700315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Learning Effective Discriminative Features with Differentiable Magnet Loss 学习可微磁损失的有效判别特征

2021 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2021-07-18 DOI: 10.1109/IJCNN52387.2021.9534158

Xiaojing Zhang, Lin Wang, Bo Yang

引用次数: 0

A Dilated Convolution-based Denoising Network for Magnetic Resonance Images 一种基于扩展卷积的磁共振图像去噪网络

2021 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2021-07-18 DOI: 10.1109/IJCNN52387.2021.9533653

P. C. Tripathi, Soumen Bag

引用次数: 2