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Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach

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arXiv 2020年

作者： Chen, Lei Wu, Le Hong, Richang Zhang, Kun Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology School of Computer Science and Information Engineering HeFei University of Technology School of Computer Science and Technology University of Science and Technology of China

Graph Convolutional Networks (GCNs) are state-of-the-art graph based representation learning models by iteratively stacking multiple layers of convolution aggregation operations and non-linear activation operations. Recently, in Collaborative Filtering (CF) based Recommender Systems (RS), by treating the user-item interaction behavior as a bipartite graph, some researchers model higher-layer collaborative signals with GCNs. These GCN based recommender models show superior performance compared to traditional works. However, these models suffer from training difficulty with non-linear activations for large user-item graphs. Besides, most GCN based models could not model deeper layers due to the over smoothing effect with the graph convolution operation. In this paper, we revisit GCN based CF models from two aspects. First, we empirically show that removing non-linearities would enhance recommendation performance, which is consistent with the theories in simple graph convolutional networks. Second, we propose a residual network structure that is specifically designed for CF with user-item interaction modeling, which alleviates the over smoothing problem in graph convolution aggregation operation with sparse user-item interaction data. The proposed model is a linear model and it is easy to train, scale to large datasets, and yield better efficiency and effectiveness on two real datasets. We publish the source code at https://***/newlei/LRGCCF. Copyright © 2020, The Authors. All rights reserved.

关键词： Collaborative filtering

Research on Penetration Strategy of UAV 2020

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Research on Penetration Strategy of UAV

Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies

作者： Daojun Han Hongzhen Xiang Haolin Chen Yi Xie Institute of Data and Knowledge Engineering Henan University Kaifeng PR China School of Computer and Information Engineering Henan University Kaifeng PR China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng PR China

ISBN: (纸本)9781450387828

Taking a military operation as an example, this paper makes an in-depth study of what tactics a single UAV should adopt when facing the interception of multiple enemy UAV groups. According to the analysis of the impact diagram, the aerial range of the UAV standoff defines a threat area, that is, when the UAV arrives at the threat area, it needs to adopt a strategy for penetration, or it will be intercepted. The strategy adopted by UAV in the threat area is analyzed, and the decision control method is made.

关键词： Decision control Threat area Influence chart analysis UAV penetration

Learning with Partial Labels from Semi-supervised Perspective

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arXiv 2022年

作者： Li, Ximing Jiang, Yuanzhi Li, Changchun Wang, Yiyuan Ouyang, Jihong College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of MOE Jilin University China College of Information Science and Technology Northeast Normal University China Key Laboratory of Applied Statistics of MOE Northeast Normal University China

Partial Label (PL) learning refers to the task of learning from the partially labeled data, where each training instance is ambiguously equipped with a set of candidate labels but only one is valid. Advances in the recent deep PL learning literature have shown that the deep learning paradigms, e.g., self-training, contrastive learning, or class activate values, can achieve promising performance. Inspired by the impressive success of deep Semi-Supervised (SS) learning, we transform the PL learning problem into the SS learning problem, and propose a novel PL learning method, namely Partial Label learning with Semi-supervised Perspective (PLSP). Specifically, we first form the pseudo-labeled dataset by selecting a small number of reliable pseudo-labeled instances with high-confidence prediction scores and treating the remaining instances as pseudo-unlabeled ones. Then we design a SS learning objective, consisting of a supervised loss for pseudo-labeled instances and a semantic consistency regularization for pseudo-unlabeled instances. We further introduce a complementary regularization for those non-candidate labels to constrain the model predictions on them to be as small as possible. Empirical results demonstrate that PLSP significantly outperforms the existing PL baseline methods, especially on high ambiguity levels. Code available: https://***/changchunli/PLSP. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

Learning Relation-Specific Representations for Few-shot knowledge Graph Completion

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arXiv 2022年

作者： Li, Yuling Yu, Kui Zhang, Yuhong Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei230601 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China

Recent years have witnessed increasing interest in few-shot knowledge graph completion (FKGC), which aims to infer unseen query triples for a few-shot relation using a few reference triples about the relation. The primary focus of existing FKGC methods lies in learning relation representations that can reflect the common information shared by the query and reference triples. To this end, these methods learn entity-pair representations from the direct neighbors of head and tail entities, and then aggregate the representations of reference entity pairs. However, the entity-pair representations learned only from direct neighbors may have low expressiveness when the involved entities have sparse direct neighbors or share a common local neighborhood with other entities. Moreover, merely modeling the semantic information of head and tail entities is insufficient to accurately infer their relational information especially when they have multiple relations. To address these issues, we propose a Relation-Specific Context Learning (RSCL) framework, which exploits graph contexts of triples to learn global and local relation-specific representations for few-shot relations. Specifically, we first extract graph contexts for each triple, which can provide long-term entity-relation dependencies. To encode the extracted graph contexts, we then present a hierarchical attention network to capture contextualized information of triples and highlight valuable local neighborhood information of entities. Finally, we design a hybrid attention aggregator to evaluate the likelihood of the query triples at the global and local levels. Experimental results on two public datasets demonstrate that RSCL outperforms state-of-the-art FKGC methods. © 2022, CC BY.

关键词： knowledge graph

Uncertainty-based Model Acceleration for Cancer Classification in Whole-Slide Images

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Uncertainty-based Model Acceleration for Cancer Classificati...

2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Gao, Zeyu Mao, Anyu Wu, Jialun Li, Yang Wang, Chunbao Ding, Caixia Gong, Tieliang Li, Chen Xi'an Jiaotong University School of Computer Science and Technology Xi'an China Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi'an China The First Affiliated Hospital of Xi'an Jiaotong University Department of Pathology Xi'an China Shaanxi Provincial Cancer Hospital Department of Pathology Xi'an China

ISBN: (纸本)9781665468190

Computational Pathology (CPATH) offers the possibility for highly accurate and low-cost automated pathological diagnosis. However, the high time cost of model inference is one of the main issues limiting the application of CPATH methods. Due to the large size of Whole-Slide Image (WSI), commonly used CPATH methods divided a WSI into a large number of image patches at relatively high magnification, then predicted each image patch individually, which is time-consuming. In this paper, we propose a novel Uncertainty-based Model Acceleration (UMA) method for reducing the time cost of model inference, thereby relieving the deployment burden of CPATH applications. Enlightened by the slide-viewing process of pathologists, only a few high-uncertain regions are regarded as 'suspicious' regions that need to be predicted at high magnification, and most of the regions in WSI are predicted at low magnification, thereby reducing the times of image patch extraction and prediction. Meanwhile, uncertainty estimation ensures prediction accuracy at low magnification. We take two fundamental CPATH classification tasks (i.e., cancer region detection and subtyping) as examples. Extensive experiments on two large-scale renal cell carcinoma classification datasets demonstrate that our UMA can significantly reduce the time cost of model inference while maintaining competitive classification performance. © 2022 IEEE.

关键词： Diseases

PathReasoner: Modeling Reasoning Path with Equivalent Extension for Logical Question Answering

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arXiv 2024年

作者： Xu, Fangzhi Lin, Qika Zhao, Tianzhe Han, Jiawei Liu, Jun School of Computer Science and Technology Xi’an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering China

Logical reasoning task has attracted great interest since it was proposed. Faced with such a task, current competitive models, even large language models (e.g., ChatGPT and PaLM 2), still perform badly. Previous promising LMs struggle in logical consistency modeling and logical structure perception. To this end, we model the logical reasoning task by transforming each logical sample into reasoning paths and propose an architecture PathReasoner. It addresses the task from the views of both data and model. To expand the diversity of the logical samples, we propose an atom extension strategy supported by equivalent logical formulas, to form new reasoning paths. From the model perspective, we design a stack of transformer-style blocks. In particular, we propose a path-attention module to joint model in-atom and cross-atom relations with the high-order diffusion strategy. Experiments show that PathReasoner achieves competitive performances on two logical reasoning benchmarks and great generalization abilities. © 2024, CC BY.

关键词： Atoms

Research on Visualization Algorithm of Association Rules Based on Concept Lattice 2020

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Research on Visualization Algorithm of Association Rules Bas...

Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies

作者： Xiajiong Shen Lijie Bao Lei Zhang Institute of Data and Knowledge Engineering Henan University Kaifeng PR China School of Computer and Information Engineering Henan University Kaifeng PR China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng PR China

ISBN: (纸本)9781450387828

The traditional association rule representation method has no way to show the essential relationship between concepts and lacks understanding of the concept level. Concept lattice is a kind of data structure which can show the relationship between concepts, concept lattice also has unique advantages in the visualization and discovery of potential knowledge of association rules. In this paper, a visualization algorithm of association rules based on concept lattice is proposed. The algorithm visually displays the association rules extracted from concept lattice. Finally, this article combines the chronic disease record data of a certain hospital to analyze and visualize the association rules. The implementation results show that the visualization method has good effects in knowledge analysis and data display.

关键词： Visualization Concept lattice Association rules knowledge discovery

Feature Rotation Invariance Learning for Point Cloud Analysis 10

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Feature Rotation Invariance Learning for Point Cloud Analysi...

10th IEEE Smart World Congress, SWC 2024

作者： Shi, Lu Cao, Qi Zhang, Guoqing Yi, Jin Huang, Yansen Cen, Yigang Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation The School of Computer Science and Technology Visual Intellgence +X International Cooperation Joint Laboratory of Moe Beijing100044 China University of Glasgow The School of Computing Science 567739 Singapore Beijing Jiaotong University With the Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education The State Key Laboratory of Advanced Rail Autonomous Operation The School of Computer and Information Technology Beijing100044 China Guizhou University Guizhou Lianjian Civil Engineering Quality Testing Monitoring Center Co. Ltd The College of Civil Engineering Guizhou550025 China

ISBN: (纸本)9798331520861

While deep learning has significantly advanced point cloud analysis, extracting effective features from their disordered structure remains challenging. Existing approaches often rely on complex network architectures or manual feature engineering to achieve rotation invariance, which increases inference latency. This paper offers a novel perspective on this challenge, showing that rotation-sensitive models can achieve performance comparable to rotation-invariant models when trained with rotation-augmented data. Given the significant differences in the feature distributions of rotation-sensitive and rotation-invariant models, we propose Feature Rotation Invariance Learning (FRIL), a framework that leverages Distance Metric Learning and rotation-based constraints to help rotation-sensitive models extract rotation-invariant features. This approach eliminates the need for additional modules, enabling robust performance against rotations. Extensive experiments demonstrate that FRIL consistently outperforms existing rotation-invariant methods across various visual tasks. © 2024 IEEE.

关键词： Contrastive Learning

Towards Certifying '∞ Robustness using Neural Networks with '∞-dist Neurons 38

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Towards Certifying '∞ Robustness using Neural Networks with...

38th International Conference on Machine Learning, ICML 2021

作者： Zhang, Bohang Cai, Tianle Lu, Zhou He, Di Wang, Liwei Key Laboratory of Machine Perception MOE School of EECS Peking University China Department of Electrical and Computer Engineering Princeton University United States Zhongguancun Haihua Institute for Frontier Information Technology China Department of Computer Science Princeton University United States Microsoft Research Center for Data Science Peking University China

ISBN: (纸本)9781713845065

It is well-known that standard neural networks, even with a high classification accuracy, are vulnerable to small '∞-norm bounded adversarial perturbations. Although many attempts have been made, most previous works either can only provide empirical verification of the defense to a particular attack method, or can only develop a certified guarantee of the model robustness in limited scenarios. In this paper, we seek for a new approach to develop a theoretically principled neural network that inherently resists '∞ perturbations. In particular, we design a novel neuron that uses '∞-distance as its basic operation (which we call '∞-dist neuron), and show that any neural network constructed with '∞-dist neurons (called '∞-dist net) is naturally a 1-Lipschitz function with respect to '∞-norm. This directly provides a rigorous guarantee of the certified robustness based on the margin of prediction outputs. We then prove that such networks have enough expressive power to approximate any 1-Lipschitz function with robust generalization guarantee. We further provide a holistic training strategy that can greatly alleviate optimization difficulties. Experimental results show that using '∞-dist nets as basic building blocks, we consistently achieve state-of-the-art performance on commonly used datasets: 93.09% certified accuracy on MNIST (∊ = 0.3), 35.42% on CIFAR-10 (∊ = 8/255) and 16.31% on TinyImageNet (∊ = 1/255). Copyright © 2021 by the author(s)

关键词： Neurons