Query：
学者姓名：张春阳
Refining：
Year
Type
Indexed by
Source
Complex
Former Name
Co-
Language
Clean All
Abstract ：
Accurate decision-making within highly interactive driving environments is vital for the safety of self-driving vehicles. Despite the significant progress achieved by the existing models for autonomous vehicle decision-making tasks, there remains untapped potential for further exploration in this field. Previous models have focused primarily on specific scenarios or single tasks, with inefficient sample utilization and weak robustness problems, making them challenging to apply in practice. Motivated by this, a robust decision-making method named DRL-EPKG is proposed, which enables the simultaneous determination of vertical and horizontal behaviors of driverless vehicles without being limited to specific driving scenarios. Specifically, the DRL-EPKG integrates human driving knowledge into a framework of soft actor-critic (SAC), where we derive expert policy by a generative model: variational autoencoders (VAE), train agent policy by employing the SAC algorithm and further guide the behaviors of the agent by regulating the Wasserstein distance between the two policies. Moreover, a multidimensional reward function is designed to comprehensively consider safety, driving velocity, energy efficiency, and passenger comfort. Finally, several baseline models are employed for comparative evaluation in three highly dynamic driving scenarios. The findings demonstrate that the proposed model outperforms the baselines regarding the success rate, highlighting the practical applicability and robustness of DRL-EPKG in addressing complex, real-world problems in autonomous driving.
Keyword ：
Autonomous vehicles Autonomous vehicles Decision-making Decision-making Deep reinforcement learning Deep reinforcement learning Human driving knowledge Human driving knowledge
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Li, Feng-Jie , Zhang, Chun-Yang , Chen, C. L. Philip . Robust decision-making for autonomous vehicles via deep reinforcement learning and expert guidance [J]. | APPLIED INTELLIGENCE , 2025 , 55 (6) . |
| MLA | Li, Feng-Jie 等. "Robust decision-making for autonomous vehicles via deep reinforcement learning and expert guidance" . | APPLIED INTELLIGENCE 55 . 6 (2025) . |
| APA | Li, Feng-Jie , Zhang, Chun-Yang , Chen, C. L. Philip . Robust decision-making for autonomous vehicles via deep reinforcement learning and expert guidance . | APPLIED INTELLIGENCE , 2025 , 55 (6) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
The goal of multi-document summarization (MDS) is to generate a comprehensive and concise summary from multiple documents, which should not only be grammatically correct but also semantically contains the refined content of the overall texts. Existing summarizers based on sequential pre-trained large language models often cognize documents as linear sequences, which overlook the hierarchical structure correlations of sentences and paragraphs within or between documents. Additionally, those models also have limitations in handling long text input. To alleviate these two problems, a multi-document summarization model is proposed, with a heterogeneous graph of sentences, paragraphs and documents, called HeterMDS, to uncover deep semantic meanings and local-global context within documents. By integrating large language model and graph encoder with bootstrapped graph latents, the proposed HeterMDS can learn a semantically rich document representation and generate a coherent, concise and fact-consistent summary. It can be flexibly applied to current pre-trained language models, effectively improving their performance in MDS. Extensive experiment results can verify the effectiveness of the proposed HeterMDS and its contained modules, and demonstrate its competitiveness against the state-of-the-art models. © 2016 IEEE.
Keyword ：
bootstrapped graph latents bootstrapped graph latents graph representation learning graph representation learning heterogeneous graph heterogeneous graph large language models large language models Multi-document summarization Multi-document summarization
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zeng, G.-H. , Liu, Y.-Q. , Zhang, C.-Y. et al. Adaptive Multi-Document Summarization Via Graph Representation Learning [J]. | IEEE Transactions on Cognitive and Developmental Systems , 2024 . |
| MLA | Zeng, G.-H. et al. "Adaptive Multi-Document Summarization Via Graph Representation Learning" . | IEEE Transactions on Cognitive and Developmental Systems (2024) . |
| APA | Zeng, G.-H. , Liu, Y.-Q. , Zhang, C.-Y. , Cai, H.-C. , Chen, C.L.P. . Adaptive Multi-Document Summarization Via Graph Representation Learning . | IEEE Transactions on Cognitive and Developmental Systems , 2024 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Graph representation learning focuses on abstracting critical information from raw graphs. Unfortunately, there always exist various kinds of uncertainties, such as attribute noise and network topology corruption, in raw graphs. Under the message passing mechanism, certainties are likely to spread throughout the whole graph. Matters like these would induce deep graph models into producing uncertain representations and restrict representation expressiveness. Considering this, we propose a pioneering framework to defend graph uncertainties by improving the robustness and capability of graph neural networks (GNNs). In our framework, we consider that weights and biases are all fuzzy numbers, thus generating representations to assimilate graph uncertainties, which are finally released by defuzzification. To describe the process of the framework, in this article, a graph convolutional network (GCN) is employed to construct a robust graph model, called FuzzyGCN. To verify the effectiveness of FuzzyGCN, it is trained in both supervised and unsupervised ways. In the supervised setting, we find that FuzzyGCN has stronger power and is more immune to data uncertainties when compared with various classical and robust GNNs. In the unsupervised setting, FuzzyGCN surpasses many state-of-the-art models in node classification and community detection over several real-world datasets.
Keyword ：
Fuzzy graph neural network (FGNN) Fuzzy graph neural network (FGNN) fuzzy graph representation learning (GRL) fuzzy graph representation learning (GRL) fuzzy number fuzzy number graph uncertainty graph uncertainty
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Lin, Yue-Na , Cai, Hai-Chun , Zhang, Chun-Yang et al. Fuzzy Neural Network for Representation Learning on Uncertain Graphs [J]. | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2024 , 32 (9) : 5259-5271 . |
| MLA | Lin, Yue-Na et al. "Fuzzy Neural Network for Representation Learning on Uncertain Graphs" . | IEEE TRANSACTIONS ON FUZZY SYSTEMS 32 . 9 (2024) : 5259-5271 . |
| APA | Lin, Yue-Na , Cai, Hai-Chun , Zhang, Chun-Yang , Yao, Hong-Yu , Philip Chen, C. L. . Fuzzy Neural Network for Representation Learning on Uncertain Graphs . | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2024 , 32 (9) , 5259-5271 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
With the rise of contrastive learning, unsupervised graph representation learning (GRL) has shown strong competitiveness. However, existing graph contrastive models typically either focus on the local view of graphs or take simple considerations of both global and local views. This may cause these models to overemphasize the importance of individual nodes and their ego networks, or to result in poor learning of global knowledge and affect the learning of local views. Additionally, most GRL models pay attention to topological proximity, assuming that nodes that are closer in graph topology are more similar. However, in the real world, close nodes may be dissimilar, which makes the learned embeddings incorporate inappropriate messages and thus lack discrimination. To address these issues, we propose a novel unsupervised GRL model by contrasting cluster assignments, called graph representation learning model via contrasting cluster assignment (GRCCA). To comprehensively explore the global and local views, it combines multiview contrastive learning and clustering algorithms with an opposite augmentation strategy. It leverages clustering algorithms to capture fine-grained global information and explore potential relevance between nodes in different augmented perspectives while preserving high-quality global and local information through contrast between nodes and prototypes. The opposite augmentation strategy further enhances the contrast of both views, allowing the model to excavate more invariant features. Experimental results show that GRCCA has strong competitiveness compared to state-of-the-art models in different graph analysis tasks.
Keyword ：
Clustering algorithms Clustering algorithms Contrastive learning Contrastive learning graph data mining graph data mining graph representation learning (GRL) graph representation learning (GRL) Prototypes Prototypes Representation learning Representation learning Social networking (online) Social networking (online) Task analysis Task analysis Topology Topology unsupervised learning unsupervised learning Unsupervised learning Unsupervised learning
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zhang, Chun-Yang , Yao, Hong-Yu , Chen, C. L. Philip et al. Graph Representation Learning via Contrasting Cluster Assignments [J]. | IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS , 2024 , 16 (3) : 912-922 . |
| MLA | Zhang, Chun-Yang et al. "Graph Representation Learning via Contrasting Cluster Assignments" . | IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 16 . 3 (2024) : 912-922 . |
| APA | Zhang, Chun-Yang , Yao, Hong-Yu , Chen, C. L. Philip , Lin, Yue-Na . Graph Representation Learning via Contrasting Cluster Assignments . | IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS , 2024 , 16 (3) , 912-922 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Pre-trained language models (PLMs) have shown remarkable performance on question answering (QA) tasks, but they usually require fine-tuning that depends on a substantial quantity of QA pairs. Therefore, improving the performance of PLMs in scenarios with only a small number of training examples, also known as a few-shot setting, is of great practical significance. Current mitigation strategies for the few-shot QA task largely rely on pre-training a QA task-specific language model from scratch, overlooking the potential of foundational PLMs to generate QA pairs, particularly in the few-shot setting. To address this issue, we propose a prompt-based QA data augmentation method aimed at automating the creation of high-quality QA pairs. It employs the prompt-based fine-tuning method, adapting the question generation process of PLMs to the few-shot setting. Additionally, we introduce a dynamic text filling training strategy. This strategy simulates the progressive human learning process, thereby alleviating overfitting of PLMs in the few-shot setting and enhancing their reasoning capability to tackle complex questions. Extensive experiments demonstrate that the proposed method outperforms existing approaches across various few-shot configurations. © 2020 IEEE.
Keyword ：
data augmentation data augmentation few-shot learning few-shot learning prompt learning prompt learning Question answering Question answering
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Qiu, J.-Q. , Zhang, C.-Y. , Chen, C.L.P. . Prompt Learning for Few-Shot Question Answering via Self-Context Data Augmentation [J]. | IEEE Transactions on Artificial Intelligence , 2024 . |
| MLA | Qiu, J.-Q. et al. "Prompt Learning for Few-Shot Question Answering via Self-Context Data Augmentation" . | IEEE Transactions on Artificial Intelligence (2024) . |
| APA | Qiu, J.-Q. , Zhang, C.-Y. , Chen, C.L.P. . Prompt Learning for Few-Shot Question Answering via Self-Context Data Augmentation . | IEEE Transactions on Artificial Intelligence , 2024 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Point clouds can capture the precise geometric information of objects and scenes, which are an important source of 3D data and one of the most popular 3D geometric data structures for cognitions in many real-world applications like automatic driving and remote sensing. However, due to the influence of sensors and varieties of objects, the point clouds obtained by different devices may suffer obvious geometric changes, resulting in domain gaps that are prone to the neural networks trained in one domain failing to preserve the performance in other domains. In order to alleviate the above problem, this paper proposes an unsupervised domain adaptation framework, named HO-GSM, as the first attempt to model high-order geometric structures of point clouds. Firstly, we construct multiple self-supervised tasks to learn the invariant semantic and geometric features of the source and target domains, especially to capture the feature invariance of high-order geometric structures of point clouds. Secondly, the discriminative feature space of target domain is acquired by using contrastive learning to refine domain alignment to specific class level. Experiments on the PointDA-10 and GraspNetPC-10 collection of datasets show that the proposed HO-GSM can significantly outperform the state-of-the-art counterparts. © 2020 IEEE.
Keyword ：
contrastive learning contrastive learning high-order geometric structures high-order geometric structures Point clouds Point clouds self-supervised learning self-supervised learning unsupervised domain adaptation unsupervised domain adaptation
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Cheng, J.-X. , Lin, H. , Zhang, C.-Y. et al. Unsupervised Domain Adaptation on Point Clouds via High-Order Geometric Structure Modeling [J]. | IEEE Transactions on Artificial Intelligence , 2024 , 5 (12) : 6121-6133 . |
| MLA | Cheng, J.-X. et al. "Unsupervised Domain Adaptation on Point Clouds via High-Order Geometric Structure Modeling" . | IEEE Transactions on Artificial Intelligence 5 . 12 (2024) : 6121-6133 . |
| APA | Cheng, J.-X. , Lin, H. , Zhang, C.-Y. , Chen, C.L.P. . Unsupervised Domain Adaptation on Point Clouds via High-Order Geometric Structure Modeling . | IEEE Transactions on Artificial Intelligence , 2024 , 5 (12) , 6121-6133 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Graph representation learning has recently garnered significant attention due to its wide applications in graph analysis tasks. It is well-known that real -world networks are dynamic, with edges and nodes evolving over time. This presents unique challenges that are distinct from those of static networks. However, most graph representation learning methods are either designed for static graphs, or address only partial challenges associated with dynamic graphs. They overlook the intricate interplay between topology and temporality in the evolution of dynamic graphs and the complexity of sequence modeling. Therefore, we propose a new dynamic graph representation learning model, called as R-GraphSAGE, which takes comprehensive considerations for embedding dynamic graphs. By incorporating a recurrent structure into GraphSAGE, the proposed RGraphSAGE explores structural and temporal patterns integrally to capture more fine-grained evolving patterns of dynamic graphs. Additionally, it offers a lightweight architecture to decrease the computational costs for handling snapshot sequences, achieving a balance between performance and complexity. Moreover, it can inductively process the addition of new nodes and adapt to the situations without labels and node attributes. The performance of the proposed R-GraphSAGE is evaluated across various downstream tasks with both synthetic and real -world networks. The experimental results demonstrate that it outperforms state-of-the-art baselines by a significant margin in most cases.
Keyword ：
Dynamic networks Dynamic networks Graph representation learning Graph representation learning Inductive learning Inductive learning Recurrent graph neural networks Recurrent graph neural networks Unsupervised learning Unsupervised learning
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Yao, Hong-Yu , Zhang, Chun-Yang , Yao, Zhi-Liang et al. A recurrent graph neural network for inductive representation learning on dynamic graphs [J]. | PATTERN RECOGNITION , 2024 , 154 . |
| MLA | Yao, Hong-Yu et al. "A recurrent graph neural network for inductive representation learning on dynamic graphs" . | PATTERN RECOGNITION 154 (2024) . |
| APA | Yao, Hong-Yu , Zhang, Chun-Yang , Yao, Zhi-Liang , Chen, C. L. Philip , Hu, Junfeng . A recurrent graph neural network for inductive representation learning on dynamic graphs . | PATTERN RECOGNITION , 2024 , 154 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Existing works have made some progress in point cloud registration, but most of them measure performance only on point cloud pairs with high overlap. In practical applications, it is often difficult to ensure that the collected point clouds overlap in large regions due to problems such as occlusion and noise. Therefore, a good low-overlap point cloud registration method is of great practical significance. However, extracting reliable correspondences from point clouds has always been a challenging task, particularly when dealing with low-overlap situation. In this paper, we propose a novel method for low-overlap point cloud registration via efficient correspondence augmentation, called AugLPCR, which not only enhances correspondences with high confidence, but also employs confidence weights to mitigate the impact of outliers. After the augmentation, the correspondences used for the transformation have a large amount of inliers, leading to improved registration performance. Extensive experiments on indoor and outdoor datasets demonstrate that the proposed AugLPCR is capable of maintaining consistent performance and achieve results comparable to or better than the state-of-the-art methods. Note to Practitioners-The motivation of this paper is to address the problem of registering two low-overlap point clouds. Mainstream algorithms for point cloud registration typically assume a sufficient overlap between point clouds. However, in practical scenarios, it is common to encounter scans with inadequate overlap. These conditions often hinder the extraction of reliable correspondences. This paper introduces an effective method for augmenting correspondences to address the problem of low inlier rates within predicted correspondences. While augmenting correspondences with high confidence, it also mitigates the influence of outliers and ambiguous points. Additionally, traditional approaches often divide superpoint regions before matching, but this can lead to the elimination of points in overlapping regions alongside outliers. To address this issue, we adjust the order of superpoint matching and region partitioning. The proposed framework can be easily applied to other correspondence-based point cloud registration models.
Keyword ：
Accuracy Accuracy Convolution Convolution correspondence augmentation correspondence augmentation Estimation Estimation Feature extraction Feature extraction Image color analysis Image color analysis Iterative methods Iterative methods Point cloud compression Point cloud compression Point cloud registration Point cloud registration point cloud visualization point cloud visualization Reflection Reflection Three-dimensional displays Three-dimensional displays Transforms Transforms
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Lin, Zhi-Huang , Zhang, Chun-Yang , Lin, Xue-Ming et al. Low-Overlap Point Cloud Registration via Correspondence Augmentation [J]. | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2024 , 22 : 9363-9375 . |
| MLA | Lin, Zhi-Huang et al. "Low-Overlap Point Cloud Registration via Correspondence Augmentation" . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 22 (2024) : 9363-9375 . |
| APA | Lin, Zhi-Huang , Zhang, Chun-Yang , Lin, Xue-Ming , Lin, Huibin , Zeng, Gui-Huang , Chen, C. L. Philip . Low-Overlap Point Cloud Registration via Correspondence Augmentation . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2024 , 22 , 9363-9375 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Domain generalization aims at learning a model with transferable knowledge from one or more source domain(s) in the presence of domain shift, enabling the model to achieve effective generalization for an unseen target domain. Most existing methods pursue domain-invariant representations of samples to address the challenges of heterogeneous distributions across domains. However, most of such methods are limited to simple data manipulation at the instance level or computing style statistics in feature space for distribution alignment. Such operations fail to effectively capture the contextual semantics across domains from both the intra and inter-views. In this paper, we propose contextual Distribution Alignment via a Contrastive Learning strategy with domain correlation, called DACL, which sufficiently exploits both intra- and inter-domain invariant representations for image domain generalization classification. Specifically, a new Fourier-based augmentation method is developed to capture high-level semantic invariant features. Second, a domain-based feature fusion module is further proposed to increase the diversity of features, which mainly extracts both intra- and inter-domain prototypes via clustering to learn cross-domain representations. Finally, we propose a contrastive learning strategy that takes domain correlation into account, which uses spatial second-order statistics as a metric to measure the relevance between multiple source domains. Extensive experiments are conducted on two domain generalization tasks over six benchmarks, demonstrating that DACL achieves state-of-the-art performance against baseline models. A series of ablation studies are performed and in-depth analyses are conducted in visualization to further verify the rationality and effectiveness of the proposed method. © 1991-2012 IEEE.
Keyword ：
contrastive learning contrastive learning Domain generalization Domain generalization domain-invariant representations domain-invariant representations feature fusion feature fusion Fourier-based augmentation Fourier-based augmentation
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Lin, H. , Zhang, C.-Y. , Philip, Chen, C.L. . Contextual Distribution Alignment via Correlation Contrasting for Domain Generalization [J]. | IEEE Transactions on Circuits and Systems for Video Technology , 2024 . |
| MLA | Lin, H. et al. "Contextual Distribution Alignment via Correlation Contrasting for Domain Generalization" . | IEEE Transactions on Circuits and Systems for Video Technology (2024) . |
| APA | Lin, H. , Zhang, C.-Y. , Philip, Chen, C.L. . Contextual Distribution Alignment via Correlation Contrasting for Domain Generalization . | IEEE Transactions on Circuits and Systems for Video Technology , 2024 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
针对现有课程群在综合实践教学上存在知识点零散、内容重叠、实践路径单一、缺乏有效评价等问题，提出一种基于知识图谱的“能力导向+问题驱动”综合性实践教学模式，分别从教学目标、问题分解、资源整合、实施方式和成绩评定等方面探讨多路径实践教学及评定方法。
Keyword ：
机器学习课程群 机器学习课程群 知识图谱 知识图谱 综合实践 综合实践 能力导向 能力导向 问题驱动 问题驱动
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 陈飞 , 张春阳 , 于元隆 . 基于知识图谱的综合实践教学设计 [J]. | 计算机教育 , 2023 , 5 (04) : 158-162 . |
| MLA | 陈飞 et al. "基于知识图谱的综合实践教学设计" . | 计算机教育 5 . 04 (2023) : 158-162 . |
| APA | 陈飞 , 张春阳 , 于元隆 . 基于知识图谱的综合实践教学设计 . | 计算机教育 , 2023 , 5 (04) , 158-162 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
