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Structural iterative lexicographic autoencoded node representation
Graph representation learning approaches are effective to automatically extract relevant hidden features from graphs. Previous related work in graph...
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Structural Adversarial Attack for Code Representation Models
As code intelligence and collaborative computing advances, code representation models (CRMs) have demonstrated exceptional performance in tasks such...
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Fusing structural information with knowledge enhanced text representation for knowledge graph completion
Although knowledge graphs store a large number of facts in the form of triplets, they are still limited by incompleteness. Hence, Knowledge Graph...
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Joint semantic embedding with structural knowledge and entity description for knowledge representation learning
Previous works mainly employ triple structural information in learning representations for knowledge graph, which results in poor performance of link...
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Structural Node Representation Learning for Detecting Botnet Nodes
Private consumers, small businesses, and even large enterprises are all more at risk from botnets. These botnets are known for spearheading...
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Structural similarity-based Bi-representation through true noise level for noise-robust face super-resolution
In today’s real-world scenarios’ of computer vision applications, enhancing low-resolution (LR) facial images corrupted with unwanted noise effects...
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Beyond Accuracy: Measuring Representation Capacity of Embeddings to Preserve Structural and Contextual Information
Effective representation of data is crucial in various machine learning tasks, as it captures the underlying structure and context of the data....
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Adaptive denoising for magnetic resonance image based on nonlocal structural similarity and low-rank sparse representation
Magnetic resonance imaging (MRI) has become a widely used medical imaging method. Affected by imaging mechanism, magnetic field inhomogeneity and...
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Structural feature representation and fusion of human spatial cooperative motion for action recognition
Aiming at the cooperative relationship of human body parts in the process of action execution, we propose an action recognition method based on the...
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GraphSAGE++: Weighted Multi-scale GNN for Graph Representation Learning
Graph neural networks (GNNs) have emerged as a powerful tool in graph representation learning. However, they are increasingly challenged by...
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Multi-view subspace clustering for learning joint representation via low-rank sparse representation
Multi-view data are generally collected from distinct sources or domains characterized by consistent and specific properties. However, most existing...
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Improving actionable warning identification via the refined warning-inducing context representation
We improve AWI via the refined warning-inducing context representation, which captures both lexical and structural information for AWI from the...
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Meaning Representation
Before the study of semantic analysis, this chapter explores meaning representation, a vital component in NLP before the discussion of semantic and...
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Efficient Network Representation Learning via Cluster Similarity
Network representation learning is a de facto tool for graph analytics. The mainstream of the previous approaches is to factorize the proximity...
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CoolGust: knowledge representation learning with commonsense knowledge guidelines and constraints
Representation learning serves as a crucial link between knowledge graphs and neural models. Knowledge graphs are typical symbolic models and require...
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Multi-modal image registration in the presence of spatially varying intensity distortion using structural representation
Non-rigid multi-modal image registration remains a challenging task due to the complex intensity relation between the two images to be registered....
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An effective representation learning model for link prediction in heterogeneous information networks
Heterogeneous Information Networks (HINs) consist of multiple categories of nodes and edges and encompass rich semantic information. Representing...
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Transfer-learning-based representation learning for trajectory similarity search
Trajectory similarity search is one of the most fundamental tasks in spatial-temporal data analysis. Classical methods are based on predefined...
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Structural gender imbalances in ballet collaboration networks
Ballet, a mainstream performing art predominantly associated with women, exhibits significant gender imbalances in leading positions. However, the...
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Structural and Compact Latent Representation Learning on Sparse Reward Environments
For the task of training RL agent in a sparse-reward, image-based observation environment, the agent should perfect both learning latent...