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Joint Representation of Functional and Structural Profiles for Identifying Common and Consistent 3-Hinge Gyral Folding Landmark
The 3-hinge is a form of cortical fold, which is the intersection of the three gyri. And it has been proved to be unique anatomically, structurally,...
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Subgraph representation learning with self-attention and free adversarial training
Due to its capacity to capture subgraph information within graph data, subgraph representation learning has garnered considerable attention in recent...
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Property graph representation learning for node classification
Graph representation learning (graph embedding) has led to breakthrough results in various machine learning graph-based applications such as node...
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Improved image representation and sparse representation for face recognition
Sparse representation is of great significance to the research of face recognition. Due to factors such as illumination, angle, and facial features,...
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Causal representation for few-shot text classification
Few-Shot Text Classification (FSTC) is a fundamental natural language processing problem that aims to classify small amounts of text with high...
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Improving node embedding by a compact neighborhood representation
Graph Embedding, a learning paradigm that represents graph vertices, edges, and other semantic information about a graph into low-dimensional...
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A graph-based code representation method to improve code readability classification
ContextCode readability is crucial for developers since it is closely related to code maintenance and affects developers’ work efficiency. Code...
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Graph neural news recommendation based on multi-view representation learning
Accurate news representation is of crucial importance in personalized news recommendation. Most of existing news recommendation model lack...
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AutoTGRL: an automatic text-graph representation learning framework
Text-graph representation learning is a critical and important area of research with extensive applications in natural language processing (NLP)....
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FLBP: Fechner local binary pattern for face representation
Most images are ultimately observed and interpreted by humans, so the ideal image descriptor should take into account the effects of human psychology...
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Deep video representation learning: a survey
This paper provides a review on representation learning for videos . We classify recent spatio-temporal feature learning methods for sequential visual...
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Multimodal fuzzy granular representation and classification
AbstractIn a complex classification task, samples are represented by various types of multimodal features, including structured data, text, images,...
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Granger causal representation learning for groups of time series
Discovering causality from multivariate time series is an important but challenging problem. Most existing methods focus on estimating the Granger...
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Recent advances in scene image representation and classification
With the rise of deep learning algorithms nowadays, scene image representation methods have achieved a significant performance boost, particularly in...
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Towards Distributed Graph Representation Learning
Distributed graph representation learning refers to the process of learning graph data representation in a distributed computing environment. In the...
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A representation learning model based on stochastic perturbation and homophily constraint
The network representation learning task of fusing node multi-dimensional classification information aims to effectively combine node...
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IMPRL-Net: interpretable multi-view proximity representation learning network
Due to the heterogeneity gap in multi-view data, researchers have been attempting to apply these data to learn a co-latent representation to bridge...
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Consensus representation-driven structured graph learning for multi-view clustering
Graph-based multi-view clustering has gained increasing attention due to its ability to effectively unveil complex nonlinear structures among data...
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Dual-Branch Contrastive Learning for Network Representation Learning
Graph Contrastive Learning (GCL) is a self-supervised learning algorithm designed for graph data and has received widespread attention in the field...
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Graph Representation Learning
Graph structure, which can represent objects and their relationships, is ubiquitous in big data including natural languages. Besides original text as...