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Found 6 papers, 2 papers with code
Contrastive Knowledge Graph Error Detection
To this end, we propose a novel framework - ContrAstive knowledge Graph Error Detection (CAGED).
Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic
Graph embedding, which represents real-world entities in a mathematical space, has enabled numerous applications such as analyzing natural languages, social networks, biochemical networks, and knowledge bases. It has been experimentally shown that graph embedding in hyperbolic space can represent hierarchical tree-like data more effectively than embedding in linear space, owing to hyperbolic space's exponential growth property.
Multi-layered Semantic Representation Network for Multi-label Image Classification
To this end, this paper designs a Multi-layered Semantic Representation Network (MSRN) which discovers both local and global semantics of labels through modeling label correlations and utilizes the label semantics to guide the semantic representations learning at multiple layers through an attention mechanism.
Ranked #7 on
Multi-Label Classification
on PASCAL VOC 2007
Generalization Error Bound for Hyperbolic Ordinal Embedding
Hyperbolic ordinal embedding (HOE) represents entities as points in hyperbolic space so that they agree as well as possible with given constraints in the form of entity i is more similar to entity j than to entity k. It has been experimentally shown that HOE can obtain representations of hierarchical data such as a knowledge base and a citation network effectively, owing to hyperbolic space's exponential growth property.
A Novel Global Spatial Attention Mechanism in Convolutional Neural Network for Medical Image Classification
In this paper, we thus propose a novel global spatial attention mechanism in CNNs mainly for medical image classification.
Cross view link prediction by learning noise-resilient representation consensus
In this paper, we study the problem of Cross View Link Prediction (CVLP) on partially observable networks, where the focus is to recommend nodes with only links to nodes with only attributes (or vice versa).
