Search Results for author: Linchuan Xu

Found 5 papers, 1 papers with code

Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic

no code implementations NeurIPS 2021 Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, Marc Cavazza

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.

Generalization Bounds Graph Embedding

Multi-layered Semantic Representation Network for Multi-label Image Classification

1 code implementation22 Jun 2021 Xiwen Qu, Hao Che, Jun Huang, Linchuan Xu, Xiao Zheng

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.

Classification Multi-Label Classification +1

Generalization Error Bound for Hyperbolic Ordinal Embedding

no code implementations21 May 2021 Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Marc Cavazza, Kenji Yamanishi

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.

Cross view link prediction by learning noise-resilient representation consensus

no code implementations WWW '17 2017 Xiaokai Wei, Linchuan Xu, Bokai Cao and Philip S. Yu

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).

Link Prediction Representation Learning

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