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Found 7 papers, 0 papers with code
Stable Geodesic Update on Hyperbolic Space and its Application to Poincare Embeddings
A hyperbolic space has been shown to be more capable of modeling complex networks than a Euclidean space.
Time-Discounting Convolution for Event Sequences with Ambiguous Timestamps
This paper proposes a method for modeling event sequences with ambiguous timestamps, a time-discounting convolution.
Riemannian TransE: Multi-relational Graph Embedding in Non-Euclidean Space
Multi-relational graph embedding which aims at achieving effective representations with reduced low-dimensional parameters, has been widely used in knowledge base completion.
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.
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.
Cumulative Stay-time Representation for Electronic Health Records in Medical Event Time Prediction
The MET of non-communicable diseases like diabetes is highly correlated to cumulative health conditions, more specifically, how much time the patient spent with specific health conditions in the past.
Tight and fast generalization error bound of graph embedding in metric space
However, recent theoretical analyses have shown a much higher upper bound on non-Euclidean graph embedding's generalization error than Euclidean one's, where a high generalization error indicates that the incompleteness and noise in the data can significantly damage learning performance.
