Search Results for author:
Found 5 papers, 1 papers with code
Multi-Objective Evolutionary Beer Optimisation
We introduce a framework which uses an evolutionary method supporting multi-objective optimisation.
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.
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.
Beer Organoleptic Optimisation: Utilising Swarm Intelligence and Evolutionary Computation Methods
Customisation in food properties is a challenging task involving optimisation of the production process with the demand to support computational creativity which is geared towards ensuring the presence of alternatives.
A Corpus of Clinical Practice Guidelines Annotated with the Importance of Recommendations
In this paper we present the Corpus of REcommendation STrength (CREST), a collection of HTML-formatted clinical guidelines annotated with the location of recommendations.
