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• W2955449169 abstract "Knowledge graph embeddings rank among the most successful methods for link prediction in knowledge graphs, i.e., the task of completing an incomplete collection of relational facts. A downside of these models is their strong sensitivity to model hyperparameters, in particular regularizers, which have to be extensively tuned to reach good performance [Kadlec et al., 2017]. We propose an efficient method for large scale hyperparameter tuning by interpreting these models in a probabilistic framework. After a model augmentation that introduces per-entity hyperparameters, we use a variational expectation-maximization approach to tune thousands of such hyperparameters with minimal additional cost. Our approach is agnostic to details of the model and results in a new state of the art in link prediction on standard benchmark data." @default.
• W2955449169 created "2019-07-12" @default.
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• W2955449169 date "2019-07-01" @default.
• W2955449169 modified "2023-10-01" @default.
• W2955449169 title "Augmenting and Tuning Knowledge Graph Embeddings" @default.
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