main webpage W Topic Physics arXiv (Cornell University) Using Pairwise Occurrence Information to Improve Knowledge Graph Completion on Large-Scale Datasets 2019 Bilinear models such as DistMult and ComplEx are effective methods for knowledge graph (KG) completion. However, they require large batch sizes, which becomes a performance bottleneck when training on large scale datasets due to memory constraints. In this pa… Article Open Physics Open Article Page

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Science about matter and energy Physics is the natural science that studies matter, its fundamental constituents, its motion and behavior through space and time, and the related entities of energy and force. Physics is one of the most fundamental scientific disciplines, with its main goal being to understand how the universe behaves. A scientist who specializes in the field of physics is called a physicist. Physics is one of the oldest academic disciplines and, through its inclusion of astronomy, perhaps the oldest. Over much of the past two millennia, physics, chemistry, biology, and certain branches of mathematics were a part of natural philosophy, but during the Scientific Revolution in the 17th century these natural sciences emerged as unique research endeavors in their own right. Critical Symbolic Virtual Narrative Open Physics Open Article Page

Exploring foci of: arXiv (Cornell University) Using Pairwise Occurrence Information to Improve Knowledge Graph Completion on Large-Scale Datasets 2019 Bilinear models such as DistMult and ComplEx are effective methods for knowledge graph (KG) completion. However, they require large batch sizes, which becomes a performance bottleneck when training on large scale datasets due to memory constraints. In this paper we use occurrences of entity-relation pairs in the dataset to construct a joint learning model and to increase the quality of sampled negatives during training. We show on three standard datasets that when these two techniques are combined, they give a sig… Open Article Page

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Explore Physics W Topic Physics arXiv (Cornell University) Using Pairwise Occurrence Information to Improve Knowledge Graph Completion on Large-Scale Datasets 2019 Bilinear models such as DistMult and ComplEx are effective methods for knowledge graph (KG) completion. However, they require large batch sizes, which becomes a performance bottleneck when training on large scale datasets due to memory constraints. In this pa… Article Critical Symbolic Virtual Narrative Open Physics Open Article Page