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Entity Embeddings

Entity Embeddings is a technique for applying deep learning to tabular data. It involves representing the categorical data of an information systems entity with multiple dimensions.

Papers

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Showing 1–10 of 151 papers

TitleStatusHype
RaTEScore: A Metric for Radiology Report GenerationCode4
OmniSearchSage: Multi-Task Multi-Entity Embeddings for Pinterest SearchCode2
Scalable Zero-shot Entity Linking with Dense Entity RetrievalCode2
Entity-aware Transformers for Entity SearchCode1
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding AggregationCode1
Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph EmbeddingCode1
Complex Temporal Question Answering on Knowledge GraphsCode1
BioBLP: A Modular Framework for Learning on Multimodal Biomedical Knowledge GraphsCode1
CoLAKE: Contextualized Language and Knowledge EmbeddingCode1
AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language ModelsCode1
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