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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 11–20 of 151 papers

TitleStatusHype
ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch SimilaritiesCode1
Entity-aware Transformers for Entity SearchCode1
Query2Particles: Knowledge Graph Reasoning with Particle EmbeddingsCode1
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding AggregationCode1
Rethinking Graph Convolutional Networks in Knowledge Graph CompletionCode1
TempoQR: Temporal Question Reasoning over Knowledge GraphsCode1
Using Random Effects to Account for High-Cardinality Categorical Features and Repeated Measures in Deep Neural NetworksCode1
Meta-Knowledge Transfer for Inductive Knowledge Graph EmbeddingCode1
HyperTeNet: Hypergraph and Transformer-based Neural Network for Personalized List ContinuationCode1
Complex Temporal Question Answering on Knowledge GraphsCode1
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