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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 41–50 of 151 papers

TitleStatusHype
Graph Neural Pre-training for Enhancing Recommendations using Side Information—0
Global entity alignment with Gated Latent Space Neighborhood Aggregation—0
CNN-based Dual-Chain Models for Knowledge Graph Learning—0
Graph Reasoning for Explainable Cold Start Recommendation—0
Embedding Knowledge Graphs in Degenerate Clifford Algebras—0
A Neural Pipeline Approach for the PharmaCoNER Shared Task using Contextual Exhaustive Models—0
CMed-GPT: Prompt Tuning for Entity-Aware Chinese Medical Dialogue Generation—0
Fast and scalable learning of neuro-symbolic representations of biomedical knowledge—0
Duality-Induced Regularizer for Semantic Matching Knowledge Graph Embeddings—0
Dual Graph Embedding for Object-Tag LinkPrediction on the Knowledge Graph—0
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