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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 51–60 of 151 papers

TitleStatusHype
Entity Embeddings : Perspectives Towards an Omni-Modality Era for Large Language Models—0
DBLPLink: An Entity Linker for the DBLP Scholarly Knowledge GraphCode0
Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector QuantizationCode0
Context-Aware Composition of Agent Policies by Markov Decision Process Entity Embeddings and Agent EnsemblesCode0
MoCoSA: Momentum Contrast for Knowledge Graph Completion with Structure-Augmented Pre-trained Language Models—0
Social World Knowledge: Modeling and Applications—0
DsMtGCN: A Direction-sensitive Multi-task framework for Knowledge Graph Completion—0
EnCore: Fine-Grained Entity Typing by Pre-Training Entity Encoders on Coreference ChainsCode0
An entity-guided text summarization framework with relational heterogeneous graph neural networkCode0
Medical Knowledge Graph QA for Drug-Drug Interaction Prediction based on Multi-hop Machine Reading Comprehension—0
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