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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 21–30 of 151 papers

TitleStatusHype
ConvD: Attention Enhanced Dynamic Convolutional Embeddings for Knowledge Graph Completion—0
CMed-GPT: Prompt Tuning for Entity-Aware Chinese Medical Dialogue Generation—0
Entity Embeddings : Perspectives Towards an Omni-Modality Era for Large Language Models—0
MMEAD: MS MARCO Entity Annotations and DisambiguationsCode1
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
AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language ModelsCode1
Social World Knowledge: Modeling and Applications—0
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