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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 121–130 of 151 papers

TitleStatusHype
CMed-GPT: Prompt Tuning for Entity-Aware Chinese Medical Dialogue Generation—0
Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks—0
Relation-aware Graph Attention Model With Adaptive Self-adversarial Training—0
Universal Embeddings of Tabular Data—0
RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification—0
RelWalk -- A Latent Variable Model Approach to Knowledge Graph Embedding—0
BRIGHT -- Graph Neural Networks in Real-Time Fraud Detection—0
Entity Context Graph: Learning Entity Representations fromSemi-Structured Textual Sources on the Web—0
Entity Embedding as Game Representation—0
DERA: Dense Entity Retrieval for Entity Alignment in Knowledge Graphs—0
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