SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 131–140 of 151 papers

TitleStatusHype
Entity Embeddings : Perspectives Towards an Omni-Modality Era for Large Language Models—0
Entity Embeddings with Conceptual Subspaces as a Basis for Plausible Reasoning—0
SE-GNN: Seed Expanded-Aware Graph Neural Network with Iterative Optimization for Semi-supervised Entity Alignment—0
Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction—0
SERAG: Semantic Entity Retrieval from Arabic Knowledge Graphs—0
Fast and scalable learning of neuro-symbolic representations of biomedical knowledge—0
GeoVectors: A Linked Open Corpus of OpenStreetMap Embeddings on World Scale—0
Global entity alignment with Gated Latent Space Neighborhood Aggregation—0
Abstract Graphs and Abstract Paths for Knowledge Graph Completion—0
Entity-Assisted Language Models for Identifying Check-worthy Sentences—0
Show:102550
← PrevPage 14 of 16Next →

No leaderboard results yet.