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 71–80 of 151 papers

TitleStatusHype
Principled Representation Learning for Entity Alignment—0
HyperTeNet: Hypergraph and Transformer-based Neural Network for Personalized List ContinuationCode1
Complex Temporal Question Answering on Knowledge GraphsCode1
SeDyT: A General Framework for Multi-Step Event Forecasting via Sequence Modeling on Dynamic Entity EmbeddingsCode0
Enhancing Natural Language Representation with Large-Scale Out-of-Domain CommonsenseCode0
GeoVectors: A Linked Open Corpus of OpenStreetMap Embeddings on World Scale—0
Improving Entity Linking by Encoding Type Information into Entity Embeddings—0
Global entity alignment with Gated Latent Space Neighborhood Aggregation—0
Graph Neural Pre-training for Enhancing Recommendations using Side Information—0
Modelling Monotonic and Non-Monotonic Attribute Dependencies with Embeddings: A Theoretical Analysis—0
Show:102550
← PrevPage 8 of 16Next →

No leaderboard results yet.