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 141–150 of 151 papers

TitleStatusHype
Unlocking the Power of Large Language Models for Entity AlignmentCode0
Context-Aware Composition of Agent Policies by Markov Decision Process Entity Embeddings and Agent EnsemblesCode0
Conflict-Aware Pseudo Labeling via Optimal Transport for Entity AlignmentCode0
Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector QuantizationCode0
Capturing Knowledge of Emerging Entities From Extended Search SnippetsCode0
TempCaps: A Capsule Network-based Embedding Model for Temporal Knowledge Graph CompletionCode0
Learning to Borrow -- Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph CompletionCode0
Learning to Borrow– Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph CompletionCode0
Relation Extraction with Contextualized Relation Embedding (CRE)Code0
A Mixture-of-Experts Model for Learning Multi-Facet Entity EmbeddingsCode0
Show:102550
← PrevPage 15 of 16Next →

No leaderboard results yet.