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Found 5 papers, 3 papers with code
On Inductive Abilities of Latent Factor Models for Relational Learning
Latent factor models are increasingly popular for modeling multi-relational knowledge graphs.
Complex and Holographic Embeddings of Knowledge Graphs: A Comparison
Embeddings of knowledge graphs have received significant attention due to their excellent performance for tasks like link prediction and entity resolution.
Knowledge Graph Completion via Complex Tensor Factorization
In statistical relational learning, knowledge graph completion deals with automatically understanding the structure of large knowledge graphs---labeled directed graphs---and predicting missing relationships---labeled edges.
Ranked #2 on
Knowledge Graphs
on FB15k
Complex Embeddings for Simple Link Prediction
In statistical relational learning, the link prediction problem is key to automatically understand the structure of large knowledge bases.
Ranked #4 on
Link Prediction
on FB122
Online Learning to Sample
Stochastic Gradient Descent (SGD) is one of the most widely used techniques for online optimization in machine learning.
