1.1k citations · 1.2k across the 6 of their papers we have counts for
3 papers · 1 filter
On Inductive Abilities of Latent Factor Models for Relational Learning
Théo Trouillon, Éric Gaussier, Christopher R. Dance +1
Latent factor models are increasingly popular for modeling multi-relational knowledge graphs. By their vectorial nature, it is not only hard to interpret why this class of models w…
Complex and Holographic Embeddings of Knowledge Graphs: A Comparison
Théo Trouillon, Maximilian Nickel
Embeddings of knowledge graphs have received significant attention due to their excellent performance for tasks like link prediction and entity resolution. In this short paper, we…
Online Learning to Sample
Guillaume Bouchard, Théo Trouillon, Julien Perez +1
Stochastic Gradient Descent (SGD) is one of the most widely used techniques for online optimization in machine learning. In this work, we accelerate SGD by adaptively learning how…