1.1k citations · 1.3k across the 12 of their papers we have counts for
14 papers
Interpretation of Natural Language Rules in Conversational Machine Reading
Marzieh Saeidi, Max Bartolo, Patrick Lewis +5
Most work in machine reading focuses on question answering problems where the answer is directly expressed in the text to read. However, many real-world question answering problems…
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…
Knowledge Graph Completion via Complex Tensor Factorization
Théo Trouillon, Christopher R. Dance, Johannes Welbl +3
In statistical relational learning, knowledge graph completion deals with automatically understanding the structure of large knowledge graphs---labeled directed graphs---and predic…
SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods
Marzieh Saeidi, Guillaume Bouchard, Maria Liakata +1
In this paper, we introduce the task of targeted aspect-based sentiment analysis. The goal is to extract fine-grained information with respect to entities mentioned in user comment…
Complex Embeddings for Simple Link Prediction
Théo Trouillon, Johannes Welbl, Sebastian Riedel +2
In statistical relational learning, the link prediction problem is key to automatically understand the structure of large knowledge bases. As in previous studies, we propose to sol…
Decomposing Real Square Matrices via Unitary Diagonalization
Théo Trouillon, Christopher R. Dance, Éric Gaussier +1
Diagonalization, or eigenvalue decomposition, is very useful in many areas of applied mathematics, including signal processing and quantum physics. Matrix decomposition is also a u…
A Factorization Machine Framework for Testing Bigram Embeddings in Knowledgebase Completion
Johannes Welbl, Guillaume Bouchard, Sebastian Riedel
Embedding-based Knowledge Base Completion models have so far mostly combined distributed representations of individual entities or relations to compute truth scores of missing link…
Approximate Inference with the Variational Holder Bound
Guillaume Bouchard, Balaji Lakshminarayanan
We introduce the Variational Holder (VH) bound as an alternative to Variational Bayes (VB) for approximate Bayesian inference. Unlike VB which typically involves maximization of a…
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…
Overlapping Trace Norms in Multi-View Learning
Behrouz Behmardi, Cedric Archambeau, Guillaume Bouchard
Multi-view learning leverages correlations between different sources of data to make predictions in one view based on observations in another view. A popular approach is to assume…
Group-sparse Embeddings in Collective Matrix Factorization
Arto Klami, Guillaume Bouchard, Abhishek Tripathi
CMF is a technique for simultaneously learning low-rank representations based on a collection of matrices with shared entities. A typical example is the joint modeling of user-item…
Fast Inference of Admixture Coefficients Using Sparse Non-negative Matrix Factorization Algorithms
Eric Frichot, François Mathieu, Théo Trouillon +2
Inference of individual admixture coefficients, which is important for population genetic and association studies, is commonly performed using compute-intensive likelihood algorith…
The OS* Algorithm: a Joint Approach to Exact Optimization and Sampling
Marc Dymetman, Guillaume Bouchard, Simon Carter
Most current sampling algorithms for high-dimensional distributions are based on MCMC techniques and are approximate in the sense that they are valid only asymptotically. Rejection…
Linear programming problems for frontier estimation
Guillaume Bouchard, Stéphane Girard, Anatoli Iouditski +1
We propose new estimates for the frontier of a set of points. They are defined as kernel estimates covering all the points and whose associated support is of smallest surface. The…