8 code implementations • 20 Jun 2016 • Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, Guillaume Bouchard
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
2 code implementations • 22 Feb 2017 • Théo Trouillon, Christopher R. Dance, Johannes Welbl, Sebastian Riedel, Éric Gaussier, Guillaume Bouchard
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
1 code implementation • 17 Sep 2017 • Théo Trouillon, Éric Gaussier, Christopher R. Dance, Guillaume Bouchard
Latent factor models are increasingly popular for modeling multi-relational knowledge graphs.
no code implementations • COLING 2016 • Marzieh Saeidi, Guillaume Bouchard, Maria Liakata, Sebastian Riedel
In this paper, we introduce the task of targeted aspect-based sentiment analysis.
Ranked #5 on Aspect-Based Sentiment Analysis (ABSA) on Sentihood
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +2
no code implementations • WS 2016 • 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 links.
no code implementations • 30 Jun 2015 • Guillaume Bouchard, Théo Trouillon, Julien Perez, Adrien Gaidon
Stochastic Gradient Descent (SGD) is one of the most widely used techniques for online optimization in machine learning.
no code implementations • 19 Jun 2015 • Guillaume Bouchard, Balaji Lakshminarayanan
We introduce the Variational Holder (VH) bound as an alternative to Variational Bayes (VB) for approximate Bayesian inference.
no code implementations • 24 Apr 2014 • 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.
no code implementations • 20 Dec 2013 • 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.
no code implementations • EMNLP 2018 • Marzieh Saeidi, Max Bartolo, Patrick Lewis, Sameer Singh, Tim Rocktäschel, Mike Sheldon, Guillaume Bouchard, Sebastian Riedel
This task requires both the interpretation of rules and the application of background knowledge.
no code implementations • NeurIPS 2010 • Mohammad E. Khan, Guillaume Bouchard, Kevin P. Murphy, Benjamin M. Marlin
We show that EM is significantly more robust in the presence of missing data compared to treating the latent factors as parameters, which is the approach used by exponential family PCA and other related matrix-factorization methods.
no code implementations • NeurIPS 2009 • Percy S. Liang, Guillaume Bouchard, Francis R. Bach, Michael. I. Jordan
Many types of regularization schemes have been employed in statistical learning, each one motivated by some assumption about the problem domain.
no code implementations • 27 Feb 2021 • Arnav Arora, Preslav Nakov, Momchil Hardalov, Sheikh Muhammad Sarwar, Vibha Nayak, Yoan Dinkov, Dimitrina Zlatkova, Kyle Dent, Ameya Bhatawdekar, Guillaume Bouchard, Isabelle Augenstein
The proliferation of harmful content on online platforms is a major societal problem, which comes in many different forms including hate speech, offensive language, bullying and harassment, misinformation, spam, violence, graphic content, sexual abuse, self harm, and many other.