1 code implementation • 10 Nov 2020 • Eda Bayram, Alberto Garcia-Duran, Robert West
The existing literature on knowledge graph completion mostly focuses on the link prediction task.
no code implementations • 25 Sep 2019 • Alberto Garcia-Duran, Robert West
The most successful prior approaches for modeling such time series are based on recurrent neural networks that learn to impute unobserved values and then treat the imputed values as observed.
4 code implementations • 13 Mar 2019 • Ye Liu, Hui Li, Alberto Garcia-Duran, Mathias Niepert, Daniel Onoro-Rubio, David S. Rosenblum
We present MMKG, a collection of three knowledge graphs that contain both numerical features and (links to) images for all entities as well as entity alignments between pairs of KGs.
no code implementations • 12 Nov 2018 • Brandon Malone, Alberto Garcia-Duran, Mathias Niepert
Extracting actionable insight from Electronic Health Records (EHRs) poses several challenges for traditional machine learning approaches.
1 code implementation • 29 Jun 2018 • Sebastijan Dumancic, Alberto Garcia-Duran, Mathias Niepert
Many real-world domains can be expressed as graphs and, more generally, as multi-relational knowledge graphs.
no code implementations • 4 May 2018 • Mathias Niepert, Alberto Garcia-Duran
We present our ongoing work on understanding the limitations of graph convolutional networks (GCNs) as well as our work on generalizations of graph convolutions for representing more complex node attribute dependencies.
no code implementations • 30 Jan 2018 • Alberto Garcia-Duran, Roberto Gonzalez, Daniel Onoro-Rubio, Mathias Niepert, Hui Li
This is exploited in sentiment analysis where machine learning models are used to predict the review score from the text of the review.
no code implementations • NeurIPS 2017 • Alberto Garcia-Duran, Mathias Niepert
We propose Embedding Propagation (EP), an unsupervised learning framework for graph-structured data.
2 code implementations • 14 Sep 2017 • Alberto Garcia-Duran, Mathias Niepert
We present KBLRN, a framework for end-to-end learning of knowledge base representations from latent, relational, and numerical features.
no code implementations • 10 May 2017 • Roberto Gonzalez, Filipe Manco, Alberto Garcia-Duran, Jose Mendes, Felipe Huici, Saverio Niccolini, Mathias Niepert
We present Net2Vec, a flexible high-performance platform that allows the execution of deep learning algorithms in the communication network.
2 code implementations • 2 Jun 2015 • Alberto Garcia-Duran, Antoine Bordes, Nicolas Usunier, Yves GRANDVALET
This paper tackles the problem of endogenous link prediction for Knowledge Base completion.
6 code implementations • NeurIPS 2013 • Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko
We consider the problem of embedding entities and relationships of multi-relational data in low-dimensional vector spaces.
Ranked #5 on Link Prediction on FB122
no code implementations • 26 Apr 2013 • Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko
We consider the problem of embedding entities and relations of knowledge bases in low-dimensional vector spaces.