1 code implementation • ACL 2016 • Iulian Vlad Serban, Alberto García-Durán, Caglar Gulcehre, Sungjin Ahn, Sarath Chandar, Aaron Courville, Yoshua Bengio
Over the past decade, large-scale supervised learning corpora have enabled machine learning researchers to make substantial advances.
2 code implementations • AKBC 2019 • Daniel Oñoro-Rubio, Mathias Niepert, Alberto García-Durán, Roberto González, Roberto J. López-Sastre
A visual-relational knowledge graph (KG) is a multi-relational graph whose entities are associated with images.
3 code implementations • EMNLP 2018 • Alberto García-Durán, Sebastijan Dumančić, Mathias Niepert
In line with previous work on static knowledge graphs, we propose to address this problem by learning latent entity and relation type representations.
no code implementations • 22 Oct 2018 • Brandon Malone, Alberto García-Durán, Mathias Niepert
The polypharmacy side effect prediction problem considers cases in which two drugs taken individually do not result in a particular side effect; however, when the two drugs are taken in combination, the side effect manifests.
no code implementations • AKBC 2019 • Bhushan Kotnis, Alberto García-Durán
It is a well-known fact that knowledge bases are far from complete, and hence the plethora of research on KB completion methods, specifically on link prediction.
no code implementations • 17 Apr 2021 • Alberto García-Durán, Robert West
Time series with missing data are signals encountered in important settings for machine learning.
1 code implementation • EMNLP 2021 • Akhil Arora, Alberto García-Durán, Robert West
We propose a light-weight and scalable entity linking method, Eigenthemes, that relies solely on the availability of entity names and a referent knowledge base.
1 code implementation • PVLDB 2022 • Manuel Leone, Stefano Huber, Akhil Arora, Alberto García-Durán, Robert West
Our findings shed light on the potential problems resulting from an impulsive application of neural methods as a panacea for all data analytics tasks.
1 code implementation • LREC 2022 • Alberto García-Durán, Akhil Arora, Robert West
We also propose a light-weight and simple solution based on the construction of indexes whose design is motivated by more complex transfer learning based neural approaches.