77 citations · 353 across the 60 of their papers we have counts for
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Modeling Semantic Relatedness using Global Relation Vectors
Shoaib Jameel, Zied Bouraoui, Steven Schockaert
Word embedding models such as GloVe rely on co-occurrence statistics from a large corpus to learn vector representations of word meaning. These vectors have proven to capture surpr…
Stacked Structure Learning for Lifted Relational Neural Networks
Gustav Sourek, Martin Svatos, Filip Zelezny +2
Lifted Relational Neural Networks (LRNNs) describe relational domains using weighted first-order rules which act as templates for constructing feed-forward neural networks. While p…
Relational Marginal Problems: Theory and Estimation
Ondrej Kuzelka, Yuyi Wang, Jesse Davis +1
In the propositional setting, the marginal problem is to find a (maximum-entropy) distribution that has some given marginals. We study this problem in a relational setting and make…
Probabilistic Relation Induction in Vector Space Embeddings
Zied Bouraoui, Shoaib Jameel, Steven Schockaert
Word embeddings have been found to capture a surprisingly rich amount of syntactic and semantic knowledge. However, it is not yet sufficiently well-understood how the relational kn…
Jointly Learning Word Embeddings and Latent Topics
Bei Shi, Wai Lam, Shoaib Jameel +2
Word embedding models such as Skip-gram learn a vector-space representation for each word, based on the local word collocation patterns that are observed in a text corpus. Latent t…
Induction of Interpretable Possibilistic Logic Theories from Relational Data
Ondrej Kuzelka, Jesse Davis, Steven Schockaert
The field of Statistical Relational Learning (SRL) is concerned with learning probabilistic models from relational data. Learned SRL models are typically represented using some kin…