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Found 2 papers, 1 papers with code
Pseudo-Riemannian Embedding Models for Multi-Relational Graph Representations
In this paper we generalize single-relation pseudo-Riemannian graph embedding models to multi-relational networks, and show that the typical approach of encoding relations as manifold transformations translates from the Riemannian to the pseudo-Riemannian case.
Molecular representation learning with language models and domain-relevant auxiliary tasks
We apply a Transformer architecture, specifically BERT, to learn flexible and high quality molecular representations for drug discovery problems.
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