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Found 6 papers, 1 papers with code
Proxy-based Zero-Shot Entity Linking by Effective Candidate Retrieval
A recent advancement in the domain of biomedical Entity Linking is the development of powerful two-stage algorithms, an initial candidate retrieval stage that generates a shortlist of entities for each mention, followed by a candidate ranking stage.
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.
Directed Graph Embeddings in Pseudo-Riemannian Manifolds
The inductive biases of graph representation learning algorithms are often encoded in the background geometry of their embedding space.
Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness
Learning meaningful representations of data that can address challenges such as batch effect correction and counterfactual inference is a central problem in many domains including computational biology.
Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs
In this work, we provide a new formulation for Graph Convolutional Neural Networks (GCNNs) for link prediction on graph data that addresses common challenges for biomedical knowledge graphs (KGs).
Random Forests on Distance Matrices for Imaging Genetics Studies
We propose a non-parametric regression methodology, Random Forests on Distance Matrices (RFDM), for detecting genetic variants associated to quantitative phenotypes representing the human brain's structure or function, and obtained using neuroimaging techniques.
