Search Results for author:
Found 6 papers, 1 papers with code
Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy
Posterior sampling, i. e., exponential mechanism to sample from the posterior distribution, provides $\varepsilon$-pure differential privacy (DP) guarantees and does not suffer from potentially unbounded privacy breach introduced by $(\varepsilon,\delta)$-approximate DP.
Knowledge-aware Attention Network for Protein-Protein Interaction Extraction
Protein-protein interaction (PPI) extraction from published scientific literature provides additional support for precision medicine efforts.
Leveraging Prior Knowledge for Protein-Protein Interaction Extraction with Memory Network
Automatically extracting Protein-Protein Interactions (PPI) from biomedical literature provides additional support for precision medicine efforts.
Chemical-induced Disease Relation Extraction with Dependency Information and Prior Knowledge
KBs are important resources for biomedical relation extraction.
Knowledge-guided Convolutional Networks for Chemical-Disease Relation Extraction
Results: This paper proposes a novel model called "Knowledge-guided Convolutional Networks (KCN)" to leverage prior knowledge for CDR extraction.
Combining Context and Knowledge Representations for Chemical-Disease Relation Extraction
KBs contain huge amounts of structured information about entities and relationships, therefore plays a pivotal role in chemical-disease relation (CDR) extraction.
