Search Results for author:
Found 4 papers, 1 papers with code
Active entailment encoding for explanation tree construction using parsimonious generation of hard negatives
Entailment trees have been proposed to simulate the human reasoning process of explanation generation in the context of open--domain textual question answering.
A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data
We discuss the recent evolutionary arch of DL models in the direction of integrating prior biological relational and network knowledge to support better generalisation (e. g. pathways or Protein-Protein-Interaction networks) and interpretability.
Assessing the communication gap between AI models and healthcare professionals: explainability, utility and trust in AI-driven clinical decision-making
This paper contributes with a pragmatic evaluation framework for explainable Machine Learning (ML) models for clinical decision support.
Transformers and the representation of biomedical background knowledge
Specialised transformers-based models (such as BioBERT and BioMegatron) are adapted for the biomedical domain based on publicly available biomedical corpora.
