Search Results for author:
Found 3 papers, 2 papers with code
HEALNet -- Hybrid Multi-Modal Fusion for Heterogeneous Biomedical Data
Technological advances in medical data collection such as high-resolution histopathology and high-throughput genomic sequencing have contributed to the rising requirement for multi-modal biomedical modelling, specifically for image, tabular, and graph data.
CGXplain: Rule-Based Deep Neural Network Explanations Using Dual Linear Programs
Rule-based surrogate models are an effective and interpretable way to approximate a Deep Neural Network's (DNN) decision boundaries, allowing humans to easily understand deep learning models.
Feature Synergy, Redundancy, and Independence in Global Model Explanations using SHAP Vector Decomposition
We offer a new formalism for global explanations of pairwise feature dependencies and interactions in supervised models.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
