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Found 3 papers, 3 papers with code
Explaining models relating objects and privacy
To explain the decision of these models, we use feature-attribution to identify and quantify which objects (and which of their features) are more relevant to privacy classification with respect to a reference input (i. e., no objects localised in an image) predicted as public.
A Comparative Analysis of Gene Expression Profiling by Statistical and Machine Learning Approaches
A collection of machine learning models including logistic regression, multilayer perceptron, and graph neural network are trained to classify samples according to their cancer type.
Studying Limits of Explainability by Integrated Gradients for Gene Expression Models
The scientific questions are formulated as classical learning problems on tabular data or on graphs, e. g. phenotype prediction from gene expression data.
