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Found 5 papers, 3 papers with code
DSEG-LIME -- Improving Image Explanation by Hierarchical Data-Driven Segmentation
Explainable Artificial Intelligence is critical in unraveling decision-making processes in complex machine learning models.
GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Data
Our method combines axis-aligned splits, which is a useful inductive bias for tabular data, with the flexibility of gradient-based optimization.
GradTree: Learning Axis-Aligned Decision Trees with Gradient Descent
Decision Trees (DTs) are commonly used for many machine learning tasks due to their high degree of interpretability.
Explaining Neural Networks without Access to Training Data
We consider generating explanations for neural networks in cases where the network's training data is not accessible, for instance due to privacy or safety issues.
xRAI: Explainable Representations through AI
The approach is based on the idea of training a so-called interpretation network that receives the weights and biases of the trained network as input and outputs the numerical representation of the function the network was supposed to learn that can be directly translated into a symbolic representation.
