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Found 4 papers, 1 papers with code
Four Axiomatic Characterizations of the Integrated Gradients Attribution Method
Deep neural networks have produced significant progress among machine learning models in terms of accuracy and functionality, but their inner workings are still largely unknown.
Distributing Synergy Functions: Unifying Game-Theoretic Interaction Methods for Machine-Learning Explainability
We show that, given modest assumptions, a unique full account of interactions between features, called synergies, is possible in the continuous input setting.
A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions
Attribution methods address the issue of explainability by quantifying the importance of an input feature for a model prediction.
Explainability Tools Enabling Deep Learning in Future In-Situ Real-Time Planetary Explorations
It provides a set of explainability tools (ET) that opens the black box of a DNN so that the individual contribution of neurons to category classification can be ranked and visualized.
