Axiomatic Attribution for Deep Networks (Integrated Gradients)

Formulated Integrated Gradients by integrating the gradient of the output with respect to input features along a straight path from a baseline, satisfying completeness and implementation invariance.

Serves as qualified reference for implementing task-model-explainability in production systems.

Assumptions

• Underlying computational topology and mathematical bounds adhere to established convexity/smoothness guarantees

Limitations

• Hardware runtime speedups, privacy budgets, and convergence depend on hyperparameters and network communication limits