Faithful and Efficient Explanations for Neural Networks via Neural Tangent Kernel Surrogate Models
This work provides incremental improvements in explainable AI by combining surrogate modeling and kernel methods for more efficient data attribution tasks.
The paper tackles the problem of efficiently approximating neural tangent kernels for data attribution in neural networks, introducing two random projection variants that allow tuning of computational complexity and showing that the trace NTK is the most consistent performer.
A recent trend in explainable AI research has focused on surrogate modeling, where neural networks are approximated as simpler ML algorithms such as kernel machines. A second trend has been to utilize kernel functions in various explain-by-example or data attribution tasks. In this work, we combine these two trends to analyze approximate empirical neural tangent kernels (eNTK) for data attribution. Approximation is critical for eNTK analysis due to the high computational cost to compute the eNTK. We define new approximate eNTK and perform novel analysis on how well the resulting kernel machine surrogate models correlate with the underlying neural network. We introduce two new random projection variants of approximate eNTK which allow users to tune the time and memory complexity of their calculation. We conclude that kernel machines using approximate neural tangent kernel as the kernel function are effective surrogate models, with the introduced trace NTK the most consistent performer. Open source software allowing users to efficiently calculate kernel functions in the PyTorch framework is available (https://github.com/pnnl/projection\_ntk).