GPEX, A Framework For Interpreting Artificial Neural Networks

Understand

The analogy between Gaussian processes (GPs) and deep artificial neural networks (ANNs) has received a lot of interest, and has shown promise to unbox the blackbox of deep ANNs.

• Existing theoretical works put strict assumptions on the ANN (e.g.
• requiring all intermediate layers to be wide, or using specific activation functions).
• Accommodating those theoretical assumptions is hard in recent deep architectures, and those theoretical conditions need refinement as new deep architectures emerge.