How can transfer entropy metrics be applied to evaluate Generative AI model complexity

Question

Can i know How can transfer entropy metrics be applied to evaluate Generative AI model complexity?

Answer 1

Transfer entropy metrics evaluate generative AI model complexity by quantifying the directional flow of information between input and output variables, helping to assess how well the model captures dependencies.

Here is the code snippet you can refer to:

In the above code, we are using the following key points:

• Simulated Data: Creates input-output sequences to evaluate dependency.
• Mutual Information: Used as a surrogate to estimate directional dependencies.
• Interpretation: Higher mutual information suggests the model effectively captures complex dependencies.

Hence, transfer entropy metrics, estimated through mutual information, provide a quantitative measure of how well generative models capture input-output dependencies, reflecting their complexity.