Normalized Relevance Measure as a Unifying Framework to Explain Neural Network Latent Structures

ArXi:2606.00557v1 Announce Type: new To understand how a neural network (NN) functions and makes predictions, it has become increasingly clear that analyzing only the input domain is insufficient -- one must also examine its internal inference mechanisms to capture the complete picture. To explain the internal inference mechanisms of such models, it is essential to analyze the importance of latent representations for a given task.