Given a deep net graph and the activation functions on the hidden vertices do we have a description of the function space spanned by it? (even if for some specific architectures and activation functions)

Or conversely : think of being given a function and an error tolerance (say in the sup norm) and a network architecture (with activation functions). Then is it known how to decide if edge weights can be assigned to the net to represent a function within the given error tolerance?

A few papers which I think addresses related questions are,


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.