In this paper, we propose a versatile graph inference framework for learning from graph signals corrupted by exponential family noise. Our framework generalizes previous methods from continuous smooth ...

This paper describes an identifier for a class of nonlinear systems based on continuous recurrent neural networks (CRNN). The identifier is proposed considering the approximation properties of ...