Reviews: Non-parametric Structured Output Networks

The paper proposes Non-parametric Neural Networks (N3) a method that combines advantages of deep models for learning strong relations between input and output variables with the capabilities of probabilistic graphical models at modeling relationships between the output variables. Towards this goal, the proposed method is designed based on three components: a) a deep neural network (DNN) which learns the parameters of local non-parametric distributions conditioned on the input variables, b) a non-parametric graphical model (NGM) which defines a graph structure on the local distributions considered by the DNN. The proposed method is sound, well motivated and each of its components are properly presented. The method is evaluated covering a good set of baselines and an ablation study showing variants of the proposed method. The evaluation shows that state of the art results are achieved by the proposed method.