# DAVIDSTUTZ

15thJULY2018

For predicting model attributes, they propose two models, called kennen-o and kennen-i, see Figure 1. kennen-o takes as input a set of $100$ predictions of the models (i.e. final probability distributions) and tries to directly learn the attributes using a MLP of two fully connected layers. Kennen-i instead crafts a single input which allows to reason about a specific model attribute. An example for kennen-i is shown in Figure 2. In experiments, they demonstrate that both models are able to predict model attributes significantly better than chance. For details, I refer to the paper.