- Some parts of the work are harder to follow and it helps to have checked [Cohen and Shashua, 2016] for background information. # Typos and Presentation - The citation of Kraehenbuehl and Koltun: it seems that the first and last name of the first author, i.e. Philipp, are swapped. - The paper seems to be using a different citation style than the rest of the NIPS submission. Is this intended? - line 111: it might make sense to not call g activation function, but rather a binary operator; similar to Cohen and Shashua, 2016. They do introduce the activation-pooling operator though that fulfils the required conditions. - line 111: I believe that the weight w_i is missing in the sum. - line 114: Why not mention that the operator has to be associative and commutative? - eq 6 and related equations: I believe that the operator after w_i should be the multiplication of the underlying vector space and not \cross_g: It is an operator between a scalar and a tensor, and not just between two scalars. - line 126: by the black *line* in the input # Further Questions - Would it make sense to include and learn AccNet as part of a larger predictor, e.g., for semantic segmentation, that make use of similar operators? - Do you plan to publish their implementation of the proposed AccNet? # Conclusion The work shows that the proposed method is expressive enough to approximate high-dimensional filtering operations while being fast. I think the paper makes an interesting contribution and I would like to see this work being published.
- line 126: by the black *line* in the input # Further Questions - Would it make sense to include and learn AccNet as part of a larger predictor, e.g., for semantic segmentation, that make use of similar operators?