From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper45/Authors
comment: We would like to thank the reviewer for reading our paper and providing valuable feedbacks. The point-by-point responses to the detailed comments are below.

1) the angular-based method with conventional Convolutional Neural Networks
In the Computer Vision field, several CNNs have been proposed to consider a rotation or an orientation of an object in an image. Since it was not apparent whether we can straightforwardly apply such CNNs to our spatio-temporal scenario, we did not use them as baselines in this paper. We will further investigate related works and add them as baselines.

2) multi-modal scenarios
When we attempt to estimate block missing values appeared in the bike sharing dataset, we can easily assume that the taxi dataset observed in the same period would have local relationships with the bike sharing dataset. To utilize such different modal data for our task, we will construct a bipartite graph matrix whose nodes corresponds to spatial locations of bikes and taxies and edges represent spatial relationships between different modalities. In a multi-modal scenario, by leveraging this graph, we plan to learn modal-between local features that enable us to extract more complex interactions between modalities in feature spaces and to get more accurate estimations than just concatenating these data.

3) the relative distances
Due to a space problem, we only showed the result with a simple graph matrix whose elements were set to 1 or 0. As you pointed out, our method with the relative distances seems to improve the performance than our method with the previous simple one. We will report the progress.

4) a skip connection
Since we are going to construct a deep Angle-based CNN for our spatio-temporal data analysis, we introduced a skip connection to avoid expected learning problems reported in the original ResNet paper. However, in this paper, we employed a shallow network structure, and the skip connection was not necessarily part of ours. Due to the space problem, we provided the result of our method with a skip connection. We have confirmed that ours without a skip connection also showed better performances than the baselines. We will report the result and modify our paper based on your comments. Moreover, we will add baselines with a skip connection to verify the efficiency of our proposed method.
title: To AnonReviewer1

From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper45/AnonReviewer1
evaluation: The paper presents an augmentation to Graphical Convolutional Networks (GCNs) to take into account angular relationships in the data. The new layer presented in this paper is named Angular-based Convolutional Networks (ACNs). The paper also includes empirical results demonstrating that ACNs perform marginally better than GCNs and much better than Fully Connected layers.

The idea of incorporating angular relationships in spatial temporal modelling is appealing, as this information is often easy to extract given that the spatial relationships are known. However, it is not obvious that when spatial information is known, using ACNs would outperform simply using normal convolution over the spatial data. It would have been good if there was a comparison of the angular-based method with conventional Convolutional Neural Networks.

It would also be good to see some more explanation on how the author plans to extend the method to multi-modal scenarios, which the paper claims to be straightforward. 

It would have also been good to some value indicating the relative distances between spatial locations in the experiments, instead of simply allocating 1 to a fixed number of closest locations. This would have been relatively easy to accomplish with the existing experimental set-up communicated in the paper. 

An additional point seeking clarification: The motivation or intuition behind adding a skip connection in the training of ACNs was also not explained adequately, and it is not immediate obvious if a similar setup was used for other Neural Network architectures evaluated.

From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper45/AnonReviewer1
relevance: 3: borderline
impact: 2: low
novelty: 3: borderline