Venue: ICLR 2018 conference

Reviews:

final_score: rating: 4: Ok but not good enough - rejection
review_content: The paper adresses an important task to build a data-driven model traffic forecasting model. The paper takes into consideration the spatio-temporal autocorralation and tackles this with a diffusion process for convolutional recurrent neural networks. The paper lacks a comparison to other models that aim to include spatio-temporal dependencies used for this problem - namely Gaussian Processes, spatial k-NN.
The paper motivates the goal to obtain smooth traffic predictions, but traffic is not a smooth process, e.g. traffic lights and intersections cause non smooth effects. therefore it is difficult to follow this argumentation. Statements as 'is usually better at predicting start and end of peak hours' (caption of Figure 6) should be supported by statistical test that stress significance of the statement.
The method performs well on the presented data - in comparison to the models that do not consider autocorrelation. This might be because tests were not performed with commonly used traffic models or the traffic data was in favour for this model - it remains unclear, whether the proposed method really contributes better predictions or higher scalibility or faster computation to the mentioned problem. How does the proposed model behave in case of a shift in the traffic distribution? How do sudden changes (accident, road closure, etc.) affect the performance?
review_title: The paper presents an interesting approach to a relevant topic but lacks a comparison to other models that include spatio-temporal dependencies for traffic forecasting.

final_score: rating: 9: Top 15% of accepted papers, strong accept
review_content: Summary of the reviews:
Pros:
•	A Novel Diffusion Convolutional Recurrent Neural Network framework for traffic forecasting
•	Apply bidirectional random walks with nice theoretical analysis to capture the spatial dependency
•	Novel applications of sequence to sequence architecture and the scheduled sampling technique into modeling the temporal dependency in the traffic domain
•	This work is very well written and easy to follow
Cons:
•	Needs some minor re-organization of contents between the main sections and appendix

Detailed comments:
D1:  Some minor re-organization of contents between the main sections and the appendix will help the reader reduce cross-section references. Some examples:
1.	Lemma 2.1 can be put into appendix since it is not proposed by this work while the new theoretical analysis of Appendix 5.2 (or at least a summary) can be moved to the main sections
2.	Figure 9 can be moved earlier to the main section since it well supports one of the contributions of the proposed method (using the DCRNN to capture the spatial correlation)
D2: Some discussions regarding the comparison of this work to some state-of-the-arts graph embedding techniques using different deep neural network architectures would be a plus
review_title: This work proposed a Diffusion Convolutional Recurrent Neural Network framework for traffic forecasting that incorporates both spatial and temporal dependency in the traffic flow. Specifically, the proposed framework captures the spatial dependency using bidirectional random walks on the graph, and the temporal dependency using the encoder-decoder architecture with scheduled sampling and the sequential modeling from the nature of recurrent neural network.

final_score: rating: 5: Marginally below acceptance threshold
review_content: The paper proposes to build a graph where the edge weight is defined using the road network distance which is shown to be more realistic than the Euclidean distance. The defined diffusion convolution operation is essentially conducting random walks over the road segment graph. To avoid the expensive matrix operation for the random walk, it empirically shows that K = 3 hops of the random walk can give a good performance. The outputs of the graph convolutionary operation are then fed into the sequence to sequence architecture with the GRU cell to model the temporal dependency. Experiments show that the proposed architecture can achieve good performance compared to classic time series baselines and several simplified variants of the proposed model. 

Although the paper argues that several existing deep-learning based approaches may not be directly applied in the current setting either due to using Euclidean distance or undirected graph structure, the comparisons are not persuasive. For example, the approach in the paper "DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting" also consider directed graph and a diffusion effect from 2 or 3 hops away in the neighboring subgraph of a target road segment. 

Furthermore, the paper proposes to use two convolution components in Equation 2, each of which corresponds to out-degree and in-degree direction, respectively. This effectively increase the number of model parameters to learn. Compared to the existing spectral graph convolution approach, it is still not clear how its performance will be by using the same number of parameters. The experiments will be improved if it can compare with "Spatio-temporal graph convolutional neural network: A deep learning framework for traffic forecasting" using roughly the same number of parameters.
review_title: The paper proposes the Diffusion Convolutional Recurrent Neural Network architecture for the spatiotemporal traffic forecasting problem. Overall, the paper is well written with incremental technical contributions.

Meta-review: The paper received highly diverging scores: 5 (R1) ,9 (R2), 4(R3). Both R1 and R3 complained about the comparisons to related methods. R3 suggested some kNN and GP baselines, while R1 mentioned concurrent work using deepnets for trafffic prediction.

R3 is real expert on field. R2 and R1, not so.
R2 review very positive, but vacuous.
Rebuttal seems to counter R1 and R3 well.

It's a close all but the AC is inclined to accept since it's an interesting application of (graph-based) deepnets.
Decision: Accept (Poster)