Finding an appropriate value for hyper-parameters is always a challenge in machine learning problems. We do not yet compare to prior tractography algorithms. The availability of a ground truth Phi let's us focus our investigation into the soundness With the additional space in the camera-ready, we can include more background and discussion on ENCODE. Our solution exploits the inherent sparseness in the optimization. However, this does not need to be run for each step of the optimization and is not expensive to do once upfront.

The reviewers generally agree that this paper offers a novel viewpoint on avoiding catastrophic forgetting. The theoretical and experimental results are well received. R3 would have preferred to see a deeper discussion on the differences with OWM. However, the authors explained during the rebuttal that their learning rule modifies both sides of the gradient update, differently to OWM. This characteristic, together with the intricacies involved in considering a sequential application, makes the overall contribution significant enough.

We got a clear sense of where more clarification would be helpful. To what solution do neural nets (trained w. GD on this network simulates the unnormalized exponentiated gradient algorithm (EGU). Previously it was thought that GD cannot take advantage of the sparsity of the solution. What is the surprising insight?

Summary and Contributions: This paper improves the StyleGAN-based image generation model by disentangling semantics based on a learnable semantics grouping operation, where the styles of the intra-group features are controlled by group-wise adaptive instance normalization and the overall features are re-balanced by inter-group adaptive group normalization. Quantitative and qualitative evaluations show certain improvements over existing methods. Strengths: - The quantitative evaluations and ablation study validates the effectiveness of the proposed improvements. The most critical limitation of this work is its novelty and theoretical soundness. However, similarity between layers of a convolutional kernel may not indicate consistent similarity between corresponding feature channels.

Weaknesses: The authors neglect to compare to probably the 2 most related works I am aware of. The authors briefly mention variational noise contrastive estimation which can also be used to train models like those presented in this work. While this method has not yet been shown to scale to high dimensional image data it should be used as a comparison for the toy data at the very least. This work: "Variational Inference for Sparse and Undirected Models" Ingraham & Marks provides a method for parameter inference in EBLVMs. This method could also be used for comparison but at the very least should be included in the related work. The proposed method requires 2 inner loop optimizations (N x K) for each model gradient update.

All reviewers agree this is interesting work that succefsully trains energy-based latent variable models with score matching. There were concerns around clarity of the algorithm, utility of latent variables, complexity of the bi-level optimization proess, and missing baselines, which should all be addressed (as promised in the rebuttal) in the final verison of the paper.

The paper systematically studies neural architecture search for graph neural networks by proposing (1) a general GNN design space, (2) a GNN task space with a quantitative similarity metric and (3) the design space evaluation. Although it is experiment-driven and lacks deep insights or theoretical analysis, the comprehensive and systemic evaluation of GNN design are important to the community. Based on the reviews, the merits of the paper outweigh the drawbacks and acceptance is recommended.

Weaknesses: The paper's biggest omission is that it only considers decision-maker utility as opposed to social welfare/decision subjects' utility. This is significant because the model and techniques proposed are inherently extractive in the following sense: the decision-maker can and will induce the subject to pay a cost of (say) .5 in order to improve the decision-maker's utility by .01. As noted in the paper, the hope is that the improvement is worth it to both the decision-maker and the subject, but there's no guarantee that this will actually be the case. I think the experiments should at least investigate this question: does social welfare ultimately increase? Are there individuals whose utility decreases compared to the non-strategic setting?