Paper ID: | 4444 |
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Title: | On Exact Computation with an Infinitely Wide Neural Net |

The claim of this paper is clear and easy to read. Although the composition of CNTK is straightforward and its exact computation in ReLU networks seems to be a bit trivial, this is a natural direction to be explored and some researchers will be interested in experimental results reported in this paper. The non-asymptotic proof is also a novel contribution and will be helpful to further develop the theory of NTK. Basically, I believe that this paper is worth to be published. I think that clarifying the following points will further increase the significance of this paper. - In table 1, CNN-V (or CNN-GAP) trained by SGD achieved the highest performance compared to CNTKs. When the number of channels increase, does the performance of CNN-V (or CNN-GAP) converge to that of CNTKs? Because Authors argue the equivalence between the sufficiently wide net and kernel regression in the former part of the manuscript, some readers will be interested in experimental justification on such equivalence in CNNs. It will be also informative to empirically demonstrate in what finite number of channels the trained CNNs perform better than the kernel regression with the CNTKs. - In main theorems, the width m is evaluated by using the notation of poly(…), i.e., m >= poly(…). It seems to be almost enough to show that the lower bound is polynomial, it will be also helpful to show the specific rate of the polynomial order (I mean, when m>=poly(n), what is k satisfying m>=O(n^k)?). Such rates are explicitly shown in a part of lemmas and theorems (ex. Theorem E.1 and E.2), it is hard to follow them in the proofs of main theorems. It seems to be better to add a brief description of the rate of the polynomial order to Supplementary Material. - It may be better to remark on locally-connected network (LCN; CNN without weight sharing). [Novak et al 2019] claims that the kernel matrix (corresponding to weakly-trained nets) without global average pooling is equivalent between CNN and LCN. I am curious as to whether this equivalence holds in the case of NTKs. As shown in Table 1, the CNTK-GAP performed better than CNTK-V. This might be because the pooling makes correlations between different patches non-negligible and the advantage of weight sharing appear. - Lemma E.3 : I cannot fully understand the meaning of "random vector F=w^{\top}G". Does this mean that F is a fixed random vector independent of w and w is generated under the constraint of F=w^{\top}G after fixing G and F? It would be better to enrich description on its definitions. Typo: In Lemma F.9: m >= poly(\omega) -> m >= poly(1/\omega) --- After the rebuttal --- I have been almost satisfied with the authors' response and keep my score. I hope that Authors can improve the polynomial rate of the bounds in follow-up works.

POST-REBUTTAL UPDATE: I have read the rebuttal and other reviews; my score remains unchanged. ======================================================= Originality: This paper builds upon a line of works on the equivalence of DNNs and kernel methods and adds results for fully-trained CNNs to that list. The authors also prove a new convergence theorem for neural tangent kernels with a better convergence bound. Quality: The paper seems technically sound. However, I did not verify the provided proofs. Clarity: The paper is well-organized and clearly written. Significance: The provided experiments show that the proposed kernel outperforms existing kernels on CIFAR-10. It would be nice to see the performance on other datasets as well. It is not clear from the experiments how significant the results from the 21-layer kernel are. Does its performance degrade further with depth? Or is the difference between a 21-layer kernel and an 11-layer kernel not significant? Also, the idea of using less data for training to aid model selection is very widespread in the neural architecture search community. It is not clear why this could have been any different for CNTKs. I don't think these experiments are very relevant to the scope of the paper unless the authors show, for example, that CNTKs generalize better from less data or can be used for the design of finite CNNs. Finally,

The paper extends the previous fully-connected-layer based Neural Tangent Kernel (NTK) to Convolutional NTK (CNTK) and provides the first efficient exact computation algorithm. It results in a significant new benchmark for the performance of the pure kernel-based method on CIFAR-10. **Pros:** 1. The paper is clearly written and well-structured. 2. Theoretically, compared to previous results, it provides a non-asymptotic bound for NTK and looses the (sequential) limit of width requirements. It also shows the equivalence of a fully-trained sufficiently wide neural net and the deterministic kernel predictor using NTK. The theoretical analysis part is solid and important. 3. Technically, it extends NTK to CNTK (focusing on ReLU-based CNNs) for exact CNTK computation and provides some tricks (rely on the ReLU activation) to optimize the computation efficiency. It is important for both researchers practitioners, e.g., better understand the generalization performance of deep learning. **Cons:** 1. The section I in the appendix specifies the implementation tricks for GPUs, but the benefits (e.g., effectiveness, improved efficiency) of these tricks are unclear. A simple justification through some benchmarking experiments could be helpful. It would be great if the authors can also comment on the potential of giving the efficient exact algorithm to compute CNTK for other activation functions. 2. The lack of experimental details makes benchmarking results a bit less convincing. For example, what is the number of channels used for each layer, and what is its range? I think line 388 shows the number of channels is treated as a hyper-parameter and the best performance is reported among all hyper-parameters. Is it indicate that different depths might have different # of channels, or even different convolutional layers might have different # of channels? Is it possible to show a figure similar to Figure 6 in [Novak et al., 2019]? Otherwise, the comment (line 290) is less conclusive. Also, more justifications are required, e.g., why CNN-GAP suddenly has a quality loss for depth-11 but improves again for depth-21. BTW, how many repeated evaluations do Table 1 perform and what score is reported (average, or median as in Table 2)? **Minor comments:** 1. Line 126, ‘out’ should be ‘our’ 2. The proposed CNTK extension and its exact computation is a combination of the existing NTK with dynamic programming (specific to ReLU activation function). This part (Section 4) is a bit technical (with limited novelty) and independent to Section 3, while the title/introduction indeed is more focus on Section 4. Strengthening Section 4, e.g., including the residual layer, extending to more activation functions, or supporting softmax and cross-entropy, would make the whole story more solid. Open-sourcing an efficient NTK/CNTK computation framework could also connect these theoretical understanding to deep learning practitioners.

This paper provides two theoretical analyses of the neural tangent kernel (NTK); (i) one is the convergence guarantee of approximate NTK by finite-width DNNs to NTK and (ii) the other is a uniform bound on the gap between the limit point of gradient flow and a solution in RKHS associated with NTK. In addition, the paper introduces an efficient algorithm to compute a CNTK which is a CNN variant of NTK and experimentally demonstrate the superior performance of CNTK over the state-of-the-art kernel method and a random feature method of NTK. In short, I think this paper makes strong contributions to NeurIPS community as commented below. Clarity: The paper is well organized and easy to read. Quality: The work is of good quality and is technically sound. Two suggestions: - However, a major concern is the positivity of the gram-matrix $H^*$ of NTK. In the theoretical analysis, the positivity is assumed to define a solution in RKHS with NTK and to provide a convergence guarantee of the gradient flow. Therefore, I would like the author to discuss the validity of this assumption if possible. - It would be useful for the reader if the proof of the convergence of the linear dynamics (4) to a solution $f_{ntk}$ could be provided. Originality & Significance: In this paper, there are two interesting theoretical contributions. Theorem 3.1 refines the previous result obtained in the original NTK paper [Jacot2018+] via the non-asymptotic convergence analysis. In addition, Theorem 3.2 seems the first result that provides a uniform bound on the gap between the limit point of the gradient flow for DNN and a solution of NTK. In other words, these results connect DNNs with kernel methods and allow us to analyze the theoretical property of DNN trained by the gradient methods via a kernel perspective. Although there is still a non-negligible gap between the empirical performance of NTK and CNNs using several heuristics (which are turned off in the experiment) and there is also controversy over whether or not NTK is really useful in generalization analysis, this paper provides theoretical and empirical evidence of its usefulness to some extent. I believe that this paper will facilitate theoretical analysis of neural networks based on NTK. ----- I have read the authors' response and I will keep the score.