Summary and Contributions: The paper considers the setting of Gradient Flow (GF) over homogeneous models (a broad class of models including neural networks with fully-connected and convolutional layers, ReLU and Leaky ReLU activations, max and average pooling, and more) trained for binary classification via minimization of exponential or logistic loss on separable data. In this setting, minimizing the loss necessarily means divergence of parameters (weights) to infinity, but the asymptotic behavior, i.e. the manner in which the parameters diverge, is of interest. The paper makes the following contributions: (i) It proves that the parameters converge in direction, which implies that prediction margins on the training examples converge if normalized. (ii) Under the additional assumption of locally Lipschitz gradients, it is proven that the gradient converges to the direction of the parameters. This implies margin maximization for the special cases of linear neural networks and a two layer network with squared ReLU activation. From a technical perspective, the analysis is based on a theory of non-smooth Kurdyka-Lojasiewicz inequalities for functions definable in an o-minimal structure. Experiments support the theory on both a model covered by the analysis ("homogeneous AlexNet"), and one that is not (DenseNet).
Strengths: The question of implicit regularization is of prime importance to the theory of deep learning, and the setting analyzed by this paper is very timely. In that regard, I believe the work is of high relevance to the NeurIPS community. The theoretical tools employed are very interesting, and although I did not verify all details, I believe the paper is solid from a technical perspective. I also found the text to be relatively well written, which is non-trivial given its level of mathematical depth.
Weaknesses: I have two main critiques on this work. The first relates to the significance of its results. In the setting studied, directional convergence, alignment and margin maximization have all been treated in several recent works (which the paper refers to). I know that at least in some of these works directional convergence and/or alignments were assumed (not proven), but nonetheless, my feeling is that the paper does not draw a sufficiently clear line separating itself from existing literature. For example, a very relevant existing work --- Lyu and Li 2019 --- is said to have left open the issues of directional convergence and alignment, but to my knowledge, that work does establish directional convergence, at least in some settings. I may be missing something here, but regardless, I think it is absolutely necessary to include in the current paper a detailed account for the exact differences between its results and those of existing literature. Otherwise the significance of its contributions is unclear. The second major comment I have relates to presentation. Some parts of the text (mostly in Sections 3 and 4) are extremely technical and dense, and it seems to me like much of the technicality arises from the need to account for non-smooth models (e.g. ones including ReLU activation). I think this aspect of the analysis is important, but recommend to the authors to consider deferring it to an appendix, and treating in the body of the paper only models that are differentiable. Hopefully that way a lot of the technical clutter will be avoided, and only the main ideas of the analysis will remain. Despite the above critique, I believe this paper is solid and does provide meaningful contributions. Therefore, I currently rate is as marginally above acceptance threshold, and will favorably consider increasing my score if the authors provide convincing arguments in their rebuttal. === UPDATE FOLLOWING AUTHOR FEEDBACK === I have thoroughly read the authors' feedback, as well as the other reviews. The authors have largely addressed my concerns (unclear distinction from prior work and overly technical presentation). Assuming the changes they have committed to will be incorporated, I recommend acceptance of this work.
Correctness: I did not verify all details in the analysis but I believe it is correct.
Clarity: Given the technical content I think the text is well written, but as described in "weaknesses" section above, I suggest deferring much of the technical content to an appendix. This will make the text much more readable in my opinion.
Relation to Prior Work: In my opinion relation to prior work is lacking --- see "weaknesses" section above.
Additional Feedback: Minor comments: * In the CIFAR experiments, it should be described how the dataset is adapted for binary classification. * Typo in line 74: the word "section" appears twice. * Line 160: if I understand correctly, the words "to ensure it" would be more appropriate than "to rule it out". * In Lemma 3.4, projections of subdifferential are used before being defined. * Typo in line 248: "an assumptions" ==> "assumptions".
Summary and Contributions: For binary classification with homogeneous networks and the exponential loss or logistic loss, this paper proves that the weights along gradient flow path convergence in direction. Moreover, under local Lipschitz condition on gradients, the angle between the weights and the gradients along gradient flow path is shown to approach zero. The paper also discuses the consequence of this results for implicit bias for margin maximization.
Strengths: The implicit bias of gradient decent is an important topic in the NeurIPS community. The logic of the results in many (but not all) previous papers was, for example, if it converges with a certain condition, then it satisfies KKT condition of margin maximization. While one might argue that the directional convergence part is relatively easier, it is still important to prove this precisely (and that is one of the points of mathematical proofs) and in this sense, the contribution of this paper is significant.
Weaknesses: It is not directly applicable to practical scenarios yet, partly because the paper considers gradient flow instead of finite time gradient dynamics. Also, many networks being used today are not homogeneous. However, these are understandable limitation for theoretical developments.
Correctness: All the claims seem to be correct.
Clarity: The paper is well written.
Relation to Prior Work: The paper clearly discusses how this work differs from previous contributions.
Additional Feedback: I read the author response, and agree with the authors for the responses for my part. I also read other reviews and agree with the concerns for the relationship to prior work. I updated my review accordingly. Except for in section 4.2, the paper does not seem to rely on the structure of deep networks. So, main results hold for general functions that satisfy those assumptions. Also, not only the setting, but also the statement about convergence is of usual interest in many math fields. This is good and interesting for wider applicability of the results. But, it also raises a potential concern that there are related previous results in other math fields outside of machine learning literature. Sub-differential and continuous dynamics are a big field. I am not clarified to judge the novelty of the results in a wider community in this sense. If someone in the related math fields outside of machine learning literature can check its novelty, that would be nice.
Summary and Contributions: The paper considers positively homogeneous networks and logistic or exponential losses, and assume a technical condition that the network is definable in some o-minimal structure. It studies the late training setting after the loss is smaller than 1/#datapoints. It shows that 1) weights learned by gradient flow converge in direction (implying the convergence of predictions, training errors, and the margin distribution etc); 2) if the network further has locally Lipschitz gradients, the gradients converge in direction and asymptotically align with the gradient flow path (implying margin maximization in a few settings). The analysis is by unbounded nonsmooth Kurdyka-Łojasiewicz inequalities.
Strengths: + The results are quite general and clean. It holds for a rich family of networks (ie linear, convolution, ReLU, and max-pooling layers). + The analysis technique (unbounded nonsmooth Kurdyka-Łojasiewicz inequalities) seems an interesting contribution to analyzing deep learning and nonlinear systems more broadly. (Although I'm not exactly sure its connection to the techniques in the previous work Lyu and Li ).
Weaknesses: - The results are not applicable to some other important family of networks like ResNets. The analysis seems to be relying on the homogeneity. Does this mean that ResNets have different behavior? Or they have similar behaviors, but the analysis is limited by the current techniques available? - Can one have discrete-time guarantees? Especially since the analysis if for late training, one would like to have an analysis saying after how many steps the training roughly converges (stabilizes). Does it take exponential time to stabilize? - The generalization behavior is only briefly mentioned. The implications on margins are related, but more explicit discussion will be appreciated. I would also like to see more discussion on the effect of late training on generalization, e.g., does the late training after loss 1/#datapoints improve the generalization or can hurt the generalization? ============after response=============== The response clarified my questions on generalization and distinction from prior work. It doesn't adequately address the questions about discrete-time analysis and non-homogeneous networks, but I understand these two are quite beyond the scope of the paper, so I increase the score from 6 to 7.
Correctness: I believe so, though I may have only checked the key lemmas.
Relation to Prior Work: Yes. More discussions on generalization will be appreciated.
Summary and Contributions: This work established that the normalized parameter vectors converge, and that under an additional assumption of locally Lipschitz gradients, the gradients also converge and align with the parameters.
Strengths: (1) This work established that the normalized parameter vectors converge; (2) Under an additional assumption of locally Lipschitz gradients, the gradients also converge and align with the parameters.
Weaknesses: (1) In terms of the results, the paper obtains a significant convergence result in the deep learning field, this work studies the binary classiﬁcation problem, where the loss is the logistic loss or exponential loss. The main technical analysis is based on this. Do you think whether it is easy to consider a general loss function with some mild assumptions? (2) In terms of the assumptions, both the locally Lipchitz gradient assumption and the initial-error assumption seem strong to me. In particular, this work requires an initial risk smaller than 1/n, where n is the number of data samples. In general, the number of data samples n is typically large, which means this work requires a sufficiently good initialization. First, the initialization assumption seems a bit stronger compared with the random initialization or around-zero initialization in the literature. Second, this work also assumes perfect classification accuracy on the data samples. Despite the rationality of the assumption in practice, using this assumption along with the sufficient good initialization, it seems not hard to show the initialization is close to an optimal classifier. Could we relax such assumptions by using additional network structures such as over-parameterization? Because the convergence literature doesn't require such assumptions when assuming the over-parameterization assumption. (3) In terms of the technical analysis, this work heavily relies on previous works [Ji and Telgarsky 2018a] and [Kurdyka et al. 2000a, 2006].
Relation to Prior Work: Yes.
Additional Feedback: This work studies the binary classiﬁcation problem, where the loss is the logistic loss or exponential loss. The main technical analysis is based on this. Do you think whether it is easy to consider a general loss function with some mild assumptions? ==========================after response=================== I read the author response. I updated my review accordingly.