*********** update after author feedback ***************** The improvements the authors note sound great and I hope this can improve the impact of the paper significantly. I would give an accept score if I were able to have a look at the new version and be happy with it (as is possible in openreview settings for example). However since improving the presentation usually takes a lot of work and it is not possible for me to verify in which way the improvements have actually been implemented, I will bump it to a 5. I do think readability and clarity is key for impact as written in my review, which is the main reason I gave a much lower score than other reviewers, some of whom have worked on exactly this intersection of algebra and G-CNNs themselves and provided valuable feedback on the content from an expert's perspective. The following comments are based on the reviewer's personal definition of clarity and good quality of presentation: that most of the times when following the paper from start to end it is clear to the reader why each paragraph is written and how it links to the objective of the main results of the paper, here claimed e.g. in the last sentence to be the development of new equivariant network architectures. The paper is one long lead-up of three pages of definitions of mathematical terms and symbols to the theorems in section 6 on equivariant kernels which represent the core results of the paper. In general, I appreciate rigorous frameworks which generalize existing methods, especially if they provide insight and enable the design of an arbitrary new instance that fits in the framework (in this case transformations on arbitrary fields). However that being said, my main concern with this paper is that I'm not sure whether the latter is actually achieved because of how the paper is presented in the current state. They describe G-CNNs in the language of field theory which is nice, but they do not elaborate on the exact impact of this, albeit interesting, achievement. Furthermore given my personal definition above, the quality in presentation is severely lacking and my main criticism about the paper. As an ML conference paper, the presentation is not transparent enough 1. for the average researcher to understand the framework to see for which equivariances they could use it 2. to see how exactly they can now design the convolutional layer in their specific instance. Now the authors could aim for the properly trained and interested mathematicians/physicists only - then, however I'm not sure why pages 3-5 of definitions are at such a prominent place in the paper. It is background knowledge that either the trained reader knows already or won't understand in the condensed way it is presented here. In particular, the definitions are given without the reader knowing why and how to connect it the familiar machine learning models/concepts. If they are experts, then these pages occlude the main take-away of the paper for the connection of these field theoretic concepts to practical G-CNN networks, which is discussed very little on the last page. Since the paper introduces everything starting with symmetry groups (admittedly basic concept) however, it does seems to aim to reach someone not too familiar with group theory. However it is inconceivable to me how they would be able to follow through all the way until induced representations and equivariant kernels in a reasonable amount of time, without actually properly learning the background by working through chapters of a book.

Having read the authors' feedback and other reviews, I am increasing my score from 5 to 6. If the paper is accepted, I would ask the authors to dedicate more space to worked examples and to differentiate their work from the existing literature in more detail. -------------------------------------------------------- In terms of novelty over previous works on equivariant CNNs, this paper is a mild step forward, bringing in sections of associated vector bundles for feature spaces and allowing for general representations there. The clarity of this mathematical language is admittedly nice and I think it will help researchers think about general equivariant CNNs in the future. However, Section 8 did not do a sufficient job of clarifying what the new theory does that previous papers could not, in terms of relevant examples. The main theorem is a classification of equivariant linear maps between such feature spaces. The organization of the paper is probably not optimal for NeurIPS, with the body of the paper Sections 2-6 reviewing general mathematical constructions. Some of this material could presumably be relegated to appendices, e.g. the proofs in Section 6, leaving more space for improved discussion.

The paper studies the following problem: If we consider a base space that admits a transitive action, and if the feature maps in neural network layers operating on this space are fields, then what is the most general way to write equivariant linear maps between the layers? The first contribution of the paper is to state and prove a theorem that says that such a linear map is a cross-correlation/convolution with a specially constrained kernel, which is called an equivariant kernel. The proof follows in a very straightforward manner from the application of MacKay's theorem aka Frobenius reciprocity, which essentially describes how induction and restriction interact with one another. Turns out that this is precisely the language needed to describe the equivariant networks talked about in this paper (and implicitly in many experimental papers). The proof is elegant and natural, and no details are omitted. Next, in a somewhat abstract manner it is also describes how such constrained kernels will look like. This to me personally is the most useful, as for practitioners, it gives a systematic procedure to derive the right kernel for convolution. This is also useful in different ways -- for example there has been recent work that posits that for continuous groups it is perhaps useful to always operate in Fourier space. To enforce locality we then need an appropriate notion of wavelets. The two approaches are equivalent, but I find the approach presented in the paper more transparent vis a vis jointly enorcing locality and equivariance. Appropriate equivariant non-linearities are also described. Lastly useful examples are given re spherical CNNs, SE(3) steerable CNNs that do a good job in making the discussion a bit more concrete (although still in the abstract space :)

This is an "emergency review" therefore it might be shorter than the norm. There is a substantial literature on steerability in the classical image processing domain. Recently, it has become clear that generalizing steerability to the action groups other than SE(2) is important for constructing certain classes of neural networks. Steerability can be described in different ways, some more abstract than others. This paper uses the language of fiber bundles, which is beautiful and enlightening but somewhat abstract. The paper makes no apologies about being abstract. I can understand that to somebody who comes more from the applications side of ML rather than the mathematical side it might be difficult to digest. On the other hand, it also states that "This paper does not contain fundamentally new mathematics (in the sense that a professional mathematician with expertise in the relevant subjects would not be surprised by our results)." I like this honesty. In actual fact, I don't think that either of the above detract from the value of the paper. This paper forms an important bridge between the neural nets literature and certain branches of algebra. I found it very enlightening. I appreciate the effort that the authors have made to show how a wide range of other works fit in their framework. I also think that the exposition is very straight forward. It does not attempt to gloss over or hide any of the underlying mathematical concepts. At the same time, it avoids getting bogged down with mathematical minutiae or a long list of definitions. The authors clearly made an attempt to say things in a way that is "as simple as possible but not simpler." It is quite an achievement to expose all the concepts that they need in 8 pages.