Reviews: A General Theory of Equivariant CNNs on Homogeneous Spaces
–Neural Information Processing Systems
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).
Neural Information Processing Systems
Jan-26-2025, 16:46:15 GMT
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