Paper:
Neuronal Capacity
We define the capacity of a learning machine to be the logarithm of the number (or volume) of the functions it can implement. We review known results, and derive new results, estimating the capacity of several neuronal models: linear and polynomial threshold gates, linear and polynomial threshold gates with constrained weights (binary weights, positive weights), and ReLU neurons. We also derive some capacity estimates and bounds for fully recurrent networks, as well as feedforward networks.

Review:
Overall assessment: I consider this work to be interesting and worth accepting, even though I am not convinced by the presentation itself, which I feel is obfuscating the real point of the paper. (More details to follow.)  ----------------------------------------------------------------------------- Details: In this work, the author(s) consider, under a specific angle, the following quite fundamental question: "what is the expressive power of a given class C of functions (concepts)?" A natural (though maybe arguable, and not suitable for *all* purposes) is to consider the logarithm of its size, log_2 |C| (or, if C is infinite, the log of its measure, for a well-chosen notion thereof; or the size of a minimum cover). Indeed, this is an approach well-studied in information theory and statistics, for instance (see the related notions of VC dimension, covering number, bracketing entropy, etc.). With this in mind, the paper seeks to obtain tights bounds for classes C of Boolean functions (f: {0,1}^n \to {0,1}) such as (i) linear threshold functions (LTF, halfspaces) (ii) polynomial threshold functions (PTF), (iii) restrictions thereof (small weights, positive weights), and (iv) networks obtained by combining them.  Now, this is indeed interesting. My main point is that these questions are quite hard, and as far as I can tell the results for (iii), and (iv) are quite weak or straightforward. The main contribution of the paper is Theorem 3.1, which provides and actual asymptotically tight bound for (ii), the class of degree-d PTFs  (concluding a decades-long line of work, the previous bounds still being off by a factor d). More specifically:  - Theorem 1 is a preliminary, and should be either a fact or a lemma - Theorem 3.1 is the main technical result, non-trivial, and (in my opinion) should be highlighted in the introduction as the key result - Theorem 4.1 is quite straightforward and easy; and only deals with (binary-weights) LTFs, not PTFs - Theorem 4.2 is not tight; and only deals with (positive-weights) LTFs, not PTFs - I am not able to really quantify the relevance of Theorems 5.1 and 5.2.  In summary, again: I feel the presentation distracts from the main result (Theorem 3.1), promising instead results about more general classes (degree-d PTFs, networks of PTFs and LTFs, more general types of functions...) which end up being unaddressed or for which the results are much less impressive (as in, they are not tight, or are simple corollaries). This is a bit disappointing, since Theorem 3.1 is very good by itself.  (Ironically, this is exactly what the supplemental material is: a math paper focusing entirely on, and dedicated to, Theorem 3.1)  ----------------------------------------------------------------------------- More detailed comments (technical, and typos) below.  - I am really not convinced Figure 1 is useful in any way to understand the paper. - l.41 Misleading. Phrased as it is, it sounds like this problem is still open, while (as you mention later) it has been settled since '89. - l.60 and everywhere: "i.e. " -> "i.e., " - eq (10) (l.89): "leq" -> "\leq" - Section 3.1 (e.g., l.99): you should also discuss Chow's theorem, which gives such an interpretation and upper bound, as it states that any LTF is uniquely determined by its n+1 first Fourier coefficients. Now, since for a Boolean function each Fourier coefficient as granularity 1/2^{n-1}... (see e.g. discussion after Theorem 2.2 in https://arxiv.org/abs/1511.07860) - l.104: similar: you should discuss the degree-d PTF extension of Chow's theorem, due to Bruck [1990]: see e.g. discussion at the end of https://arxiv.org/abs/1206.0985 - Section 4.1: for the sake of the literature review, mention that binary-weight LTFs are commonly referred to as "signed majorities". Also, this section is quite misleading. You start by discussion binary-weight PTFs (degree d, for general d), but prove essentially nothing (except a possibly very loose easy upper bound) for d>1; only considering d=1 (Theorem 4.1). Moreover, a really relevant and important case (as you mention!) is the case of bounded weights, say when the weights are constrained to be poly(n) (see e.g. https://arxiv.org/abs/1508.03061 (STOC'17) and references within). Can you say anything for that case? - l.147: trailing period. - Theorem 4.2: where was T[N,1] introduced? I couldn't find that notation -- it looks like you are using it in lieu of C_1[N,1]?  Moreover, I don't buy the "As a result" statement (and the "reduced by a linear factor" which follows). You have N^2(1+o(1)), so that the second order term you neglect is o(N^2). Since N is also o(N^2), you *cannot* write and discuss this -N term! It could be completely cancelled or drowned by the o(N^2) (which, as far as the statement of the theorem promises, cound be anything, say N^{1.999999}) - ll.178-179: why is it a reasonable thing to do? Detail. - Eq. (17) "\approx =" -> "\approx" Also, more generally (Eq. 16, Eq. 19, Theorem 5...), you never have defined what \approx means for you. Is it informal? Is it notation for the Landau asymptotic \sim? You need to explain, as otherwise one cannot make sense of it. For instance, in (17) using the Landau asymptotic equivalence (which only "guarantees" anything meaningful for the first dominating term) would be strange, as you give 3 terms. Do you mean instead "= N^2 +N + o(N)"? "= N^2 +N + O(1)"? "= N^2 +N - 1 +o(1)" (which given the fact that sizes are integers, would lead to an actual exact equality...)?  In short: you are abusing asymptotic notation here, giving too many terms (leading to a mistaken feeling of precision) l.210: typo ("threshold") l.236: typo ("layered") l.272: typo (ref [17]): "O'Donnell"  Again, the section title and introduction promise results about degree-d PTFs, but the only actual result is for d=1.