Paper:
We apply the canonical forms of gradient Morse complexes (barcodes) to explore topology of loss surfaces. We present a new algorithm for calculations of the objective function's barcodes of minima. Our experiments confirm two principal observations: (1) the barcodes of minima are located in a small lower part of the range of values of loss function of neural networks and (2) increase of the neural network's depth brings down the minima's barcodes. This has natural implications for the neural network learning and the ability to generalize.

Review:
The paper aims to study the topology of loss surfaces of neural networks using tools from algebraic topology. From what I understood, the idea is to effectively (1) take a grid over the parameters of a function (say a parameters of a neural net), (2) evaluate the function at those points, (3) compute sub-levelset persistent homology and (4) study the resulting barcode (for 0/1-dim features) (i.e., the mentioned "canonical form" invariants). Some experiments are presented on extremely simple toy data.
Overall, the paper is very hard to read, as different concepts and terminology appear all over the place without a precise definition (see comments below). Given the problems in the writing of the paper, my assessment is that this idea boils down to computing persistent homology of the sub-levelset filtration of the loss surface sampled at fixed parameter realizations. I do not think that this will be feasible to do, even for small-scale real-world neural networks, simply due to the difficulty of finding a suitable grid, let alone the vast number of function evaluations involved.
The paper is also unclear in many parts. A selection is listed below:
(1) What do you mean by gradient flow? One can define a gradient flow in a linear space X and for a function F: X->R, e.g., as  a smooth curve R->X, such that x'(t) = -\nabla F(x(t)); is that what is meant? 
(2) What do you mean by "TDA package"? There are many TDA packages these days (maybe the CRAN TDA package?)
(3) "It was tested in dimensions up to 16 ..." What is meant by dimension here? The dimensionality of the parameter space?
(4) The author's talk about the "minima's barcode" - I have no idea what is meant by that either; the barcode is the result of sub-levelset persistent homology of a function -> it's not associated to a minima.
(5) Is Theorem 2.3. not just a restatement of a theorem from Barannikov '94? At least the proof in the appendix seems to be .
(6) Right before Theorem 2.3., what does the notation F_sC_* mean? This needs to be introduced somewhere.
From my perspective, the whole story revolves around how to compute persistence barcodes from the sub-levelset filtration of the loss surface, obtained from function values taken on a grid over the parameters. The paper devotes quite some time to the introduction of these concepts, but not in a very clear or understandable manner. The experiments are effectively done on toy data, which is fine, but the paper stops at that point. I do not buy the argument that "it is possible to apply it [the method] to large-scale modern neural networks". Without a clear strategy to extend this, or at least some preliminary "larger"-scale results, the paper does not meet the ICLR threshold. The more theoretical part is too convoluted and, from my perspective, just a restatement of earlier results.