Paper:
Neural network training depends on the structure of the underlying loss landscape, i.e. local minima, saddle points, flat plateaus, and loss barriers. In relation to the structure of the landscape, we study the permutation symmetry of neurons in each layer of a deep neural network, which gives rise not only to multiple equivalent global minima of the loss function but also to critical points in between partner minima. In a network of d−1 hidden layers with n k neurons in layers k = 1, . . . , d, we construct continuous paths between equivalent global minima that lead through a 'permutation point' where the input and output weight vectors of two neurons in the same hidden layer k collide and interchange. We show that such permutation points are critical points which lie inside high-dimensional subspaces of equal loss, contributing to the global flatness of the landscape. We also find that a permutation point for the exchange of neurons i and j transits into a flat high-dimensional plateau that enables all n k ! permutations of neurons in a given layer k at the same loss value. Moreover, we introduce higher-order permutation points by exploiting the hierarchical structure in the loss landscapes of neural networks, and find that the number of K-th order permutation points is much larger than the (already huge) number of equivalent global minima -at least by a polynomial factor of order K. In two tasks, we demonstrate numerically with our path finding method that continuous paths between partner minima exist: first, in a toy network with a single hidden layer on a function approximation task and, second, in a multilayer network on the MNIST task. Our geometric approach yields a lower bound on the number of critical points generated by weight-space symmetries and provides a simple intuitive link between previous theoretical results and numerical observations.

Review:
This paper studies the permutation symmetry of deep neural networks. It was known that by reordering neurons and their connections in each layer, the input -> output map the neural network represents can be preserved. This corresponded to a set of unconnected equivalent points in the weight space. The authors study the weight-space connectivity of these points through midpoints they call “permutation points”. They demonstrate that such points are members of high-dimensional manifolds of equivalent points. After that, they look at empirical experiments and explicitly construct a path between two equivalent weight-space points on a toy task and MNIST.
I generally like the paper and its geometrical lens on the problem. I find the figures very helpful in understanding what is going on. However, there are a few points that I wasn’t entirely clear on. I will detail them below.
-- Point 1 --
Connecting equivalent minima vs connecting SGD-found minima. 
If I understood the paper correctly, the derivation connects two weight space points A and B whose weights and biases, once loaded to the neural network, would have the exact same answers on all inputs X i.e. f_A(X) == f_B(X), i.e. they are a pair of equivalent points. I understand that those are the ones we obtain by using the permutation symmetries.
However, some of the papers cited look at the low-loss paths between pairs of optima found by training with SGD from independent initializations, which in turn represent different functions. I.e. for two such optima C and D, the predictions on the val/test set are (sometimes) different, showing that the functions are not the same. I found the initial evidence in:
Large Scale Structure of Neural Network Loss Landscapes. Stanislav Fort and Stanislaw Jastrzebski. NeurIPS 2019. (https://arxiv.org/abs/1906.04724)
And also in much more detail in another OpenReview submission:
Deep Ensembles: A Loss Landscape Perspective. (https://openreview.net/forum?id=r1xZAkrFPr)
I found your results very compelling, however, the two problems seem to be quite different -- on one hand you are connecting a pair of minima that are in fact *identical* by construction. On the other hand the empirical work in literature (especially in the two papers I provided above) deals with pairs of minima that in fact do differ on the test set (at least).
Would you mind commenting on how the two approaches relate to each other? 
-- Point 2 --
Higher order connectivity
In Large Scale Structure of Neural Network Loss Landscapes. Stanislav Fort and Stanislaw Jastrzebski. NeurIPS 2019. (https://arxiv.org/abs/1906.04724), the authors look at higher-order connectivity between SGD-found optima (e.g. connecting 3 optima on a 2-manifold, 4 optima on a 3-manifold etc.). They also have a particularly simple path-finding algorithm. This seems relevant to the approach you are presenting, although the points in Point 1 still stand.
-- Point 3 --
Previous work on connecting two optima using layer-wise weight merging
Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets. Rohith Kuditipudi, Xiang Wang, Holden Lee, Yi Zhang, Zhiyuan Li, Wei Hu, Sanjeev Arora, Rong Ge. (https://arxiv.org/abs/1906.06247)
They prove that a low-loss path between 2 optima exists provided you can apply a p_keep = 0.5 dropout on each of the optima without incurring a significant loss punishment for it. This paper seems very related to your approach. Would you mind commenting on the differences?
-- Conclusion -- 
I like the paper and the idea in general. I appreciate the geometrical lens the authors took. My main point of confusion relates to the connection between this work and the low-loss connectivity between inequivalent optima found in literature, which (at least to me) seems to be the more interesting of the two connectivities.