Depth creates no more spurious local minima in linear networks
We show that for any convex differentiable loss, a deep linear network has no spurious local minima as long as it is true for the two layer case. This reduction greatly simplifies the study on the existence of spurious local minima in deep linear networks. When applied to the quadratic loss, our result immediately implies the powerful result by Kawaguchi (2016). Further, with the recent work by Zhou& Liang (2018), we can remove all the assumptions in (Kawaguchi, 2016). This property holds for more general “multi-tower” linear networks too. Our proof builds on the work in (Laurent & von Brecht, 2018) and develops a new perturbation argument to show that any spurious local minimum must have full rank, a structural property which can be useful more generally
The paper shows an interesting result: deep linear NN has introduced no more spurious local minima than two layer NN and provides an intuitive and short proof for the results, which improve and generalize the previous results under milder assumptions. Overall, the paper is well written and clear in comparison and explanation. The weakness is that the main theoretical contribution seems to be merely Lemma 1, and all other theorems are a direct corollary. Also, it would be of great interest to see concrete results on non-linear neural networks, since that is exactly what is used in common practice.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Depth creates no more spurious local minima in linear networks
We show that for any convex differentiable loss, a deep linear network has no spurious local minima as long as it is true for the two layer case. This reduction greatly simplifies the study on the existence of spurious local minima in deep linear networks. When applied to the quadratic loss, our result immediately implies the powerful result by Kawaguchi (2016). Further, with the recent work by Zhou& Liang (2018), we can remove all the assumptions in (Kawaguchi, 2016). This property holds for more general “multi-tower” linear networks too. Our proof builds on the work in (Laurent & von Brecht, 2018) and develops a new perturbation argument to show that any spurious local minimum must have full rank, a structural property which can be useful more generally
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The paper shows an interesting result: deep linear NN has introduced no more spurious local minima than two layer NN and provides an intuitive and short proof for the results, which improve and generalize the previous results under milder assumptions. Overall, the paper is well written and clear in comparison and explanation. The weakness is that the main theoretical contribution seems to be merely Lemma 1, and all other theorems are a direct corollary. Also, it would be of great interest to see concrete results on non-linear neural networks, since that is exactly what is used in common practice.
assistant
The paper shows an interesting result: deep linear NN has introduced no more spurious local minima than two layer NN and provides an intuitive and short proof for the results, which improve and generalize the previous results under milder assumptions. Overall, the paper is well written and clear in comparison and explanation. The weakness is that the main theoretical contribution seems to be merely Lemma 1, and all other theorems are a direct corollary. Also, it would be of great interest to see concrete results on non-linear neural networks, since that is exactly what is used in common practice.