An Empirical Analysis of Deep Network Loss Surfaces
The training of deep neural networks is a high-dimension optimization problem with respect to the loss function of a model. Unfortunately, these functions are of high dimension and non-convex and hence difficult to characterize. In this paper, we empirically investigate the geometry of the loss functions for state-of-the-art networks with multiple stochastic optimization methods. We do this through several experiments that are visualized on polygons to understand how and when these stochastic optimization methods find minima.
This paper provides an extensive analysis of the error loss function for different optimization methods. The presentation is well done and informative. The experimental procedure is clarified sufficiently well. Theoretical evaluations like this are crucial for a wide range of applications and help to better understand and improve the convergence behavior for a given system. Pros: - Important analysis - Good visualizations Cons: - The paper describes mostly the observation that the optima vary for different methods, however doesn*t attempt to explain why it happens and how to solve it (aside from batch-norm) - Some fonts are very small (e.g. Fig. 5)
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
An Empirical Analysis of Deep Network Loss Surfaces
The training of deep neural networks is a high-dimension optimization problem with respect to the loss function of a model. Unfortunately, these functions are of high dimension and non-convex and hence difficult to characterize. In this paper, we empirically investigate the geometry of the loss functions for state-of-the-art networks with multiple stochastic optimization methods. We do this through several experiments that are visualized on polygons to understand how and when these stochastic optimization methods find minima.
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>



This paper provides an extensive analysis of the error loss function for different optimization methods. The presentation is well done and informative. The experimental procedure is clarified sufficiently well. Theoretical evaluations like this are crucial for a wide range of applications and help to better understand and improve the convergence behavior for a given system. Pros: - Important analysis - Good visualizations Cons: - The paper describes mostly the observation that the optima vary for different methods, however doesn*t attempt to explain why it happens and how to solve it (aside from batch-norm) - Some fonts are very small (e.g. Fig. 5)
assistant
This paper provides an extensive analysis of the error loss function for different optimization methods. The presentation is well done and informative. The experimental procedure is clarified sufficiently well. Theoretical evaluations like this are crucial for a wide range of applications and help to better understand and improve the convergence behavior for a given system. Pros: - Important analysis - Good visualizations Cons: - The paper describes mostly the observation that the optima vary for different methods, however doesn*t attempt to explain why it happens and how to solve it (aside from batch-norm) - Some fonts are very small (e.g. Fig. 5)