A Boo(n) for Evaluating Architecture Performance
We point out important problems with the common practice of using the best single model performance for comparing deep learning architectures, and we propose a method that corrects these flaws. Each time a model is trained, one gets a different result due to random factors in the training process, which include random parameter initialization and random data shuffling. Reporting the best single model performance does not appropriately address this stochasticity. We propose a normalized expected best-out-of-n performance (Boo_n) as a way to correct these problems.
 This manuscript raises an important issue regarding the current lack of standardization regarding methods for evaluating and reporting algorithm performance in deep learning research. While I believe that raising this issue is important and that the method proposed is a step in the right direction, I have a number of concerns which I will list below. One risk is that if the proposed solution is not adequate or widely agreeable then we may find a proliferation of solutions from which different groups might pick and choose as it suits their results! The method of choosing the best model under *internal* cross-validation to take through to *external* cross-validation against a second hold-out set should be regarded as one possible stochastic solution to the optimisation problem of hyper-parameter selection. The authors are right to emphasize that this should be considered part of the cost of the technique, but I would not suggest that one specify a *benchmark* number of trials (n=5) for comparison. Rather I would suggest that this is a decision that needs to be explored and understood by the researchers presenting the method in order to understand the cost/benefit ratio for their algorithm provided by attempting to refine their guess of the optimal hyperparameters. This would then allow for other methods not based on internal cross-validation to be compared on a level footing. I think that the fundamental issue of stochasticity of concern for repeatability and generalisability of these performance evaluation exercises is not in the stochastic optimisation search but in the use of a single hold-out sample. Would it not be wise to insist on a mean performance (a mean Boo_n or other) over multiple random partitions of the entire dataset into training and hold-out? I wonder if in theory both the effect of increasing n and the mean hold-out performance could be learnt efficiently with a clever experimental design. Finally, I am concerned with the issue of how to compute the suggested Boo_n score. Use of a parameteric Gaussian approximation is a strong assumption, while bootstrap methods for order statistics can be rather noisy. It would be interesting to see a comparison of the results from the parametric and non-parameteric Boo_n versions applied to the test problems.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
A Boo(n) for Evaluating Architecture Performance
We point out important problems with the common practice of using the best single model performance for comparing deep learning architectures, and we propose a method that corrects these flaws. Each time a model is trained, one gets a different result due to random factors in the training process, which include random parameter initialization and random data shuffling. Reporting the best single model performance does not appropriately address this stochasticity. We propose a normalized expected best-out-of-n performance (Boo_n) as a way to correct these problems.
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 manuscript raises an important issue regarding the current lack of standardization regarding methods for evaluating and reporting algorithm performance in deep learning research. While I believe that raising this issue is important and that the method proposed is a step in the right direction, I have a number of concerns which I will list below. One risk is that if the proposed solution is not adequate or widely agreeable then we may find a proliferation of solutions from which different groups might pick and choose as it suits their results! The method of choosing the best model under *internal* cross-validation to take through to *external* cross-validation against a second hold-out set should be regarded as one possible stochastic solution to the optimisation problem of hyper-parameter selection. The authors are right to emphasize that this should be considered part of the cost of the technique, but I would not suggest that one specify a *benchmark* number of trials (n=5) for comparison. Rather I would suggest that this is a decision that needs to be explored and understood by the researchers presenting the method in order to understand the cost/benefit ratio for their algorithm provided by attempting to refine their guess of the optimal hyperparameters. This would then allow for other methods not based on internal cross-validation to be compared on a level footing. I think that the fundamental issue of stochasticity of concern for repeatability and generalisability of these performance evaluation exercises is not in the stochastic optimisation search but in the use of a single hold-out sample. Would it not be wise to insist on a mean performance (a mean Boo_n or other) over multiple random partitions of the entire dataset into training and hold-out? I wonder if in theory both the effect of increasing n and the mean hold-out performance could be learnt efficiently with a clever experimental design. Finally, I am concerned with the issue of how to compute the suggested Boo_n score. Use of a parameteric Gaussian approximation is a strong assumption, while bootstrap methods for order statistics can be rather noisy. It would be interesting to see a comparison of the results from the parametric and non-parameteric Boo_n versions applied to the test problems.
assistant
 This manuscript raises an important issue regarding the current lack of standardization regarding methods for evaluating and reporting algorithm performance in deep learning research. While I believe that raising this issue is important and that the method proposed is a step in the right direction, I have a number of concerns which I will list below. One risk is that if the proposed solution is not adequate or widely agreeable then we may find a proliferation of solutions from which different groups might pick and choose as it suits their results! The method of choosing the best model under *internal* cross-validation to take through to *external* cross-validation against a second hold-out set should be regarded as one possible stochastic solution to the optimisation problem of hyper-parameter selection. The authors are right to emphasize that this should be considered part of the cost of the technique, but I would not suggest that one specify a *benchmark* number of trials (n=5) for comparison. Rather I would suggest that this is a decision that needs to be explored and understood by the researchers presenting the method in order to understand the cost/benefit ratio for their algorithm provided by attempting to refine their guess of the optimal hyperparameters. This would then allow for other methods not based on internal cross-validation to be compared on a level footing. I think that the fundamental issue of stochasticity of concern for repeatability and generalisability of these performance evaluation exercises is not in the stochastic optimisation search but in the use of a single hold-out sample. Would it not be wise to insist on a mean performance (a mean Boo_n or other) over multiple random partitions of the entire dataset into training and hold-out? I wonder if in theory both the effect of increasing n and the mean hold-out performance could be learnt efficiently with a clever experimental design. Finally, I am concerned with the issue of how to compute the suggested Boo_n score. Use of a parameteric Gaussian approximation is a strong assumption, while bootstrap methods for order statistics can be rather noisy. It would be interesting to see a comparison of the results from the parametric and non-parameteric Boo_n versions applied to the test problems.