Confidence Calibration in Deep Neural Networks through Stochastic Inferences
We propose a generic framework to calibrate accuracy and confidence (score) of a prediction through stochastic inferences in deep neural networks. We first analyze relation between variation of multiple model parameters for a single example inference and variance of the corresponding prediction scores by Bayesian modeling of stochastic regularization. Our empirical observation shows that accuracy and score of a prediction are highly correlated with variance of multiple stochastic inferences given by stochastic depth or dropout. Motivated by these facts, we design a novel variance-weighted confidence-integrated loss function that is composed of two cross-entropy loss terms with respect to ground-truth and uniform distribution, which are balanced by variance of stochastic prediction scores. The proposed loss function enables us to learn deep neural networks that predict confidence calibrated scores using a single inference. Our algorithm presents outstanding confidence calibration performance and improves classification accuracy with two popular stochastic regularization techniques---stochastic depth and dropout---in multiple models and datasets; it alleviates overconfidence issue in deep neural networks significantly by training networks to achieve prediction accuracy proportional to confidence of prediction.
 I was pleased to be assigned this paper as it sounded like a worthy aim and an interesting topic. However, having attempted to read it several times, I find it hard to take anything from the paper. My first criticism is that the English in the paper is poor, which makes it hard to follow. Take the following sentence from the abstract: "We first analyze relation between variation of multiple model parameters for a single example inference and variance of the corresponding prediction scores by Bayesian modeling of stochastic regularization." Despite reading this multiple times I can*t make sense of it. The paper is full of sentences such as this. I had expected the paper to make more of a link to the scoring rule literature given that this is where ideas about how to score predictions, ensuring calibration of predictions etc, has been developed. The Brier score and loglikelihood are used in the results, but the link is not made explicit. The use of the word *calibration* in this paper is different to how it is used in the scoring rule literature, which is confusing (e.g. *NLL and Brier score are another metrics for calibration* - whereas usually we*d talk about proper scoring rules, which these both are, as assessing some combination of calibration and sharpness, where these terms are given precise meanings). The results, if I*ve understood them correctly, seem to be positive, but not stellar. A slight improvement is made on the baseline method, but the improvement is very minor.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Confidence Calibration in Deep Neural Networks through Stochastic Inferences
We propose a generic framework to calibrate accuracy and confidence (score) of a prediction through stochastic inferences in deep neural networks. We first analyze relation between variation of multiple model parameters for a single example inference and variance of the corresponding prediction scores by Bayesian modeling of stochastic regularization. Our empirical observation shows that accuracy and score of a prediction are highly correlated with variance of multiple stochastic inferences given by stochastic depth or dropout. Motivated by these facts, we design a novel variance-weighted confidence-integrated loss function that is composed of two cross-entropy loss terms with respect to ground-truth and uniform distribution, which are balanced by variance of stochastic prediction scores. The proposed loss function enables us to learn deep neural networks that predict confidence calibrated scores using a single inference. Our algorithm presents outstanding confidence calibration performance and improves classification accuracy with two popular stochastic regularization techniques---stochastic depth and dropout---in multiple models and datasets; it alleviates overconfidence issue in deep neural networks significantly by training networks to achieve prediction accuracy proportional to confidence of prediction.
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>



 I was pleased to be assigned this paper as it sounded like a worthy aim and an interesting topic. However, having attempted to read it several times, I find it hard to take anything from the paper. My first criticism is that the English in the paper is poor, which makes it hard to follow. Take the following sentence from the abstract: "We first analyze relation between variation of multiple model parameters for a single example inference and variance of the corresponding prediction scores by Bayesian modeling of stochastic regularization." Despite reading this multiple times I can*t make sense of it. The paper is full of sentences such as this. I had expected the paper to make more of a link to the scoring rule literature given that this is where ideas about how to score predictions, ensuring calibration of predictions etc, has been developed. The Brier score and loglikelihood are used in the results, but the link is not made explicit. The use of the word *calibration* in this paper is different to how it is used in the scoring rule literature, which is confusing (e.g. *NLL and Brier score are another metrics for calibration* - whereas usually we*d talk about proper scoring rules, which these both are, as assessing some combination of calibration and sharpness, where these terms are given precise meanings). The results, if I*ve understood them correctly, seem to be positive, but not stellar. A slight improvement is made on the baseline method, but the improvement is very minor.
assistant
 I was pleased to be assigned this paper as it sounded like a worthy aim and an interesting topic. However, having attempted to read it several times, I find it hard to take anything from the paper. My first criticism is that the English in the paper is poor, which makes it hard to follow. Take the following sentence from the abstract: "We first analyze relation between variation of multiple model parameters for a single example inference and variance of the corresponding prediction scores by Bayesian modeling of stochastic regularization." Despite reading this multiple times I can*t make sense of it. The paper is full of sentences such as this. I had expected the paper to make more of a link to the scoring rule literature given that this is where ideas about how to score predictions, ensuring calibration of predictions etc, has been developed. The Brier score and loglikelihood are used in the results, but the link is not made explicit. The use of the word *calibration* in this paper is different to how it is used in the scoring rule literature, which is confusing (e.g. *NLL and Brier score are another metrics for calibration* - whereas usually we*d talk about proper scoring rules, which these both are, as assessing some combination of calibration and sharpness, where these terms are given precise meanings). The results, if I*ve understood them correctly, seem to be positive, but not stellar. A slight improvement is made on the baseline method, but the improvement is very minor.