Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors
Obtaining reliable uncertainty estimates of neural network predictions is a long standing challenge. Bayesian neural networks have been proposed as a solution, but it remains open how to specify their prior. In particular, the common practice of a standard normal prior in weight space imposes only weak regularities, causing the function posterior to possibly generalize in unforeseen ways on inputs outside of the training distribution. We propose noise contrastive priors (NCPs) to obtain reliable uncertainty estimates. The key idea is to train the model to output high uncertainty for data points outside of the training distribution. NCPs do so using an input prior, which adds noise to the inputs of the current mini batch, and an output prior, which is a wide distribution given these inputs. NCPs are compatible with any model that can output uncertainty estimates, are easy to scale, and yield reliable uncertainty estimates throughout training. Empirically, we show that NCPs prevent overfitting outside of the training distribution and result in uncertainty estimates that are useful for active learning. We demonstrate the scalability of our method on the flight delays data set, where we significantly improve upon previously published results.
 This paper presents an approach to obtain uncertainty estimates for neural network predictions that has good performance when quantifying predictive uncertainty at points that are outside of the training distribution. The authors show how this is particularly useful in an active learning setting where new data points can be selected based on metrics that rely on accurate uncertainty estimates. Interestingly, the method works by perturbing all data inputs instead of only the ones at the boundary of the training distribution. Also, there is no need to sample outside of the input distribution in order to have accurate uncertainty estimates in that area. The paper is clear and very well written with a good balance between the use of formulas and insights in the text. The experimental section starts with a toy 1d active learning task that shows the advantage of good uncertainty estimates when selecting new data points. The authors also present a larger regression task (8 input dimensions and 700k data points in the training set) in which they obtain good performance compared to other models able to quantify epistemic uncertainty. In my opinion, the experiments do a good job at showing the capabilities of the algorithm. If anything, since the authors use the word "deep" in the title of the paper I would have expected some experiments on deep networks and a very large dataset.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors
Obtaining reliable uncertainty estimates of neural network predictions is a long standing challenge. Bayesian neural networks have been proposed as a solution, but it remains open how to specify their prior. In particular, the common practice of a standard normal prior in weight space imposes only weak regularities, causing the function posterior to possibly generalize in unforeseen ways on inputs outside of the training distribution. We propose noise contrastive priors (NCPs) to obtain reliable uncertainty estimates. The key idea is to train the model to output high uncertainty for data points outside of the training distribution. NCPs do so using an input prior, which adds noise to the inputs of the current mini batch, and an output prior, which is a wide distribution given these inputs. NCPs are compatible with any model that can output uncertainty estimates, are easy to scale, and yield reliable uncertainty estimates throughout training. Empirically, we show that NCPs prevent overfitting outside of the training distribution and result in uncertainty estimates that are useful for active learning. We demonstrate the scalability of our method on the flight delays data set, where we significantly improve upon previously published results.
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 presents an approach to obtain uncertainty estimates for neural network predictions that has good performance when quantifying predictive uncertainty at points that are outside of the training distribution. The authors show how this is particularly useful in an active learning setting where new data points can be selected based on metrics that rely on accurate uncertainty estimates. Interestingly, the method works by perturbing all data inputs instead of only the ones at the boundary of the training distribution. Also, there is no need to sample outside of the input distribution in order to have accurate uncertainty estimates in that area. The paper is clear and very well written with a good balance between the use of formulas and insights in the text. The experimental section starts with a toy 1d active learning task that shows the advantage of good uncertainty estimates when selecting new data points. The authors also present a larger regression task (8 input dimensions and 700k data points in the training set) in which they obtain good performance compared to other models able to quantify epistemic uncertainty. In my opinion, the experiments do a good job at showing the capabilities of the algorithm. If anything, since the authors use the word "deep" in the title of the paper I would have expected some experiments on deep networks and a very large dataset.
assistant
 This paper presents an approach to obtain uncertainty estimates for neural network predictions that has good performance when quantifying predictive uncertainty at points that are outside of the training distribution. The authors show how this is particularly useful in an active learning setting where new data points can be selected based on metrics that rely on accurate uncertainty estimates. Interestingly, the method works by perturbing all data inputs instead of only the ones at the boundary of the training distribution. Also, there is no need to sample outside of the input distribution in order to have accurate uncertainty estimates in that area. The paper is clear and very well written with a good balance between the use of formulas and insights in the text. The experimental section starts with a toy 1d active learning task that shows the advantage of good uncertainty estimates when selecting new data points. The authors also present a larger regression task (8 input dimensions and 700k data points in the training set) in which they obtain good performance compared to other models able to quantify epistemic uncertainty. In my opinion, the experiments do a good job at showing the capabilities of the algorithm. If anything, since the authors use the word "deep" in the title of the paper I would have expected some experiments on deep networks and a very large dataset.