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.
 The paper considers the problem of uncertainty estimation of neural networks and proposes to use Bayesian approach with noice contrastive prior. The paper is nicely written, but there are several issues which require discussion: 1. The authors propose to use so-called noise contrastive prior, but the actual implementation boils down to adding Gaussian noise to input points and respective outputs. This seems to be the simplest possible prior in data space (well known for example in Bayesian linear regression). That would be nice if authors can comment on the differences of proposed NCP with standard homoscedastic priors in regression. 2. The paper title mentions *RELIABLE UNCERTAINTY ESTIMATES*, but in fact the paper doesn*t discuss the realibility of obtained uncertainty estimates directly. Experiments only consider active learning, which allows to assess the quality of UE only indirectly. To verify the title one needs to directly compare uncertainty estimates with errors of prediction on preferably vast selection of datasets. 3. The paper performs experiments basically on two datasets, which is not enough to obtain any reliable conclusions about the performance of the method. I recommend to consider much wider experimental evaluation, which is especially importan for active learning, which requires very accurate experimental evaluation 4. It is not clear how to choose hyperparameters (noise variances) in practice. The paper performs some sensitivity analysis with resepct to variance selection, but the study is again on one dataset. Finally, I think that the paper targets important direction of uncertainty estimation for neural networks, but currently it is not mature in terms of results obtained.
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>



 The paper considers the problem of uncertainty estimation of neural networks and proposes to use Bayesian approach with noice contrastive prior. The paper is nicely written, but there are several issues which require discussion: 1. The authors propose to use so-called noise contrastive prior, but the actual implementation boils down to adding Gaussian noise to input points and respective outputs. This seems to be the simplest possible prior in data space (well known for example in Bayesian linear regression). That would be nice if authors can comment on the differences of proposed NCP with standard homoscedastic priors in regression. 2. The paper title mentions *RELIABLE UNCERTAINTY ESTIMATES*, but in fact the paper doesn*t discuss the realibility of obtained uncertainty estimates directly. Experiments only consider active learning, which allows to assess the quality of UE only indirectly. To verify the title one needs to directly compare uncertainty estimates with errors of prediction on preferably vast selection of datasets. 3. The paper performs experiments basically on two datasets, which is not enough to obtain any reliable conclusions about the performance of the method. I recommend to consider much wider experimental evaluation, which is especially importan for active learning, which requires very accurate experimental evaluation 4. It is not clear how to choose hyperparameters (noise variances) in practice. The paper performs some sensitivity analysis with resepct to variance selection, but the study is again on one dataset. Finally, I think that the paper targets important direction of uncertainty estimation for neural networks, but currently it is not mature in terms of results obtained.
assistant
 The paper considers the problem of uncertainty estimation of neural networks and proposes to use Bayesian approach with noice contrastive prior. The paper is nicely written, but there are several issues which require discussion: 1. The authors propose to use so-called noise contrastive prior, but the actual implementation boils down to adding Gaussian noise to input points and respective outputs. This seems to be the simplest possible prior in data space (well known for example in Bayesian linear regression). That would be nice if authors can comment on the differences of proposed NCP with standard homoscedastic priors in regression. 2. The paper title mentions *RELIABLE UNCERTAINTY ESTIMATES*, but in fact the paper doesn*t discuss the realibility of obtained uncertainty estimates directly. Experiments only consider active learning, which allows to assess the quality of UE only indirectly. To verify the title one needs to directly compare uncertainty estimates with errors of prediction on preferably vast selection of datasets. 3. The paper performs experiments basically on two datasets, which is not enough to obtain any reliable conclusions about the performance of the method. I recommend to consider much wider experimental evaluation, which is especially importan for active learning, which requires very accurate experimental evaluation 4. It is not clear how to choose hyperparameters (noise variances) in practice. The paper performs some sensitivity analysis with resepct to variance selection, but the study is again on one dataset. Finally, I think that the paper targets important direction of uncertainty estimation for neural networks, but currently it is not mature in terms of results obtained.