Predictive Uncertainty through Quantization
High-risk domains require reliable confidence estimates from predictive models. Deep latent variable models provide these, but suffer from the rigid variational distributions used for tractable inference, which err on the side of overconfidence. We propose Stochastic Quantized Activation Distributions (SQUAD), which imposes a flexible yet tractable distribution over discretized latent variables. The proposed method is scalable, self-normalizing and sample efficient. We demonstrate that the model fully utilizes the flexible distribution, learns interesting non-linearities, and provides predictive uncertainty of competitive quality.
 The authors consider uncertainty estimation in deep latent variable models. They propose to use quantised latent variable and argue that this solves the overconfidence problem, commonly encountered in variational inference. The proposed approach relies on optimizing an information bottleneck objective instead of the ELBO. While the approach is of interest, a number of questions, central to the work, remain. For example, it is not clear how parameter .beta is chosen/optimised, how the number of bins C is chosen and how the annealing scheme is tuned. The authors do not discuss the quantisation parameters, such as bin size and location, which are likely to have a major effect on the performance (and the complexity). Then the authors propose to use a hierachical set of latent variables without properly justifying the need, nor discuss how to select the depth and its impact on the performance. Finally the authors propose yet another extension based on a matrix-factorization with little justification. Overall, this paper does not fully develop the ideas proposed in the paper or discuss them in sufficient detail. The experiments do not provide additional intuition on what*s going on and why this helps and are insufficiently documented/made accessible to be convincing. For example, I am not sure what to conclude from experiments that rely on no (or "light") hyperparameter tuning, when the proposed method has many and not discussion is provided about how to set them or how sensitive results are to their actual value. More importantly, the initial claim that uncertainty is better captured relies on SGR, a metric which is not standard and mentioned in passing without being properly defined. The evaluation further depends on a "selective classifier" which is not detailed, but critical to understanding the experiments. Finally, the presentation of Section 3 could be significantly improved. For example, I would suggest distinguishing the neural network parameters of the encoder and the decoder as well as the encoder and decoder networks. I would also refrain using notations like "..." or and always specify what is left and right of an equality. Please spell out all abbreviations at least once in the paper and define all important quantities and concepts.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Predictive Uncertainty through Quantization
High-risk domains require reliable confidence estimates from predictive models. Deep latent variable models provide these, but suffer from the rigid variational distributions used for tractable inference, which err on the side of overconfidence. We propose Stochastic Quantized Activation Distributions (SQUAD), which imposes a flexible yet tractable distribution over discretized latent variables. The proposed method is scalable, self-normalizing and sample efficient. We demonstrate that the model fully utilizes the flexible distribution, learns interesting non-linearities, and provides predictive uncertainty of competitive quality.
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 authors consider uncertainty estimation in deep latent variable models. They propose to use quantised latent variable and argue that this solves the overconfidence problem, commonly encountered in variational inference. The proposed approach relies on optimizing an information bottleneck objective instead of the ELBO. While the approach is of interest, a number of questions, central to the work, remain. For example, it is not clear how parameter .beta is chosen/optimised, how the number of bins C is chosen and how the annealing scheme is tuned. The authors do not discuss the quantisation parameters, such as bin size and location, which are likely to have a major effect on the performance (and the complexity). Then the authors propose to use a hierachical set of latent variables without properly justifying the need, nor discuss how to select the depth and its impact on the performance. Finally the authors propose yet another extension based on a matrix-factorization with little justification. Overall, this paper does not fully develop the ideas proposed in the paper or discuss them in sufficient detail. The experiments do not provide additional intuition on what*s going on and why this helps and are insufficiently documented/made accessible to be convincing. For example, I am not sure what to conclude from experiments that rely on no (or "light") hyperparameter tuning, when the proposed method has many and not discussion is provided about how to set them or how sensitive results are to their actual value. More importantly, the initial claim that uncertainty is better captured relies on SGR, a metric which is not standard and mentioned in passing without being properly defined. The evaluation further depends on a "selective classifier" which is not detailed, but critical to understanding the experiments. Finally, the presentation of Section 3 could be significantly improved. For example, I would suggest distinguishing the neural network parameters of the encoder and the decoder as well as the encoder and decoder networks. I would also refrain using notations like "..." or and always specify what is left and right of an equality. Please spell out all abbreviations at least once in the paper and define all important quantities and concepts.
assistant
 The authors consider uncertainty estimation in deep latent variable models. They propose to use quantised latent variable and argue that this solves the overconfidence problem, commonly encountered in variational inference. The proposed approach relies on optimizing an information bottleneck objective instead of the ELBO. While the approach is of interest, a number of questions, central to the work, remain. For example, it is not clear how parameter .beta is chosen/optimised, how the number of bins C is chosen and how the annealing scheme is tuned. The authors do not discuss the quantisation parameters, such as bin size and location, which are likely to have a major effect on the performance (and the complexity). Then the authors propose to use a hierachical set of latent variables without properly justifying the need, nor discuss how to select the depth and its impact on the performance. Finally the authors propose yet another extension based on a matrix-factorization with little justification. Overall, this paper does not fully develop the ideas proposed in the paper or discuss them in sufficient detail. The experiments do not provide additional intuition on what*s going on and why this helps and are insufficiently documented/made accessible to be convincing. For example, I am not sure what to conclude from experiments that rely on no (or "light") hyperparameter tuning, when the proposed method has many and not discussion is provided about how to set them or how sensitive results are to their actual value. More importantly, the initial claim that uncertainty is better captured relies on SGR, a metric which is not standard and mentioned in passing without being properly defined. The evaluation further depends on a "selective classifier" which is not detailed, but critical to understanding the experiments. Finally, the presentation of Section 3 could be significantly improved. For example, I would suggest distinguishing the neural network parameters of the encoder and the decoder as well as the encoder and decoder networks. I would also refrain using notations like "..." or and always specify what is left and right of an equality. Please spell out all abbreviations at least once in the paper and define all important quantities and concepts.