Towards a Neural Statistician
An efficient learner is one who reuses what they already know to tackle a new problem. For a machine learner, this means understanding the similarities amongst datasets. In order to do this, one must take seriously the idea of working with datasets, rather than datapoints, as the key objects to model. Towards this goal, we demonstrate an extension of a variational autoencoder that can learn a method for computing representations, or statistics, of datasets in an unsupervised fashion. The network is trained to produce statistics that encapsulate a generative model for each dataset. Hence the network enables efficient learning from new datasets for both unsupervised and supervised tasks. We show that we are able to learn statistics that can be used for: clustering datasets, transferring generative models to new datasets, selecting representative samples of datasets and classifying previously unseen classes. We refer to our model as a neural statistician, and by this we mean a neural network that can learn to compute summary statistics of datasets without supervision.
The authors introduce a variant of the variational autoencoder (VAE) that models dataset-level latent variables. The idea is clearly motivated and well described. In my mind the greatest contribution of this paper is the movement beyond the relatively simple graphical model structure of the traditional VAEs and the introduction of more interesting structures to the deep learning community. Comments: - It*s not clear to me why this should be called a *statistician*. Learning an approximate posterior over summary statistics is not the only imaginable way to summarize a dataset with a neural network. One could consider a maximum likelihood approach, etc. In general it felt like the paper could be more clear, if it avoided coining new terms like *statistic network* and stuck to the more accurate *approximate posterior*. - The experiments are nice, and I appreciate the response to my question regarding *one shot generation*. I still think that language needs to be clarified, specifically at the end of page 6. My understanding of Figure 5 is the following: Take an input set, compute the approximate posterior over the context vector, then generate from the forward model given samples from the approximate posterior. I would like clarification on the following: (a) Are the data point dependent vectors z generated from the forward model or taken from the approximate posterior? (b) I agree that the samples are of high-quality, but that is not a quantified statement. The advantage of VAEs over GANs is that we have natural ways of computing log-probabilities. To that end, one *proper* way of computing the *one shot generation* performance is to report log p(x | c) (where c is sampled from the approximate posterior) or log p(x) for held-out datasets. I suspect that log probability performance of these networks relative to a vanilla VAE without the context latent variable will be impressive. I still don*t see a reason not to include that.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Towards a Neural Statistician
An efficient learner is one who reuses what they already know to tackle a new problem. For a machine learner, this means understanding the similarities amongst datasets. In order to do this, one must take seriously the idea of working with datasets, rather than datapoints, as the key objects to model. Towards this goal, we demonstrate an extension of a variational autoencoder that can learn a method for computing representations, or statistics, of datasets in an unsupervised fashion. The network is trained to produce statistics that encapsulate a generative model for each dataset. Hence the network enables efficient learning from new datasets for both unsupervised and supervised tasks. We show that we are able to learn statistics that can be used for: clustering datasets, transferring generative models to new datasets, selecting representative samples of datasets and classifying previously unseen classes. We refer to our model as a neural statistician, and by this we mean a neural network that can learn to compute summary statistics of datasets without supervision.
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 introduce a variant of the variational autoencoder (VAE) that models dataset-level latent variables. The idea is clearly motivated and well described. In my mind the greatest contribution of this paper is the movement beyond the relatively simple graphical model structure of the traditional VAEs and the introduction of more interesting structures to the deep learning community. Comments: - It*s not clear to me why this should be called a *statistician*. Learning an approximate posterior over summary statistics is not the only imaginable way to summarize a dataset with a neural network. One could consider a maximum likelihood approach, etc. In general it felt like the paper could be more clear, if it avoided coining new terms like *statistic network* and stuck to the more accurate *approximate posterior*. - The experiments are nice, and I appreciate the response to my question regarding *one shot generation*. I still think that language needs to be clarified, specifically at the end of page 6. My understanding of Figure 5 is the following: Take an input set, compute the approximate posterior over the context vector, then generate from the forward model given samples from the approximate posterior. I would like clarification on the following: (a) Are the data point dependent vectors z generated from the forward model or taken from the approximate posterior? (b) I agree that the samples are of high-quality, but that is not a quantified statement. The advantage of VAEs over GANs is that we have natural ways of computing log-probabilities. To that end, one *proper* way of computing the *one shot generation* performance is to report log p(x | c) (where c is sampled from the approximate posterior) or log p(x) for held-out datasets. I suspect that log probability performance of these networks relative to a vanilla VAE without the context latent variable will be impressive. I still don*t see a reason not to include that.
assistant
The authors introduce a variant of the variational autoencoder (VAE) that models dataset-level latent variables. The idea is clearly motivated and well described. In my mind the greatest contribution of this paper is the movement beyond the relatively simple graphical model structure of the traditional VAEs and the introduction of more interesting structures to the deep learning community. Comments: - It*s not clear to me why this should be called a *statistician*. Learning an approximate posterior over summary statistics is not the only imaginable way to summarize a dataset with a neural network. One could consider a maximum likelihood approach, etc. In general it felt like the paper could be more clear, if it avoided coining new terms like *statistic network* and stuck to the more accurate *approximate posterior*. - The experiments are nice, and I appreciate the response to my question regarding *one shot generation*. I still think that language needs to be clarified, specifically at the end of page 6. My understanding of Figure 5 is the following: Take an input set, compute the approximate posterior over the context vector, then generate from the forward model given samples from the approximate posterior. I would like clarification on the following: (a) Are the data point dependent vectors z generated from the forward model or taken from the approximate posterior? (b) I agree that the samples are of high-quality, but that is not a quantified statement. The advantage of VAEs over GANs is that we have natural ways of computing log-probabilities. To that end, one *proper* way of computing the *one shot generation* performance is to report log p(x | c) (where c is sampled from the approximate posterior) or log p(x) for held-out datasets. I suspect that log probability performance of these networks relative to a vanilla VAE without the context latent variable will be impressive. I still don*t see a reason not to include that.