Variational Autoencoder with Arbitrary Conditioning
We propose a single neural probabilistic model based on variational autoencoder that can be conditioned on an arbitrary subset of observed features and then sample the remaining features in "one shot". The features may be both real-valued and categorical. Training of the model is performed by stochastic variational Bayes. The experimental evaluation on synthetic data, as well as feature imputation and image inpainting problems, shows the effectiveness of the proposed approach and diversity of the generated samples.
 This paper introduces the VAEAC model, inspired by CVAEs, it allows conditioning on any subset of the latent features. This provides a model able to achieve good results on image inpainting and feature imputation tasks. The paper appears to be technically sound, and the experiments are thoughtfully designed. The writing is clear and the model is easy to understand. The closest work to this of the Universal Marginalizer is compared to well, with more compelling examples in the appendix. I would have preferred if more of the experimental results were in the main paper instead of in the appendix especially as the authors state they chose to highlight their better results in the main paper. While not the first model to try to handle modeling data with missing features, it is still a fairly original and elegant formulation. Minor details: In equation (8) should x be x_b?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Variational Autoencoder with Arbitrary Conditioning
We propose a single neural probabilistic model based on variational autoencoder that can be conditioned on an arbitrary subset of observed features and then sample the remaining features in "one shot". The features may be both real-valued and categorical. Training of the model is performed by stochastic variational Bayes. The experimental evaluation on synthetic data, as well as feature imputation and image inpainting problems, shows the effectiveness of the proposed approach and diversity of the generated samples.
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 introduces the VAEAC model, inspired by CVAEs, it allows conditioning on any subset of the latent features. This provides a model able to achieve good results on image inpainting and feature imputation tasks. The paper appears to be technically sound, and the experiments are thoughtfully designed. The writing is clear and the model is easy to understand. The closest work to this of the Universal Marginalizer is compared to well, with more compelling examples in the appendix. I would have preferred if more of the experimental results were in the main paper instead of in the appendix especially as the authors state they chose to highlight their better results in the main paper. While not the first model to try to handle modeling data with missing features, it is still a fairly original and elegant formulation. Minor details: In equation (8) should x be x_b?
assistant
 This paper introduces the VAEAC model, inspired by CVAEs, it allows conditioning on any subset of the latent features. This provides a model able to achieve good results on image inpainting and feature imputation tasks. The paper appears to be technically sound, and the experiments are thoughtfully designed. The writing is clear and the model is easy to understand. The closest work to this of the Universal Marginalizer is compared to well, with more compelling examples in the appendix. I would have preferred if more of the experimental results were in the main paper instead of in the appendix especially as the authors state they chose to highlight their better results in the main paper. While not the first model to try to handle modeling data with missing features, it is still a fairly original and elegant formulation. Minor details: In equation (8) should x be x_b?