Deep processing of structured data
We construct a general unified framework for learning representation of structured data, i.e. data which cannot be represented as the fixed-length vectors (e.g. sets, graphs, texts or images of varying sizes). The key factor is played by an intermediate network called SAN (Set Aggregating Network), which maps a structured object to a fixed length vector in a high dimensional latent space. Our main theoretical result shows that for sufficiently large dimension of the latent space, SAN is capable of learning a unique representation for every input example. Experiments demonstrate that replacing pooling operation by SAN in convolutional networks leads to better results in classifying images with different sizes. Moreover, its direct application to text and graph data allows to obtain results close to SOTA, by simpler networks with smaller number of parameters than competitive models.
 The paper at hand describes an approach to enable neural networks to take arbitrary structured data (basically any data that is not easily represented as a fixed-dimensional vector) as input. The paper describes how such data can be represented as a set (e.g. a sequence is represented as a set of index + data) and then an auxiliary network called the set aggregating network (SAN) is used to represent the data in a high dimensional latent space. In addition to the idea, the paper provides a theoretical analysis of the approach which shows that with a sufficiently high dimensional representation the network is able to learn a unique representation for each input example. Good in this paper: - nicely on topic - this is truly about learning representations - interesting idea with some (albeit not-surprising) theoretical analysis Not yet great in this paper: - the paper feels a bit premature in multiple ways, to me most importantly the experiments appear to be really rushed. Looking at table 1 - it is really unclear how to read this. The table is hardly explained and it would be good to actually compare the method to the state of the art. I understand that the authors care more about generality here - but it*s much easier to build something generic that is very far from the state of the art than to build something that is closer to the state of the art. Also - I feel it would have been interesting to allow for a more direct comparison of the SAN results with the other methods. Similarly, in Table 2 - how far away is this from the state of the art. - the variable size image-recognition task seems a bit artificial - I believe that scaling images to a fixed size is a reasonable idea and is well understood and also something that humans can work with. Dealing with variable size images for the purpose of not having a fixed size vector seems artificial and unnecessary - in this case here specifically the images are same size to begin with. By using SAN you loose a lot of the understanding of computer vision research of the last decade (e.g. it*s clear that CNNs are a good idea - with SAN - you cannot really do that anymore) - so, I feel this experiment here doesn*t add anything. I feel these comments can probably be addressed by rethinking the experimental evaluation. At this point, I think the paper provides a nice idea with a theoretical analysis - but it doesn*t provide enough experimental evidence that this works. Minor comments: - p1: Analogically -> just drop the word - p1: citation of Brin & Page -> this seems a bit out of place - yes, it is a method working on graphs, but it is not relevant in the context of the paper - to the best of my knowledge there are no neural networks in this. - p2: where Radon - >where the Radon - p2: "One can easily see " -> I cannot. Please ellaborate
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Deep processing of structured data
We construct a general unified framework for learning representation of structured data, i.e. data which cannot be represented as the fixed-length vectors (e.g. sets, graphs, texts or images of varying sizes). The key factor is played by an intermediate network called SAN (Set Aggregating Network), which maps a structured object to a fixed length vector in a high dimensional latent space. Our main theoretical result shows that for sufficiently large dimension of the latent space, SAN is capable of learning a unique representation for every input example. Experiments demonstrate that replacing pooling operation by SAN in convolutional networks leads to better results in classifying images with different sizes. Moreover, its direct application to text and graph data allows to obtain results close to SOTA, by simpler networks with smaller number of parameters than competitive models.
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 at hand describes an approach to enable neural networks to take arbitrary structured data (basically any data that is not easily represented as a fixed-dimensional vector) as input. The paper describes how such data can be represented as a set (e.g. a sequence is represented as a set of index + data) and then an auxiliary network called the set aggregating network (SAN) is used to represent the data in a high dimensional latent space. In addition to the idea, the paper provides a theoretical analysis of the approach which shows that with a sufficiently high dimensional representation the network is able to learn a unique representation for each input example. Good in this paper: - nicely on topic - this is truly about learning representations - interesting idea with some (albeit not-surprising) theoretical analysis Not yet great in this paper: - the paper feels a bit premature in multiple ways, to me most importantly the experiments appear to be really rushed. Looking at table 1 - it is really unclear how to read this. The table is hardly explained and it would be good to actually compare the method to the state of the art. I understand that the authors care more about generality here - but it*s much easier to build something generic that is very far from the state of the art than to build something that is closer to the state of the art. Also - I feel it would have been interesting to allow for a more direct comparison of the SAN results with the other methods. Similarly, in Table 2 - how far away is this from the state of the art. - the variable size image-recognition task seems a bit artificial - I believe that scaling images to a fixed size is a reasonable idea and is well understood and also something that humans can work with. Dealing with variable size images for the purpose of not having a fixed size vector seems artificial and unnecessary - in this case here specifically the images are same size to begin with. By using SAN you loose a lot of the understanding of computer vision research of the last decade (e.g. it*s clear that CNNs are a good idea - with SAN - you cannot really do that anymore) - so, I feel this experiment here doesn*t add anything. I feel these comments can probably be addressed by rethinking the experimental evaluation. At this point, I think the paper provides a nice idea with a theoretical analysis - but it doesn*t provide enough experimental evidence that this works. Minor comments: - p1: Analogically -> just drop the word - p1: citation of Brin & Page -> this seems a bit out of place - yes, it is a method working on graphs, but it is not relevant in the context of the paper - to the best of my knowledge there are no neural networks in this. - p2: where Radon - >where the Radon - p2: "One can easily see " -> I cannot. Please ellaborate
assistant
 The paper at hand describes an approach to enable neural networks to take arbitrary structured data (basically any data that is not easily represented as a fixed-dimensional vector) as input. The paper describes how such data can be represented as a set (e.g. a sequence is represented as a set of index + data) and then an auxiliary network called the set aggregating network (SAN) is used to represent the data in a high dimensional latent space. In addition to the idea, the paper provides a theoretical analysis of the approach which shows that with a sufficiently high dimensional representation the network is able to learn a unique representation for each input example. Good in this paper: - nicely on topic - this is truly about learning representations - interesting idea with some (albeit not-surprising) theoretical analysis Not yet great in this paper: - the paper feels a bit premature in multiple ways, to me most importantly the experiments appear to be really rushed. Looking at table 1 - it is really unclear how to read this. The table is hardly explained and it would be good to actually compare the method to the state of the art. I understand that the authors care more about generality here - but it*s much easier to build something generic that is very far from the state of the art than to build something that is closer to the state of the art. Also - I feel it would have been interesting to allow for a more direct comparison of the SAN results with the other methods. Similarly, in Table 2 - how far away is this from the state of the art. - the variable size image-recognition task seems a bit artificial - I believe that scaling images to a fixed size is a reasonable idea and is well understood and also something that humans can work with. Dealing with variable size images for the purpose of not having a fixed size vector seems artificial and unnecessary - in this case here specifically the images are same size to begin with. By using SAN you loose a lot of the understanding of computer vision research of the last decade (e.g. it*s clear that CNNs are a good idea - with SAN - you cannot really do that anymore) - so, I feel this experiment here doesn*t add anything. I feel these comments can probably be addressed by rethinking the experimental evaluation. At this point, I think the paper provides a nice idea with a theoretical analysis - but it doesn*t provide enough experimental evidence that this works. Minor comments: - p1: Analogically -> just drop the word - p1: citation of Brin & Page -> this seems a bit out of place - yes, it is a method working on graphs, but it is not relevant in the context of the paper - to the best of my knowledge there are no neural networks in this. - p2: where Radon - >where the Radon - p2: "One can easily see " -> I cannot. Please ellaborate