Deep Learning with Sets and Point Clouds
We introduce a simple permutation equivariant layer for deep learning with set structure. This type of layer, obtained by parameter-sharing, has a simple implementation and linear-time complexity in the size of each set. We use deep permutation-invariant networks to perform point-could classification and MNIST digit summation, where in both cases the output is invariant to permutations of the input. In a semi-supervised setting, where the goal is make predictions for each instance within a set, we demonstrate the usefulness of this type of layer in set-outlier detection as well as semi-supervised learning with clustering side-information.
Pros : - New and clear formalism for invariance on signals with known structure - Good numerical results Cons : - The structure must be specified. - The set structure dataset is too simple - There is a gap between the large (and sometimes complex) theory introduced and the numerical experiments ; consequently a new reader could be lost since examples might be missing Besides, from a personal point of view, I think the topic of the paper and its content could be suitable for a big conference as the author improves its content. Thus, if rejected, I think you should not consider the workshop option for your paper if you wish to publish it later in a conference, because big conferences might consider the workshop papers of ICLR as publications. (that*s an issue I had to deal with at some points)
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Deep Learning with Sets and Point Clouds
We introduce a simple permutation equivariant layer for deep learning with set structure. This type of layer, obtained by parameter-sharing, has a simple implementation and linear-time complexity in the size of each set. We use deep permutation-invariant networks to perform point-could classification and MNIST digit summation, where in both cases the output is invariant to permutations of the input. In a semi-supervised setting, where the goal is make predictions for each instance within a set, we demonstrate the usefulness of this type of layer in set-outlier detection as well as semi-supervised learning with clustering side-information.
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>



Pros : - New and clear formalism for invariance on signals with known structure - Good numerical results Cons : - The structure must be specified. - The set structure dataset is too simple - There is a gap between the large (and sometimes complex) theory introduced and the numerical experiments ; consequently a new reader could be lost since examples might be missing Besides, from a personal point of view, I think the topic of the paper and its content could be suitable for a big conference as the author improves its content. Thus, if rejected, I think you should not consider the workshop option for your paper if you wish to publish it later in a conference, because big conferences might consider the workshop papers of ICLR as publications. (that*s an issue I had to deal with at some points)
assistant
Pros : - New and clear formalism for invariance on signals with known structure - Good numerical results Cons : - The structure must be specified. - The set structure dataset is too simple - There is a gap between the large (and sometimes complex) theory introduced and the numerical experiments ; consequently a new reader could be lost since examples might be missing Besides, from a personal point of view, I think the topic of the paper and its content could be suitable for a big conference as the author improves its content. Thus, if rejected, I think you should not consider the workshop option for your paper if you wish to publish it later in a conference, because big conferences might consider the workshop papers of ICLR as publications. (that*s an issue I had to deal with at some points)