GeoSeq2Seq: Information Geometric Sequence-to-Sequence Networks
The Fisher information metric is an important foundation of information geometry, wherein it allows us to approximate the local geometry of a probability distribution. Recurrent neural networks such as the Sequence-to-Sequence (Seq2Seq) networks that have lately been used to yield state-of-the-art performance on speech translation or image captioning have so far ignored the geometry of the latent embedding, that they iteratively learn. We propose the information geometric Seq2Seq (GeoSeq2Seq) network which abridges the gap between deep recurrent neural networks and information geometry. Specifically, the latent embedding offered by a recurrent network is encoded as a Fisher kernel of a parametric Gaussian Mixture Model, a formalism common in computer vision. We utilise such a network to predict the shortest routes between two nodes of a graph by learning the adjacency matrix using the GeoSeq2Seq formalism; our results show that for such a problem the probabilistic representation of the latent embedding supersedes the non-probabilistic embedding by 10-15\%.
 In this paper, the authors propose to integrate the Fisher information metric with the Seq2Seq network, which abridges the gap between deep recurrent neural networks and information geometry. By considering of the information geometry of the latent embedding, the authors propose to encode the RNN feature as a Fisher kernel of a parametric Gaussian Mixture Model, which demonstrate an experimental improvements compared with the non-probabilistic embedding. The idea is interesting. However, the technical contribution is rather incremental. The authors seem to integrate some well-explored techniques, with little consideration of the specific challenges. Moreover, the experimental section is rather insufficient. The results on road network graph is not a strong support for the Seq2Seq model application.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
GeoSeq2Seq: Information Geometric Sequence-to-Sequence Networks
The Fisher information metric is an important foundation of information geometry, wherein it allows us to approximate the local geometry of a probability distribution. Recurrent neural networks such as the Sequence-to-Sequence (Seq2Seq) networks that have lately been used to yield state-of-the-art performance on speech translation or image captioning have so far ignored the geometry of the latent embedding, that they iteratively learn. We propose the information geometric Seq2Seq (GeoSeq2Seq) network which abridges the gap between deep recurrent neural networks and information geometry. Specifically, the latent embedding offered by a recurrent network is encoded as a Fisher kernel of a parametric Gaussian Mixture Model, a formalism common in computer vision. We utilise such a network to predict the shortest routes between two nodes of a graph by learning the adjacency matrix using the GeoSeq2Seq formalism; our results show that for such a problem the probabilistic representation of the latent embedding supersedes the non-probabilistic embedding by 10-15\%.
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>



 In this paper, the authors propose to integrate the Fisher information metric with the Seq2Seq network, which abridges the gap between deep recurrent neural networks and information geometry. By considering of the information geometry of the latent embedding, the authors propose to encode the RNN feature as a Fisher kernel of a parametric Gaussian Mixture Model, which demonstrate an experimental improvements compared with the non-probabilistic embedding. The idea is interesting. However, the technical contribution is rather incremental. The authors seem to integrate some well-explored techniques, with little consideration of the specific challenges. Moreover, the experimental section is rather insufficient. The results on road network graph is not a strong support for the Seq2Seq model application.
assistant
 In this paper, the authors propose to integrate the Fisher information metric with the Seq2Seq network, which abridges the gap between deep recurrent neural networks and information geometry. By considering of the information geometry of the latent embedding, the authors propose to encode the RNN feature as a Fisher kernel of a parametric Gaussian Mixture Model, which demonstrate an experimental improvements compared with the non-probabilistic embedding. The idea is interesting. However, the technical contribution is rather incremental. The authors seem to integrate some well-explored techniques, with little consideration of the specific challenges. Moreover, the experimental section is rather insufficient. The results on road network graph is not a strong support for the Seq2Seq model application.