Representing dynamically: An active process for describing sequential data
We propose an unsupervised method for building dynamic representations of sequential data, particularly of observed interactions. The method simultaneously acquires representations of input data and its dynamics. It is based on a hierarchical generative model composed of two levels. In the first level, a model learns representations to generate observed data. In the second level, representational states encode the dynamics of the lower one. The model is designed as a Bayesian network with switching variables represented in the higher level, and which generates transition models. The method actively explores the latent space guided by its knowledge and the uncertainty about it. That is achieved by updating the latent variables from prediction error signals backpropagated to the latent space. So, no encoder or inference models are used since the generators also serve as their inverse transformations. The method is evaluated in two scenarios, with static images and with videos. The results show that the adaptation over time leads to better performance than with similar architectures without temporal dependencies, e.g., variational autoencoders. With videos, it is shown that the system extracts the dynamics of the data in states that highly correlate with the ground truth of the actions observed.
 This paper, to me, is a clear rejection from these basic observations: A *model* is not a computation graph and should never be presented that way. Calling a computation graph a Bayesian network without even writing down how *inference* can ever result in such a computation graph is a basic error. The authors claim this: "So, no encoder or inference models are used since the generators also serve as their inverse transformations." Well, then this is not a Bayesian network. The authors spend a lot of time analyzing constant inputs theoretically, and I*m not sure why this is even relevant.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Representing dynamically: An active process for describing sequential data
We propose an unsupervised method for building dynamic representations of sequential data, particularly of observed interactions. The method simultaneously acquires representations of input data and its dynamics. It is based on a hierarchical generative model composed of two levels. In the first level, a model learns representations to generate observed data. In the second level, representational states encode the dynamics of the lower one. The model is designed as a Bayesian network with switching variables represented in the higher level, and which generates transition models. The method actively explores the latent space guided by its knowledge and the uncertainty about it. That is achieved by updating the latent variables from prediction error signals backpropagated to the latent space. So, no encoder or inference models are used since the generators also serve as their inverse transformations. The method is evaluated in two scenarios, with static images and with videos. The results show that the adaptation over time leads to better performance than with similar architectures without temporal dependencies, e.g., variational autoencoders. With videos, it is shown that the system extracts the dynamics of the data in states that highly correlate with the ground truth of the actions observed.
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, to me, is a clear rejection from these basic observations: A *model* is not a computation graph and should never be presented that way. Calling a computation graph a Bayesian network without even writing down how *inference* can ever result in such a computation graph is a basic error. The authors claim this: "So, no encoder or inference models are used since the generators also serve as their inverse transformations." Well, then this is not a Bayesian network. The authors spend a lot of time analyzing constant inputs theoretically, and I*m not sure why this is even relevant.
assistant
 This paper, to me, is a clear rejection from these basic observations: A *model* is not a computation graph and should never be presented that way. Calling a computation graph a Bayesian network without even writing down how *inference* can ever result in such a computation graph is a basic error. The authors claim this: "So, no encoder or inference models are used since the generators also serve as their inverse transformations." Well, then this is not a Bayesian network. The authors spend a lot of time analyzing constant inputs theoretically, and I*m not sure why this is even relevant.