Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data
We introduce Deep Variational Bayes Filters (DVBF), a new method for unsupervised learning and identification of latent Markovian state space models. Leveraging recent advances in Stochastic Gradient Variational Bayes, DVBF can overcome intractable inference distributions via variational inference. Thus, it can handle highly nonlinear input data with temporal and spatial dependencies such as image sequences without domain knowledge. Our experiments show that enabling backpropagation through transitions enforces state space assumptions and significantly improves information content of the latent embedding. This also enables realistic long-term prediction.
The paper proposes to use the very standard SVGB in a sequential setting like several previous works did. However, they proposes to have a clear state space constraints similar to Linear Gaussian Models: Markovian latent space and conditional independence of observed variables given the latent variables. However the model is in this case non-linear. These assumptions are well motivated by the goal of having meaningful latent variables. The experiments are interesting but I*m still not completely convinced by the regression results in Figure 3, namely that one could obtain the angle and velocity from the state but using a function more powerful than a linear function. Also, why isn*t the model from (Watter et al., 2015) not included ? After rereading I*m not sure I understand why the coordinates should be combined in a 3x3 checkerboard as said in Figure 5a. Then paper is well motivated and the resulting model is novel enough, the bouncing ball experiment is not quite convincing, especially in prediction, as the problem is fully determined by its initial velocity and position.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data
We introduce Deep Variational Bayes Filters (DVBF), a new method for unsupervised learning and identification of latent Markovian state space models. Leveraging recent advances in Stochastic Gradient Variational Bayes, DVBF can overcome intractable inference distributions via variational inference. Thus, it can handle highly nonlinear input data with temporal and spatial dependencies such as image sequences without domain knowledge. Our experiments show that enabling backpropagation through transitions enforces state space assumptions and significantly improves information content of the latent embedding. This also enables realistic long-term prediction.
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 proposes to use the very standard SVGB in a sequential setting like several previous works did. However, they proposes to have a clear state space constraints similar to Linear Gaussian Models: Markovian latent space and conditional independence of observed variables given the latent variables. However the model is in this case non-linear. These assumptions are well motivated by the goal of having meaningful latent variables. The experiments are interesting but I*m still not completely convinced by the regression results in Figure 3, namely that one could obtain the angle and velocity from the state but using a function more powerful than a linear function. Also, why isn*t the model from (Watter et al., 2015) not included ? After rereading I*m not sure I understand why the coordinates should be combined in a 3x3 checkerboard as said in Figure 5a. Then paper is well motivated and the resulting model is novel enough, the bouncing ball experiment is not quite convincing, especially in prediction, as the problem is fully determined by its initial velocity and position.
assistant
The paper proposes to use the very standard SVGB in a sequential setting like several previous works did. However, they proposes to have a clear state space constraints similar to Linear Gaussian Models: Markovian latent space and conditional independence of observed variables given the latent variables. However the model is in this case non-linear. These assumptions are well motivated by the goal of having meaningful latent variables. The experiments are interesting but I*m still not completely convinced by the regression results in Figure 3, namely that one could obtain the angle and velocity from the state but using a function more powerful than a linear function. Also, why isn*t the model from (Watter et al., 2015) not included ? After rereading I*m not sure I understand why the coordinates should be combined in a 3x3 checkerboard as said in Figure 5a. Then paper is well motivated and the resulting model is novel enough, the bouncing ball experiment is not quite convincing, especially in prediction, as the problem is fully determined by its initial velocity and position.