Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whitening
We propose Power Slow Feature Analysis, a gradient-based method to extract temporally slow features from a high-dimensional input stream that varies on a faster time-scale, as a variant of Slow Feature Analysis (SFA). While displaying performance comparable to hierarchical extensions to the SFA algorithm, such as Hierarchical Slow Feature Analysis, for a small number of output-features, our algorithm allows fully differentiable end-to-end training of arbitrary differentiable approximators (e.g., deep neural networks). We provide experimental evidence that PowerSFA is able to extract meaningful and informative low-dimensional features in the case of (a) synthetic low-dimensional data, (b) visual data, and also for (c) a general dataset for which symmetric non-temporal relations between points can be defined.
 In this paper the authors present a differentiable objective for slow feature analysis, to facilitate end-to-end training. I am not clear on the novelty of this formulation, as it appears to have been proposed in a similar form in previous works (e.g., A maximum-likelihood interpretation for slow feature analysis by Turner and Sahani - Eq., (2)) and can probably be considered straightforward. Nevertheless, the approximate whitening layer and the way it is used is a smart approach for this problem. The experiments are interesting and shed light on the properties of the method. In summary, the paper may lack technical novelty in some respect, but the experiments are convincing in terms of proof-of-concept, and the approach is smart.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whitening
We propose Power Slow Feature Analysis, a gradient-based method to extract temporally slow features from a high-dimensional input stream that varies on a faster time-scale, as a variant of Slow Feature Analysis (SFA). While displaying performance comparable to hierarchical extensions to the SFA algorithm, such as Hierarchical Slow Feature Analysis, for a small number of output-features, our algorithm allows fully differentiable end-to-end training of arbitrary differentiable approximators (e.g., deep neural networks). We provide experimental evidence that PowerSFA is able to extract meaningful and informative low-dimensional features in the case of (a) synthetic low-dimensional data, (b) visual data, and also for (c) a general dataset for which symmetric non-temporal relations between points can be defined.
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 present a differentiable objective for slow feature analysis, to facilitate end-to-end training. I am not clear on the novelty of this formulation, as it appears to have been proposed in a similar form in previous works (e.g., A maximum-likelihood interpretation for slow feature analysis by Turner and Sahani - Eq., (2)) and can probably be considered straightforward. Nevertheless, the approximate whitening layer and the way it is used is a smart approach for this problem. The experiments are interesting and shed light on the properties of the method. In summary, the paper may lack technical novelty in some respect, but the experiments are convincing in terms of proof-of-concept, and the approach is smart.
assistant
 In this paper the authors present a differentiable objective for slow feature analysis, to facilitate end-to-end training. I am not clear on the novelty of this formulation, as it appears to have been proposed in a similar form in previous works (e.g., A maximum-likelihood interpretation for slow feature analysis by Turner and Sahani - Eq., (2)) and can probably be considered straightforward. Nevertheless, the approximate whitening layer and the way it is used is a smart approach for this problem. The experiments are interesting and shed light on the properties of the method. In summary, the paper may lack technical novelty in some respect, but the experiments are convincing in terms of proof-of-concept, and the approach is smart.