Venue: NeurIPS 2019 Workshop Neuro_AI

Reviews:

final_score: rating: null
review_content: importance_comment: This paper continues a line of work from the 2000s that has not had significant recent interest. I am glad it is getting tried with modern compute scale and tools, and I believe the results are promising. This submission is not sufficient on its own to convince me though that this approach will tell us new things about the brain or about artificial neural networks.
rigor_comment: The algorithm was presented very clearly, and I believe all claims to be correct. I was surprised that x was set by projection rather than inference, and would have liked better understanding for why this was effective or desirable (though this may not be possible w/in length constraints).
clarity_comment: The writing was very good, and the algorithm and results were very clearly presented, especially considering length constraints.

intersection_comment: The paper presented an unsupervised machine learning algorithm, which was used to try to describe representation learning in the brain.

comment: This was a sensible algorithm for unsupervised feature learning, algorithm and results were clear, and results were reasonably good.
review_title: review

final_score: rating: null
review_content: importance_comment: Interesting extension to bilinear sparse coding models, but there is insufficient evidence in the work to support the claims in the abstracts - particularly that it captures the statistics of the transformations between frames.
rigor_comment: There are no quantifications of the performance of the model particularly in comparison to the original model that they are extending. The reconstruction of a single image in Fig1e is not a convincing test of the model - one would want to see how well the feature dynamics predict the next frame, if they are indeed sufficient to capture changes in the videos frame by frame.
clarity_comment: Figure legends/descriptions are too short, not totally clear what is shown in Figure 2.
intersection_comment: Unsupervised approaches might be interesting to some in the AI community.
review_title: Bilinear sparse coding model with dynamic features, but unclear if dynamics work

final_score: rating: null
review_content: importance_comment: They make modifications to an existing generative model of natural images. They do not make direct comparisons to previous models or study quantitatively the results of the model with respect to its parameters. It is difficult to judge whether the new model is important because it has not been evaluated except by eye it does seem to reconstruct an image.
rigor_comment: They show images of a single reconstruction but no quantification of reconstruction quality or comparison to previous methods. In the spirit of insight it would have been very nice to have a quantification of error with respect to parameters (priors on slow identity, fast form).  If it had been evaluated and its efficacy varied in an interesting way with respect to the parameters of the model this could be a potentially important model to understand why the nervous system trades off between object identity associated features, transformation features, and speed.

The statement that: ‘GAN’s and VAE features are not typically interpretable.’ Seemed broad and was unsupported by any citations and to my knowledge GAN’s and VAE’s have been used specifically to find interpretable features.



clarity_comment: Paper was organized, figures clear and readable. Some development of the model could have been left to the references and didn't add much to their contribution (e.g. Taylor approximation to a Lie model) .

When they say ‘steerable’ filter I was a little confused, do they just mean the basis vectors learned vary smoothly with respect to some affine transform parameter? 

Their statement of the novelty of their method: ‘(1) allowing each feature to have its own transformation’ was not clear. Does this mean previous methods learned the same transformation for all features. 

intersection_comment: They make an interesting connection to speed of processing that rapid changes better represented by the magnocellular pathway would be associated with transformations and slow parvo with identity. It was not clear though where they experimentally varied/tested this prior in their algorithm. So while an interesting connection they did not make clear where they substantively pursue it. 

They draw an analogy between the ventral and dorsal stream of cortex and bilinear models of images.
comment: The main place to improve is to have some quantitative analysis of the quality of their model perhaps MSE of image reconstruction. Then this evaluation could be used to study impacts of the parameters of their model which could then lead to neural hypotheses.

They have some qualitative evaluation in images of filters but they could explore the parameter space to understand what led to these features. 

One of their stated novel contribution was that their filters were convolutional but they do not discuss the potential connection convolutional filters have to transformation of features which seemed like a gap. Weight sharing across shifted filters separates out feature and position yet many of their learned transformations are also translations. Is this an issue of spatial scale? This warranted some potentially interesting discussion though admittedly 4 pages isn’t a lot of space.
review_title: Interesting connection between bilinear models and neuro but no quantitative analysis of model.

Decision: Accept (Poster)