Venue: NeurIPS 2019 Workshop Neuro_AI

Reviews:

final_score: rating: null
review_content: importance_comment: Training RAM networks faster and making their execution more flexible is potentially useful, but unfortunately I see the experiments provided as too weak to support this paper's approach.
rigor_comment: Unless I'm missing something, it looks like the authors fail to replicate the setup they are trying to extend. Fig. 1 shows a baseline training error rate of 20%. The original RAM paper reports a test error rate of 1%, and linear regression yield an error rate of around 9%, so to me this points to a bug. Confusingly, Fig. 2 shows a baseline training error rate of 5%. Since these are both inconsistent and so far from the original performance measurements, it makes interpreting the extensions' performance measurements very difficult. Also, since the authors refer often to the original paper but never mention this very large performance disparity, the omission seems borderline dishonest.
clarity_comment: At a high level, the writing is clear, but some of the technical parts could use a revision pass. e.g. the use of the word "bound" on line 112 is potentially confusion, as is the sentence about merging objectives in line 105. The loss names are also a bit unintuitive.
intersection_comment: This is relevant to the workshop, but it's more of a psychology-inspired approach to a solving machine vision problems than a bridge between ML and neuroscience, and it would fit in about as well e.g. in the main conference at CVPR as it would here.
review_title: Exploration of an interesting extension idea is hindered by issues replicating the original RAM results

final_score: rating: null
review_content: importance_comment: The authors use insights from neuroscience to an important problem in artificial intelligence: the problem of active sampling.

rigor_comment: The paper is transparent and benchmarks its approach against existing approaches.  

clarity_comment: The paper is conceptually easy to follow, although there are several minor spelling / grammar / typographical issues. 

intersection_comment: The paper directly applies a conceptual approach from neuroscience to improve an existing, widely cited technique in active image sampling. 

comment: This paper addresses an important problem in artificial intelligence, in the context of existing techniques, and uses neuroscience as inspiration to propose new techniques.

Greater detail in the crucial paragraphs developing the concepts and computations of J-uncertainty and J-intrinsic would be helpful. In particular, why does the new cost term protect the RAM approach from performance degradation with higher numbers of glimpses? This problem is raised and appears to be addressed in the experimental results, but it is unclear why the insights from neuroscience help make this possible. 

In Figure 2, the “both new terms, dynamic” plot does not extend into the regime where performance degradation is most extreme; while this may be a result of the technique used to evaluate the dynamic case, it somewhat undercuts the claim that they dynamic case is roughly equivalent in performance.
review_title: Neuroscience-inspired recurrent attention model

final_score: rating: null
review_content: importance_comment: Active sampling presents a framework for improving the efficiency of artificial neural networks in tasks requiring interaction between an agent and an environment. Approaches like this are important to try to reduce the training time needed and online processing requirements for artificial agents to make decisions in the real world.
rigor_comment: The authors present their interpretation of Gottlieb's three motives for implementing active sampling. They conclude that the Recurrent Attention Model implements the first of these three (increase expected reward), and propose objective functions that achieve the remaining two: 1) reducing the uncertainty of belief states, and 2) something related to the intrinsic utility or disutility of anticipating a positive or negative outcome.

The quote from Gottlieb (2018) outlining these objectives leaves a lot to interpretation, but the authors present a reasonable method for reducing the uncertainty of belief states that has the bonus feature of providing control over the number of glimpses required to make a decision.  However, I do not see how the belief in the sparsity of the output is related to the utility of making a prediction.

Nevertheless, the authors show that their new objective function improves the convergence of the recurrent attention model Both new terms in isolation improving convergence individually, although the output sparsity objective appears to do most of the heavy lifting. They also show that by using their uncertainty measure to dynamically determine the number of glimpses they increase test accuracy, most of which seem to be accounted for by minimizing the uncertainty in belief rather than output sparsity. 
clarity_comment: The motivation, method, and results of the paper were well written and easy to follow. The only difficulty I had was in reading figure 1, which was excessively small.
intersection_comment: This paper is very clearly at the intersection of neuroscience and artificial intelligence, using a well-defined theory from neuroscience to improve a popular model in the AI literature.
comment: Strengths:

The additional objectives inspired by neuroscience make convergence faster on training accuracy and increase test accuracy on MNIST. They also provide a system to dynamically control the number of glimpses required to make a decision.

Areas for improvement:

I would like to see either a more convincing rationale for your sparsity objective, or an objective that directly addresses the intrinsic utility of an outcome. I appreciate that MNIST may not be the best task for this, and also that utility will be task- and agent-specific.
review_title: Modifications to Recurrent Attention Model inspired by active sampling in neuroscience improves convergence and generalization.

Decision: Accept (Poster)