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What is the experimental setup ['arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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In any case, the results in Figure 1 and the appendix are useful for showing that the baselines used in prior works were not as strong as they could be ['non', 'non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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However, there is one key weakness which prevents me from being more positive with respect to acceptance : an evaluation of the proposed visualization in practical use through a user study is absent ['non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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The second has to do with the interpretation of the results. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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"It is probable that revolutionary computational systems can be created in this way with only moderate expenditure of resources and effort"" Of course whole fields are working on this problem." ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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A lengthy study, I agree, but a necessity in light of other recent works highlighting how dangerous is to use GANs for this kind of tasks ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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This paper evaluates 5 different models for motion tracking in 4D OCT. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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Tables and figures are inconveniently far from where they are referenced in the text ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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For example, is there something different about the feature maps that support this ['non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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Thirdly, from the discussion of the findings, quotes appear unpacked ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'arg', 'arg', 'arg'] paper quality
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Can we get the same conclusions on a different domain where other model-based methods have been successful; e.g. continuous control tasks? ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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Transfer learning and dealing with small datasets is an important area of research - The paper proposes a novel method, enabling pretraining on several different tasks instead of only one dataset (e.g. ImageNet) like done most of the times - Results show clear performance increase on small datasets - Proper experiment setup and validation - Clearly written and comprehensible - Code is openly available - Little comparison to other state-of-the-art methods for transfer learning ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'non', 'arg', 'arg', 'arg', 'arg', 'non', 'arg', 'arg', 'arg', 'arg', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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Section 1 pitches the method as solving the credit assignment problem, citing problems with weight symmetry etc, that apply to many forms of learning. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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Despite the fact that this set has been the standard for evaluating blood vessel segmentation algorithms since 2004, the resolution of the images is extremelly different from the current ones ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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In this paper, the authors aimed to improve the representations learned by Neural Image Compression (NIC) algorithms when applied to Whole Slide Images (WSI) for pathology analysis. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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The authors test their algorithm on a dataset of 95 subjects for neuromuscular disease. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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To me the proposed approach does not seem particularly novel and the idea that hierarchy can be useful for multi-task learning is also not new ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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I have just a few comments below: ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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Many design choices for the algorithms are not clearly explained ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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This demonstrates that even when the Bayes-optimal classifier is robust, we may need to explicitly regularize/incentivize neural networks to learn the correct decision boundary. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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Are there other patters with features not presented in these three ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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Experimental setup: - One somewhat concerning (but perhaps unavoidable) thing about the experimental setup is that all the considered datasets are not perfectly linearly separable , i.e. the Bayes-optimal classifier has non-zero test error in expectation, and moreover the data variance is full-rank in the embedded space. ['non', 'non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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For example, many of the interactions between myriad excitatory and inhibitory types across brains regions and neuromodulators, of which dopamine is just one of several, is largely unknown ['non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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This idea is simple and works well ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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I think it could at least be improved for clarity . ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'non'] paper quality
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"The proposed localisation map is actually the result of distance transform, and has been initially used in : ""Counting in The Wild"", C. Arteta, V. Lempitsky, A. Zisserman, In ECCV 2016." ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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Could it be extended to work with only a fraction of the nuclei annotated ? ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'non'] paper quality
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I think this is a key experiment, really necessary to validate if the method is performing well or not ['non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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The results in Figure 3 are very far from the state of the art ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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STRENGTHS + Decoupling instruction-to-action mapping by introducing goals as a learned intermediate representation has advantages, particularly for goal-directed instructions ['non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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Experiments have shown that the convergence speed and results are improved, but not significant ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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"UNLIMITING"" I found it quite hard to understand the point of Bi et al. for rejecting screen-to-screen pointing, at least the way it is explained in this paper" ['non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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For larger scale domains, I fear this could become an important obstacle to effective model training ['non', 'non', 'non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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The experiments measure the recon accuracy. ['non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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More detail for this application of AdVIL would be nice ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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The choice de-speckle network architecture is somewhat not sound, with the multiplicative residual connection near the outputs of the network and the median filtering operation ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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The paper takes a crudely 'neuroscience inspired' concept (though, admittedly it could simply be 'task structure' inspired) and builds a simple model from it, which it benchmarks on a appropriately designed simplest-working-example. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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"Relevant to the discussion of learning from demonstration for language understanding is the following paper by Duvallet et al. Duvalet, Kollar, and Stentz, ""Imitation learning for natural language direction following through unknown environments,"" ICRA 2014 - The paper is overly verbose and redundant in places" ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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I am quite confused about what exactly the author are claiming is the core contribution of their work ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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The basic premise is very strange ['arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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I would suggest to include the F1-score and the area under the Precision/Recall curve, instead , which have been used already in other studies (see [1] and [2], for example, or Orlando et al. 2017 in the submitted draft). ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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The experiments shown in Table 1 compare several different network settings. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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From table 1, it is clear that ECE is much lower for the proposed method ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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It seems the robot arm task is very similar to the navigation task, due to robot arm's end effector being position controlled directly ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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Do we really need a labelled ground truth here ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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How different are the familiar and unfamiliar instructions ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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Premise is that feedback alignment networks are also more robust to adversarial attacks. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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"No trouble understanding the material or writing By focusing on the more biologically plausible ""feedback alignment"" networks, the paper does sit at the intersection of neuro and AI" ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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The training should be done by using the small network ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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The same problem also occurs for the conclusion about the robustness of SRL approaches ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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The compositional action classification task is harder and shows that incorporating LSTMs for temporal reasoning leads to non-trivial performance improvements over frame averaging. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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Overall the technical aspects of this paper seem sound ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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The main contribution of the work was adding a normalization step to the network, and learning the affine transformation parameters during the training. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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However, there are a few (in my opinion) critical concerns that currently bar me from strongly recommending acceptance of the paper ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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Foremost, the presented criteria are actually not real criteria (expect maybe C1) but rather general guidelines to visually inspect 'accuracy over randomization curves ['non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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In my opinion this paper is generally of good quality and clarity, modest originality and significance ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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It was not clear though where they experimentally varied/tested this prior in their algorithm ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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Please provide variance measures on your results (within model configuration, across scene examples). ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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"The dataset is an extension of CLEVR using simple motions of primitive 3D objects to produce videos of primitive actions (e.g. pick and place a cube), compositional actions (e.g. ""cone is rotated during the sliding of the sphere""), and finally a 3D object localization tasks (i.e. where is the ""snitch"" object at the end of the video)." ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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It further establishes the sample complexity to determine optimal actions. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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"We require a new class of theories that dispose of the simplistic stimulus-driven encode/ transmit/decode doctrine. """ ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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Can't simple heuristics perform at least as well ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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Association for Computing Machinery, New York, NY, USA, 37643774. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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12), - and for some reason that makes it ok to consider that screen-to-screen pointing is compatible with Bi et al.'s model (which does not consider A). ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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While eligibility traces have received some attention in neuroscience their relevance to learning has not been thoroughly explored, so this paper makes a welcome contribution that fits well within the workshop goals ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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These results remain valid , even if the proposed approach is not as context-independent as hoped ['arg', 'arg', 'arg', 'arg', 'non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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While I agree with the statement as such , the GAN development makes a stronger statement about the nature of the learning trajectory ['non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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Update: I feel the idea of this paper is straightforward, and the contribution is incremental ['non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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This work is an extension to the work of Sukbaatar et al. (2016) with two main differences: 1) Selective communication: agents are able to decide whether they want to communicate. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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Despite these weaknesses with regards to the study reporting and discussion, the paper is interesting and showcases good and novel work and I think the GI community would benefit from its presentation (albeit with some changes as suggested above). ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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The method part is well-written and easy to understand ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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For example between the hears and the leaf the syrup is either a series of dots or a continuous line. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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To the best of my knowledge, it has the highest performance in the DRIVE data set compared to several other techniques. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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The system does not seem to follow a particular rationale ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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1] Oktay O, Ferrante E, Kamnitsas K, Heinrich M, Bai W, Caballero J, et al. Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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It is unclear on what basis one can say that real-world datasets are more like the symmetric case or the asymmetric case ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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Interestingly, they also construct a dataset where they Bayes-optimal classifier is robust and neural networks *do* learn a robust classifier (adversarial squares sans label noise). ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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In fact, the major claim is that using a cascade of linear layers instead of a single layer can lead to better performance in deep neural networks. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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Only a single (large) dataset is used, while there are many publicly available datasets that could be included for additional experiments ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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is a tool for authoring object component behaviour within VR. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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Also, it would be very interesting to use these models to predict situations that might trigger maladaptive behaviors, by finding scenarios in which the pathological behavior becomes optimal. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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I fail to understand the the authors augmentation ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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Experiments: The authors show the empirical advantages offered by the proposed method over the existing literature ['non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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Also, in the experiments, it is said that one can combing normalizing flows with TRPO without describing the details ['non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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Compared to the proposed work, where latents represent clinically relevant mandible landmarks, an auto-encoder approach as in ACNN is more general: relevant landmarks as in the mandible cannot be identified for arbitrary anatomies , and a separate training of decoder and decoder as proposed here crucially depends on a semantically meaningful latent space with a supervised mapping to the dense representation (e.g. hand-labeled landmarks vs. voxel labelmaps). ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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Furthermore, even the tests in the appendix are not comprehensive enough to to warrant the conclusion as written ['non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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However, I feel that the work lacked clarity when it came to interpretation of the results ['non', 'non', 'non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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The decoder network is made possible by a newly proposed architecture that is based on inception-like transpose convolutional blocks. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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I think this joint training might result in even better outcomes. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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A Discussion of Adversarial Examples are not Bugs they are Features (pseudo-url): Nakkiran (2019) actually constructs a dataset (called adversarial squares) where the Bayes-optimal classifier is robust but neural networks learn a non-robust classifier due to label noise and overfitting. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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Adversarially robust generalization requires more data (pseudo-url): Schmidt et al show a setup where many more samples are required for adversarial robustness than for standard classification error. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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The comparison to PCD-1 in Fig. 3 seems a bit unfair in that the learning curve ends at 8000 iterations, while PCD-1 continues to improve NLL ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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It is the first work to identify aortic value prosthesis types using a general representation learning technique. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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The main contribution is it provides a way to reduce the number of interactions with the environment. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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Some points to address are listed in the following: The early stopping is not clear ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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While it does not seemingly add anything conceptual , the exact implementation is arguably new ['non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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If there is an issue with Tran et al you should state it clearly, if not, you should accept their results ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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If a sonographer is able to acquire these images, they are also able to perform these measurements ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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Seemed broad and was unsupported by any citations and to my knowledge GANs and VAEs have been used specifically to find interpretable features. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality
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Results show the effectiveness of the proposed method ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality
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