conferences_annotated/sentence_level/train.csv

	sentence_id	text	position
379	iclr19_261_3_17	 You should link to this literature (mostly in NLP) and contrast your task/model with theirs.	 NEG
976	midl19_51_2_16	 5- Obtaining quantitative comparison results for staining accuracy is not feasible due to the reasons clearly defined by the authors.	 NEG
374	iclr19_261_3_11	 Please provide variance measures on your results (within model configuration, across scene examples).	 NEG
1208	midl20_96_3_19	 I am advising regulatory decision makers and do active research in clinical environments.	 NA
541	iclr20_1042_2_19	 Similarly, the proposed rejection sampling scheme of OCDVAE is not consistent with the theory of VAEs and it's a post-hoc tweak that is not theoretically expected to provide a pdf of data with lower KL divergence to the true data pdf.	 NEG
801	iclr20_880_2_21	 The training should be done by using the small network.	 NEG
683	iclr20_526_3_4	 As others have found in the past, a variational approximation to the partition function contribution to the loss function (i.e. the negative phase) results in the loss of the variational lower bound on log likelihood and the connection between the resulting approximation and the log likelihood becomes unclear.	 NA
982	midl19_52_2_1	 The authors test their algorithm on a dataset of 95 subjects for neuromuscular disease.	 NA
231	graph20_61_2_9	 The proposed methodology of design and development relies on well established practices: eliciting requirements through focus groups, designing using action design research framework, implementation through agile development, evaluating the system through uncontrolled longitudinal studies and feedback sessions.	 POS
147	graph20_39_3_8	 While identifying the uniqueness of each patients medical conditions and how/why they record information is important, I think this could be greatly shortened to the most pertinent points to demonstrate the differences.	 POS
1321	neuroai19_36_1_9	" As well as whether or how adversarial attacks (as framed) might have relevance to neuroscience."""	 NEG
167	graph20_45_2_2	 This approach preserves the readability of correlational patterns from the original PCP while making cluster assignments more obvious than alternatives relying on edge bundling and on just the use of line color.	 NA
72	graph20_29_3_40	 This would be good to report, either way, even though only a small number of trials was removed overall.	 NEG
70	graph20_29_3_38	 CLARITY Removing tap points that are further than a fixed distance away from the target center will likely affect W levels differently.	 NA
925	midl19_49_1_21	 other comments: - The authors use 2D images to represent leaflet shapes, I'm concerned whether 2D photograph is precise enough.	 NEG
503	iclr19_938_3_8	 MAAC does not consistently outperform baselines, and it is not clear how the stated explanations about the difference in performance apply to other problems.	 NEG
171	graph20_45_2_6	 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.	 NEG
130	graph20_39_2_12	 Overall, the analysis lacks clarity, rigour and situated in the literature.	 NEG
168	graph20_45_2_3	 The implementation of the proposed visualization requires tackling several interesting aspects including a scheme to connect lines between duplicated axes by drawing Hermite spline segments that preserve the line slopes at the axes and a layout optimization based on an A* algorithm to compute the shortest path ordering of duplicated axes.	 NA
264	iclr19_1091_1_2	 The paper is easy to read, and seems technically sound.	 POS
507	iclr19_997_3_0	 Summary This paper proposes an evolutionary-based method for the multi-objective neural architecture search, where the proposed method aims at minimizing two objectives: an error metric and the number of FLOPS.	 NA
708	iclr20_526_3_29	 While I understand the stance taken by the authors that these methods leverage the tractability of the conditional distributions, these strategies are sufficiently general to be considered widely applicable and a true competitor for AdVIL.	 NEG
1187	midl20_90_2_8	 The results are also very nice.	 POS
272	iclr19_1091_1_10	 In the main text, no results are presented that warrant such a conclusion.	 NEG
799	iclr20_880_2_19	 In fact, each composing matrix is initialized randomly.	 NA
420	iclr19_304_3_34	 While for criterion 1 you define overfitting as 'above the diagonal line and underfitting as below the line, which is at least measurable depending on sample density of the randomization, such criteria are missing for C2 and C3.	 NEG
1102	midl20_119_2_3	 Improvement on plaque detection is signification.	 POS
879	midl19_36_2_4	" Predicting the confidence map with fully convolutional networks was initially done by : ""Microscopy Cell Counting with Fully Convolutional Regression Networks"", W. Xie, J.A."	 NA
696	iclr20_526_3_17	 I note that I am aware of the theoretical representation differences between directed and undirected models, I am wondering how these differences actually matter in practical applications at scale.	 NEG
1109	midl20_127_4_4	 If a sonographer is able to acquire these images, they are also able to perform these measurements.	 NEG
42	graph20_29_3_10	" Perhaps worse, the paper immediately jumps from this patched-together explanation, straight to calling it a ""novel finding"", and then to suggesting design guidelines from it, as if it was now a proven fact."	 NEG
56	graph20_29_3_24	 I might be wrong.	 NA
1151	midl20_71_1_8	 Overall, the problem the paper tackles is critical, and the proposed network component is effective to some extent.	 POS
1002	midl19_52_2_21	 The reason for high performance of the proposed method can be explained with the required number of parameters to train the method.	 NA
132	graph20_39_2_14	 Lastly, in HCI, there is a movement towards ideas about participatory design, user-centred design, value-sensitive design and so on.	 NA
1193	midl20_96_3_4	 Several experiments are proposed and results are presented.	 NA
977	midl19_51_2_17	 It is necessary to provide more qualitative information regarding the staining results in addition to confirmation from two expert pathologists.	 NEG
38	graph20_29_3_6	 The paper doesn't even acknowledge that this lack of success could simply be due to a lower external validity than the authors hoped for.	 NEG
306	iclr19_1333_1_4	" This makes it for me not possible to advice publication as is."""	 NA
492	iclr19_866_1_25	 In fact Mei et al. 2016 requires no human annotation or linguistic knowledge.	 NA
837	midl19_14_2_11	 It is not clear from the explanation in Section 3.1 how the authors deal with the differences in resolution between DRIVE and IDRID data sets.	 NEG
927	midl19_49_1_23	 Though this is not the issue to be considered in this work.	 NA
270	iclr19_1091_1_8	 The discussion of the results reflects this, but the introduction and conclusion suggest otherwise.	 NEG
408	iclr19_304_3_22	 Is that an assumption?	 NA
691	iclr20_526_3_12	 Relevance and Significance: This paper is highly relevant to the ICLR community and -- to the extent that one believes that training and inference in MRFs is important -- also significant.	 POS
1233	neuroai19_23_1_2	 The work is lacking a discussion of the most recent work in the similarity of visual processing in convnets to brain data, which incorporate recurrence into convnets (Nayebi et al. 2018, Kubilius et al. 2018 and 2019), thereby potentially allowing for similar behavior as a PredNet.	 NEG
166	graph20_45_2_1	 The visualization represents clusters of data points in multivariate data by duplicating axes from the canonical PCP visualization to represent 2D subspaces of the multivariate data.	 NA
713	iclr20_526_3_34	 More detail for this application of AdVIL would be nice.	 NEG
71	graph20_29_3_39	 I imagine that more of these errors occurred in the W=10mm condition.	 NA
556	iclr20_1493_2_14	 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).	 NA
946	midl19_51_1_14	 The contribution is therefore incremental, building on top of well-known techniques.	 NEG
971	midl19_51_2_11	 4- The authors conclude that the despeckling NN is crucial to obtain realistic images, however, the results presented in Figures 8 and 9 do not provide enough information to support this conclusion.	 NEG
961	midl19_51_2_1	 The aim for this work is to provide an image that is familiar to the pathologists such that it will remove the need for specific training for CM interpretation.	 NA
1132	midl20_56_4_14	 In Table 3., the result of the proposed method is slightly higher than the CSM.	 NA
1318	neuroai19_36_1_6	" 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."	 POS
1098	midl20_108_3_14	" The work also raises some interesting points regarding multi-task training for pathology and with further work could be a good paper."""	 POS
603	iclr20_2046_2_8	 For example, what kind of additional benefit will it bring when integrating the priority queue into the MCTS algorithms?	 NEG
547	iclr20_1493_2_4	 The contribution of the two datasets (the symmetric and asymetric CelebA) is, in my opinion, an extremely important contribution in studying adversarial robustness and on their own these datasets warrant further study.	 POS
668	iclr20_2157_3_15	" Is it just smoothing? """	 NEG
316	iclr19_1399_1_9	 The formalization that the authors proposed is basically the definition of curriculum learning.	 NEG
1256	neuroai19_26_1_12	" Authors could also add some context by considering related works in the computational neuroscience literature, e.g. Stroud et al. Nature Neurosciencevolume 21, pages 17741783 (2018) and pseudo-url (though the latter is very recent)."""	 NEG
1199	midl20_96_3_10	 The task itself would imply that a deep network classifier is potentially an overkill.	 NA
4	graph20_25_2_4	 The results show that although it took longer for participants to create their passwords with BendyPass, they were able to recall and enter them quicker with BendyPass than with PIN.	 NA
595	iclr20_2046_2_0	 This paper proposes A*MCTS, which combines A* and MCTS with policy and value networks to prioritize the next state to be explored.	 NA
789	iclr20_880_2_9	 Now, if internal matrices have more dimensions of the rank of the original matrix, the product of the internal matrices is exactly the original matrix.	 NA
748	iclr20_727_1_3	 They show by means of extensive experiments on real as well as synthetic data that their approach is able to attain and often surpass state of the art predictive models which rely on parametric modelling of the intensity function.	 POS
1186	midl20_90_2_7	 Contrast normalization yielded the best results for detecting meniscus tears, and layer normalization for detecting the remaining pathologies.The algorithm was explained very well.	 POS
900	midl19_41_1_0	 To investigate whether a conditional mapping can be learned by a generative adversarial network to map CTP inputs to generated MR DWI that more clearly delineates hyperintense regions due to ischemic stroke.	 NA
642	iclr20_2094_1_16	 Namely, the state representation is ambiguous: pseudo-formula is obviously not a boolean variable, but a boolean vector (where each component is associated with an item).	 NEG
1194	midl20_96_3_5	 automatic patient data anonymity and data cleansing are important topics - the results look good with a big but (see below) - this is clearly an application paper, testing well known methods in a new scenario.	 POS
817	midl19_13_2_2	 This is an important advantage for leveraging hundreds of recorded cases without having available segmentations.	 NA
1045	midl19_59_3_13	 Not 100% clear if the IMM method used in the experiments is the method described in section 3.2 (alpha=1/T) ?	 NEG
1237	neuroai19_23_1_6	 For this result to be convincing, I would like to see some reasons why the authors think PredNet is outperforming previous models.	 NA
898	midl19_40_3_16	 Those three papers should be included in the state-of-the-art section: - Constrained convolutional neural networks for weakly supervised segmentation, Pathak et al., ICCV 2015 - DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks, Rajchl et al., TMI, 2016 - Constrained-CNN losses for weakly supervised segmentation, Kervadec et al., MIDL 2018 Since the AJI and object-level Dice are not standard and introduced in other papers, it would be easier to put their formulation back in the paper, so the reader does not have to go look for it.	 NEG
452	iclr19_601_3_6	" Even if intuitively understandable, all parameters in equations should be explicitly described (e.g., h,w,H,W in eq.1)"""	 NEG
94	graph20_36_1_8	 The system does not seem to follow a particular rationale.	 NEG
95	graph20_36_1_9	 The fact that participants complained about the lack of information about syrup pouring reveals that this is more a trial and error approach than an informed design procedure.	 NEG
1149	midl20_71_1_6	 If it's the latter one, is the convolution done with a 4D filter?	 NEG
839	midl19_14_2_13	 The segmentation architecture does not use batch normalization.	 NA
534	iclr20_1042_2_12	 This is not true in a beta-VAE.	 NEG
153	graph20_43_1_0	" This paper presents two variations of the standard Fitts' law study, to understand the effect of (1) a situation where targets initially appear with a given size (called the ""visual width"" in the paper) but are revealed to have a larger clickable size revealed once the cursor gets close (called the ""motor width"") or vice versa; and (2) different gaps between targets arranged side-by-side."	 NA
997	midl19_52_2_16	 Did the authors considered to utilize complex valued networks for this task?	 NEG
585	iclr20_1724_2_2	 The construction of the dataset focuses on demonstrating that compositional action classification and long-term temporal reasoning for action understanding and localization in videos are largely unsolved problems, and that frame aggregation-based methods on real video data in prior work datasets, have found relative success not because the tasks are easy but because of dataset bias issues.	 NA
861	midl19_14_2_35	 The abstract should be improved.	 NEG
58	graph20_29_3_26	" p. 2) That seems quite a stretched ""contribution"", at least in the absence of actual data about how long designers do spend on testing width values today."	 NEG
463	iclr19_659_2_10	 This problem is important for practical usage.	 NA
562	iclr20_1493_2_21	 Discussion/interpretation of the results: - Sufficient vs necessary: While the experimental design and results are both of very high quality, I am slightly confused about the interpretation of the results.	 POS
61	graph20_29_3_29	 These are differences between values that are already expressed in percents.	 NA
901	midl19_41_1_1	 To perform image-to-image translation from multi-modal CT perfusion maps to diusion weighted MR outputs To make use of generated MR data inputs to perform ischemic stroke lesion segmentation.	 NA
24	graph20_25_2_25	 Association for Computing Machinery, New York, NY, USA, 37643774.	 NA
1384	neuroai19_59_3_9	 For instance, it is hard to see differences between the cue periods in the bottom two heatmaps, but differences may appear in some numerical measure of the average discriminability over these regions.	 NEG
878	midl19_36_2_3	 In my view, the proposed methods are not completely novel, I think the authors are suggested to cite them, just name a few.	 NEG
244	graph20_61_2_22	 Figure 4: I would suggest to split the figure into 2 rows (3.5 and 3.6) and annotate columns in black font over white paper background, instead of white font over blue application background: with a low zoom level on my PDF reader, I had first confused these annotations with potential widgets in the application.	 NEG
25	graph20_26_3_0	 Thank you for submitting a revised version of this submission, and addressing concerns raised in the previous round of reviews.	 NA
222	graph20_61_2_0	 This submission reports on the creation of a system to help medical residents and their reviewers to assess their learning using an information visualization dashboard, designed for and with them in a participatory process, deployed in their setting, and evaluated with them through a longitudinal study.	 NA
795	iclr20_880_2_15	 The approximation act as the non-linear layers among linear layers.	 NA
105	graph20_36_1_19	 It would have been a good start for a design rationale.	 NEG
652	iclr20_2094_1_26	 Etc. (f) Even if the aforementioned issues are fixed, it seems that the framework is using many hyper-parameters (\gamma, \beta, \alpha_t, etc.) which are left unspecified.	 NEG
509	iclr19_997_3_2	 In the exploration step, architectures are sampled by using genetic operators such as the crossover and the mutation.	 NA
131	graph20_39_2_13	 With a few grammatical typos, it reads as a thread of different perspective, with little grounding in HCI and related field.	 NEG
1055	midl20_100_1_6	 I suggest you either argue for the novelty or remove the claim from the paper.	 NEG
565	iclr20_1493_2_24	 In fact, if real-world datasets end up being like the asymmetric dataset, then the results of this paper would actually indicate the *opposite* of the above statement.	 NEG
508	iclr19_997_3_1	 The proposed method consists of an exploration step and an exploitation step.	 NA
1396	neuroai19_59_3_21	 The paper in the process reveals some (expected) results about how spiking RNNs behave on a working memory task.	 POS
75	graph20_35_1_1	 The first study explores how users respond to new node ideas suggested by the tool and whether that creates more detailed maps.	 NA
36	graph20_29_3_4	 BLAMING AGE Honestly, I found it quite a weak argument to put the lack of generalization of the approach on age (p. 10).	 NEG
1230	neuroai19_2_2_20	" And emphasize that this only solves credit assignment for certain types of learning problems (at the moment)."""	 NEG
57	graph20_29_3_25	" by reducing the time and cost of conducting user studies, our model will let them focus on other important tasks such as visual design and backend system development, which will indirectly contribute to implementing better, novel UIs."""	 NA
196	graph20_56_1_3	 The approach is interesting and the use cases described demonstrate the technique well.	 POS
1333	neuroai19_37_3_11	 Hardly what I'd call moderate effort.	 NEG
1332	neuroai19_37_3_10	" 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."	 NA
1075	midl20_100_1_26	 Having said that, if the model predictions does not change, then AUC does not change.	 NA
477	iclr19_866_1_10	 The trajectory encoder operates differently for goal-oriented vs. trajectory-oriented instructions, however it is not clear how a given instruction is identified as being goal- vs. trajectory-oriented.	 NEG
86	graph20_36_1_0	 This paper presents a projection system to help unexperienced people to draw latte art on a cappuccino.	 NA
334	iclr19_242_2_10	 The improvement on test errors does not look significant.	 NEG
899	midl19_40_3_17	" Replacing (a), (b), ... by Image, ground truth, ... in figures 2, 3, and 4 would improve readability. """	 NA
1366	neuroai19_54_3_5	 1) If I understand correctly, attribution is computed only for a single OSR stimulus video.	 NEG
781	iclr20_880_2_1	 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.	 NA
1082	midl20_100_1_33	 Finally, I would very much have liked to to see a frame from one of the videos.	 NEG
1087	midl20_108_3_3	 The authors evaluated the quality of these representations on multiple tasks, illustrating the added benefit of their multi-task system and the utility of using multiple tasks to supervised the feature extraction.	 NA
538	iclr20_1042_2_16	 I.e., there are two different probabilistic models modeling the same data in inconsistent ways and one or the other is used depending on the part of the system.	 NEG
666	iclr20_2157_3_13	 I am also not clear on where the image attribution prior comes from for the image task.	 NEG
418	iclr19_304_3_32	 My main concern here, besides the motivations that I did not fully understand (s.b.	 NEG
1125	midl20_56_4_3	 The idea of learning convolution weights for different input image quality is novel.	 POS
1306	neuroai19_34_2_8	 Generally, great paper.	 POS
1340	neuroai19_37_3_18	 When it hears an incoming pattern of spikes that matches a pattern it knows, it responds with a spike of its own.	 NA
558	iclr20_1493_2_16	 Excessive Invariance causes Adversarial Vulnerability (pseudo-url): Jacobsen et al offers an explanation for adversarial examples based on the fact that NNs are not sensitive to many task-relevant changes in inputs, which seems to tie in nicely to the discussion in this paper, as under the presented setup the Bayes-optimal classifier will certainly exploit (and be somewhat sensitive) to such changes.	 NA
1183	midl20_90_2_4	 The method was tested on two different datasets, which is impressive.	 POS
1010	midl19_52_2_29	" c- Quantitative results can be mentioned in the abstract. """	 NEG
717	iclr20_57_3_1	 To solve this problem, the authors first applied distant supervision technique to harvest hard-negative training examples and then transform the original task to a multi-task learning problem by splitting the original labels to positive, hard-negative, and easy-negative examples.	 NA
1197	midl20_96_3_8	 Why hasn't the semi-supervised paradigm be explored in more detail instead of only using a few biasing iterations with user input?	 NEG
1024	midl19_56_3_13	 Their voxel resolution is only sligthly smaller than in this work (120x120x40), with a similar latent dimensionality (64D, here: 3*29=87).	 NA
1017	midl19_56_3_6	 Authors could comment on how their model could be incorporated into (e.g. deep) segmentation approaches, because I do not see an immediate way to do that without requiring the (precise) image-based localization of mandible landmarks in a test volume.	 NA
1127	midl20_56_4_7	 It conducts extensive experiments for three different settings and the results demonstrate the effectiveness of the proposed method.1).	 POS
1095	midl20_108_3_11	 It would be helpful to put the results in context with all other methods such as automatic and semi-automatic methods.	 NEG
8	graph20_25_2_8	 The paper is well written: the work is motivated well, the related work is mostly comprehensive, and the design and evaluation sections are clear and have enough detail for others to attempt to reproduce/replicate the study.	 POS
802	iclr20_880_2_22	 If results are significantly different, then the authors can reject the hypothesis.	 NA
1200	midl20_96_3_11	 Bluntly: surgical parts are predominantly red, non-surgical parts anything and blue/green.	 NA
1228	neuroai19_2_2_18	 Seeing if these meta-learnt rules line up with previously characterized biological learning rules is particularly interesting.	 NEG
1085	midl20_108_3_1	 The authors extended unsupervised NIC to a multi-task supervised system.	 NA
1054	midl20_100_1_5	 Plenty of works combine autoencoders with LSTMs.	 NEG
74	graph20_35_1_0	 This paper presents QCue, a tool to assist mind-mapping through suggested context related to existing nodes and through question that expand on less developed branches, including two studies, a detailed description of the algorithm design, and rater evaluation of their results.	 NA
739	iclr20_720_2_12	 In comparison to past frameworks, the approach of this paper seems less theoretically motivated.	 NEG
522	iclr20_1042_2_0	 This paper tackles the problem of catastrophic forgetting when data is organized in a large number of batches of data (tasks) that are sequentially made available.	 NA
335	iclr19_242_2_11	 If given more computing resources, and under same timing constraint, we have many other methods to improve performance.	 NA
88	graph20_36_1_2	 The results suggest that participants perform better with the system.	 NA
858	midl19_14_2_32	 I would certainly accept the paper is this experiment were included and the results were convincing.	 NA
217	graph20_56_1_24	 Why dont they include the feedback?	 NEG
886	midl19_40_3_4	 The trained network is then fine tuned with a direct CRF loss, as in Tang et al. Evaluation is performed on two datasets in several configurations (with and without CRF loss, and variation on the labels used) ; showing the effects of the different parts of the method.	 NA
173	graph20_45_2_8	" The paper would have been significantly stronger if the expected benefits were measured in a practical scenario."""	 NEG
926	midl19_49_1_22	 3D scanner such as CT, MRI, optical scanner could be more suitable for this work?	 NEG
905	midl19_49_1_0	 This paper presents a clustering method using deep autoencoder for aortic value shape clustering.	 NA
319	iclr19_1399_1_12	 While these results are scientifically interesting, I don't expect it to be of practical use.	 POS
1216	neuroai19_2_2_6	 Section 1 pitches the method as solving the credit assignment problem, citing problems with weight symmetry etc, that apply to many forms of learning.	 NA
693	iclr20_526_3_14	 In most modeling situations, one would simply impose the directed graphical model directly and skip the formalization in terms of an MRF.	 NA
266	iclr19_1091_1_4	 The contribution is minor, and the reasoning behind it could be better motivated.	 NEG
6	graph20_25_2_6	 The main strength of the paper is the experimental user study design with users who are visually impaired.	 POS
138	graph20_39_2_20	" I would encourage the authors to situate the research questions into the broader literature and determine whether they fit into some of the well-established methods informing the designing of health-related technologies. """	 NEG
163	graph20_43_1_10	 Finally, I found the study results to be difficult to interpret, as many of the results subsections are ANOVA output with little interpretation and commentary to help the reader understand what was found.	 NEG
1148	midl20_71_1_5	 2) what is the dimension of input, is it W D or H W D$ ?	 NEG
1232	neuroai19_23_1_1	 However, the contribution of the authors does not appear to extend beyond combining existing data sets with existing network architectures.	 NEG
1013	midl19_56_3_2	 The paper is written clearly.	 POS
1376	neuroai19_59_3_1	 The importance is tempered by the findings only covering what is to be expected, and not pushing beyond this or describing a path to push beyond this.	 NEG
523	iclr20_1042_2_1	 To avoid catastrophic forgetting, the authors learn a VAE that generates the training data (both inputs and labels) and retrain it using samples from the new task combined with samples generated from the VAE trained in the previous tasks (generative replay).	 NA
737	iclr20_720_2_10	 Additionally, it is well known that Option-Critic approaches (when unregularized) tend to learn options that terminate every step [2].	 NA
704	iclr20_526_3_25	 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.	 NEG
578	iclr20_1493_2_38	 While completely alleviating this concern may once again be quite difficult/impossible, it could be significantly alleviated by generating training samples dynamically (at every iteration) instead of generating a dataset in one shot and training on it.	 NEG
665	iclr20_2157_3_12	 So with that the paper positions itself not as a survey but as a method paper but lacks evidence that the method expected gradients performs better.	 NEG
19	graph20_25_2_19	 In summary, this is an interesting paper that will contribute to the GI community.	 NA
779	iclr20_855_3_15	" A way to improve the paper would be to make it clear from the beginning that these results are about Dyna-style algorithms in the Atari domain. """	 NEG
777	iclr20_855_3_13	 The paper is written as if the conclusions could be extended to model-based methods in general.	 NA
673	iclr20_305_3_5	 With this modeling step, the authors formulate an event-based policy gradient, which considers models for which goal to send to followers and when.	 NA
809	iclr20_934_1_6	 The proposed method is very similar with the unsupervised GraphSAGE, which also optimizes Eq.(7).	 NEG
137	graph20_39_2_19	 This makes the paper weak, lacking impactful significance, and thus leaning would not argue strongly towards acceptance.	 NEG
557	iclr20_1493_2_15	 While I think the datasets presented in this work are much more interesting and certainly more realistic, this work should be put in context.	 POS
1142	midl20_70_4_4	" I only regret the fact that this is a short paper, and there is therefore not enough space for a more formal description and discussion of the methodology."""	 NEG
702	iclr20_526_3_23	 I am somewhat alarmed at the use of 100 updates of the joint model q(v,h) (K1 = 100) for every update of the other parameters.	 NEG
744	iclr20_720_2_20	" 2] ""When Waiting is not an Option: Learning Options with a Deliberation Cost"" Jean Harb, Pierre-Luc Bacon, Martin Klissarov, and Doina Precup."	 NA
782	iclr20_880_2_2	 As the title reports, expanding layers seems to be the key to obtain extremely interesting results.	 NA
368	iclr19_261_3_3	 This is a very interesting task and the dataset/models are a very useful contribution to the community.	 POS
1315	neuroai19_36_1_3	 Adversarial attacks are artificial: attacker has access to gradient of the loss function.	 NA
672	iclr20_305_3_4	 A `termination' menas that an agent should stop executing the previous selected action; the leader signals as such to the agent.	 NA
327	iclr19_242_2_1	 The authors claim the proposed method has better generalization performance.	 NA
317	iclr19_1399_1_10	 There is no novelty about this.	 NEG
1063	midl20_100_1_14	" If you do use it, you cannot argue that you learn from ""a small number of labeled samples"" as done in the final paragraph of the paper."	 NEG
1031	midl19_56_3_20	 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.	 NA
528	iclr20_1042_2_6	 A normal flow is to first describe the model and what the involved variables mean, and then talk about what the loss for learning it should be, not the other way around.	 NA
526	iclr20_1042_2_4	 Unfortunately, there are several things that left me unconvinced about this paper: 1) Presentation of the paper - Variables x, y, z are introduced and talked about without explanation.	 NEG
87	graph20_36_1_1	 There is a user study comparing participants performance with the system, and with watching explanatory videos only.	 NA
479	iclr19_866_1_12	 A contrastive loss would seemingly be more appropriate for learning the instruction-goal distance function.	 NEG
338	iclr19_242_2_14	 The experiments are not strong.	 NEG
658	iclr20_2157_3_5	 I think a few papers to have a look at are a survey article about graph based biasing pseudo-url as well as methods for using graph convolutions with biases based on graphs: pseudo-url and pseudo-url .	 NA
148	graph20_39_3_9	 I would have also liked to see some of the images of the visualizations for myself.	 NEG
1189	midl20_96_3_0	 The presented paper aims to label and remove irrelevant sequences from laparoscopic videos.	 NA
1289	neuroai19_32_1_15	 So while an interesting connection they did not make clear where they substantively pursue it.	 NEG
1015	midl19_56_3_4	 There are certain original aspects in this work (latent en-/decoding, inception-based decoder network, latent space interpolation, generalization to previously unseen shapes etc.), but the work may not be as original as authors suggest, since they may not be aware of a very similar work (see Cons), where some of the discussed concepts have already been proposed and explored.	 NEG
246	graph20_61_2_24	" Congratulations for opensourcing the code to potentially help other institutions with medical programs (""across Canada"", or beyond?)."	 NA
365	iclr19_261_3_0	 This paper presents CoDraw, a grounded and goal-driven dialogue environment for collaborative drawing.	 NA
660	iclr20_2157_3_7	 It is not clear which model is used in Figure 2.	 NEG
1051	midl20_100_1_2	 The method is compared to five embryologists and results clearly shows that learning directly from the clinical outcome outperfoms embryologists by a large margin.	 POS
198	graph20_56_1_5	 The basics of the technique are well-described: the user draws a shape that the system then selects matches for, based on two similarity metrics (one calculated by Pearson's coefficient and the other by a PCA algorithm).	 POS
1080	midl20_100_1_31	 A mior nitpick: You define all abbreviations except for UBar.	 NEG
797	iclr20_880_2_17	 If this does not lead to the same improvement, there should be a value in the expansion.	 NEG
404	iclr19_304_3_17	" What do you mean by ""easier to learn""?"	 NA
688	iclr20_526_3_9	 Specifically, it states that the generator is minimizing a Jenson-Shannon divergence which has a fixed point at the true data density.	 NA
483	iclr19_866_1_16	 Where do they come from?	 NEG
158	graph20_43_1_5	 While I appreciate the overall motivation, I'm not sure if a Fitts' law study is the right approach for going about understanding the effects of these kinds of interfaces.	 NEG
315	iclr19_1399_1_8	 It is difficult for me to accept it.	 NA
256	iclr19_1049_1_1	 The method does not make major changes to the network structure, but by modifying the calculations in the network.	 NA
1014	midl19_56_3_3	 Methods, materials and validation are of a sufficient quality.	 POS
1116	midl20_135_3_2	 Some points to address are listed in the following: The early stopping is not clear.	 NEG
384	iclr19_261_3_25	 Speaker-follower models for vision-and-language navigation.	 NA
177	graph20_53_2_4	" The system requires that the virtual objects are implemented in a way that they do not only present an outside facade but also contain primitives of its components not displayed on the outside (i.e., ""internal faces"")."	 NA
655	iclr20_2157_3_1	 The structure of the paper is strange because it discusses attribution priors but then they are not used for the method.	 NEG
314	iclr19_1399_1_7	 The authors claim the formalization of the problem to be one of their contributions.	 NA
357	iclr19_242_2_35	 Like the authors said, they did not propose new data augmentation method, and their contribution is how to combine data augmentation with large-batch training.	 NA
298	iclr19_1291_3_11	 For example: Is there any difference between the results of table 1, if we look at the cooperative setup?	 NEG
455	iclr19_659_2_2	 Experimental results demonstrate that the proposed method can achieve better performance than non-ensemble one under the same training steps, and the decision space can also be stabilized.	 NA
543	iclr20_1493_2_0	 This paper proposes studying adversarial examples from the perspective of Bayes-optimal classifiers.	 NA
1026	midl19_56_3_15	 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).	 NEG
732	iclr20_720_2_5	 Additionally, it is very much not clear why someone, for example, would select the approach of this paper in comparison to popular paradigms like Option-Critic and Feudal Networks.	 NEG
527	iclr20_1042_2_5	 The graphical model or factorization assumptions are not even mentioned until after the loss has been defined.	 NEG
1167	midl20_77_4_14	" Section 3: combing should be combining """	 NEG
159	graph20_43_1_6	 Or, put in a different way, I'm not sure if the study results are all that valuable for designers (given that it's looking at 1D pointing), or whether this type of interface is common enough that it's useful to have a new Fitts' law formula to account for it.	 NEG
1104	midl20_119_2_5	" This will provide more insights or explanations."""	 NA
360	iclr19_242_2_38	 However, the authors quote a previous paper that use different data augmentation and (potentially) other experimental settings.	 NA
1229	neuroai19_2_2_19	 Define the model more explicitly.	 NEG
202	graph20_56_1_9	 I had to re read the paper back and forward to finally tease out what I think is the way it works.	 NEG
963	midl19_51_2_3	 This will potentially bring us closer to rapid evaluation of lesions during surgical operation using fast CM.	 POS
674	iclr20_305_3_6	 The authors compare this approach on 4 environments with M3RL, which also solves (extensions of) principal-agent problems.	 NA
112	graph20_36_1_26	 This might have affected the metric, with no real impact on the perceived result.	 NA
771	iclr20_855_3_7	 Then, in Figure 2, human normalized scores are reported for varying amounts of experience for the variants of Rainbow, and compared against SiMPLe with 100k interactions, with the claim that the authors couldn't run the method for longer experiences.	 NA
307	iclr19_1399_1_0	 In my opinion this paper is generally of good quality and clarity, modest originality and significance.	 POS
731	iclr20_720_2_4	 The two improvements in section 3.2 seem quite low level and are only applicable to this particular approach to hierarchical RL.	 NEG
422	iclr19_304_3_36	 You present a number for C2 in Section 5, but that is only applicable to the present data set (i.e. assuming that training accuracy is 1).	 NEG
684	iclr20_526_3_5	 To deal with this issue, the authors argue (in Lemma 1) that the gradient of their approximate objective is at least in the same direction as the ELBO (lower bound) objective.	 NA
1034	midl19_59_3_2	 Comparison to (unsupervised) domain adaptation methods would also have been interesting (e.g. gradient reversal (Ganin et al. 2014, Kamnitsas et al. 2016)).	 NEG
816	midl19_13_2_1	 The method introduces a self attention mechanism using weakly supervised labels, thereby avoiding the need to use more exhaustive annotations such as segmentations.	 NA
459	iclr19_659_2_6	 This idea is simple and works well.	 POS
1322	neuroai19_37_3_0	 The paper provides a broadly useful synthesis of key differences between ANN and SNN approaches.	 POS
1261	neuroai19_29_1_4	 The authors should have identified a task where networks trained on MNIST perform poorly, and then propose a different strategy or architecture.	 NEG
1079	midl20_100_1_30	 If there is an issue with Tran et al you should state it clearly, if not, you should accept their results.	 NEG
928	midl19_49_1_24	 The paper is not well organized.	 NEG
449	iclr19_601_3_3	 Experiments are convincing.	 POS
841	midl19_14_2_15	 The vessel segmentation performance is evaluated on the DRIVE data set.	 NA
80	graph20_35_1_6	 The two studies are well-described and designed studies.	 POS
1363	neuroai19_54_3_2	 3) Suggest testable hypotheses.	 NA
1118	midl20_135_3_4	 It is not clear whether T1 and T2 is available for all cases (mostly) In Table 1, bold results are not always the best, this is very misleading.	 NEG
215	graph20_56_1_22	 Having reviewed this approach with experts, the authors state that the experts did not get it, and so they choose to describe the system with a use-case method.	 NA
1381	neuroai19_59_3_6	 The statistical tools are fairly well described and appear to be well-suited for illustrating the phenomena of interest.	 POS
30	graph20_26_3_5	 However, I noted that there are several typos throughout the text, and I recommend a thorough editing pass for the camera ready.	 NEG
242	graph20_61_2_20	 The choice for visualizing rotation schedules using an interval chart rather than a more space-consuming Gantt chart widespread in time/project management is smart.	 POS
1278	neuroai19_32_1_4	 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).	 NEG
641	iclr20_2094_1_15	 d) The problem formulation is very unclear.	 NEG
209	graph20_56_1_16	 The nice video provided was helpful in showing this technique.	 POS
720	iclr20_57_3_4	 This implementation showed improvement of performance on both tasks.	 NA
435	iclr19_495_1_7	 Also, in the experiments, it is said that one can combing normalizing flows with TRPO without describing the details.	 NEG
936	midl19_51_1_4	 The general organization of the paper is sound This paper tackles a problem that is relevant to the whole medical community.	 POS
1308	neuroai19_34_2_10	 Would have been great to include another Imagenet-trained architecture, since different architectures have widely varying macaque brain predictivity, and that of VGG16 is not particularly high (Schrimpf et al., 2018 BrainScore).	 NEG
1020	midl19_56_3_9	 Further, there is always the chance that authors are not aware of every piece of related literature (in all of computer graphics), as it might be the case here.	 NEG
1273	neuroai19_3_3_8	" 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. """	 NA
551	iclr20_1493_2_8	 I outline these below.	 NA
544	iclr20_1493_2_1	 They construct a pair of synthetic but somewhat realistic datasetsin one case, the Bayes-optimal classifier is *not* robust, demonstrating that the Bayes-optimal classifier may not be robust for real-world datasets.	 NA
918	midl19_49_1_14	 The experiments measure the recon accuracy.	 NA
906	midl19_49_1_1	 It is the first work to identify aortic value prosthesis types using a general representation learning technique.	 NA
746	iclr20_727_1_1	 Instead of learning the conditional intensity for the point process, as is usually the case, the authors instead propose an elegant method based on Normalizing Flows to directly learn the probability distribution of the next time step.	 POS
403	iclr19_304_3_16	 As in that case correlation in the data can be destroyed by the introduction of randomness making the data easier to learn.	 NA
1380	neuroai19_59_3_5	 A comparison with Bellec et al. 2018, which looks at working memory tasks in spiking networks, would also have been appropriate.	 NEG
362	iclr19_242_2_40	 Moreover, instead of showing the consistent benefits of large batch, the authors tune the batchsize as a hyperparameter for different experiments.	 NEG
271	iclr19_1091_1_9	 The same problem also occurs for the conclusion about the robustness of SRL approaches.	 NEG
466	iclr19_659_2_13	 I think more examples, such as in section 8.1, should be put in the main text.	 NEG
1245	neuroai19_26_1_1	 The heavy lifting is seemingly done by well known architectures: default RNN & a feed-forward NN.	 NA
1027	midl19_56_3_16	 In contrast, ACNN auto-encoders train their encoder and decoder in conjunction.	 NA
277	iclr19_1091_1_15	 Due to the shared feature extractor, the contradictory objectives (and hence the need for tuning of the weights in the cost function) are still a potential problem.	 NEG
1343	neuroai19_37_3_22	" We require a new class of theories that dispose of the simplistic stimulus-driven encode/ transmit/decode doctrine. """	 NA
1326	neuroai19_37_3_4	" The paper opens ""In recent years we have made significant progress identifying computational principles that underlie neural function."	 NA
829	midl19_14_2_3	 The adversarial loss allows to leverage complementary data sets that do not have all the regions of interest segmented.	 NA
1067	midl20_100_1_18	 You argue that including embryologists decisions in the prediction is an easier task.	 NA
1074	midl20_100_1_25	 This holds for all the popular performance measures.	 NEG
442	iclr19_495_1_14	 I wonder how good the results are if these more advanced versions are used.	 NA
299	iclr19_1291_3_12	 Does their model outperform a model which has global communication with IR?	 NEG
954	midl19_51_1_22	 Is there some reference for multiplicative residual connections?	 NEG
375	iclr19_261_3_12	 Are the machinemachine pairs consistently performing well together?	 NEG
881	midl19_36_2_6	" 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. """	 NA
1044	midl19_59_3_12	" How is training till ""convergence"" (section 4.3) defined?"	 NA
1058	midl20_100_1_9	 If you want your work applied in clinics, this is much more important than improving the results.	 NEG
851	midl19_14_2_25	 The results for vessel segmentation in IDRID images do not look as accurate as those in the DRIVE data set.	 NEG
472	iclr19_866_1_5	 STRENGTHS + Decoupling instruction-to-action mapping by introducing goals as a learned intermediate representation has advantages, particularly for goal-directed instructions.	 POS
1181	midl20_90_2_2	 The normalization was followed by a BlurPool layer to solve the shift variance.	 NA
563	iclr20_1493_2_22	 First, if my understanding of the paper is correct, the experiments show that (a) the Bayes-optimal classifier can be non-robust in real-world settings, and (b) even when the Bayes-optimal classifier is robust, NNs can learn a non-robust decision boundary.	 NA
1266	neuroai19_3_3_1	 This model allows for more flexibility in modelling human behaviors in normal and pathological states.	 POS
388	iclr19_304_3_1	 Instead of using a hold-out set they propose to randomly flip the labels of certain amounts of training data and inspect the corresponding 'accuracy vs. randomization curves.	 NA
22	graph20_25_2_23	 Thumprint: Socially-Inclusive Local Group Authentication Through Shared Secret Knocks.	 NA
715	iclr20_526_3_36	" Given the comparison to PCD in the RBM setting, I am somewhat surprised that AdVIL is so competitive with VCD in the case of the DBM. """	 POS
124	graph20_39_2_6	 Second, I particularly appreciate the authors' use of different methods (focus group, interviews, and observation) but fail to see an understanding of the needed sensitivity towards participants with some form of a chronic condition.	 NEG
423	iclr19_304_3_37	 Criterion 2 (b) is not clear.	 NEG
1253	neuroai19_26_1_9	 So it fits well with the workshop theme.	 POS
1348	neuroai19_37_3_27	 There was an absence of nuance.	 NEG
957	midl19_51_1_25	 Is the math right?	 NEG
1397	neuroai19_59_3_22	 The proof-of-concept work (among others) that this can be done with spiking RNN may inspire more work in this area.	 POS
142	graph20_39_3_3	 I enjoyed the paper.	 POS
50	graph20_29_3_18	 In the example given in p. 1 (choosing between 5 or 7-mm circular icons), it is unclear why the designer would need a model, or to know by how much a 7-mm icon would improve accuracy.	 NEG
1303	neuroai19_34_2_5	 Figures exceptionally detailed and thoroughly labelled.	 POS
980	midl19_51_2_20	" This could potentially add a bias to the results presented here. """	 NA
623	iclr20_2046_2_28	 How would this affect the results?	 NA
1011	midl19_56_3_0	 Summary: Authors present AnatomyGen, a CNN-based approach for mapping from low-dimensional anatomical landmark coordinates to a dense voxel representation and back, via separately trained decoder and encoder networks.	 NA
81	graph20_35_1_7	 The level of detail in the algorithm description is a particular strength, giving a clear picture of how it works and why those choices were made.	 POS
703	iclr20_526_3_24	 For larger scale domains, I fear this could become an important obstacle to effective model training.	 NEG
482	iclr19_866_1_15	 How many are there?	 NEG
321	iclr19_1399_1_14	 I realize that they were obtained with a simple network, however, showing improvements in this regime is not that convincing.	 NEG
1163	midl20_77_4_10	 Maybe get rid of performing motions?	 NA
956	midl19_51_1_24	 Can we prove that at least visually?	 NEG
1329	neuroai19_37_3_7	 Arguably ACh and noradrenaline are more important for network states and dynamics, and equally important for plasticity as dopamine.	 NA
66	graph20_29_3_34	 the error rate difference was |29 38| = 9%.	 NA
798	iclr20_880_2_18	 3) the small improvement of the expanded network can be given by the different initialization.	 NA
1234	neuroai19_23_1_3	 How would you expect those networks to perform when trained on unlabeled video data?	 NA
990	midl19_52_2_9	 How does the temporal and spatial blocks work?	 NEG
1023	midl19_56_3_12	 CNN-based shape modeling and latent space discovery and was realized for heart ventricle shapes with an auto-encoder, and integrated into Anatomically Constrained Neural Networks (ACNNs) [1].	 NA
417	iclr19_304_3_31	 But you state it as if those measures are actually correct, which you didnt show yet.	 NEG
632	iclr20_2094_1_6	 There are various classes of BPPs, and it would be relevant to briefly present them.	 NA
1334	neuroai19_37_3_12	 Claims of efficiency of more brain-like approaches compared to AI are disingenuous.	 NEG
64	graph20_29_3_32	 However, that still makes a 10% prediction error quite high in my book, and worthy of contextualization.	 NA
139	graph20_39_3_0	 This paper describes the exploration of designing data visualizations of daily medical records by patients, and what kinds of visualizations may assist providers in best keeping track with an patients medical status.	 NA
1123	midl20_56_4_1	 A novel dynamic weight prediction model is proposed to learn to predict the kernel weights for each convolution based on different context settings.	 NA
968	midl19_51_2_8	 2- It is not clear why the histology images were used for denoising network training.	 NEG
491	iclr19_866_1_24	 The paper incorrectly references Mei et al. 2016 when stating that methods require a large amount of human supervision (data annotation) and/or linguistic knowledge.	 NEG
939	midl19_51_1_7	 The quantitative results delivered by the de-speckling images, which seem to be computed using simulated realization of random speckle noise, look also convincing.	 POS
974	midl19_51_2_14	 For instance, Figure 9 needs to use the same images presented in Figure 8 to provide enough support for the need of despeckling network.	 NEG
532	iclr20_1042_2_10	 Tables and figures are inconveniently far from where they are referenced in the text.	 NEG
65	graph20_29_3_33	 Perhaps I misunderstood something.	 NA
1351	neuroai19_53_1_1	 This sheds new light on how artificial network algorithms might be implementable by the brain.	 POS
783	iclr20_880_2_3	 Moreover, the proposed approach is extremely simple and it is well explained in Section 2 with equations (1) and (2).	 POS
1170	midl20_85_3_2	 From table 1, it is clear that ECE is much lower for the proposed method.	 POS
296	iclr19_1291_3_9	 Authors provide 3 baselines: 1) no communication, but IR 2) no communication, no IR 3) global communication, no IR (commNet) I think having a baseline that has global communication with IR can show the effect of selective communication better.	 NA
591	iclr20_1724_2_8	 It is a well-argued, thoughtful dataset contribution that sets up a reasonable video understanding dataset.	 POS
419	iclr19_304_3_33	 is the lack of measurable criteria.	 NEG
438	iclr19_495_1_10	 BTW, in the Section 4.3, what does [-1, 1]^2 mean?	 NEG
261	iclr19_1049_1_6	" Minor Example 2: ""A"" -> ""AI""."""	 NEG
724	iclr20_57_3_8	 The objective function L3 is not well justified.	 NEG
1139	midl20_70_4_1	 The novelty of the proposed framework is to take the label structure into account and to learn label dependencies, based on the idea of conditional learning in (Chen et al., 2019) and the lung disease hierarchy of the CheXpert dataset (Irvin and al., 2019).	 POS
281	iclr19_1091_1_19	 Why is it worthwhile to study this task separately?	 NEG
626	iclr20_2094_1_0	 This paper aims at solving geometric bin packing (2D or 3D) problems using a deep reinforcement learning framework.	 NA
876	midl19_36_2_1	 The authors consider the problem of nuclei detection, and propose to decompose the task into three subtasks, trying to predict the confidence map, localization map and a weight map.	 NA
935	midl19_51_1_3	 They present an architecture making use of two network, a de-noise/de-speckle network (trained independently on one of the two types of CM images used in this work) followed by a generative network (cycle gan).	 NA
1354	neuroai19_53_1_4	 Given its technical details it was reasonably straightforward to follow.	 POS
188	graph20_53_2_15	 I strongly recommend that this be moved to a subsection of the previous section, i.e., the Results section.	 NEG
1121	midl20_135_3_7	" obtained an F1-score of 0.68 -> 0.686? """	 NA
251	graph20_61_2_29	 2016. doi: 10.1109/TVCG.2015.2467613 - Papers from the IEEE VIS'16 Workshop: Logging Interactive Visualizations & Visualizing Interaction Logs pseudo-url DESIGN CHOICES AND INSIGHTS GAINED I found the design considerations to be mostly obvious and known to designers and developers of user interfaces and information visualization.	 NA
583	iclr20_1724_2_0	 The paper introduces CATER: a synthetically generated dataset for video understanding tasks.	 NA
903	midl19_41_1_3	 The authors had better compare segmentation result between CTP with orginal MRI and CTP with CGAN MRI.	 NEG
967	midl19_51_2_7	 The authors should validate their selection of two step approach (NN + filter) compared to an end-to-end FCN (with an additional loss like TV) for the despeckling network.	 NEG
1180	midl20_90_2_1	 The main contribution of the work was adding a normalization step to the network, and learning the affine transformation parameters during the training.	 NA
514	iclr19_997_3_7	 Cons - The contribution of the proposed method is not clear to me.	 NEG
820	midl19_13_2_5	 The different loss functions are all based on previously proposed approaches and exploited in this case for this dual background/foreground problem.	 NEG
706	iclr20_526_3_27	 Perhaps PCD-1 results in performance that is far better than AdVIL.	 NEG
487	iclr19_866_1_20	 It would be better to evaluate on one of the few common benchmarks for robot language understanding, e.g., the SAIL corpus, which considers trajectory-oriented instructions.	 NEG
663	iclr20_2157_3_10	" It is not clear if the paper is presenting ""expected gradients"" or existing attribution priors."	 NEG
273	iclr19_1091_1_11	 The appendix includes some tests in this direction, but conclusions should not be based on material that is only available in the appendix.	 NEG
685	iclr20_526_3_6	 The result is fairly obvious, but the conditions for validity have interesting consequences for the training algorithm, as it relates the approximation error to the norm of the gradient of the ELBO loss.	 POS
1159	midl20_77_4_6	 Was the setup the same as in Gessert et al (2019), i.e. with a robot moving the object and mirrors moving the OCT FOV?	 NA
1281	neuroai19_32_1_7	 Seemed broad and was unsupported by any citations and to my knowledge GANs and VAEs have been used specifically to find interpretable features.	 NA
448	iclr19_601_3_2	 The paper is fairly well written and structured, and it seems technically sound.	 POS
411	iclr19_304_3_25	 Page 4, Monotony.	 NA
458	iclr19_659_2_5	 Using ensembles of Q-function can naturally reduce the variance of decisions, so it can speed up the training procedure for certain tasks.	 NA
1276	neuroai19_32_1_2	 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.	 NEG
1218	neuroai19_2_2_8	 The efficiency of backprop should be mentioned in the intro if it is something this work is aiming to address.	 NEG
667	iclr20_2157_3_14	 Where is this extra information?	 NEG
1307	neuroai19_34_2_9	 Clear presentation of thorough work, exploring an important question.	 POS
149	graph20_39_3_10	 Another concern I have is about the disparity between the emphasis on how each patients medical history (and in turn, visualization) is unique, and then the proposal of general design guidelines for creating patient visualizations.	 NEG
1353	neuroai19_53_1_3	 It would have been nice to present a figure showing how e-prop yields eligibility traces resembling STDP, as this is one of the key connections of this work to biology.	 NEG
955	midl19_51_1_23	 How do we know that the network is learning 1/F (inverse of speckle noise)?	 NEG
733	iclr20_720_2_6	" The authors mention that Feudal approaches ""employ different rewards for different levels of the hierarchy rather than optimizing a single objective for the entire model as we do."""	 NA
218	graph20_56_1_25	 Surely they found out useful information.	 NA
239	graph20_61_2_17	 Data characterization is assorted with visibly clear understanding and explanation of the domain.	 POS
1293	neuroai19_32_1_19	 They have some qualitative evaluation in images of filters but they could explore the parameter space to understand what led to these features.	 NA
725	iclr20_57_3_9	 It would be important to see if the proposed method is also beneficial with the state of the art neural networks on the two applications.	 NEG
192	graph20_53_2_19	" General minor issues: - ""users authoring process"" -> ""users' authoring process"""""	 NA
951	midl19_51_1_19	 I feel it would have been extremely interesting to evaluate the performance of those same clinicians (and others) diagnosing cancer using both H&E stained image and CM images of the same patient (or patient distributions) vs a control group.	 NA
191	graph20_53_2_18	 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).	 POS
1202	midl20_96_3_13	 Do we really need a labelled ground truth here?	 NEG
828	midl19_14_2_2	 This is important when processing these images, where anatomical and pathological structures usually share similar visual properties and lead to false positive detections (e.g. red lesions and vessels, or bright lesions and the optic disc).	 NA
832	midl19_14_2_6	 The strategy proposed to tackle this issue is not novel as adversarial losses have been used before for image segmentations.	 NEG
644	iclr20_2094_1_18	 We dont know if it is an episodic MDP (which is usually the case in DRL approaches to combinatorial optimization tasks).	 NA
984	midl19_52_2_3	 Moreover, they have done some ablation studies to show the importance of the receptive field and temporal frames for MRF reconstruction.	 NA
1316	neuroai19_36_1_4	 For FA networks, it's unclear why an attacker could not access true gradient, and be forced to use the approximate gradient.	 NEG
1356	neuroai19_53_1_6	 Gives important new results about how eligibility traces can be used to approximate gradients when adequately combined with a learning signal.	 POS
16	graph20_25_2_16	 This brings up another issue: the PIN baseline is the current de facto standard, but other baselines (e.g., physical PIN from the previous paragraph) would position the work better and help justify use of BendyPass very different and unfamiliar interaction modality.	 NEG
1275	neuroai19_32_1_1	 They do not make direct comparisons to previous models or study quantitatively the results of the model with respect to its parameters.	 NEG
1124	midl20_56_4_2	 Experiments show that the proposed method outperforms the model trained on the context-agnostic setting and acquires similar results to models trained by context-specific settings.1).	 NA
582	iclr20_1493_2_43	" I would be more than happy to significantly improve my score if these concerns can be addressed in the revision and corresponding rebuttal."""	 NA
1327	neuroai19_37_3_5	" While not yet complete, we have sufficient evidence that a synthesis of these ideas could result in an understanding of how neural computation emerges from a combination of innate dynamics and plasticity"" What follows is a useful survey of a selection of ideas, by far not complete."	 NEG
983	midl19_52_2_2	 They compare their method with two state of the art deep learning methods and illustrate superior performance on NRMSE, PSNR, SSIM and R2 metrics.	 NA
627	iclr20_2094_1_1	 Namely, the framework is based on the actor-critic paradigm, and uses a conditional query learning model for performing composite actions (selections, rotations) in geometric bin packing.	 NA
1297	neuroai19_32_1_23	" This warranted some potentially interesting discussion though admittedly 4 pages isnt a lot of space."""	 NA
323	iclr19_1399_1_16	 I suggest checking the papers citing Bengio et al. (2009) to find lots of closely related papers.	 NA
1140	midl20_70_4_2	 The method is then shown to significantly outperform the state-of-the-art methods of (Irvin and al., 2019; Allaouzi and Ahmed, 2019).	 POS
631	iclr20_2094_1_5	 b) In the related work section, very little is said about Bin Packing Problems.	 NEG
1097	midl20_108_3_13	 The method is well explained and the validation is strong with convincing results versus state of the art methods.	 POS
778	iclr20_855_3_14	 Can we get the same conclusions on a different domain where other model-based methods have been successful; e.g. continuous control tasks?	 NA
1110	midl20_127_4_5	 Such a system might speed up this process.	 NA
437	iclr19_495_1_9	 The experiments also talk about 2D bandit problem, and again, without any descriptions.	 NEG
119	graph20_39_2_1	 The authors utilise a range of methods in order to better understand the attitude and perspective of both participants to provide relevant and appropriate design insights for developing tools to support the visualisation of data collected during a clinical visit.	 NA
1086	midl20_108_3_2	 A hard-parameters sharing network was presented with a shared, compressed representation branching out in task-specific networks.	 NA
790	iclr20_880_2_10	 Whereas, if internal matrices have a number of dimensions lower than the rank of the original matrix, these matrices act as filters on features or feature combination.	 NA
341	iclr19_242_2_17	 It is unclear what is the default batchsize for Imagenet.	 NEG
815	midl19_13_2_0	 This paper presents a method for the instrument recognition task from laparoscopic images, using two generators and two discriminators to generate images which are then presented to the network to classify surgical gestures.	 NA
2	graph20_25_2_2	 The evaluation consisted of two sessions (taking place one week apart) in which participants first created their passwords and then used them to sign in.	 NA
229	graph20_61_2_7	" I would suggest to use active voice instead of passive to clarify who contributed what (""The system was developed"", ""...was installed"")."	 NEG
1238	neuroai19_23_1_7	 For example, is there something different about the feature maps that support this?	 NEG
1347	neuroai19_37_3_26	" Largely contradicts this one ""It is probable that revolutionary computational systems can be created in this way with only moderate expenditure of resources and effort""  I felt the paper could have done more to link with current state-of-the-art AI approaches."	 NEG
76	graph20_35_1_2	 The second study expands on those findings to balance the depth and breadth of mind maps creation.	 NA
1291	neuroai19_32_1_17	 The main place to improve is to have some quantitative analysis of the quality of their model perhaps MSE of image reconstruction.	 NA
1126	midl20_56_4_5	 The method part is well-written and easy to understand.	 POS
371	iclr19_261_3_7	 The humanhuman similarity score is pretty far above those of the best models, even though MTurkers are not optimized (and likely not as motivated as an NN) to solve this task.	 NEG
180	graph20_53_2_7	 The paper further assessed the tool in an exploratory study looking at usability and induced workload, with promising results.	 POS
993	midl19_52_2_12	 4- How does the specifics of the network architecture influence the performance?	 NEG
577	iclr20_1493_2_37	 In particular, with such low-variance directions, at standard dataset sizes the distributions generated here are most likely statistically indistinguishable from their robust/non-robust counterparts (you can see hints of this in the fact that the CNN gets .	 NA
1158	midl20_77_4_5	 However, one weakness of the paper was that the details of the experimental setup for data generation were not clear without following up the Gessert et al (2019) reference.	 NEG
1021	midl19_56_3_10	" Authors claim to introduce many concepts for the first time, such as the ""first demonstration that a deep generative architecture can generate high fidelity complex human anatomies in a [...] voxel space [from low-dimensional latents]""."	 NEG
1038	midl19_59_3_6	 The medical decathlon (pseudo-url) would have provided easy access to more datasets and tasks.	 NA
1201	midl20_96_3_12	 How would a generic linear classifier on the image histograms perform here, or perceptual hashing with a linear classifier on top?	 NA
1192	midl20_96_3_3	 Iterative refinement is claimed to be semi-supervised learning.	 NA
895	midl19_40_3_13	 It is improving the final performances, speeding up convergence, both ?	 NA
589	iclr20_1724_2_6	 Finally, the localization task is challenging, especially when camera motion is introduced, with much space for improvement left for future work.	 NA
1046	midl19_59_3_14	" in section 5: ""Table 2 shows, that both IMM and T-IMM...""."	 NA
785	iclr20_880_2_5	 However, in its present form, it is hard to understand why the claim is correct.	 NEG
1070	midl20_100_1_21	 It is not obvious how to best get around this issue, since the first embryologist screening probably has false negatives, but you need to take it into account.	 NEG
311	iclr19_1399_1_4	 The analysis of the results is quite insightful.	 POS
301	iclr19_1291_3_14	" Why is CommNet work worse than IRIC and IC in table 2?"""	 NEG
722	iclr20_57_3_6	 The paper lacks insight about a principled way to label such examples, the costs associated with such labeling, and impacts of the labeling quality on accuracy.	 NEG
689	iclr20_526_3_10	 In the current development, Theorem 1 only states that the optimization process will converge to the stationary points of the approximate ELBO objective (L1 in the paper's notation).	 NA
812	iclr20_934_1_10	 Since the proposed method uses the multi-channel representation, how to set the number of channels pseudo-formula ?	 NEG
37	graph20_29_3_5	 Age difference is one among many possible explanations, but one in which this paper rushes in nevertheless, at the expense of any other.	 NEG
353	iclr19_242_2_31	 My main concern is that the benefit of this method is unclear.	 NEG
1283	neuroai19_32_1_9	 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) .	 NEG
714	iclr20_526_3_35	 Also, I would like to see the test estimated NLL (via AIS) learning curves for VCD and AdVIL.	 NEG
236	graph20_61_2_14	" Also, before initiating collaborations, I would say that all parties must first be aware of each others contributions, so I would rephrase the reason as a ""lack of communication"" among them."	 NEG
1164	midl20_77_4_11	 Section 2: In description of n-Path-CNN3D, extent should be extend Section 2, Dataset: For data generation, we consider various smooth curved trajectories with different motion magnitudes this is a bit vague, can you provide more information?	 NEG
729	iclr20_720_2_2	 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.	 NEG
1150	midl20_71_1_7	 3) more details about the convGRU may be useful, for example its architecture.	 NEG
867	midl19_14_2_43	" 2] Maninis, Kevis-Kokitsi, et al. ""Deep retinal image understanding."""	 NA
499	iclr19_938_3_4	 Furthermore, the different baselines perform differently: there is no method that consistently performs well.	 NA
1152	midl20_71_1_9	" The conclusion is more like a validation for the usefulness of the temporal information, while technical novelty may not be very sufficient in this case."""	 NEG
553	iclr20_1493_2_11	 For example, a few very closely related works are as follows: - Adversarial examples are not Bugs, they are Features (pseudo-url): Ilyas et al (2019) demonstrate that adversarial perturbations are not in meaningless directions with respect to the data distribution, and in fact a classifier can be recovered from a labeled dataset of adversarial examples.	 NA
55	graph20_29_3_23	 I doubt many designers would consider a clickable, 2.4-mm high font or icon on a touch screen in any case.	 NA
302	iclr19_1333_1_0	 This paper proposes a new set of heuristics for learning a NN for generalising a set of NNs trained for more specific tasks.	 NA
794	iclr20_880_2_14	 There are some possibilities, which have not been explored: 1) the performance improvement derives from the approximation induced by the representation of float or double in the matrices.	 NEG
286	iclr19_1091_1_24	" Why are the robotics priors not in Table 1?"""	 NEG
1064	midl20_100_1_15	 In the discussion you almost exclusively focus on the work by Tran et al and why comparing with that work is unfair.	 NEG
219	graph20_56_1_26	 It sounds like a classic case of theres nothing wrong with our system, just change the user.	 NEG
882	midl19_40_3_0	 This paper attempt to do nuclei segmentation in a weakly supervised fashion, using point annotations.	 NA
387	iclr19_304_3_0	 Overview: The authors aim at finding and investigating criteria that allow to determine whether a deep (convolutional) model overfits the training data without using a hold-out data set.	 NA
1022	midl19_56_3_11	 However, I am aware of at least one work where such concepts have been proposed and explored already.	 NEG
390	iclr19_304_3_3	 I have several issues with this work.	 NEG
1047	midl19_59_3_15	 I guess this should actually be table 4.	 NEG
352	iclr19_242_2_30	 I will keep my score and argue for the rejection of this paper.	 NA
193	graph20_56_1_0	 The authors describe the design and implementation of a shape-based brushing technique targeted at selecting a particular type of data - trajectories.	 NA
235	graph20_61_2_13	 The passive voice of the sentence does not help to identify who posited this reason: the authors of the submission or Vieira et al. [36]?	 NEG
1227	neuroai19_2_2_17	 There are exiting directions in both AI and neuroscience this work could be take.	 NA
694	iclr20_526_3_15	 I would appreciate a more forceful motivation of the relevance of MRFs rather than just stating it as a important model with applications.	 NEG
480	iclr19_866_1_13	 The goal search process relies on a number of user-defined parameters - The nature of the instructions used for experimental evaluations is unclear.	 NEG
567	iclr20_1493_2_26	 I believe a more measured conclusion (perhaps that we *need* more regularization methods, but even then we may not be able to get perfect robustness and accuracy) would better fit the strong results presented in the paper.	 NEG
116	graph20_36_1_30	 The results presented in appendix do not seem so different, and I think the result will be even more similar with a little practice.	 NEG
1108	midl20_127_4_3	" This paper aims to solve the above problems by..."", but the authors use 2D ultrasound images made by a sonographer, so the system therefore does not solve these problems."	 NEG
653	iclr20_2094_1_27	" Under such circumstances, it is quite impossible to reproduce experiments. """	 NEG
1169	midl20_85_3_1	 To achieve this , the idea of introducing Dirichlet distribution after neural network is used from Evidential Deep Learning (EDL) paper.	 NA
1295	neuroai19_32_1_21	 Weight sharing across shifted filters separates out feature and position yet many of their learned transformations are also translations.	 NA
757	iclr20_727_1_12	 Minor point: - The extension of the method to Marked Temporal Point Processes in the Evaluation section seems out of place, esp.	 NEG
444	iclr19_495_1_16	 Update: I feel the idea of this paper is straightforward, and the contribution is incremental.	 NEG
1089	midl20_108_3_5	 The introduction and description of the state of the art, in addition to the main limitations of popular algorithms is very clear and interesting to read.	 POS
120	graph20_39_2_2	 First, the authors attempted to identify a gap in the literature concerning how visualisation designs can support the review and analysis of user-generated data.	 NA
1072	midl20_100_1_23	 The only way training size can influence AUC is by influencing the training of the model.	 NA
1198	midl20_96_3_9	 A radical ablation study is clearly missing here.	 NEG
1048	midl19_59_3_16	" Figure 1 could have been a bit more clear """	 NEG
62	graph20_29_3_30	 In my experience, many pointing studies have error rates ranging from 0 to, say, 15%, perhaps more when the tasks or input devices make it particularly difficult.	 NA
743	iclr20_720_2_16	 If this is the case, I feel like the empirical results are not novel enough to create value for the community and too tied to a particular approach to hierarchy which does not align with much of the past work on HRL.	 NEG
920	midl19_49_1_16	 It is not convincing to claim that the clustering is correct since even a noise can be decoded into a normal image.	 NEG
453	iclr19_659_2_0	 This paper proposes the deep reinforcement learning with ensembles of Q-functions.	 NA
515	iclr19_997_3_8	 The proposed method is compared with the existing multi-objective methods in terms of classification accuracy, but if we focus on that point, the performance (i.e., error rate and FLOPs) of the proposed method is almost the same as those of the random search judging from Table 4.	 NEG
570	iclr20_1493_2_29	 The RBF SVM, for small enough bandwidth can express any function and is convex, so no argument needs to be made about its ability to find the Bayes-optimal classifier.	 NA
223	graph20_61_2_1	 Quality The methodology employed for conducting this research sources methods from diverse fields and is relevant.	 POS
1028	midl19_56_3_17	 How do authors suggest to apply their approach to anatomies where it is impossible (in terms of feasibility and manual effort) to place a sufficiently large number of unique landmarks on the anatomy (e.g. smooth shapes, such as left ventricle in ACNN)?	 NEG
613	iclr20_2046_2_18	 This is also the case for Theorems 2-4.	 NA
843	midl19_14_2_17	 There are other existing data sets such as HRF (pseudo-url), CHASEDB1 (pseudo-url) and DR HAGIS (pseudo-url) with higher resolution images that are more representative of current imaging devices.	 NA
221	graph20_56_1_28	" Id like to see an inclusion of the user review. """	 NA
1346	neuroai19_37_3_25	" We require a new class of theories that dispose of the simplistic stimulus-driven encode/ transmit/decode doctrine. """	 NA
516	iclr19_997_3_9	 It would be better to compare the proposed method to the existing multi-objective methods in terms of classification accuracy and other objectives.	 NEG
465	iclr19_659_2_12	 Minor things: +The main idea is described too sketchily.	 NEG
52	graph20_29_3_20	 I assume that strong design guidelines already exist for this?	 NA
1280	neuroai19_32_1_6	 The statement that: GANs and VAE features are not typically interpretable.	 NA
485	iclr19_866_1_18	 Similarly, what is the nature of the different action spaces?	 NEG
875	midl19_36_2_0	 The paper is well-written, and easy to read and understand.	 POS
1331	neuroai19_37_3_9	 Which leads me to a few concerns.	 NEG
818	midl19_13_2_3	 Overall a clearly written paper, with nice visual results.	 POS
386	iclr19_261_3_28	" On learning to refer to things based on their discriminative properties. """	 NA
155	graph20_43_1_2	 Overall, I found the design of the study to be sound, as is the data analysis and modeling methodology.	 POS
1137	midl20_56_4_19	" Therefore I recommend the weak accept. """	 NA
600	iclr20_2046_2_5	 Experimental results show that the proposed algorithm outperform the MCTS algorithms.	 POS
1113	midl20_127_4_8	" The boxplot shows that six outliers are resolved by the AF-Net, so it can be debated if that is clinically relevant to reduce (6/435=)1.4% of the errors."""	 NEG
248	graph20_61_2_26	 Rendering in SVG with d3 might pose issues regarding accessibility, where efforts for compliance are left at the discretion of application developers rather than library developers.	 NEG
761	iclr20_76_2_2	 Theresults quantify how smooth Gaussian data should be to avoid the curse of dimensionality, and indicate that for kernel learning the relevant dimension of the data should be defined in terms of how the distance between nearest data points depends on sample numbers.	 NA
870	midl19_25_3_0	 The paper is well written and describes an interesting and relatively novel approach to solving multi-class classification in a clinical domain where overlap between classes is frequently a possibility.	 POS
855	midl19_14_2_29	 Since the HRF data set contains images from normal, glaucomatous and diabetic retinopathy patients, I would suggest to use that one.	 NA
441	iclr19_495_1_13	 However, there are more powerful variants of normalizing flows such as the Multiplicative Normalizing Flows or the Glow.	 NEG
1270	neuroai19_3_3_5	 One needs to go see Appendix C to understand what the model used (SQL) consists in.	 NEG
1284	neuroai19_32_1_10	 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?	 NA
542	iclr20_1042_2_20	" 3) Experiments Finally, the experimental results do not look very compelling, it seems to be overall worse than the baselines in the two image datasets and slightly better in the audio dataset, so it's unclear that this approach is superior."""	 NEG
1042	midl19_59_3_10	" The way table 2 is presented at the moment it seems like T-IMM is better than all methods also for ""100%""."	 NEG
701	iclr20_526_3_22	" What is meant by ""RBM loss"" in Fig. 2(d), I do not see this defined?"	 NEG
117	graph20_36_1_31	" In summary, the idea is interesting, but the design rationale is unclear, and it is unclear the results justify using this system."""	 NEG
1312	neuroai19_36_1_0	 Premise is that feedback alignment networks are also more robust to adversarial attacks.	 NA
628	iclr20_2094_1_2	 Experiments are performed on several instances of 2D-BPP and 3D-BPP, Overall, bin packing problems are challenging tasks for DRL, and I would encourage the authors to pursue this research topic.	 NA
1393	neuroai19_59_3_18	 Does limiting the synaptic time constants limit the intrinsic time constants, and if so by how much?	 NEG
206	graph20_56_1_13	 I assuming - as one would consider the obvious choice - that directionality is taken from the direction of the sketched brush at the time the user draws it.	 NA
291	iclr19_1291_3_4	 The paper is well written, easy to follow, and everything has been explained quite well.	 POS
1222	neuroai19_2_2_12	 So I think the present work needs to be repitched slightly as solving credit assignment in an online/few shot learning setting.	 NEG
381	iclr19_261_3_19	 Learning to follow navigational directions.	 NA
819	midl19_13_2_4	 Mainly an incremental paper, proposing a combination of well established GAN-based networks to accomplish a classification task.	 NEG
972	midl19_51_2_12	 For example, it is not clear what are the non-desirable artifacts, where are the eliminated nuclei and why the network has a harder time to learn.	 NEG
1069	midl20_100_1_20	 In your case, you train on data that has already been filtered to only include positive decisions by embryologists, otherwise the eggs would not have been implanted.	 NA
200	graph20_56_1_7	 There appears to be a set of small multiples for each of the two metrics.	 NA
596	iclr20_2046_2_1	 It further establishes the sample complexity to determine optimal actions.	 NA
897	midl19_40_3_15	 Section 2.3 should make the differences (if any) with Tang et al. explicit.	 NEG
1178	midl20_85_3_13	" Overall, the idea is fine. """	 POS
854	midl19_14_2_28	 That would be equivalent to assume that the new data set(s) does (do) not contain the annotations, and will allow to quantify the performance there.	 NA
1328	neuroai19_37_3_6	 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.	 NEG
1389	neuroai19_59_3_14	" It seems that one of the main points of the work is that ""longer intrinsic timescales correspond to more stable coding"", but I didn't find that this point was made very convincingly."	 NEG
804	iclr20_934_1_0	 This paper proposed a dual graph representation method to learn the representation of nodes in a graph.	 NA
1061	midl20_100_1_12	 You do not report results for the embryologist trained LSTM, so what do you use this LSTM for?	 NEG
981	midl19_52_2_0	 This paper proposes to use a CNN architecture to reconstruct MR Fingerprinting parametric maps.	 NA
792	iclr20_880_2_12	 Hence, without non-linear functions, where is the added value of the method?	 NEG
769	iclr20_855_3_5	 The first is the presentation of the empirical results.	 NA
1174	midl20_85_3_6	 Now, it is difficult to connect use of prior and improvement in ECE.	 NEG
813	iclr20_934_1_11	 How does this parameter affect the performance?	 NEG
1115	midl20_135_3_1	 It is an interesting idea and the quality is overall rather good for an abstract paper.	 POS
505	iclr19_938_3_10	 It is unclear how the model actually operates and uses attention during execution.	 NEG
891	midl19_40_3_9	" How resilient is the method to ""forgotten"" nuclei ; i.e. nucleus without a point in the labels ?"	 NEG
348	iclr19_242_2_24	 It contradicts with the authors other explanation.	 NEG
1037	midl19_59_3_5	 When used on another dataset they do not show gains anymore.	 NA
1141	midl20_70_4_3	 The paper reads well and the methodology seems to be interesting.	 POS
1392	neuroai19_59_3_17	 How does this relate to their synaptic time constants?	 NEG
340	iclr19_242_2_16	 In figure 1 (b), the results of M=4,8,16,32 are very similar, and it looks unstable.	 NEG
786	iclr20_880_2_6	 In fact, the model presented in the paper has a major obscure point.	 NEG
615	iclr20_2046_2_20	 However, it would be better to have some discussion earlier right after these theorems are presented.	 NEG
154	graph20_43_1_1	 Models are fit which account for these differences, on both new data gathered from 12 participants, and data sets gathered from several past studies.	 NA
1292	neuroai19_32_1_18	 Then this evaluation could be used to study impacts of the parameters of their model which could then lead to neural hypotheses.	 NA
1263	neuroai19_29_1_6	 The question of how the brain and artificial network can perform relational reasoning is critical in both fields, since many believe that it may be one of the primary ingredients of intelligence.	 NA
77	graph20_35_1_3	 Both studies compare the new mind mapping tool to digital options without computer assistance.	 NA
1387	neuroai19_59_3_12	" For instance, the claim of ""stronger cue-specific differences across the cue stimulus window"" between fast and slow intrinsic timescale neurons in the RNN model isn't clearly supported by the heatmap in Figure 3 -- the cue-specific differences for the short instrinsic timescale group to me appears to be at least as great as that of the long intrinsic timescale group within the cue stimulus window."	 NEG
287	iclr19_1291_3_0	 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.	 NA
322	iclr19_1399_1_15	 Even the results with the VGG network are very far from the best available models.	 NEG
686	iclr20_526_3_7	 I have a minor issue with the discussion (in the last paragraph of sec. 3.2) stating that the theoretical statement of the proposed objective relies on a much weaker assumption than the nonparametric assumption made in the theoretical justification of GANs.	 NEG
1076	midl20_100_1_27	 Maybe you meant the size of the test set?	 NA
1252	neuroai19_26_1_8	 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.	 NA
913	midl19_49_1_8	 One major concern is whether the results are reliable: 1.	 NEG
1267	neuroai19_3_3_2	 Although innovative and promising, the work is quite preliminary and would benefit from comparison and validation with real human behavior.	 NEG
1188	midl20_90_2_9	" However, if different models were trained for predicting each parameter, not only training but also prediction would not be efficient."""	 NEG
82	graph20_35_1_8	 One small point that could be clarified is why a between subjects design was chosen over a counterbalanced within subjects.	 NEG
512	iclr19_997_3_5	 Pros - The performance of the proposed method is better than the existing multi-objective architecture search methods in the object classification task.	 POS
1395	neuroai19_59_3_20	 The authors use an artificial network model to shed light on the biological mechanisms enabling and shaping working memory in the brain.	 NA
964	midl19_51_2_4	 2- Two step approach combining despeckling and generative networks are reasonable for the task.	 POS
464	iclr19_659_2_11	 However, the authors didnt show these results in the paper.	 NEG
1128	midl20_56_4_8	 Opposite to the Method part, it's hard to read the abstract and introduction.	 NEG
359	iclr19_242_2_37	 I have suggested the authors to compare with stronger baselines to demonstrate the benefits.	 NA
1367	neuroai19_54_3_6	 Is the attribution analysis stable for different stimulus frequencies?	 NA
929	midl19_49_1_25	 Details of training should be more clearly written.	 NEG
1135	midl20_56_4_17	 Results show the effectiveness of the proposed method.	 POS
333	iclr19_242_2_9	 Even provided more computing resources, the proposed method is not faster than small batch training.	 NEG
10	graph20_25_2_10	 The paper never justifies why Bend Passwords [33] is the best design to adapt for users who are visually impaired.	 NEG
738	iclr20_720_2_11	 So, if you generically apply Option-Critic, it would in fact be possible to disentangle the inductive bias of hierarchy from the inductive bias of temporal abstraction by using options that always terminate.	 NA
470	iclr19_866_1_3	 Such a modular approach has the advantage that the instruction-to-goal and goal-to-policy mappings can be trained separately and, in principle, allow for swapping in different modules.	 NA
831	midl19_14_2_5	 The contribution is original in the sense that complementing data sets is a really challenging task, difficult to address with current available solutions.	 POS
32	graph20_29_3_0	 Through four studies, this paper proposes to lift a theoretical limitation in the application range of the Dual Gaussian Distribution Model, namely that it could also work when touch acquisition occurs from a touchscreen to that same touchscreen.	 NA
150	graph20_39_3_11	 It seemed that the initial statement was that general guidelines were not useful because of the uniqueness at each patient.	 NA
413	iclr19_304_3_27	 Although you didnt show anything but only state assumptions or claims (which may be reasonable but are not backed up here).	 NEG
580	iclr20_1493_2_41	 A suggestion rather than a concern and not impacting my current score: but it would be very interesting to see what happens for robustly trained classifiers on the symmetric and asymmetric datasets.	 NA
382	iclr19_261_3_21	 Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings.	 NA
793	iclr20_880_2_13	 How the proposed method can have better results.	 NA
1077	midl20_100_1_28	 In that case, it is the ratio of positive/negative that is relevant.	 NA
376	iclr19_261_3_13	 Are the humans?	 NEG
1324	neuroai19_37_3_2	 Its an opinion piece.	 NA
614	iclr20_2046_2_19	 The authors give some concrete examples in Section 6.2 for these bounds.	 POS
1330	neuroai19_37_3_8	 The dynamics of neuromodulation is largely unknown.	 NA
378	iclr19_261_3_16	 Framing: there is a lot of work in collaborative / multi-agent dialogue models which you have missed see refs below to start.	 NEG
89	graph20_36_1_3	 This is overall an interesting idea of interactive system supporting skill acquisition.	 POS
692	iclr20_526_3_13	 One this last point, it seems ironic to me that the proposed strategy for training the MRF is through the use of three separate directed graphical models (an encoder q(h | x), a decoder and a VAE to model the approximate prior over the latents h).	 NA
601	iclr20_2046_2_6	 Cons: However, there are several issues that should be addressed including the presentation of the paper: The algorithm seeks to combine A* search with MCTS (combined with policy and value networks), and is shown to outperform the baseline MCTS method.	 NEG
213	graph20_56_1_20	 One would expect that trying some combination would be an obvious step, especially given the unclear feedback from the expert review.	 NEG
833	midl19_14_2_7	 However, it is the first time that it is applied for complementing data sets and have some interesting modifications that certainly ensures novelty in the proposal.	 POS
332	iclr19_242_2_8	 It is unclear to me what is the benefit of the proposed method.	 NEG
133	graph20_39_2_15	 A utilisation of these perspectives in framing the research ideas would have done more good to the paper than proposing a new design space for visualisation of user-generated data.	 NEG
346	iclr19_242_2_22	 I fail to understand the the authors augmentation.	 NEG
555	iclr20_1493_2_13	 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.	 NA
7	graph20_25_2_7	 It is particularly important to evaluate technology with target stakeholders.	 POS
28	graph20_26_3_3	 In particular, clarifications around the motivation behind the path tracing task, and additional related work that have utilized path tracing to determine endpoints (e.g., [17], [18]) and to mark or detect features along a path (e.g., [66]) were helpful in positioning the contributions of this work in relation to prior work.	 POS
320	iclr19_1399_1_13	 The results in Figure 3 are very far from the state of the art.	 NEG
1225	neuroai19_2_2_15	 In understanding the model, it would be useful to more explicitly define the model.	 NEG
187	graph20_53_2_14	 For some reason, the actual qualitative aspects of the study are then reported as a subsection in the discussion (6.3 - Comment Observations).	 NA
1206	midl20_96_3_17	 Writing, experimental setup and methodological proposals need to be improved and condensed.	 NEG
634	iclr20_2094_1_8	 Again, a brief discussion about those results would be relevant.	 NEG
552	iclr20_1493_2_10	 Prior work: the paper seems to ignore a plethora of prior work around studying adversarial robustness and understanding its roots.	 NEG
304	iclr19_1333_1_2	 The issue of model selection (clearly the main issue here) is not addressed.	 NEG
31	graph20_26_3_6	" For example, page 3: HoloLense -> Hololens. """	 NA
1083	midl20_100_1_34	" I am aware of the page limitation, so maybe MIDL should allow an extra page solely for an image of the raw data."""	 NA
796	iclr20_880_2_16	 2) the real improvement seems to be given by the initialization which has been obtained by using the non-linear counterpart of the expansion; to investigate whether this is the case, the model should be compared with a compact model where the initialization is obtained by using the linear product of the non-linear counterpart of the expanded network.	 NEG
1157	midl20_77_4_4	 The discussion of the results reveals findings that may well be of interest to others.	 POS
174	graph20_53_2_1	 is a tool for authoring object component behaviour within VR.	 NA
766	iclr20_855_3_2	 Using the modified verison of Rainbow (OTRainbow), the authors replicate an experimental comparison with SiMPLe (Kaiser et al, 2019), showing that Rainbow DQN can be a harder baseline to beat than previously reported (Figure 1).	 NA
245	graph20_61_2_23	" For further inspiration on visualization for comparing (resident) profiles, I'd suggest to browse other works by Plaisant et al. in addition to [29]: pseudo-url pseudo-url IMPLEMENTATION DETAILS The implementation details report on constraints that may be too project-specific (with occurrences of ""project"" or ""the University"") and would gain to be generalized."	 NEG
1205	midl20_96_3_16	 There will be domain shift problems for the simple methods but same is true for the presented method.	 NA
145	graph20_39_3_6	 There are a few comments I have about the paper that I describe below.	 NEG
90	graph20_36_1_4	 The system remains simple.	 POS
682	iclr20_526_3_3	 That said, it does seems like a fairly creative combination of existing approaches.	 NEG
310	iclr19_1399_1_3	 The method does show some modest improvements in the experiments provided by the authors.	 POS
93	graph20_36_1_7	 What are the design choices?	 NEG
1156	midl20_77_4_3	 The methods employed seem reasonable and quantitative evaluation is performed to compare them.	 POS
924	midl19_49_1_20	 It is a self-contradictory statement.	 NA
1173	midl20_85_3_5	 Also, I would be convinced that the variance would increase for out of distribution test samples because you used a prior that enforced uncertainty of all labels.	 NEG
1035	midl19_59_3_3	 Method only evaluated on one dataset (BRATS).	 NEG
243	graph20_61_2_21	 The decisions on color scales adjustments to highlight under-performance while shadowing over-performance on EPA count per rotation is well motivated by contextual needs.	 POS
214	graph20_56_1_21	 The last point leads me to what I see as *the* major weakness of the paper.	 NA
998	midl19_52_2_17	 6- The quantitative results are yielded using multiple segmentation masks due to MR physics related concerns.	 NEG
853	midl19_14_2_27	 It would be interesting to simulate such an experiment by taking an additional data set with vessel annotations (e.g., some of those that I suggested before, HRF, CHASEDB1 or DR HAGIS) and evaluate the performance there, without using any of their images for training.	 NEG
425	iclr19_304_3_39	 Does that mean that you assume that whenever the training accuracy drops lower than that of the model without regularization, it starts to underfit?	 NA
1221	neuroai19_2_2_11	 The credit assignment problem exists in these cases also.	 NEG
226	graph20_61_2_4	 Signifiance The system has been designed and developed and evaluated so that it ended up being useful to domain experts (medical residents and their reviewers).	 POS
1133	midl20_56_4_15	 There can be more discussion here.The authors propose a framework to utilize one model under different acquisition context scenarios.	 NA
0	graph20_25_2_0	 The submission presents evaluation of BendyPass, a prototype based on Bend Passwords design [33], with visually impaired people.	 NA
401	iclr19_304_3_14	 What do you mean by the data being independent?	 NEG
84	graph20_35_1_10	 The results are individually compelling, but what does it mean all together?	 POS
162	graph20_43_1_9	 It seems awkward to use such a similar term here, when C-D manipulation is not the focus.	 NEG
114	graph20_36_1_28	 What is the objective: people's perception or a metric?	 NEG
670	iclr20_305_3_2	 The leader is modeled as a semi-MDP with event-based policy gradients and modules to model/predict followers' actions.	 NA
1062	midl20_100_1_13	 If you dont use it, remove it from the section.	 NEG
889	midl19_40_3_7	 Few questions: - Since the method is quite simple and elegant, I expect it could be adapted to other tasks.	 POS
625	iclr20_2046_2_30	" In the first paragraph of Section 6.2, there is a typo: V*=V_{l*}=\eta should be V*-V_{l*}=\eta ?"""	 NEG
952	midl19_51_1_20	 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.	 NEG
807	iclr20_934_1_3	 Overall, the idea is presented clearly and the writing is well structured.	 POS
258	iclr19_1049_1_3	 The method is flexible and different entities correspond to different rules.	 POS
624	iclr20_2046_2_29	 In fact, the proof of the theorems could be moved to appendices.	 NA
826	midl19_14_2_0	 The authors present a deep learning method for fundus image analysis based on a fully convolutional neural network architecture trained with an adversarial loss.	 NA
234	graph20_61_2_12	" One reason for this gap seems to be the lack of collaboration among the developers, end-users and visualization experts."""	 NA
135	graph20_39_2_17	 There is the question of how the data and the proposed guidelines might bring about some implications for design (Dourish, 2006) and practice.	 NA
616	iclr20_2046_2_21	 The experimental results are carried out under the very simplified settings for both the proposed algorithm and the baseline MCTS.	 NEG
836	midl19_14_2_10	 Taking this into account, I would suggest the authors to incorporate at least one paragraph in Related works (Section 2) describing the current existing approaches to do that.	 NA
842	midl19_14_2_16	 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.	 NEG
959	midl19_51_1_27	" It is necessary to run a study to confirm that in a similar way that CM images were confirmed having diagnostic value and could therefore be used instead of H&E stained images."""	 NEG
592	iclr20_1724_2_9	 The authors recognize that since the dataset is synthetically generated it is not necessarily predictive of how methods would perform with real-world data, but still it can serve a useful and complementary role similar to the one CLEVR has served in image understanding.	 POS
343	iclr19_242_2_19	 The baselines are fairly weak, the authors did not compare with any other method.	 NEG
1146	midl20_71_1_3	 The improvement gained by the proposed method validates the effectiveness of recurrent units, and the most significant gain is from the false positive rates.	 NA
293	iclr19_1291_3_6	 The comparison between their model with three baselines was extensive; they reported the mean and variance over different runs.	 POS
364	iclr19_242_2_42	" I think it could at least be improved for clarity. """	 NEG
178	graph20_53_2_5	 This is briefly addressed in the limitations, but I would have found some discussion of this aspect very helpful, especially earlier when introducing the research motivation.	 NEG
659	iclr20_2157_3_6	 Some of these should serve as baselines.	 NEG
996	midl19_52_2_15	 Does the order of concatenation influence the results?	 NEG
14	graph20_25_2_14	 Other designs exist (e.g., work by Das et al. (2017) is just one example.	 NA
165	graph20_45_2_0	 The paper proposes a new visualization scheme that combines the properties of scatterplots and parallel coordinates plots (PCPs): the Cluster-Flow Parallel Coordinates Plot (CF-PCP).	 NA
59	graph20_29_3_27	 AMOUNT OF ERROR Throughout the paper, prediction errors (additive) up to 10% are described as small, and that is surprising (5% in Exp 1, 10% in Exp 2, 7% in Exp 3, 10% in Exp 4).	 NEG
709	iclr20_526_3_30	 With respect to Deep Boltzmann Machine (DBM), I would prefer to see quantitative comparisons against published results.	 NEG
676	iclr20_305_3_11	 Supporting arguments The approach seems sound and conceptually related to a multi-agent generalization of STRAW pseudo-url, where a planner predicts / commits to an action-plan for a single agent.	 POS
780	iclr20_880_2_0	 This paper is extremely interesting and quite surprising.	 POS
424	iclr19_304_3_38	" I neither understand ""As the accuracy curve is also monotone decreasing with increasing regularization we will also detect the convexity by a steep drop in accuracy as depicted by the marked point in the Figure 1(b)"" nor do I understand ""accuracy over regularization curve (plotted in log-log space) is constant""?"	 NEG
292	iclr19_1291_3_5	 The experiments are competent in the sense that the authors ran their model in four different environments (predator and prey, traffic junction, StarCraft explore, and StarCraft combat).	 POS
1065	midl20_100_1_16	 Instead, you should have made the comparison and highlighted the differences clearly.	 NEG
1242	neuroai19_23_1_11	 It does not seem like predictive coding is the main thing going on in V1 (Stringer et al., Science 2019), so Id be curious how the authors think that should be taken into account in the future.	 NA
329	iclr19_242_2_4	 The proposed method is very simple.	 NEG
429	iclr19_495_1_1	 Although the concept of normalizing flow is simple, and it has been applied to other models such as VAE, there seems no work on applying it for policy optimization.	 POS
1012	midl19_56_3_1	 The decoder network is made possible by a newly proposed architecture that is based on inception-like transpose convolutional blocks.	 NA
79	graph20_35_1_5	 Overall, this paper is an interesting exploration of a novel area of computer supported brainstorming.	 POS
1052	midl20_100_1_3	 The main weakness of the paper is in the methods section.	 NEG
937	midl19_51_1_5	 It has the potential to improve pathology and cancer diagnosis by making it simpler and quicker The results of this work look visually convincing.	 POS
834	midl19_14_2_8	 The paper is well written and organized, with minor details to address in this matter (see CONS).	 POS
749	iclr20_727_1_4	 The writers have put their contributions in context well and the presentation of the paper itself is very clear.	 POS
890	midl19_40_3_8	 Do you have any ideas in mind ?	 NA
254	graph20_61_2_32	" Audio quality of the voice over could be improved with a proper microphone and recording settings. """	 NA
1361	neuroai19_54_3_0	 The authors state three high-level improvements they want to make to CNN-based models of neural systems: 1 & 2) Capturing computational mechanisms and extracting conceptual insights.	 NA
136	graph20_39_2_18	 Although the issues of implication for design has been misunderstood and widely misrepresented, what the proposed design guideline sought to point to might be regarded as some form of outlining implications for a design practice that is minimal and non-representative.	 NEG
852	midl19_14_2_26	 However, since IDRID does not have vessel annotations, it is not possible to quantify the performance there.	 NA
723	iclr20_57_3_7	 The experiments are not making a convincing case that similar improvements could be obtained on a larger class of problems.	 NEG
859	midl19_14_2_33	 It is not clear if the values for the existing methods in Table 2 correspond to the winning teams of the IDRID challenge.	 NEG
969	midl19_51_2_9	 Even though it is mentioned by the authors that these images resemble to noisy RCM, this should be either referenced or shown.	 NEG
496	iclr19_938_3_1	 They used an attention mechanism over agent policies as an input to a central value function.	 NA
950	midl19_51_1_18	" The main contribution of the paper is scarcely justified by the statement ""...they confirmed that the images were similar to those in routine""."	 NEG
805	iclr20_934_1_1	 In particular, it learns the embedding of paired nodes simultaneously for multiple times, and use the mean values as the final representation.	 NA
129	graph20_39_2_11	 The analysis of the patient's interview provided a bigger picture of the different perspectives, and which makes the different factors more relational and understandable.	 NA
337	iclr19_242_2_13	 Or apply distributed knowledge distillation like in (Anil 2018 Large scale distributed neural network training through online distillation) 3.	 NA
1388	neuroai19_59_3_13	 I would be curious to know if making the input weaker or only giving it to a random subset of neurons makes this phenomenon more apparent.	 NEG
608	iclr20_2046_2_13	 For example, in line 8 of Algorithm 2, why only the top 3 child nodes are added to the queue?	 NEG
195	graph20_56_1_2	 The authors do an excellent job of describing the problem and grounding the approach in previous work.	 POS
909	midl19_49_1_4	 The entire workflow is quite clear and complete.	 POS
584	iclr20_1724_2_1	" 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)."	 NA
103	graph20_36_1_17	 But it also makes me think about the actual difficulty of performing such art (I never tried myself).	 NA
1285	neuroai19_32_1_11	 Their statement of the novelty of their method: (1) allowing each feature to have its own transformation was not clear.	 NEG
1299	neuroai19_34_2_1	 The present paper makes the important case that random networks should be included as a matter of course in DCNN modelling projects, and sounds a note of caution about the field's temptation to over-interpret the particular features learned by high-performing trained networks.	 NA
719	iclr20_57_3_3	 This authors evaluted their approach on two tasks: Text Classification and Sequence Labeling.	 NA
884	midl19_40_3_2	 The idea is to generate two labels maps from the points: a Voronoi partitioning for the first one, and a clustering between foreground, background and neutral classes for the second.	 NA
776	iclr20_855_3_12	 But models can also be used for value function estimation (Model Based Value Expansion) and reducing gradient variance(using pathwise derivatives).	 NA
849	midl19_14_2_23	 It would also be interesting to analyze the differences in a qualitative way, as in Fig. 3 (b).	 NEG
894	midl19_40_3_12	 Since there is so much dissimilarity between ImageNet and the target domains, I expect it to be mostly a glorified edge detector.	 NA
227	graph20_61_2_5	 I advocate for accepting this submission.	 NA
1309	neuroai19_34_2_11	 I'm not a big fan of the asterisks in Figures 3A and 3B used to indicate the best layers in various model tests.	 NEG
126	graph20_39_2_8	 We need more detail to determine whether what the data suggest reflect the subjective perspective of the different users that participated in the study.	 NEG
405	iclr19_304_3_19	 Better training error?	 NA
775	iclr20_855_3_11	 The paper chooses a single method class of model-based methods to do this comparison, namely dyna-style algorithms that use the model to generate new data.	 NA
821	midl19_13_2_6	 The presented evaluation is limited, with training done on only 8 datasets, which in this particular case is a limitation due to the importance of presenting the networks with different backgrounds from various surgical sites and perspectives during surgery.	 NEG
711	iclr20_526_3_32	 It seems as though, in the application of AdVIL to the DBM, the authors are exploiting the structure of the model in how they define their sampling procedure.	 NEG
1313	neuroai19_36_1_1	" The authors show because the ""gradient"" in the feedback pathway is a rough approximation, it is hard to use this gradient to train an adversarial attack."	 NA
1060	midl20_100_1_11	 As I read it, UBar is the same LSTM just trained on clinical outcomes.	 NA
303	iclr19_1333_1_1	 This particular recipe might be reasonable, but the semi-formal flavour is distracting.	 NEG
267	iclr19_1091_1_5	 The most important point of critique is that the conclusion that the split representation is the best is at best premature.	 NEG
545	iclr20_1493_2_2	 In the other case, the Bayes-optimal classifier is robust, but neural networks fail to learn the robust decision boundary.	 NA
212	graph20_56_1_19	 I found it odd that at the authors retained both metrics, delivering different results, without trying some blended version that might reduce complexity for the user.	 NEG
896	midl19_40_3_14	 Minor improvements for the camera ready version, in no particular order: Tang et al. 2018 was actually published at ECCV 2018, the bibliographic entry should be updated.	 NA
916	midl19_49_1_11	 Please compare to other representation learning methods such as sparse coding (e.g. spherical K-means, dictionary learning), dimension reduction (e.g. PCA, t-sne).	 NA
675	iclr20_305_3_8	 Decision (accept or reject) with one or two key reasons for this choice.	 NA
814	iclr20_934_1_13	" Some unsupervised network embedding baseline methods, such as DeepWalk and Node2Vec, should be included into the experiment section. """	 NEG
342	iclr19_242_2_18	 In Table 1, the proposed method tuned M as a hyperparameter.	 NA
549	iclr20_1493_2_6	 The paper also definitively proves that there are realistic datasets where the Bayes-optimal classifier is non-robust, which goes against quite a bit of conventional wisdom in the field and opens up many new paths for research.	 POS
966	midl19_51_2_6	 Error measures presented in Table 1 needs to help readers to identify the benefit of the proposed neural network.	 NEG
295	iclr19_1291_3_8	 Right now, the authors explain a bit about the model performance in Starcraft combat, but I found the explanation confusing.	 NEG
695	iclr20_526_3_16	 What is unique about the MRF formalism that -- for practical applications -- could not be effectively captured in a directed graphical model?	 NEG
73	graph20_29_3_41	" Fig. 12 should also show the actual success rates measured in these studies."""	 NA
605	iclr20_2046_2_10	 These discussions are critical to understand the merit of the proposed algorithms.	 NA
104	graph20_36_1_18	 I expected more discussion on this point in the paper.	 NEG
645	iclr20_2094_1_19	 Also, it seems that the MDP is specified for a single instance of 3D-BPP.	 NA
1365	neuroai19_54_3_4	 The technical aspects of the paper seem correct, though I have some higher-level conceptual concerns.	 POS
1040	midl19_59_3_8	" It shows that for ""100%"" T-IMM actually is not significantly better than most of the other initialization strategies."	 NEG
439	iclr19_495_1_11	 I have seen {-1, 1}^2, but not [-1, 1]^2).	 NA
1019	midl19_56_3_8	 First, it is not fully clear where this number 3 comes from, and second, the quality of the work speaks for itself.	 NEG
91	graph20_36_1_5	 This will not be a revolution, but it might be of interest.	 NEG
1112	midl20_127_4_7	 The authors also do not include a Section with a discussion.	 NEG
1217	neuroai19_2_2_7	 But the related work in Section 2 then goes on to talk about the efficiency of backprop for solving online learning and few-shot learning tasks.	 NA
908	midl19_49_1_3	 Clustering of aortic value prosthesis shapes has a high contribution to personalized medicine.	 POS
102	graph20_36_1_16	 I wish there was a condition with these schematics only.	 NEG
238	graph20_61_2_16	 Requirement analysis was conducted through focus groups including active participation of domain experts (including involving them in sketching their desired features for data presentation).	 POS
1203	midl20_96_3_14	 Can't simple heuristics perform at least as well?	 NEG
942	midl19_51_1_10	 The study has potential and could have interesting applications in clinical settings.	 POS
938	midl19_51_1_6	 Both the de-speckle network and the GAN appear to deliver very good results, at least at first glance.	 POS
5	graph20_25_2_5	 This submission contributes new knowledge about how users who are visually impaired can enter passwords.	 POS
1262	neuroai19_29_1_5	 Overall the writing is relatively clear, but it would have been beneficial to describe the hypotheses more explicitly, e.g. what neural activity would be expected for a place, grid, or concept representation with respect to MNIST.	 NEG
965	midl19_51_2_5	 3- Qualitative stained image results look promising  Cons: 1- Median filter is used after the despeckling network, however it is not clear the added benefit of using median filter in despeckling process.	 NEG
18	graph20_25_2_18	 Thus, ideally the evaluation would compare other ways that participants can enter PIN passwords.	 NEG
121	graph20_39_2_3	 What is missing is a clear articulation of the research problem and question within the literature provided.	 NEG
661	iclr20_2157_3_8	 It is also not clear from the literature if these models are really working so I think these results should be presented in a more detail.	 NEG
1172	midl20_85_3_4	 It is not clear why calibration is reported and not simple measures of uncertainty like variance or entropy?	 NEG
274	iclr19_1091_1_12	 Furthermore, even the tests in the appendix are not comprehensive enough to to warrant the conclusion as written.	 NEG
433	iclr19_495_1_5	 For example, normalizing flows are defined in Section 4, and then it is directly claimed that normalizing flows can be applied to policy optimization, without giving details on how it is actually applied, e.g., what is the objective function?	 NEG
811	iclr20_934_1_8	 Thus, the novelty is incremental.	 NEG
1300	neuroai19_34_2_2	 Comprehensive data measurement and modelling pipeline.	 POS
1314	neuroai19_36_1_2	 The basic premise is very strange.	 NEG
181	graph20_53_2_8	 This consisted of a small user study (N=16) featuring qualitative and quantitative measures.	 NA
184	graph20_53_2_11	" The quantitative data is described as ""qualitative"" for some reason, even when referring to barplots in Figure 9."	 NA
406	iclr19_304_3_20	 I dont understand the assumptions.	 NEG
865	midl19_14_2_39	" iii) Explanation of the method... [1] Zhao, Yitian, et al. ""Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retinal Images."""	 NA
520	iclr19_997_3_13	 Please elaborate on the procedure and settings of the Bayesian network used in this paper.	 NEG
1003	midl19_52_2_22	 Please elaborate on this.	 NA
380	iclr19_261_3_18	 References Vogel & Jurafsky (2010).	 NA
427	iclr19_304_3_41	" In my view, this evaluation of the (vague) criteria is not fit for showing their possible merit. """	 NEG
78	graph20_35_1_4	 They find that QCue produces more balanced and detailed mind maps and that some mind mapping tasks may be better suited to this type of computer intervention than others.	 NA
1009	midl19_52_2_28	 term in Fig.2.	 NA
1394	neuroai19_59_3_19	 The same type of comments apply to the second part of the results, which demonstrates that a task that doesn't require working memory results in neurons with shorter intrinsic timescales compared to the working memory task.	 NA
607	iclr20_2046_2_12	 Many design choices for the algorithms are not clearly explained.	 NEG
1236	neuroai19_23_1_5	 Does PredNet outperform other user-submitted models?	 NA
1385	neuroai19_59_3_10	 The technical details are presented clearly on the whole.	 POS
1268	neuroai19_3_3_3	 No comparison with human data.	 NEG
1057	midl20_100_1_8	 These must be provided in a supplement to allow reproducability.	 NEG
1091	midl20_108_3_7	 The decision to supervised the feature extraction in a multi-task setting is good and makes sense.	 POS
1101	midl20_119_2_2	 The motivation and methodology are well explained with proper reference works.	 POS
1129	midl20_56_4_9	 Some typo problems lie here.	 NEG
1223	neuroai19_2_2_13	 Or discuss how it can be extended to more general learning problems.	 NEG
958	midl19_51_1_26	 It is necessary to prove that the generated images retain their important diagnostic value.	 NEG
559	iclr20_1493_2_17	 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.	 NA
846	midl19_14_2_20	 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).	 NEG
690	iclr20_526_3_11	 Clarity: I found the paper to be very well written with a clear exposition of the material and sound development of the technical details.	 POS
1350	neuroai19_53_1_0	 The authors consider how biologically motivated synaptic eligibility traces can be used for backpropagation-like learning, in particular by approximating local gradient computations in recurrent neural networks.	 NA
1298	neuroai19_34_2_0	 The surprisingly high power of randomly weighted DCNNs is a point that has popped up a couple of times in recent human fMRI / MEG work.	 NA
436	iclr19_495_1_8	 I can't get how exactly normalizing flows + TRPO works.	 NEG
490	iclr19_866_1_23	 I wouldn't consider the results reported in Section 4.5 to be ablative studies.	 NEG
1390	neuroai19_59_3_15	" The work would have benefited from a discussion of the implications of longer intrinsic timescale neurons retaining task-relevant information for longer -- in particular, this finding feels a bit ""trivial"" without the case being made for why this should push understanding in the field."	 NEG
1277	neuroai19_32_1_3	 They show images of a single reconstruction but no quantification of reconstruction quality or comparison to previous methods.	 NEG
69	graph20_29_3_37	 Second, 29% and 38% error seems alarmingly high.	 NEG
808	iclr20_934_1_4	 But the novelty is limited.	 NEG
1184	midl20_90_2_5	 The results of the model was compared also to the state of the art.From the following sentence, I understand that for each pathology, a different model was trained.	 NA
11	graph20_25_2_11	 There are many other potential designs out there and the paper does not fully explore the potential design space before picking Bend Passwords [33].	 NEG
144	graph20_39_3_5	 The writing is clear and the paper is easy to read.	 POS
917	midl19_49_1_13	 This study did not give a gold-standard for shape clustering (though it could be difficult).	 NEG
639	iclr20_2094_1_13	 A. Khan has also found approximation algorithms for the 3D Knapsack problem with rotations.	 NA
176	graph20_53_2_3	 The tool is a very useful and novel contribution, although I have some questions about the validity of the use case scenario.	 NEG
994	midl19_52_2_13	 Why do the authors reuse the input of a temporal block to its output and how does this influence the performance?	 NEG
440	iclr19_495_1_12	 It seems that the authors only use the basic normalizing flow structures studied in Rezende&Mohamed (2015) and Dinh et al (2016).	 NEG
521	iclr19_997_3_14	" It would be better to provide discussions of recent neural architecture search methods solving the single-objective problem. """	 NEG
1378	neuroai19_59_3_3	 In particular, the setting of synaptic decay constants is an important detail in a paper about working memory.	 NA
1004	midl19_52_2_23	 8-The lack of scalability and the requirement of computational time is highlighted in the introduction and abstract.	 NA
143	graph20_39_3_4	 It is a qualitatively-driven paper, but I believe it provides much insight into what providers would like in patient visualizes, and takes into account how patients already record their information.	 POS
753	iclr20_727_1_8	 Another, relatively small point which the authors glance over is the matter of efficient training.	 NEG
519	iclr19_997_3_12	 For example, what is the drawbacks of the number of parameters, what is the advantages of FLOPs for multi-objective optimization?	 NEG
822	midl19_13_2_7	 Indeed the critical factor is not to capture the instrument's appearance but rather model how variable the anatomical environment is.	 NA
428	iclr19_495_1_0	 This paper generalizes basic policy gradient methods by replacing the original Gaussian or Gaussian mixture policy with a normalizing flow policy, which is defined by a sequence of invertible transformations from a base policy.	 NA
612	iclr20_2046_2_17	 The authors need to give more discussion and explanation about it.	 NEG
1032	midl19_59_3_0	 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.	 POS
156	graph20_43_1_3	 I also think that the overall motivation of understanding whether interfaces with distinct visual and motor widths (to use the paper's terms) is interesting.	 POS
539	iclr20_1042_2_17	 As an example, q(z) could be arbitrarily multimodal as far as the encoder is concerned, but the Weibull seems to force one mode per class.	 NA
461	iclr19_659_2_8	 My main concern about the paper is the time cost.	 NA
576	iclr20_1493_2_36	 It is unclear if what is lacking from the NN is explicit regularization, or just more data.	 NA
255	iclr19_1049_1_0	 This work proposes a variant of the column network based on the injection of human guidance.	 NA
979	midl19_51_2_19	 6- I suggest the authors to use train validation and test split or a cross-validation, since the results presented here are from a validation set without a test set.	 NEG
1185	midl20_90_2_6	 If this is true, the model is not efficient.	 NEG
1006	midl19_52_2_25	 I believe the computational time can be added for each method in Table 1.	 NEG
560	iclr20_1493_2_18	 And it seems to have very relevant connections to your work.	 NA
208	graph20_56_1_15	 IN fact, the whole way the user draws the shape is poorly described.	 NEG
910	midl19_49_1_5	 The introduction part is a little misleading for me.	 NEG
1	graph20_25_2_1	 The prototype is a simplified version of Bend Passwords [33] geared towards users who are visually impaired.	 NA
269	iclr19_1091_1_7	 Other than that, the different approaches tested all work well in different tasks.	 POS
265	iclr19_1091_1_3	 However, the conclusions do not directly follow from the results, so should be made more precise.	 NEG
914	midl19_49_1_9	 The experiments shown in Table 1 compare several different network settings.	 NA
230	graph20_61_2_8	 INTRODUCTION The motivation and context is sound, with references on how information visualization and dashboards support learning analytics or educational data visualization.	 POS
1375	neuroai19_59_3_0	 The question of how networks maintain memory over long timescales is a longstanding and important one, and to my knowledge this question hasn't been thoroughly explored in spiking, trained recurrent neural networks (RNN).	 NA
1119	midl20_135_3_5	 It is strange that the T1, T2 generalize well to the validation set but not to the test.	 NEG
1370	neuroai19_54_3_9	 The flow/high-level organization of the paper works well.	 POS
864	midl19_14_2_38	 ii) Learning to leverage the information of complementary data sets is a challenging task.	 NA
1066	midl20_100_1_17	 What is interesting is not who is better, but how, and how well, the task can be solved.	 NEG
873	midl19_25_3_3	 Only a single (large) dataset is used, while there are many publicly available datasets that could be included for additional experiments.	 NEG
1246	neuroai19_26_1_2	 While it does not seemingly add anything conceptual, the exact implementation is arguably new.	 NEG
398	iclr19_304_3_11	 You mention complexity of data and model several times in the paper but never define what you mean by that.	 NEG
1271	neuroai19_3_3_6	 The work has promising implications for computational psychiatry, but probably not for RL at this point.	 POS
45	graph20_29_3_13	" That, in turn, makes it quite difficult to understand the counter-argument developed in this paper---and especially since ""The evidence comes from a study by Bi et al."" (p. 4), which makes one wonder why Bi et al. put that ""limitation"" up in the first place."	 NEG
161	graph20_43_1_8	" In addition to the above concerns about the contribution of the paper, the term ""motor size"" is already used in Blanch et al.'s CHI 2004 work to refer to the situation where the control-display gain is manipulated to create objects with a higher or lower size in motor space as compared to their visual space on screen, work which is not cited in this paper."	 NEG
991	midl19_52_2_10	 They seem to work in different dimensions of the signals.	 NA
727	iclr20_720_2_0	 While this paper has some interesting experiments.	 POS
629	iclr20_2094_1_3	 Unfortunately, I believe that the current manuscript is at a too early stage for being accepted at ICLR, due to the following reasons: (a) The paper is littered with spelling/grammar mistakes (just take the second sentence: With the developing -> development).	 NEG
288	iclr19_1291_3_1	 2) Individualized reward: Agents receive individual rewards; therefore, agents are aware of their contribution towards the goal.	 NA
1059	midl20_100_1_10	 In the methods section you describe training an autoencoder on unlabeled data, then training an LSTM using autoencoder embeding and embryologist grades.	 NA
1359	neuroai19_53_1_9	 It also would have been nice to comment on the relationship of this work to unsupervised (e.g. Hebbian-based) learning rules.	 NEG
575	iclr20_1493_2_35	 This concern does not make the contribution of the symmetric dataset less valuable, but a discussion of such caveats would help further elucidate the similarities and differences of this setup from real datasets.	 NEG
850	midl19_14_2_24	 The authors of [2] provided a website with all the results on the DRIVE database (pseudo-url), so their segmentations could be taken from there.	 NA
911	midl19_49_1_6	 The authors emphasize that the objective is to cluster the geometric shape of leaflets, and it is hard to represent the shapes in high-dimensional space (last paragraph of introduction).	 NA
1254	neuroai19_26_1_10	 I'd say a fairly 'standard' work for the setting.	 POS
1264	neuroai19_29_1_7	" Its also critical to understanding the function of the hippocampus and entorhinal cortex in humans."""	 NA
932	midl19_51_1_0	 The paper presents an approach to aid interpretation of pathology images coming from confocal microscopes (CM images).	 NA
1100	midl20_119_2_1	 It's shown that such self-expressiveness constraint can help to preserve subtle structures during image translation, which is critical for medical tasks, such as plaque detection.	 NA
1207	midl20_96_3_18	 I have been working in this field for many years and published papers about these topics.	 NA
247	graph20_61_2_25	 The responsive design choice is great for multiple device access with various form factors.	 POS
476	iclr19_866_1_9	 This is in contrast to semantic parsing and symbol grounding models, which exploit the compositionality of language to generalize to new instructions.	 NA
85	graph20_35_1_11	" This research is well-written and a good contribution to the area of brainstorming, and it would be interesting to get more of a complete sense of the results."""	 POS
469	iclr19_866_1_2	 The second module is responsible for mapping goals from this embedding space to control policies.	 NA
225	graph20_61_2_3	 Originality The review of related work is varied across relative disciplines and well positioned.	 POS
774	iclr20_855_3_10	 The second has to do with the interpretation of the results.	 NA
331	iclr19_242_2_6	 It looks to me the better generalization comes from more complicated data augmentation, not from the proposed large batch training.	 NEG
233	graph20_61_2_11	 RELATED WORK The related work is well balanced with a review on visualization dashboards and visualization in medical training with references from diverse related research communities.	 POS
588	iclr20_1724_2_5	 The compositional action classification task is harder and shows that incorporating LSTMs for temporal reasoning leads to non-trivial performance improvements over frame averaging.	 NA
475	iclr19_866_1_8	 The goal-policy mapping approach would presumably restrict the robot to goals experienced during training, preventing generalization to new goals.	 NEG
599	iclr20_2046_2_4	 And it combines A* search with MCTS to improve the performance over the traditional MCTS approaches based on UCT or PUCT tree policies.	 POS
421	iclr19_304_3_35	 Instead, you present vague of sharp drops and two modes but do not present rigorous definitions.	 NEG
182	graph20_53_2_9	 The latter assessed usability (SUS) and workload (NASA TLX) and custom miscellaneous items.	 NA
988	midl19_52_2_7	 2- The extensive tests on a real dataset instead of phantom cases is definitely a strength of the paper.	 POS
1000	midl19_52_2_19	 Are the results on the entire parametric maps in line with the current results?	 NEG
49	graph20_29_3_17	 The described examples feel rather artificial.	 NEG
1305	neuroai19_34_2_7	 Mostly neuroscientific, but addresses the important topic of how models from machine learning can best be used in neuro research.	 POS
1029	midl19_56_3_18	" Authors suggest that their solution ""is not constrained by statistical modes of variation"", as e.g. by PCA-based SSM methods."	 NA
529	iclr20_1042_2_7	" Text contradicting the equation: ""In order to balance the individual loss terms, we normalize according to dimensions and weight the KL divergence with a constant of 0.1""."	 NEG
1258	neuroai19_29_1_1	 The work of Hill et al. (2019) very clearly addresses these questions by devising tasks that require generalization across domains, showing how training regime is sufficient to overcome the difficulties of these tasks, even in shallow networks.	 NA
486	iclr19_866_1_19	 The domains considered for experimental evaluation are particularly simple.	 NEG
497	iclr19_938_3_2	 Authors compare their approach with COMA (discrete actions and counterfactual (semi-centralized) baseline) and MADDPG (also uses centralized value function and continuous actions) MAAC is evaluated on two 2d cooperative environments, Treasure Collection and Rover Tower.	 NA
106	graph20_36_1_20	 The experiment procedure give little details about participants background.	 NEG
443	iclr19_495_1_15	 Maybe they can uniformly outperform Gaussian policy?	 NA
646	iclr20_2094_1_20	 But this looks wrong since it should include the distribution of all instances of 3D-BPP.	 NA
1036	midl19_59_3_4	" Often new methods are manually ""overfitted"" to one dataset."	 NA
726	iclr20_57_3_10	" Table 3 (text classification result) does not list baselines."""	 NEG
190	graph20_53_2_17	 The paper does discuss limitations, but I think that this section should also address the fact that the study was largely preliminary / exploratory in nature; there was no comparison condition, nor a discussion of what a baseline condition might look like for this context.	 NEG
282	iclr19_1091_1_20	 The GTC metric is not very well established (yet).	 NEG
29	graph20_26_3_4	 I am satisfied with the changes in the modified manuscript, and changing am my recommendation to accept.	 POS
169	graph20_45_2_4	 The results are demonstrated on several example datasets and contrasted against visualizations using traditional PCP and scatterplots.	 NA
1001	midl19_52_2_20	 7- What is the number of parameters required for each method in Table 1?	 NEG
1204	midl20_96_3_15	 Assessing in-focus will even get rid of blurred frames and frames as discussed in the Appendix.	 NA
100	graph20_36_1_14	 Are there other patters with features not presented in these three?	 NEG
197	graph20_56_1_4	 However, the paper is weakened by several writing and organizational aspects, and by an odd off-hand report of user feedback.	 NEG
495	iclr19_938_3_0	 Summary Authors present a decentralized policy, centralized value function approach (MAAC) to multi-agent learning.	 NA
647	iclr20_2094_1_21	 e) The Actor-Critic framework, coupled with a conditional query learning algorithm, is unfortunately unintelligible due to the fact that many notations are left unspecified.	 NEG
987	midl19_52_2_6	 1- This paper is well written and the message is clear to the reader.	 POS
44	graph20_29_3_12	" 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."	 NEG
710	iclr20_526_3_31	 Here again, MNIST would be a useful dataset.	 NEG
373	iclr19_261_3_9	 Have you tried baselines like these?	 NA
678	iclr20_305_3_14	 Make it clear that these points are here to help, and not necessarily part of your decision assessment.	 NA
566	iclr20_1493_2_25	 It is unclear on what basis one can say that real-world datasets are more like the symmetric case or the asymmetric case.	 NEG
847	midl19_14_2_21	 The method in [2] should be included in the comparison of vessel segmentation algorithms.	 NEG
460	iclr19_659_2_7	 The main contribution is it provides a way to reduce the number of interactions with the environment.	 NA
1155	midl20_77_4_2	 On the positive side, the extension of the Gessert model to motion forecasting seems like a useful one.	 POS
39	graph20_29_3_7	" As the authors state themselves p. 9, ""A common way to check external validity is to apply obtained parameters to data from different participants."""	 NA
434	iclr19_495_1_6	 and why one needs to compute gradients of the entropy (Section 4.1)?	 NA
973	midl19_51_2_13	 The authors should provide support to these conclusions.	 NEG
1239	neuroai19_23_1_8	 What precisely about predictive coding makes the similarity to brain data expected?	 NEG
1007	midl19_52_2_26	 Minor suggestions a- Some recent work on using the complex-valued neural networks (Virtue Patrick et al., arxiv), geometry of deep learning (Golbabaee et al., arxiv)and recurrent neural networks (Oksuz et al.,arxiv) for MRF dictionary matching can be mentioned in the literature review with their strengths and weakneses.	 NA
456	iclr19_659_2_3	 This paper is well-written.	 POS
260	iclr19_1049_1_5	 Experiments have shown that the convergence speed and results are improved, but not significant.	 NEG
35	graph20_29_3_3	 I also have a number of concerns that I would like to see addressed in a revision.	 NEG
827	midl19_14_2_1	 The method allows to detect a series of relevant anatomical/pathological structures in fundus pictures (such as the retinal vessels, the optic disc, hemorrhages, microaneurysms and soft/hard exudates).	 NA
309	iclr19_1399_1_2	 Hyperparameters were honestly optimized.	 POS
276	iclr19_1091_1_14	 Because the parts of the state that are needed for multiple different prediction tasks (reconstruction, inverse model, etc.) need to be in the final state representation multiple times.	 NA
636	iclr20_2094_1_10	 thesis 2015; Christensen et. al. Computer Science Review 2017).	 NA
654	iclr20_2157_3_0	 The paper presents expected gradients which is a method which looks at a difference from a baseline defined by the training data.	 NA
622	iclr20_2046_2_27	 In practice, this is not true because even at the leaf node the value could still be estimated by an inaccurate value network (e.g., AlphaGo or AlphaZero).	 NEG
844	midl19_14_2_18	 I would suggest to incorporate results on at least one of these data sets to better understand the behavior of the algorithm on these images.	 NA
393	iclr19_304_3_6	 Because of that, the experimental evaluation remains vague as well, as the criteria are tested on one data set by visual inspection.	 NEG
107	graph20_36_1_21	 How did authors ensure homogeneity of the groups?	 NEG
349	iclr19_242_2_26	 In section 4.2, I fail to understand why the proposed method can affect the norm of gradient.	 NEG
524	iclr20_1042_2_2	 In this way, there's no need to store all past data and even the first learned batch keeps being refreshed and should not be forgotten.	 NA
784	iclr20_880_2_4	 This paper can have a tremendous impact in the research in deep networks if results are well explained.	 POS
47	graph20_29_3_15	 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).	 NA
203	graph20_56_1_10	 Overall, the writing and the organization of the paper suffered from similar issues.	 NEG
21	graph20_25_2_21	 REFERENCES Sauvik Das, Gierad Laput, Chris Harrison, and Jason I. Hong.	 NA
402	iclr19_304_3_15	 Independent and identically distributed?	 NA
1107	midl20_127_4_2	" Main problem: The authors mention ""the AF are sonographer dependent, and its accuracy depends on the sonographer's experience."	 NEG
1154	midl20_77_4_1	 The models are variants of that proposed in Gessert et al (2019), which is here extended in different ways to perform motion forecasting/prediction using a sequence of OCT volumes, rather than motion estimation between 2 OCT volumes.	 NA
989	midl19_52_2_8	 3- The description of the network architecture is not clear for the reader.	 NEG
741	iclr20_720_2_14	 While the experiments show the value of hierarchy, they do not show the value of this particular method of creating hierarchy.	 NEG
1310	neuroai19_34_2_12	 It doesn't provide any additional information to the data lines themselves, and it leads the reader to expect these indicate statistically significant comparisons.	 NEG
1147	midl20_71_1_4	 Meanwhile, a few clarifications may be necessary: 1) in term of runtime, does the addition of GRUs take much more training time and memory comparing to the concatenation of 3D volumes?	 NEG
33	graph20_29_3_1	 This paper is well written and shows good experiment design and consistent analyses.	 POS
787	iclr20_880_2_7	 Equation (1) and (2) are extremely clear.	 POS
923	midl19_49_1_19	 and Hausdorff distance to measure the recon accuracy between original image and reconstructed image.	 NA
1043	midl19_59_3_11	 But the higher performance is not significant.	 NEG
1345	neuroai19_37_3_24	" I feel this statement: ""Our challenge is to understand how this occurs."	 NA
1243	neuroai19_23_1_12	" Typo line 24 Moreover, we show that as (we) train the model Typo line 87 Second, the model does not rely on labeled data and learn(s)"""	 NA
579	iclr20_1493_2_39	 It would be very interesting to see whether these results differ at all from the one-shot approach here.	 NA
481	iclr19_866_1_14	 Are they free-form instructions?	 NEG
931	midl19_49_1_27	" All architectures listed in Table 1 should be stated clearly in experiments section not only in method section. """	 NEG
356	iclr19_242_2_34	 Moreover, the proposed method also use N times more augmented samples.	 NA
1248	neuroai19_26_1_4	 I think a more persuasive bench marking could be done.	 NEG
759	iclr20_76_2_0	 In order to rationalize the existence of non-trivial exponents that can be independent of the specific kernel used, this paper introduces the Teacher-Student framework for kernels.	 NA
92	graph20_36_1_6	 To begin with, there is little details about the design rationale.	 NEG
1175	midl20_85_3_8	 What is the experimental setup?	 NEG
1341	neuroai19_37_3_20	 Repeat this process recursively tens to trillions of times, and suddenly you have a brain controlling a body in the world or doing something else equally clever.	 NA
803	iclr20_880_2_23	" If the authors can reject (1), (2) and (3), they should find a plausible explaination why performance improves in their experiments."""	 NEG
745	iclr20_727_1_0	 The authors propose a method for learning models for discrete events happening in continuous time by modelling the process as a temporal point process.	 NA
279	iclr19_1091_1_17	 Please indicate why these tasks are chosen.	 NA
764	iclr20_855_3_0	 This paper presents an emprical study of how a properly tuned implementation of a model-free RL method can achieve data-efficiency similar to a state-of-the-art model-based method for the Atari domain.	 NA
604	iclr20_2046_2_9	 How could it improve over the traditional tree policy (e.g., UCT) for the selection step in MCTS?	 NEG
1383	neuroai19_59_3_8	 For instance, while the heatmaps in Figure 3 provide visual evidence for their claims (except see my comments below), the work could have benefitted from a quantification of this evidence.	 NEG
400	iclr19_304_3_13	 Page 4, Assumption.	 NA
257	iclr19_1049_1_2	 Human knowledge is embodied in a defined rule formula.	 NA
944	midl19_51_1_12	 I understand that the available space is limited and therefore it's difficult to bring in the paper all the information that would be necessary, but the introduction should be extended to include previous work both in terms of DL and medical research.	 NEG
1096	midl20_108_3_12	 Moreover, is there is a reason you did not validate on all TUPAC16 tasks?The is well written paper with a clear description of the state of the art and the reasoning behind the presented method.	 POS
1219	neuroai19_2_2_9	 While much human learning may be more naturally cast as online learning, not all of it is.	 NA
1161	midl20_77_4_8	 Also, can the authors comment on what the accuracy requirement is for motion tracking in OCT?	 NA
494	iclr19_866_1_27	" There are several grammatical errors - The captions for Figures 3 and 4 are copied from Figure 1."""	 NEG
772	iclr20_855_3_8	 Unless a comparison can be made with the same amounts of experience, I don't see how Figure 2 can be interpreted objectively.	 NEG
550	iclr20_1493_2_7	 However, there are a few (in my opinion) critical concerns that currently bar me from strongly recommending acceptance of the paper.	 NA
201	graph20_56_1_8	 One main weakness of the paper is manifested here: I found the description of the bins, and how they are calculated, quite confusing.	 NEG
312	iclr19_1399_1_5	 Weaknesses: - The experiments are done on CIFAR-10, CIFAR-100 and subsets of CIFAR-100.	 NA
290	iclr19_1291_3_3	 The authors also claim these two extensions enable their model to converge faster and better.	 NA
1213	neuroai19_2_2_3	 Either way this is important work, with many interesting future directions.	 POS
763	iclr20_76_2_4	" Therefore, the efficacy of the proposed model can not be well demontrated."""	 NEG
1136	midl20_56_4_18	 But the writing needs to be improved.	 NEG
949	midl19_51_1_17	 Unfortunately the authors didn't report indications in this sense in their paper.	 NEG
183	graph20_53_2_10	 Some issues in the study reporting: - What was the scale range for the prior experience questions?	 NEG
1176	midl20_85_3_9	 Did you train on some other dataset and test on skin lesion dataset?	 NEG
1349	neuroai19_37_3_28	" While it covers important ground, I think the arguments need more refinement and focus before they can inspire productive discussion."""	 NEG
1068	midl20_100_1_19	 I am not convinced.	 NEG
1030	midl19_56_3_19	" While I agree that the linear latent space assumption of PCA is too simplistic and the global effect of PCA latents on the whole shape often undesirable, the ordering of latents according to ""percent of variance explained"" is actually desirable in terms of interpretability."	 NEG
250	graph20_61_2_28	" I would suggest the following references to inform analysis of user logs: - H. Guo, S. R. Gomez, C. Ziemkiewicz and D. H. Laidlaw, ""A Case Study Using Visualization Interaction Logs and Insight Metrics to Understand How Analysts Arrive at Insights,"" in IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 1, pp. 51-60, 31 Jan."	 NEG
823	midl19_13_2_8	 A more complete evaluation with different surgical scenarios would be needed to demonstrate this feature.	 NA
1099	midl20_119_2_0	 This paper proposes to add a self-expressiveness regularization term to learn a union of subspaces for image-to-image translation in medical domain.	 NA
940	midl19_51_1_8	 I agree with the authors statement in the end of the paper where they say they could train both GAN and de-speckle network end to end.	 NA
391	iclr19_304_3_4	 Foremost, the presented criteria are actually not real criteria (expect maybe C1) but rather general guidelines to visually inspect 'accuracy over randomization curves.	 NEG
1092	midl20_108_3_8	 Multi-task learning can extract a shared representation that is generalisable and this is evidenced in the results in the TUPAC16 set.	 NA
1191	midl20_96_3_2	 Motivation is based on anonymisation and data cleansing.	 NA
904	midl19_41_1_4	" The gain using CGAN MRI looks marginal, which would be better to apply ablation study. """	 NEG
730	iclr20_720_2_3	 While it is possible that I am missing something, I have tried going through the paper a few times and the contribution is not immediately obvious.	 NEG
554	iclr20_1493_2_12	 While not in conflict with this work, it does closely relate and discuss many of the same issues discussed in this work, so relating them would be fruitful.	 NEG
366	iclr19_261_3_1	 The authors argue convincingly that an interactive and grounded evaluation environment helps us better measure how well NLG/NLU agents actually understand and use their language rather than evaluating against arbitrary ground-truth examples of what humans say, we can evaluate the objective end-to-end performance of a system in a well-specified nonlinguistic task.	 NA
345	iclr19_242_2_21	 For theorem 1, it is hard to say how much the theoretical analysis based on linear approximation near global minimizer would help understand the behavior of SGD.	 NA
535	iclr20_1042_2_13	 The weighting of the KL that the authors introduce is going to bias the learned generator towards the high probability regions.	 NA
1209	midl20_96_3_20	" I am advocating open data access and reproducible research."""	 NA
1372	neuroai19_54_3_11	 e.g. what was the nonlinearity used in the model CNN?	 NEG
919	midl19_49_1_15	 However, recon accuracy highly depends on decoder network.	 NA
211	graph20_56_1_18	 These critical areas of confusion around how the process actually unfolds from start to finish should have been more clearly described.	 NEG
502	iclr19_938_3_7	 The centralized nature is also semantically improbable, as the observations might be high-dimensional in nature, so exchanging these between agents becomes impractical with complex problems.	 NEG
537	iclr20_1042_2_15	 A Weibull distribution is used to model the same data, again, in a different way.	 NA
1220	neuroai19_2_2_10	 There may be much interest in how we learn from so few samples in certain settings, but we also learn some relationships/tasks in a classical associationist manner which is well modeled by 'slow' gradient-descent like learning (e.g. Rescorla Wagner).	 NA
175	graph20_53_2_2	 With this, users can select part of a VR object, assign an animation behaviour, and preview it.	 NA