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"After the publication at MICCAI 2019 of the work ""Distribution Matching Losses Can Hallucinate Features in Medical Image Translation"" and similar other works, it has started becoming apparent that the simple visual similarity between samples generated by a GAN and true samples from a specific distribution doesn't ensure that diagnostic value is kept." ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
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. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
Can you comment? ['non', 'non', 'non', 'non'] paper quality |
Notably, several classes of geometric bin packing problems admit polynomial-time approximation algorithms (for extended surveys about this topic, see e.g. Arindam Khans Ph.D. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
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 ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
Few questions: - Since the method is quite simple and elegant , I expect it could be adapted to other tasks. ['non', 'non', 'non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
In fact, the proof of the theorems could be moved to appendices. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
They show images of a single reconstruction but no quantification of reconstruction quality or comparison to previous methods ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
Then this evaluation could be used to study impacts of the parameters of their model which could then lead to neural hypotheses. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
A comparison with Bellec et al. 2018, which looks at working memory tasks in spiking networks, would also have been appropriate ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
The authors do not compare the inference speed of the proposed method with others ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
Even provided more computing resources, the proposed method is not faster than small batch training ['non', 'non', 'non', 'non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
Specify that it is on the validation set if so, and clarify these points: number of epochs was set to 150, early stopping to 10 epochs Why is this clipping used? ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
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). ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
A short discussion of other training algorithms (such as surrogate gradient or surrogate loss methods) and why the given one was chosen instead would have been helpful ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
Could it be extended to work with only a fraction of the nuclei annotated ? ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'non'] paper quality |
The first 10 lines contains too much wording for a statement that should be much easier to explain ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
The authors directly tried to associate biological learning rules with deep network learning rules in AI. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
I suggest checking the papers citing Bengio et al. (2009) to find lots of closely related papers. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
This paper proposes a pulmonary nodule malignancy classification based on the temporal evolution of 3D CT scans analyzed by 3D CNNs. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
They draw an analogy between the ventral and dorsal stream of cortex and bilinear models of images. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
Space is of course limited, but the mathematics presented seem to pass all sanity checks and gives sufficiently rigor to the authors' approach ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
"The leader sends messages to followers, an ""event"" is a pair (timestep, message of leader to a follower)." ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
However, if different models were trained for predicting each parameter, not only training but also prediction would not be efficient ['non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
The method introduces a self attention mechanism using weakly supervised labels, thereby avoiding the need to use more exhaustive annotations such as segmentations. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
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 ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
Even the results with the VGG network are very far from the best available models ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
Since the authors are using inner matrices with a number of dimensions higher than the number of dimensions of the original matrix, there is no approximation and, then, no selection of features or feature combinations. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
It is unclear to me what is the benefit of the proposed method ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
However, the authors quote a previous paper that use different data augmentation and (potentially) other experimental settings. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
The proof-of-concept work (among others) that this can be done with spiking RNN may inspire more work in this area ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
It would have been useful to put these in context of the results of the algonauts contest, which pitched supervised methods such as Alexnet against user-submitted content. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
Is using a pre-trained network really helping ? ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'non'] paper quality |
Some unsupervised network embedding baseline methods, such as DeepWalk and Node2Vec, should be included into the experiment section . ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'non'] paper quality |
The motivation and methodology are well explained with proper reference works ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
Is the work trying to address the credit assignment problem in general, or just when applied to online learning tasks ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
This paper still represent a niche application of a more general DL technique that has been already used for a large number of similar applications ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
What precisely about predictive coding makes the similarity to brain data expected ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
Overall the technical aspects of this paper seem sound ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
"Operationally, I'm not quite sure how these are different, so, to me this goal is roughly ""be explainable"", and progress towards it could be measured e.g. in MDLs." ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
"No trouble understanding the material or writing By focusing on the more biologically plausible ""feedback alignment"" networks, the paper does sit at the intersection of neuro and AI" ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
How does the temporal and spatial blocks work ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
However, the contribution of the authors does not appear to extend beyond combining existing data sets with existing network architectures ['non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
The authors propose a method for learning models for discrete events happening in continuous time by modelling the process as a temporal point process. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
Does limiting the synaptic time constants limit the intrinsic time constants, and if so by how much ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
In this paper, the authors aimed to improve the representations learned by Neural Image Compression (NIC) algorithms when applied to Whole Slide Images (WSI) for pathology analysis. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
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 ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
Please, clarify that point in the text. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
This may bring some advantage for the proposed algorithm. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
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). ['non', 'non', 'non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
In summary, it is not a bad paper , but the experimental results are not sufficient to conclude that much ['non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
If the authors can reject (1), (2) and (3), they should find a plausible explaination why performance improves in their experiments ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
"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" ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
Section 3.3 is confusing to me ['arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
Some typo problems lie here ['arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
The contribution is minor, and the reasoning behind it could be better motivated ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
Detailed: Page 3, last paragraph: Why did you not use bias terms in your model? ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
It is not clear why calibration is reported and not simple measures of uncertainty like variance or entropy ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
Unless a comparison can be made with the same amounts of experience, I don't see how Figure 2 can be interpreted objectively ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
That said, it does seems like a fairly creative combination of existing approaches ['non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
Since there is so much dissimilarity between ImageNet and the target domains, I expect it to be mostly a glorified edge detector. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
However, I find the paper written in a way assuming readers very familiar with related concept and algorithms in reinforcement learning ['non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
What is unique about the MRF formalism that -- for practical applications -- could not be effectively captured in a directed graphical model ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
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. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
But models can also be used for value function estimation (Model Based Value Expansion) and reducing gradient variance(using pathwise derivatives). ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
The premise of the work must be clarified ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
For larger scale domains, I fear this could become an important obstacle to effective model training ['non', 'non', 'non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
One issue, from a purely organizational standpoint, is the fact that information about previous work is either omitted or scattered around the text ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
The domains considered for experimental evaluation are particularly simple ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
Minor things: + The main idea is described too sketchily ['non', 'non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
It is improving the final performances, speeding up convergence, both ? ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
You mention complexity of data and model several times in the paper but never define what you mean by that ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
b- Please explain (a.u.) ['non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
The second has to do with the interpretation of the results. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
For example, what kind of additional benefit will it bring when integrating the priority queue into the MCTS algorithms ['non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
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. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
In that case, it is the ratio of positive/negative that is relevant. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
Do we really need a labelled ground truth here ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
"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]""." ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
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. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
"2] Maninis, Kevis-Kokitsi, et al. ""Deep retinal image understanding.""" ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
Gives important new results about how eligibility traces can be used to approximate gradients when adequately combined with a learning signal ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
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. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
Specifically, it proposes to use a state representation consisting of 2 (or 3) parts that are trained separately on different aspects of the relevant state: reward prediction, image reconstruction and (inverse) model learning. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
Page 4, Monotony. ['non', 'non', 'non', 'non', 'non'] paper quality |
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. ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
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. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
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 ['non', 'non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
It would be better to provide discussions of recent neural architecture search methods solving the single-objective problem . ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'non'] paper quality |
It is fine that you give your method a name (although I personally dislike it), but a bit weird not to explain it ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
This paper is aimed at tackling a general issue in NLP: Hard-negative training data (negative but very similar to positive) can easily confuse standard NLP model. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
The contribution is original in the sense that complementing data sets is a really challenging task, difficult to address with current available solutions ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
I have a few minor comments / questions / editing notes that would be good to address: - The random baseline isn't described in the main text , it would be good to briefly mention it (this will also help to clarify why the value is particularly high for tasks 1 and 2) - The grid resolution ablation results presented in the supplement are actually quite important -- they demonstrate that with a small increase in granularity of the grid the traditional tracking methods begin to be the best performers. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
In the exploitation step, architectures are generated by a Bayesian Network. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
How would you expect those networks to perform when trained on unlabeled video data? ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
Does PredNet outperform other user-submitted models? ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
The figures are hard to parse because of the very short captions ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
Methods described clearly and in good detail ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
It is not convincing to claim that the clustering is correct since even a noise can be decoded into a normal image ['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg'] paper quality |
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. ['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non'] paper quality |
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