paper_id stringlengths 10 19 | venue stringclasses 15
values | focused_review stringlengths 192 10.2k | point stringlengths 23 618 |
|---|---|---|---|
NIPS_2019_772 | NIPS_2019 | of this approach (e.g., it does not take into account language compositionally). I appreciate that the authors used different methods to extract influential objects: Human attention (in line with previous works), text explanation (to rely on another modality), and question parsing (to remove the need of extra annotatio... | - I would use a different notation for SV(.,.,.) as it is not symmetric. For instance SV_{a}(v_i || v_j) would avoid confusion (I am using KL notation here) - Non-formal expression should be avoided: Ex: "l32 What's worse" - The references section is full of format inconsistencies. Besides, some papers are published wi... |
ICLR_2022_497 | ICLR_2022 | I have the following questions to which I wish the author could respond in the rebuttal. If I missed something in the paper, I would appreciate it if the authors could point them out.
Main concerns: - In my understanding, the best scenarios are those generated from the true distribution P (over the scenarios), and ther... | - The paper introduces CVAE-SIP and CVAE-SIPA in Sec 5 -- after discussing the training methods, so I am wondering if they follow the same training scheme. In particular, it is not clear to me by saying “append objective values to the representations” at the beginning of Sec 5. |
m50eKHCttz | ICLR_2024 | Overall, I think the paper is quite comprehensive. A few points that may be lacking:
1. The results in studying properties of student models is a bit surprising to me. This isn’t a huge weakness, but more exploration of why CNN student models improve with scale and why transformer student models seem to worsen with wou... | 1. The results in studying properties of student models is a bit surprising to me. This isn’t a huge weakness, but more exploration of why CNN student models improve with scale and why transformer student models seem to worsen with would strengthen these results. |
NIPS_2017_631 | NIPS_2017 | - I don't understand why Section 2.1 is included. Batch Normalization is a general technique as is the proposed Conditional Batch Normalization (CBN). The description of the proposed methodology seems independent of the choice of model and the time spent describing the ResNet architecture could be better used to provid... | - On that note, I understand the neurological motivation for why early vision may benefit from language modulation, but the argument for why this should be done through the normalization parameters is less well argued (especially in Section 3). The intro mentions the proposed approach reduces over-fitting compared to f... |
ICLR_2021_860 | ICLR_2021 | Weakness 1. The proposed measurement is not helpful for designing new methods. Note that the mutual information in mixup is lower than baseline while mixup still outperforms baseline. 2. Compared to mixup and cutmix, the improvement reported in Table 2 is marginal. 3. The experiments on ImageNet is unconvincing. Both o... | 2. Compared to mixup and cutmix, the improvement reported in Table 2 is marginal. |
MzDakXdBbM | EMNLP_2023 | This work presents some weaknesses regarding the information provided:
- There is no study around LLM-based data sampling, where Chat-GPT or PaLM-2 detects which queries should be rewritten. There is no measurement of the performance of those models for that classification task.
- There is no data on the nature and qua... | - There is no study around LLM-based data sampling, where Chat-GPT or PaLM-2 detects which queries should be rewritten. There is no measurement of the performance of those models for that classification task. |
NIPS_2016_370 | NIPS_2016 | , and while the scores above are my best attempt to turn these strengths and weaknesses into numerical judgments, I think it's important to consider the strengths and weaknesses holistically when making a judgment. Below are my impressions. First, the strengths: 1. The idea to perform improper unsupervised learning is ... | 1. The idea to perform improper unsupervised learning is an interesting one, which allows one to circumvent certain NP hardness results in the unsupervised learning setting. |
NIPS_2020_989 | NIPS_2020 | 1. The notations, equations in the method section are not clear. In Line 110 for instance, the equation $\Upsilon(x)=\{\Upsilon(x)_l\}$ is confusing. 2. The discriminator on the left side of Figure 1 is not the network used by the existing I2I methods (e.g., BicycleGAN concatenates the one-hot vector with the image as ... | 2. The discriminator on the left side of Figure 1 is not the network used by the existing I2I methods (e.g., BicycleGAN concatenates the one-hot vector with the image as the input.) 3. Two highly-related frameworks targeting multi-domain I2I [1,2] are not cited, discussed, and compared in the paper. |
ARR_2022_233_review | ARR_2022 | Additional details regarding the creation of the dataset would be helpful to solve some doubts regarding its robustness. It is not stated whether the dataset will be publicly released.
1) Additional reference regarding explainable NLP Datasets: "Detecting and explaining unfairness in consumer contracts through memory n... | 6) In Table 3, it is not clear whether the line with +epsilon refers to the human performance when the gold explanation is available or to the roberta performance when the golden explanation is available? In any case, both of these two settings would be interesting to know, so I suggest, if it is possible, to include t... |
CkrqCY0GhW | ICLR_2024 | 1. The reviewer did not get why Section 4 is needed (with such a large space), since most of the introductions are baseline methods. Also, I did not know why RCI/AdaPlanner/Synapse are used for baselines.
2. Only test on 50 compositional web automation tasks. Are the methods and evaluations/insights generalizable to ot... | 2. Only test on 50 compositional web automation tasks. Are the methods and evaluations/insights generalizable to other tasks? |
r2nwBwodth | ICLR_2025 | 1. The model proposed in this paper is an adaptation of the MAE model. The masking mechanism draws inspiration from wav2vec and data2vec, aiming to reconstruct the statistical information of the input signal. However, these contributions seem limited in scope.
2. The experimental results of this model are only comparab... | 2. The experimental results of this model are only comparable to data2vec, which was published two years ago, suggesting that its performance may not surpass more recent methods in the field. |
BMIjPXooNq | EMNLP_2023 | - The paper only studies one split from each of two synthetic datasets. It’s hard to know whether the conclusions can be translated to other splits and datasets.
- The effectiveness of leveraging dataset cartography for CL is unclear. In most cases, no curriculum appears to perform better or is on par with a strategy t... | - The paper only studies one split from each of two synthetic datasets. It’s hard to know whether the conclusions can be translated to other splits and datasets. |
ACL_2017_96_review | ACL_2017 | lack statistics of the datsets (e.g. average length, vocabulary size) the baseline (Moses) is not proper because of the small size of the dataset the assumption "sarcastic tweets often differ from their non sarcastic interpretations in as little as one sentiment word" is not supported by the data. - General Discussion:... | - the authors wrote that "the Fiverr workers might not take this strategy": to me it is not the spirit of corpus-based NLP. A model must be built to fit given data, not that the data must follow some assumption that the model is built on. |
ICLR_2021_2110 | ICLR_2021 | 1). My first concern is about the unrealistic assumptions. For example, Eq (5) “channel condition” requires g1 * g2 = C2, which doesn’t make sense to me: there is no intuition, and most existing convs doesn’t satisfy this assumption: (1) regular conv g1=g2=1 != C2 doesn’t satisfy this; (2) spatial separable conv g1=g2=... | 2). Second, the CIFAR results show the new layers are not much better than others. As shown in Figure 3, the largest gain is <1%, and sometimes the o-ResNet (~88%) is slightly worse than d-ResNet (which indicates the propose layers might be not "optimal"?) The improvements on ImageNet in Table 4 seem to be promising, b... |
ICLR_2022_1791 | ICLR_2022 | Weakness:
The whole framework is built upon an assumption that the video can be (near) perfectly decomposed into foreground objects and background, which is a very toy assumption and cannot be used in any complicated real video data.
This paper assumes knowing the underlying physics dynamics (in this case pendulum), wh... | 2) For the pendulum, only one real video data is evaluated; |
NIPS_2022_655 | NIPS_2022 | Weakness: 1. The conclusion seems to be only for GCN. I wonder GAT[1] may exhibit a smaller degree bias, even smaller than graph contrastive learning methods. 2. From Figure 6 in Appendix A, the advantage of graph contrastive learning methods over GCN on Photo dataset is not obvious. The numerical values of their slope... | 1. The conclusion seems to be only for GCN. I wonder GAT[1] may exhibit a smaller degree bias, even smaller than graph contrastive learning methods. |
NIPS_2017_351 | NIPS_2017 | - As I said above, I found the writing / presentation a bit jumbled at times.
- The novelty here feels a bit limited. Undoubtedly the architecture is more complex than and outperforms the MCB for VQA model [7], but much of this added complexity is simply repeating the intuition of [7] at higher (trinary) and lower (una... | - What values were learned for the linear coefficients for combining the marginalized potentials in equations (1)? It would be interesting if different modalities took advantage of different potential orders. |
NIPS_2017_71 | NIPS_2017 | - The paper is a bit incremental. Basically, knowledge distillation is applied to object detection (as opposed to classification as in the original paper).
- Table 4 is incomplete. It should include the results for all four datasets.
- In the related work section, the class of binary networks is missing. These networks... | - The paper is a bit incremental. Basically, knowledge distillation is applied to object detection (as opposed to classification as in the original paper). |
NIPS_2017_631 | NIPS_2017 | - I don't understand why Section 2.1 is included. Batch Normalization is a general technique as is the proposed Conditional Batch Normalization (CBN). The description of the proposed methodology seems independent of the choice of model and the time spent describing the ResNet architecture could be better used to provid... | - I would have liked to see how different questions change the feature representation of a single image. Perhaps by applying some gradient visualization method to the visual features when changing the question? |
fjf3YenThE | ICLR_2024 | 1. The paper lacks a proper related work section, which makes it challenging for readers to quickly grasp the background and understand the previous works. It is crucial to include a comprehensive discussion on related works, especially regarding the variance-reduced ZO hard-thresholding algorithm and the variance redu... | 2. The paper suffers from a lack of necessary references, such as papers on SAGA, SARAH, and SVRG methods. When these methods are initially mentioned, it is essential to provide corresponding references. Additionally, there are errors in the appendix due to bibtex errors, which should be carefully reviewed and correcte... |
NIPS_2017_114 | NIPS_2017 | - More evaluation would have been welcome, especially on CIFAR-10 in the full label and lower label scenarios.
- The CIFAR-10 results are a little disappointing with respect to temporal ensembles (although the results are comparable and the proposed approach has other advantages)
- An evaluation on the more challenging... | - I'd be interested to see if the exponential moving average of the weights provides any benefit on it's own, without the additional consistency cost. |
3Jl0sjmZx9 | ICLR_2024 | 1. It is not clear how to generate the I, C and R', which is critical in this paper. Also, I'm not sure if the quality of generated data by the unified pipeline is good or not, though the authors mention there are professions who help check them.
2. The comparison in Table 3 shows the advantage of the proposed method i... | 2. The comparison in Table 3 shows the advantage of the proposed method in this paper, which is mainly due to the domain-specific encoder. However, will the computational complexity be much larger? |
YPvI7SofeZ | ICLR_2025 | - A lot of use of quite specific jargon, which makes it harder to follow and otherwise very clearly written paper.
- The section on multi-step RL is very short and by only referencing results in the appendix, not self-contained material of this paper. The introduction (L 84) claims an “investigation to address larger l... | - Fig 1. Axis labels should be bigger. Split positioning of legend is a bit confusing. |
NIPS_2018_612 | NIPS_2018 | Weakness: - Two types of methods are mixed into a single package (CatBoost) and evaluation experiments, and the contribution of each trick would be a bit unclear. In particular, it would be unclear whether CatBoost is basically for categorical data or it would also work with the numerical data only. - The bias under di... | - The bias under discussion is basically the ones occurred at each step, and their impact to the total ensemble is unclear. For example, randomization as seen in Friedman's stochastic gradient boosting can work for debiasing/stabilizing this type of overfitting biases. |
NIPS_2022_2513 | NIPS_2022 | Weakness:
1.The tech contribution of MicroSeg is very limited. Region proposal network for class-agnostic detecting novel objects is already widely used, such as [a].
2.SSUL uses the off-the-shelf saliency-map detector to detect unseen classes while the paper uses the pretrained Mask2Former to produce the region propos... | 4.Missing the speed comparison and model parameters with other methods. What are the model sizes of the proposed MicroSeg combined with Mask2Former? [a] Gu, Xiuye, et al. "Open-vocabulary object detection via vision and language knowledge distillation. ICLR, 2022. [b] Open-World Instance Segmentation: Exploiting Pseudo... |
ARR_2022_317_review | ARR_2022 | - Lack of novelty: - Adversarial attacks by perturbing text has been done on many NLP models and image-text models. It is nicely summarized in related work of this paper. The only new effort is to take similar ideas and apply it on video-text models.
- Checklist (Ribeiro et. al., ACL 2020) had shown many ways to stress... | - If you could propose any type of perturbation which is specific to video-text models (and probably not that important to image-text or just text models) will be interesting to see. Otherwise, this work, just looks like a using an already existing method on this new problem (video-text) which is just coming up. |
NIPS_2016_537 | NIPS_2016 | weakness of the paper is the lack of clarity in some of the presentation. Here are some examples of what I mean. 1) l 63, refers to a "joint distribution on D x C". But C is a collection of classifiers, so this framework where the decision functions are random is unfamiliar. 2) In the first three paragraphs of section ... | 1) l 63, refers to a "joint distribution on D x C". But C is a collection of classifiers, so this framework where the decision functions are random is unfamiliar. |
NIPS_2019_962 | NIPS_2019 | for exceptions. + Experiments are convincing. + To the best of my knowledge, the idea of using unsupervised keypoints for reinforcement learning is novel and promising. One can expect a variety of follow-up work. + Using keypoints as input state of a Q function is reasonable and reduces the dimensionality of the proble... | - L93: "by marginalising the keypoint-detetor feature-maps along the image dimensions (as proposed in [15])". This would be better explained and self-contained by saying that a soft-argmax is used. |
NIPS_2018_947 | NIPS_2018 | weakness of the paper, in its current version, is the experimental results. This is not to say that the proposed method is not promising - it definitely is. However, I have some questions that I hope the authors can address. - Time limit of 10 seconds: I am quite intrigued as to the particular choice of time limit, whi... | - Pruning via equivalence classes: I could not understand what is the partial "current cost" you mention here. Thanks for clarifying. |
NIPS_2022_2523 | NIPS_2022 | The main contribution of this paper is to introduce the upsample operation into the ResTv1 to compensate for the lost information from the downsample. Though this simple design can provide the performance benefit, the generalizability of this design seems narrow, does it only work well for the specific efficient Transf... | 3) I think a more detailed description of Fig.3 is needed for a better understanding, e.g., the meaning of coordinates and curves. Yes, the authors address the efficiency to some extent. They point out the gap between the theoretical FLOPs and the actual speed, and consider the actual running speed more when designing ... |
NIPS_2017_104 | NIPS_2017 | ---
There aren't any major weaknesses, but there are some additional questions that could be answered and the presentation might be improved a bit.
* More details about the hard-coded demonstration policy should be included. Were different versions of the hard-coded policy tried? How human-like is the hard-coded policy... | * How does the model perform on the same train data it's seen already? How much does it overfit? |
NIPS_2020_1425 | NIPS_2020 | 1. The proposed method may not be able to accelerate the whole framework with GPU, as the edge prediction can not be computed in parallel. The title may need to modify, otherwise more experiment results on speed should be provided to valid the statement "accelerating self-attention". 2. Need more detailed analysis on h... | 2. Need more detailed analysis on how much memory can be saved compared to other methods, such as XLNet or reversible Transformer/CNN (REFORMER: THE EFFICIENT TRANSFORMER). The explored datasets actually do not need huge memory. It would be better to explore some tasks with much longer sequences. |
QoiOmXy3A7 | EMNLP_2023 | 1. The paper is hard to read; the abstract and introduction are well written however, the method and evaluation is hard to follow (see suggestions section).
2. Few details like numbers in the Table 2 are for Gen model with prototype or both are missing.
3. No other baselines except for random is evaluated, making it di... | 3. No other baselines except for random is evaluated, making it difficult to evaluate how good the method is as compared to others. Meta-learning also conceptually creates prototypes and an instance may belong to one of the prototypes, may be some baselines could use that intuition. |
NIPS_2020_675 | NIPS_2020 | * Monotonic Alignment Search algorithm used in training is deterministic and the training procedure doesn't represent uncertainty over possible alignments. At sampling time, the duration prediction module is also deterministic. Since it was trained with mean-squared-error, it is unable to produce natural and varied pro... | * Monotonic Alignment Search algorithm used in training is deterministic and the training procedure doesn't represent uncertainty over possible alignments. At sampling time, the duration prediction module is also deterministic. Since it was trained with mean-squared-error, it is unable to produce natural and varied pro... |
ICLR_2023_2312 | ICLR_2023 | 1. Literature Review
The paper regrettably fails to acknowledge a vast body of related literature, on (i) intention-conditioned trajectory prediction, (ii) variational graph methods for trajectory prediction, and (iii) models that explicitly model social interactions for forecasting. At the very least, these references... | 3. Notation There are a few notational errors. For instance, the variable used for the sequence cannot be the same as the individual elements: x < t = [ x 1 , . |
NIPS_2016_287 | NIPS_2016 | weakness, however, is the experiment on real data where no comparison against any other method is provided. Please see the details comments below.1. While [5] is a closely related work, it is not cited or discussed at all in Section 1. I think proper credit should be given to [5] in Sec. 1 since the spacey random walk ... | 3. This work combines ideas from [4], [5], and [14] so it is very important to clearly state the relationships and differences with these earlier works. |
ACL_2017_726_review | ACL_2017 | - Claims of being comparable to state of the art when the results on GeoQuery and ATIS do not support it. General Discussion: This is a sound work of research and could have future potential in the way semantic parsing for downstream applications is done. I was a little disappointed with the claims of “near-state-of-th... | - Figure 3 is a little confusing, I could not follow the sharp dips in performance without paraphrasing around the 8th/9th stages. |
NIPS_2018_612 | NIPS_2018 | weakness is not including baselines that address the overfitting in boosting with heuristics. Ordered boosting is non-trivial, and it would be good to know how far simpler (heuristic) fixes go towards mitigating the problem. Overall, I think this paper will spur new research. As I read it, I easily came up with variati... | * l. 131: "called ordered boosting" * l. 135-137: The "shift" terminology seems less understandable than talking about biased estimates. |
ARR_2022_141_review | ARR_2022 | of the paper: • The mix of different approaches and tasks leads to a confusion for readers.
• The logic flow of the paper needs to be improved.
• Not sure why the proposed model work for the experimental datasets, since those datasets do not have such textual supervision as co-citing sentences.
• There are no compariso... | • Not sure why the proposed model work for the experimental datasets, since those datasets do not have such textual supervision as co-citing sentences. |
NIPS_2018_559 | NIPS_2018 | - my only objection to the paper is that it packs up quite a lot of information, and because of the page-limits it doesnât include all the details necessary to reconstruct the model. This means cuts were made, some of which are not warranted. Sure, the appendix is there, but the reader needs to get all the necessary ... | - Line 108 - penalize using many different numerical constants - please provide a few examples before pointing to the supplement. |
NIPS_2018_114 | NIPS_2018 | 1. Generalizability. In general, I think the authors need to show how this approach can work on more problems. For example, it looks to me that for most deep net problem A2 is not true. Also, some empirical verification of assumption A1 alone on other problems would be useful to convince me why this approach can genera... | 1. For A1, I agree that if we have explicit written f_i(x) then we can compute radius in a easy way. My original concern is when the function is too complicated that the radius do not have a easy close form, then can we at least empirically evaluate the radius. I guess if the authors want to focus outside DL then this ... |
NIPS_2016_537 | NIPS_2016 | weakness of the paper is the lack of clarity in some of the presentation. Here are some examples of what I mean. 1) l 63, refers to a "joint distribution on D x C". But C is a collection of classifiers, so this framework where the decision functions are random is unfamiliar. 2) In the first three paragraphs of section ... | 6) in section 2.1 the phrase "group action" is used repeatedly, but it is not clear what this means. |
ACL_2017_553_review | ACL_2017 | Very few--possibly avoid some relatively "empty" statements: 191 : For example, if our task is to identify words used similarly across contexts, our scoring function can be specified to give high scores to terms whose usage is similar across the contexts.
537 : It is educational to study how annotations drawn from the ... | 278 : some words were detected by multiple methods with CCLA |
NIPS_2017_235 | NIPS_2017 | weakness even if true but worth discussing in detail since it
could guide future work.) [EDIT: I see now that this is *not* the case, see response below]
I also feel that the discussion of Chow-Liu is missing a very
important aspect. Chow-Liu doesn't just correctly recover the
true structure when run on data generated ... | 2) Alice draws two joint samples $X$ and $X'$ Both are conditioned on the some random value for $X_S$. |
ICLR_2022_2796 | ICLR_2022 | - The experiments are on very small datasets and in toy settings. - Some parts of the theory are insufficiently explored. For example, under what scenarios can we expect invertibility of E z [ G ( z ) G ( z ) T ]
? Perhaps this could be shown to hold in simple settings, e.g., G ( z ) := ReLU ( W z )
with the WDC assump... | - Some parts of the theory are insufficiently explored. For example, under what scenarios can we expect invertibility of E z [ G ( z ) G ( z ) T ] ? Perhaps this could be shown to hold in simple settings, e.g., G ( z ) := ReLU ( W z ) with the WDC assumption. Tools from NTK theory could potentially be helpful here, sin... |
NIPS_2016_279 | NIPS_2016 | Weakness: 1. The main concern with the paper is the applicability of the model to real-world diffusion process. Though the authors define an interesting problem with elegant solutions, however, it will be great if the authors could provide empirical evidence that the proposed model captures the diffusion phenomena in r... | 2. Though the IIM problem is defined on the Ising network model, all the analysis is based on the mean-field approximation. Therefore, it will be great if the authors can carry out experiments to show how similar is the mean-field approximation compared to the true distribution via methods such as Gibbs sampling. Detai... |
NIPS_2018_963 | NIPS_2018 | and Clarifications]: - My major concerns are with the experimental setup: (a) The paper bears a similarity in various implementation details to Pappert et.al. [5] (e.g. adaptive scaling etc.), but it chose to compare with the noisy network paper [8]. I understand [5] and [8] are very similar, but the comparison to [5] ... | - On the conceptual note: In the paper, the proposed approach of encouraging diversity of policy has been linked to "novelty search" literature from genetic programming. However, I think that taking bonus as KL-divergence of current policy and past policy is much closer to perturbing policy with a parameter space noise... |
NIPS_2022_807 | NIPS_2022 | . The technical contribution of this work is limited. The algorithms presented and their analysis in my understanding is mainly lifted from the existing literature. The Active Reward Learning guarantees are not really novel or surprising. The reward free section lacks citations but these results are already present in ... | 2) I think the paper has merit because of the introduction of this setup. |
NIPS_2020_1504 | NIPS_2020 | * In the context of posterior inference (i.e. p corresponds to a posterior), the stochastic Stein discrepancy investigated here is technically different from the common practice: here separate mini-batches of data points are drawn for each sample point, while in practice we usually use a fixed mini-batch for all sample... | * I have concerns about the correctness of the proof; see below. |
BCRZq5nNZu | ICLR_2024 | - Originality
- Contributions are incremental and novelty is limited.
- Quality
- [Page-1, Section-1, Para-3] When a model sequentially learns over a sequence of datasets (in this case chunks) without having the measures of retaining the past knowledge it tends to forget the past learning as evident in the CL literatur... | - Clarity - It is difficult to keep track of the different data preparation techniques for "offline SGD", "standard CL" and "chunking" methods. It would be better to have clear algorithms and/or pictorial illustrations for the same. |
ICLR_2022_518 | ICLR_2022 | 1. I am not sure if the experiments would be very appealing to the deep learning community because they do not compare with the many latest temporal network SOTAs, and the two tasks seem to mostly target audience from robotics and control. Technically speaking, it mainly uses temporal network dataset whose features hav... | 7) does not seem quite new to me. It seems essentially just a temporal convolution followed by graph diffusion which is not totally new ([6] for example adopted a very similar idea). [1] Inductive Representation Learning on Temporal Graphs [2] Inductive Representation Learning in Temporal Networks via Causal Anonymous ... |
NIPS_2018_539 | NIPS_2018 | Weakness: - Although authors try to explain the STABLE model in a Bayesian framework using variational lower bound, as we see in the final form of the algorithm, it doesn't seem different from a simple regularization method illustrated in Section 3.2.1 when discriminator in Figure 3 corresponds to L2 distance. - The ex... | - The experimental protocol seems favorable to the proposed method and unfair to previous methods such as loss correction or S-adaptation. For example, one may inject noise transition prior on S-adaptation by setting corresponding \theta values to zero and renormalize after each update. |
ICLR_2023_2235 | ICLR_2023 | 1. In section 3.1 they do not specify what certain notations mean , eg the difference between the two transaction tables on the right of figure 2.
2. Jump from section 3.2 to 3.3 is big especially for people who are unfamiliar with algorithms they point to such as FP-growth Han et al. (2000) and apriori Agrawal et al. ... | 2. Jump from section 3.2 to 3.3 is big especially for people who are unfamiliar with algorithms they point to such as FP-growth Han et al. (2000) and apriori Agrawal et al. (1994). They use an example for section 3.1 but then they drop the example for subsequent sections in the algortihm . |
x31F1VmiV7 | ICLR_2024 | - the rationality of retriever: 1). this paper claims that the retriever generates diverse sensitive words, while in the implementation, it actually samples words from 50 candidates, which reside in a very limited search space. 2). the approximation of the attacker is coarse, since the retriever only returns top-3 word... | 1). this paper claims that the retriever generates diverse sensitive words, while in the implementation, it actually samples words from 50 candidates, which reside in a very limited search space. |
ICLR_2022_1454 | ICLR_2022 | 1: The contribution of SGDEM over AEGD is limited. Although theoretical analysis is provided to verify the effectiveness of the proposed algorithm, the advantages of SGDEM over the AEGD are unclear. As an improved version of AEGD, I believe detailed comparisons of theoretical results between these two methods are requi... | 4: The experiments are only conducted for vision tasks while NLP is a very important application in deep learning. The optimization method should also be essentially tested for NLP tasks. |
NIPS_2017_434 | NIPS_2017 | ---
This paper is very clean, so I mainly have nits to pick and suggestions for material that would be interesting to see. In roughly decreasing order of importance:
1. A seemingly important novel feature of the model is the use of multiple INs at different speeds in the dynamics predictor. This design choice is not
ab... | 2. Section 4.2: To what extent should long term rollouts be predictable? After a certain amount of time it seems MSE becomes meaningless because too many small errors have accumulated. This is a subtle point that could mislead readers who see relatively large MSEs in figure 4, so perhaps a discussion should be added in... |
ACL_2017_779_review | ACL_2017 | However, there are many points that need to be address before this paper is ready for publication.
1) Crucial information is missing Can you flesh out more clearly how training and decoding happen in your training framework? I found out that the equations do not completely describe the approach. It might be useful to u... | 2) Organization The paper is not very well organized. For example, results are broken into several subsections, while they’d better be presented together. The organization of the tables is very confusing. Table 7 is referred before table 6. This made it difficult to read the results. |
MVosmEvLSb | ICLR_2025 | 1) The problem is not well motivated with respect to utility for the ML community
2) The paper seems limited to analyzing two methods proposed by authors for a new problem. The authors should mention their contributions in comparison to existing literature more clearly.
3) Assuming no error in $X_S$ seems to be a very ... | 5) For example, kindly see the mutual incoherence assumption made in the standard support recovery paper: [https://ieeexplore.ieee.org/document/4839045](https://ieeexplore.ieee.org/document/4839045). One can get some intuition about the problem from such an assumption on mutual incoherence but not from the assumptions ... |
ACL_2017_727_review | ACL_2017 | Quantitative results are given only for the author's PSL model and not compared against any traditional baseline classification algorithms, making it unclear to what degree their model is necessary. Poor comparison with alternative approaches makes it difficult to know what to take away from the paper.
The qualitative ... | - The authors give no intuition behind why unigrams are used to predict frames, while bigrams/trigrams are used to predict party. |
VSBBOEUcmD | EMNLP_2023 | - Since the proposed LLM-based approach aims to benefit from low-resource target domains, it is necessary to evaluate how the size of available target text affects performance. However, the paper only investigates one fixed amount of target text, leaving important questions unanswered. For example, does the amount of a... | - Since the proposed LLM-based approach aims to benefit from low-resource target domains, it is necessary to evaluate how the size of available target text affects performance. However, the paper only investigates one fixed amount of target text, leaving important questions unanswered. For example, does the amount of a... |
NIPS_2021_1947 | NIPS_2021 | Writing the paper and linking and describing sections need improvement.
Lat, Lng and distances are based on certain geometric models of earth as earth's shape is Geoid. Google Maps uses one, Satellite, Navy, Flights others. What system is adhered here is not clear and have not been thought of it seems. What happens if ... | - wastage of references and text area. Follow Train-Eval-Split following Andrew Ng's ML Yearning book. How does demographics and world events bias the POI prediction? A scope of future work. There is no negative impact per se, apart from privacy issue of mobility data that may not be fully anonymized following k-anonym... |
ICLR_2022_1212 | ICLR_2022 | 1) The experimental settings are not hard enough to evaluate the performance of FSL. There is no doubt that there is information loss when the devices transmit only the ranking of scores. This kind of information loss is not serious when the rankings on different devices are similar (the local subnetwork structures are... | 3) The idea of utilizing “supermask” seems novel, but this paper seems just simply combining “supermask” with FL. It is okey to do “A plus B” things, but you need to provide some scientific contributions like providing a theoretical analysis about why “supermask plus FL” works, and what challenges that you solved make ... |
NIPS_2020_790 | NIPS_2020 | Generally speaking, the paper is interesting with solid theoretical results. I have few questions: * In the analysis of the CRS model, the full CRS is considered. However, the factorized CRS is considered for the empirical results. Do the derived theoretical guarantees follow for the factorized CRS? Is it possible to s... | * In the empirical results section, It is evident that the performance gap between PL and CRS is much lower than the one between CRS and Mallows across most of the databases. Are there any insights about such a behavior? |
NIPS_2019_932 | NIPS_2019 | weakness is that some of the main results come across as rather simple combinations of existing ideas/results, but on the other hand the simplicity can also be viewed as a strength. I donât find the Experiments section essential, and would have been equally happy to have this as a purely theory paper. But the experim... | - No need for capitalization in âGroup Testingâ - Give a citation when group testing first mentioned on p3 - p3: Remove the word âtypicalâ from âthe typical group testing measurementâ, I think it only increases ambiguity/confusion. |
NIPS_2019_1207 | NIPS_2019 | - Moderate novelty. This paper combines various components proposed in previous work (some of it, it seems, unbeknownst to the authors - see Comment 1): hierarchical/structured optimal transport distances, Wasserstein-Procrustes methods, sample complexity results for Wasserstein/Sinkhorn objectives. Thus, I see the con... | - Please provide details (size, dimensionality, interpretation) about the neural population datasets, at least on the supplement. Many readers will not be familiar with it. References: |
NIPS_2017_575 | NIPS_2017 | - While the general architecture of the model is described well and is illustrated by figures, architectural details lack mathematical definition, for example multi-head attention. Why is there a split arrow in Figure 2 right, bottom right? I assume these are the inputs for the attention layer, namely query, keys, and ... | - The complexity argument claims that self-attention models have a maximum path length of 1 which should help maintaining information flow between distant symbols (i.e. long-range dependencies). It would be good to see this empirically validated by evaluating performance on long sentences specifically. Minor comments: |
cMMxJxzYkZ | EMNLP_2023 | - Some of the results tables claim that statistical significance testing has taken place, but it’s unclear which things are being compared in the statistical tests. This seems important to clarify since some of the differences are a bit smaller.
- This paper is empirically significant in establishing a new SOTA on this... | - This paper is empirically significant in establishing a new SOTA on this benchmark, but the technical novelty of the approach is more limited. It’s not proposing a new modelling approach, and the ideas for improving over chatgpt (e.g. two stage reasoning with emotion prediction, using a knowledge base, etc) are simil... |
NIPS_2020_1605 | NIPS_2020 | 1) Discrete regression outputs: The output space for the final regression model is *discrete*, and this may not be entirely desirable in applications where the regression function is expected to make continuous predictions. I understand that prior works on fair regression also discretize the regression problem to turn ... | 1) Discrete regression outputs: The output space for the final regression model is *discrete*, and this may not be entirely desirable in applications where the regression function is expected to make continuous predictions. I understand that prior works on fair regression also discretize the regression problem to turn ... |
NIPS_2018_874 | NIPS_2018 | --- None of these weaknesses stand out as major and they are not ordered by importance. * Role of and relation to human judgement: Visual explanations are useless if humans do not interpret them correctly (see framework in [1]). This point is largely ignored by other saliency papers, but I would like to see it addresse... | * How do these explanations change with hyperparameters like choice of activation function (e.g., for non piecewise linear choices). How do LRP/DeepLIFT (for non piecewise linear activations) perform? |
ARR_2022_297_review | ARR_2022 | 1. While it is fair to say that two annotators might have different answers to the same question and both might be correct, it would be better to verify that the answers provided are all valid. The authors manually validate a small subset of the dataset but, for a high quality dataset, it would be better to validate al... | 2. The paper should include automatic metrics for the generation task. While the metrics have their own problems, it would be a good way to compare systems without expensive human evaluation. |
ARR_2022_209_review | ARR_2022 | - The proposed method heavily relies on BERT-based encoders and BERT has a word limit of 512 tokens. But most discharge summaries in MIMIC-III have much more than 512 tokens. This may mean a lot of information in discharge summaries is truncated and the model may not be able to build a comprehensive representation of p... | - In the abstract, what is "increasing F1 by up to t points and precision @Top-K by a large margin of over 25%" based on? The paper may make the statement clearer by mentioning the specific setup that achieves the largest improvement margin. |
ICLR_2022_1926 | ICLR_2022 | 1. The empirical results may be only marginally significant. For example, in Table 2, the proposed method cannot surpass SOTA under several settings. Plus the current version only conducts experiments on bert-base-uncased. It would be helpful to validate the proposed method using at least one more pre-trained language ... | 1. The empirical results may be only marginally significant. For example, in Table 2, the proposed method cannot surpass SOTA under several settings. Plus the current version only conducts experiments on bert-base-uncased. It would be helpful to validate the proposed method using at least one more pre-trained language ... |
NIPS_2017_357 | NIPS_2017 | - the manuscript is mainly a continuation of previous work on OT-based DA
- while the derivations are different, the conceptual difference is previous work is limited
- theoretical results and derivations are w.r.t. the loss function used for learning (e.g.
hinge loss), which is typically just a surrogate, while the re... | - the proved bound (Theorem 3.1) is not uniform w.r.t. the labeling function $f$. Therefore, it does not suffice as a justification for the proposed minimization procedure. |
ARR_2022_343_review | ARR_2022 | 1) Questionable usefulness of experiments on small datasets - As the paper itself states in the beginning, a possible weakness of earlier works is that their experiments were conducted on small datasets. In such cases it is unclear whether conclusions also apply to current MT systems trained on large datasets.
- This c... | 3) Figures and presentation:- I believe that the color scheme in tables is confusing at times. In Table 1, I think it is confusing that a deeper shade of red means better results. In Table 2 it is non-intuitive that the first row is CHRF and the second row is COMET - and the table is quite hard to read. |
ACL_2017_768_review | ACL_2017 | . First, the classification model used in this paper (concat + linear classifier) was shown to be inherently unable to learn relations in "Do Supervised Distributional Methods Really Learn Lexical Inference Relations?" ( Levy et al., 2015). Second, the paper makes superiority claims in the text that are simply not subs... | - In table 4, the F1 of "random" should be 0.25. |
NIPS_2019_1408 | NIPS_2019 | - The paper is not that original given the amount of work in learning multimodal generative models: â For example, from the perspective of the model, the paper builds on top of the work by Wu and Goodman (2018) except that they learn a mixture of experts rather than a product of experts variational posterior. â In ... | - The paper did a commendable job in attempting to perform experiments to justify the 4 properties they outlined in the introduction. |
ACL_2017_614_review | ACL_2017 | As a reader of a ACL paper, I usually ask myself what important insight can I take away from the paper, and from a big picture point of view, what does the paper add to the fields of natural language processing and computational linguistics. How does the task of lexical substitutability in general and this paper in par... | - Citations in Section 3.1.4 are missing. Addition: I have read the author response and I am sticking to my earlier evaluation of the paper. |
NIPS_2019_346 | NIPS_2019 | weakness of the paper is a lack of theoretical results on the proposed methodology. Most of the benefits of the new model have been demonstrated by simulations. It would be very helpful if the authors could provide some theoretical insights on the relation between the model parameters and the tail dependence measures, ... | 1. In Figures 2 and 3, it may be clearer to see the fitting errors if we overlay the oracle and the fitted lines in the same plot. Update: Thanks to the authors for the feedback. I believe Items 2 and 5 above are well addressed. On the other hand, as pointed out by another reviewer as well, a lack of theoretical result... |
ICLR_2023_2122 | ICLR_2023 | 1. This work has limited technical novelties. The multiscale attention strategy is to simply learn attention maps at different scales. Such technical novelties are not enough for publication in ICLR 2. The experimental results are not convinced. It seems that the authors only report the results of the developed method.... | 3. In the experiments, the authors do not provide any ablation study experiments. |
NIPS_2018_857 | NIPS_2018 | Weakness: The main idea of learning detectors with carefully generated chips is good and reasonable, but is implemented by a set of simple practical techniques. 3) Weakness: This is an extension of SNIP [24], and focuses mostly on speed-up. Thus its novelty is significantly limited. | 3) Weakness: This is an extension of SNIP [24], and focuses mostly on speed-up. Thus its novelty is significantly limited. |
4DoSULcfG6 | ICLR_2024 | Although the attack and the observation is interesting, I think the paper has the following weak points:
1. Time complexity. Clearly from Algorithm 1, to run the adaptive poisoning, the attacker has to run the training model much more times than the baseline algorithms, making the proposed algorithm less practical. How... | 3. Clarity (minor points). The paper needs to improve the clarity. For example, many definitions are used without being defined, e.g., LIRA, challenge point, in+out model. It is better to provide those definitions in the preliminary to make the paper more self-contained. |
ARR_2022_288_review | ARR_2022 | - While this model is one of its kind in this area, the language model’s scope is too narrow to have a wide range of applications. Other similar BERT-based models (e.g. BioBERT, SciBERT) has a wider coverage. The authors should strengthen the claim of the importance of this model by discussing important problems in thi... | - Although ConfliBERT is a novel language model, the concept, methodology, implementation follows the BERT model. While it can contribute to the area of political science and computational social science in general, it is not clear how this work contributes to the area of NLP. |
bKCc3USOyv | ICLR_2025 | 1. The motivation for introducing quaternion operations by the authors doesn’t seem very natural. Is it simply because previous methods were insufficient in modeling attribute features, leading to the introduction of this mechanism? Why not consider many conventional techniques for incorporating attribute features?
2. ... | 6. The authors' baseline methods lack more recent works, especially those from 2024. |
NIPS_2016_287 | NIPS_2016 | weakness, however, is the experiment on real data where no comparison against any other method is provided. Please see the details comments below.1. While [5] is a closely related work, it is not cited or discussed at all in Section 1. I think proper credit should be given to [5] in Sec. 1 since the spacey random walk ... | 5. Sec. 3.2 and Sec. 3.3: The real data experiments study only the proposed method and there is no comparison against any existing method on real data. Furthermore, there is only some qualitative analysis/discussion on the real data results. Adding some quantitative studies will be more helpful to the readers and resea... |
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