paper_id stringlengths 10 19 | venue stringclasses 14
values | focused_review stringlengths 128 8.15k | point stringlengths 47 624 |
|---|---|---|---|
NIPS_2018_514 | NIPS_2018 | and Questions: 1) Can the results be improved so that ProxSVRG+ becomes better than SCSG for all minibatch sizes b? 2) How does the PL condition you use compare with the PL conditions proposed in âGlobal Convergence of Arbitrary-Block Gradient Methods for Generalized Polyak-Åojasiewicz Functionsâ, arXiv:1709.03014... | 2) How does the PL condition you use compare with the PL conditions proposed in âGlobal Convergence of Arbitrary-Block Gradient Methods for Generalized Polyak-Åojasiewicz Functionsâ, arXiv:1709.03014 ? |
NIPS_2020_911 | NIPS_2020 | 1. While the idea of jointly discovering, hallucinating, and adapting is interesting, there is a complete lack of discussing the impact of adding additional parameters and additional computational effort due to the multi-stage training and the multiple discriminators. The authors should provide this analysis for a fair... | 1. While the idea of jointly discovering, hallucinating, and adapting is interesting, there is a complete lack of discussing the impact of adding additional parameters and additional computational effort due to the multi-stage training and the multiple discriminators. The authors should provide this analysis for a fair... |
2RQokbn4B5 | ICLR_2025 | 1. The analysis of the correlation between dataset size and the Frobenius norm and the singular values is underwhelming. It is not clear if this trend holds across different model architectures, and if so, no theoretical evidence is advanced for this correlation.
2. The proposed method for dataset size recovery is way ... | 1. The analysis of the correlation between dataset size and the Frobenius norm and the singular values is underwhelming. It is not clear if this trend holds across different model architectures, and if so, no theoretical evidence is advanced for this correlation. |
NIPS_2021_2257 | NIPS_2021 | - Missing supervised baselines. Since most experiments are done on datasets of scale ~100k images, it is reasonable to assume that full annotation is available for a dataset at this scale in practice. Even if it isn’t, it’s an informative baseline to show where these self-supervised methods are at comparing to a fully ... | - Section 4.2 experiments with AutoAugment as a stronger augmentation strategy. One possible trap is that AutoAugment’s policy is obtained by supervise training on ImageNet. Information leaking is likely. Questions - In L114 the authors concluded that for linear classification the pretraining dataset should match the t... |
NIPS_2019_1408 | NIPS_2019 | Weakness: 1. Although the four criteria (proposed by the author of this paper) for multi-modal generative models seem reasonable, they are not intrinsic generic criteria. Therefore, the argument that previous works fail for certain criteria is not strong. 2. Tabular data (seeing each attribute dimension as a modality) ... | 2. Tabular data (seeing each attribute dimension as a modality) is another popular form of multi-modal data. It would interesting, although not necessary, to see how this model works for tabular data. |
ARR_2022_64_review | ARR_2022 | 1. The idea is a bit incremental and simply the extension of previous monolingual LUKE.
2.For the language-agnostic characters of entity representations, the paper has weak analysis on the alignment of entity representations.
The authors could add more analysis about the multilingual alignment of entity representations... | 2.For the language-agnostic characters of entity representations, the paper has weak analysis on the alignment of entity representations. The authors could add more analysis about the multilingual alignment of entity representations and it would be better to have visualizations or case studies for different types of la... |
NIPS_2018_831 | NIPS_2018 | - I wasn't fully clear about the repeat/remember example in Section 4. I understand that the unrolled reverse computation of a TBPTT of an exactly reversible model for the repeat task is equivalent to the forward pass of a regular model for the remember task, but aren't they still quite different in major ways? First, ... | - More details on using attention would be useful, perhaps as an extra appendix. |
NIPS_2017_496 | NIPS_2017 | weakness of the paper is the experimental evaluation. The only experimental results are reported on synthetic datasets, i.e. MNIST and MNIST multi-set. As the objects are quite salient on the black background, it is difficult to judge the advantage of the proposed attention mechanism. In natural images, as discussed in... | - The references list contains duplicates and the publication venues and/or the publication years of many of the papers are missing. |
ICLR_2022_2403 | ICLR_2022 | 1: The theoretical analysis in Theorem 1 is unclear and weak. It is unclear that what the error bound in Theorem 1 means. The authors need to analyze and compare the theoretical results to other comparable methods.
2: The title is ambiguous and may lead to inappropriate reviewers.
3: I see no code attached to this subm... | 1: The theoretical analysis in Theorem 1 is unclear and weak. It is unclear that what the error bound in Theorem 1 means. The authors need to analyze and compare the theoretical results to other comparable methods. |
ICLR_2023_3381 | ICLR_2023 | The authors claim that they bridge an important gap between IBC [2] and RvS by modeling the dependencies between the state, action, and return with an implicit model on Page 6. However, noticing that IBC proposes to use the implicit model to model the dependencies between the state and action, I think the contribution ... | 2) why the explicit methods perform better than implicit methods on the locomotion tasks. The pseudo-code of the proposed method is missing. [1] Søren Asmussen and Peter W Glynn. Stochastic simulation: algorithms and analysis, volume 57. Springer, 2007. [2] P. Florence, C. Lynch, A. Zeng, O. A. Ramirez, A. Wahid, L. Do... |
NIPS_2021_1527 | NIPS_2021 | Weakness:
The unbalanced data scenario has not been properly explored by experiments. Under what circumstances can it be counted as an unbalanced data scenario, and what is the data ratio? Therefore, the experiments should not pay more attention to one given setting like TED, WMT, etc., but should construct unbalanced ... | 2) the use of low-resource language pairs further finetune the multilingual model and use the method like R3F to maintain the generalization ability of the model. In some low-resource language translations from 1.2->2.0, although the improvement of 0.8 can be claimed, it is insignificant in a practical sense. Missing R... |
NIPS_2016_314 | NIPS_2016 | I found in the paper includes: 1. The paper mentions that their model can work well for a variety of image noise, but they show results only on images corrupted using Gaussian noise. Is there any particular reason for the same? 2. I can't find details on how they make the network fit the residual instead of directly le... | 1. The paper mentions that their model can work well for a variety of image noise, but they show results only on images corrupted using Gaussian noise. Is there any particular reason for the same? |
VwyTrglgmW | ICLR_2024 | 1. The authors claim that the existing PU learning methods will suffer a gradual decline in performance as the dimensionality of the data increases. It would be better if the authors can visualize this effect. This is very important as this is the research motivation of this paper.
2. Since the authors claim that the h... | 1. The authors claim that the existing PU learning methods will suffer a gradual decline in performance as the dimensionality of the data increases. It would be better if the authors can visualize this effect. This is very important as this is the research motivation of this paper. |
NIPS_2019_1397 | NIPS_2019 | weakness of the manuscript. Clarity: The manuscript is well-written in general. It does a good job in explaining many results and subtle points (e.g., blessing of dimensionality). On the other hand, I think there is still room for improvement in the structure of the manuscript. The methodology seems fully explainable b... | - Regarding the empirical version of the objective (3), it might be appropriate to put it in the supplementary materials. |
NIPS_2019_1420 | NIPS_2019 | Weakness - Not completely sure about the meaning of the results of certain experiments and the paper refuses to hypothesize any explanations. Other results show very little difference between the alternatives and unclear whether they are significant. - Lot of result description is needlessly convoluted e.g. "less likel... | - Lot of result description is needlessly convoluted e.g. "less likely to produce less easier to teach and less structured languages when no listener gets reset". ** Suggestions - A related idea of speaker-listener communication from a teachability perspective was studied in [1] - In light of [2], it's pertinent that w... |
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 approximation error is defined as the gap between the objective values, which is somehow ambiguous unless one has seen the values in the table. It would be better to provide a mathematical characterization. |
NIPS_2018_641 | NIPS_2018 | weakness. First, the main result, Corollary 10, is not very strong. It is asymptotic, and requires the iterates to lie in a "good" set of regular parameters; the condition on the iterates was not checked. Corollary 10 only requires a lower bound on the regularization parameter; however, if the parameter is set too larg... | 3. ln. 180--182: Corollar 10 only shows that uncertainty sampling moves in descent directions of the expected 0-1 loss; this does not necessarily mean that uncertainty sampling is not minimizing the expected convex surrogate. |
NIPS_2016_394 | NIPS_2016 | - The theoretical results don't have immediate practical implications, although this is certainly understandable given the novelty of the work. As someone who is more of an applied researcher who occasionally dabbles in theory, it would be ideal to see more take-away points for practitioners. The main take-away point t... | - The proposed model produces only 1 node changing cluster per time step on average because the reassignment probability is 1/n. This allows for only very slow dynamics. Furthermore, the proposed evolution model is very simplistic in that no other edges are changed aside from edges with the (on average) 1 node changing... |
NIPS_2021_1954 | NIPS_2021 | Some state-of-the-art partial multi-label references are missing, such as 1) Partial Multi-Label Learning with Label Distribution 2) Noisy label tolerance: A new perspective of Partial Multi-Label Learning 3) Partial multi-label learning with mutual teaching.
The explanation of Theorem 1 is weak; the author should prov... | 2) Noisy label tolerance: A new perspective of Partial Multi-Label Learning |
NIPS_2019_168 | NIPS_2019 | of the submission. * originality: This is a highly specialized contribution building up novel results on two main fronts: The derivation of the lower bound on the competitive ratio of any online algorithm and the introduction of two variants of an existing algorithm so as to meet this lower bound. Most of the proofs an... | * significance: As mentioned above this is a very specialized paper likely to interest some experts in the online convex optimization communities. Although narrow in scope, it contains interesting theoretical results advancing the state of the art in dealing with these specific problems. |
ICLR_2021_457 | ICLR_2021 | My main concern is that it is not completely clear to me how the authors suggest using the dataset for developing AI that is more ethical. I can clearly understand that one can use it to train an auxiliary model that will test/verify/give value for RL etc. I can also see that using it to fine tune language models and t... | 1.There are missing details about division to train and test sets, numbers as well as how the division was made (simply random? Any other considerations?). These details should be added. |
NIPS_2019_651 | NIPS_2019 | (large relative error compared to AA on full dataset) are reported. - Clarity: The submission is well written and easy to follow, the concept of coresets is well motivated and explained. While some more implementation details could be provided (source code is intended to be provided with camera-ready version), a re-imp... | - Clarity: The submission is well written and easy to follow, the concept of coresets is well motivated and explained. While some more implementation details could be provided (source code is intended to be provided with camera-ready version), a re-implementation of the method appears feasible. |
NIPS_2022_1708 | NIPS_2022 | Scalability: The proposed encoding method is templated-based (Line 155-156). Although the input encoding scheme (Section 7.1) may be a trivial problem, the encoding scheme may still affect the performance. Searching for the optimal encoding scheme is an expensive process, which may bring a high cost of hand-crafted eng... | 1) Building text descriptions for each task still requires human labor. We do not know what textual format is optimal for policy learning. It varies from task to task, model to model. On the other hand, as I stated in Question 1, the long-text input could restrict the scalability of this framework. |
NIPS_2022_1637 | NIPS_2022 | 1. The examples of scoring systems in the Introduction seem out of date, there are many newer and recognized clinical scoring systems. It also should briefly introduce the traditional framework of the scoring system and its difference in methodology and performance with the proposed method. 2. As shown in figure 3, the... | 2. As shown in figure 3, the performance improvement of proposed methods seems not so significant, the biggest improvement in the bank dataset was ~0.02. Additionally, using some tables to directly show the key improvements may be more intuitive and detailed. |
ICLR_2021_1043 | ICLR_2021 | , which justify the score: • The theoretical developments presented in the paper build on the Rademacher complexity, but ignore the conclusions drawn by Zhang et al. in Section 2.2 of their ICLR 2017 paper (Understanding deep learning requires rethinking generalization). • The theoretical developments build on the assu... | • Experimental validation are not convincing. Only shallow networks are considered (2 or 3 layers), and the optimization strategy, including the grid search strategy for hyperparameters selection, is not described. Minor issue: positioning with respect to related works is limited. For example, layer redundancy (which i... |
z62Xc88jgF | ICLR_2024 | 1. Although the use of this type of loss in this setting might be new, this work does not prove any new theoretical results.
2. That being said, experiment is a very important component in this paper, however, I find the evaluation metric of the solution very interesting. More specifically, let $u$ be the output of neu... | 1. Although the use of this type of loss in this setting might be new, this work does not prove any new theoretical results. |
ICLR_2022_1014 | ICLR_2022 | 1. It seems to me that a very straightforward hypothesis about these two parts would be that the trivial part is what’s very simple, either highly consistent to what’s in the training set, or the images with very typical object pose in the center of the images; and for the impossible part, it might be the images with a... | 1. It seems to me that a very straightforward hypothesis about these two parts would be that the trivial part is what’s very simple, either highly consistent to what’s in the training set, or the images with very typical object pose in the center of the images; and for the impossible part, it might be the images with a... |
NIPS_2016_93 | NIPS_2016 | / Major concerns: - It is difficult to evaluate whether the MovieQA result should be considered significant given that +10% gap exists between MemN2N on dataset with explicit answers (Task 1) and RBI + FP on dataset with other forms of supervision, especially Task 3. If I understood correctly, the different tasks are c... | - For bAbI, it seems the model was only tested on single supporting fact dataset (Task 1 of bAbI). How about other tasks? |
ICLR_2022_2318 | ICLR_2022 | Weakness： 1. This paper is built on the SPAIR framework and focuses on point cloud data, which is somehow incremental. 2. There is no ablation study to validate the effectiveness of the proposed components and the loss. 3. It is hard to follow Sec. 3.2. The author may improve it and give more illustrations and examples... | 3. It is hard to follow Sec. 3.2. The author may improve it and give more illustrations and examples. |
ICLR_2022_2070 | ICLR_2022 | Weakness:
1 The idea is a bit too straightforward, i.e., using the attributes of the items/users and their embeddings to bridge any two domains.
2 The technical contribution is limited, i.e., there is no significant technical contribution and extension based on a typical model for the cross-domain recommendation settin... | 2 The technical contribution is limited, i.e., there is no significant technical contribution and extension based on a typical model for the cross-domain recommendation setting. |
ICLR_2021_842 | ICLR_2021 | 1. Performance gains on downstream tasks of detection and instance segmentation are much lower -- how would the authors propose to improve these? 2. If the primary goal is to improve SSL performance on small models, I would have liked to see more analysis on how different design choices of setting up contrastive learni... | 1. Adding fully-supervised baselines for small models in table 1 will be useful in understanding the gap between full supervision and SSL for these models. |
ICLR_2022_445 | ICLR_2022 | Weakness: Method:
1. Novelty:
Incremental Contribution: The proposed LaMOO is a direct generalization from the LaMCTS method to multi-objective optimization (MOO). The novel part is to use dominance number as criteria for search space partition and hypervolume for promising region selection. These are all straightforwa... | 4. Time Complexity: What is the time complexity of the proposed algorithm? In each step of LaMOO, it has to repeatedly calculate the hypervolume of different regions for promising region selection. However, the computation of hypervolume could be time-consuming, especially for problems with many objectives (e.g., >3). ... |
NIPS_2018_219 | NIPS_2018 | Weakness: 1. I found the paper hard to follow. Unfamiliar with local differential privacy, I found it hard to comprehend. The definition is in Section 2. I would urge the authors to present it in Section 1 2. The accuracy estimates provided in the paper are probabilistic. Without proper experiments it is impossible to ... | 5. No conclusion is provided Updated after Author response: I am still not happy that the authors did not do any expts. While theoretical results only provide a bound, the usefulness can only be found by thorough evaluation. I would also urge the authors to add a conclusion section since the takeaways become more infor... |
NIPS_2020_1476 | NIPS_2020 | - As mentioned before, the dataset used in the experiments are all very small. It would be more convincing to see some result on medium or even large dataset such as ImageNet. But this is just a minor issue and it will not affect the overall quality of the paper. - Which model did you used in section 5 for image recogn... | - As mentioned before, the dataset used in the experiments are all very small. It would be more convincing to see some result on medium or even large dataset such as ImageNet. But this is just a minor issue and it will not affect the overall quality of the paper. |
NIPS_2018_109 | NIPS_2018 | that limit the contribution. In particular: 1) the method does not seem to improve on the robustness and sensitivity as claimed in the motivation of using evolutionary methods in the first place. In fig 3 the new method is noisier in 2 domains and equally noisy as the competitors in the rest. 2) The paper claims SOTA i... | - you have listed some of the limitations of evolutionary methods, but I think there are much deeper things to say regarding leveraging state, reactiveness, and learning during an episode. Being honest and direct would work well for this work - the title is way to generic and vague - be precise when being critical. Wha... |
ICLR_2021_2674 | ICLR_2021 | Though the training procedure is novel, a part of the algorithm is not well-justified to follow the physics and optics nature of this problem. A few key challenges in depth from defocus are missing, and the results lack a full analysis. See details below:
- the authors leverage multiple datasets, including building the... | - the paper doesn't describe how is the focal stack synthesized, what's the forward model of using a defocus map and an image to synthesize defocused image? how do you handle the edges where depth discontinuities happen? |
NIPS_2016_9 | NIPS_2016 | Weakness: The authors do not provide any theoretical understanding of the algorithm. The paper seems to be well written. The proposed algorithm seems to work very all on the experimental setup, using both synthetic and real-world data. The contributions of the papers are enough to be considered for a poster presentatio... | 2. The first claimed contribution of the paper is that unlike other existing algorithms, the proposed algorithm does not take as many points or does not need apriori knowledge about dimensions of subspaces. It would have been better if there were some empirical justification about this. |
NIPS_2021_537 | NIPS_2021 | Weakness: The main weakness of the approach is the lack of novelty. 1. The key contribution of the paper is to propose a framework which gradually fits the high-performing sub-space in the NAS search space using a set of weak predictors rather than fitting the whole space using one strong predictor. However, this high-... | 2. If we look at the specific components of the approach, they are not novel as well. The weak predictor used are MLP, Regression Tree or Random Forest, all of which have been used for NAS performance prediction before [2,3,7]. The sampling strategy is similar to epsilon-greedy and exactly the same as that in BRP-NAS[5... |
NIPS_2021_2224 | NIPS_2021 | . 1. The proposed S1DB-ED algorithm is too similar to RMED (Komiyama et al. 2015), so I think the novelty of this part is limited. The paper needs to give a sufficient discussion on the comparison with RMED. 2. The comparison baselines in experiments are not sufficient. The paper only compares the proposed two algorith... | .1. The proposed S1DB-ED algorithm is too similar to RMED (Komiyama et al. 2015), so I think the novelty of this part is limited. The paper needs to give a sufficient discussion on the comparison with RMED. |
PCm1oT8pZI | ICLR_2024 | 1. The authors do not give a comprehensive discussion of previous work on this topic.
2. The experimental justification of this work is not sufficient, only compared to the basic backdoor-based strategy. | 1. The authors do not give a comprehensive discussion of previous work on this topic. |
ARR_2022_169_review | ARR_2022 | 1. The paper claims that it exploits unlabelled target language data. However, in line 363, it seems that the paper actually uses event-presence labels $e_{i}$ for each target language sample. First, $e_{i}$ is probably extracted directly from labels $y_{i}$; it is when $y_{i}$ says that some word is an event trigger t... | 5. It is not known if the OT sample selection process in 2.4.3 only runs once or runs iteratively as EP module is updated during the training steps. Are optimizing the loss of equation (10), i.e. the training steps, and solving OT in equation (3) conducted by turns iteratively? It will be much easier for readers to kno... |
NIPS_2021_2418 | NIPS_2021 | - The class of problems is not very well motivated. The CIFAR example is contrived and built for demonstration purposes. It is not clear what application would warrant sequentially (or in batches) and jointly selecting tasks and parameters to simultaneously optimize multiple objective functions. Although one could achi... | - The authors take time to discuss how KG handles the continuous task setting, but there are no experiments with continuous tasks - It’s great that entropy methods for conditional optimization are derived in Section 7 in the appendix, but why are these not included in the experiments? How does the empirical performance... |
hkjcdmz8Ro | ICLR_2024 | 1. The technique contribution is week. The proposed method utilizes the LLM to refine the prompt. Thus, the performance of the proposed method heavily relies on the designed system prompt and LLMs. Moreover, the proposed method is based on heuristics, i.e., there is no insight for the proposed approach. But I understan... | 3. In GCG, authors showed that their approach could be transferred to other LLMs. Thus, GCG could craft adversarial prompts and transfer them to other LLMs. It would be good if such a comparison could be included. A minor point: The jailbreaking percentage is low for certain LLMs. |
NIPS_2021_835 | NIPS_2021 | The authors addressed the limitations and potential negative societal impact of their work. However, there are some concerns as follows: 1.The main concern is the innovation of this paper. Firstly, laplacian score is proposed by Ref.13 for feature selection as an unsupervised measure. Secondly, i think that the main co... | 2.Authors introduce the importance of unsupervised feature selection from a diffusion perspective and i think this is a very novel thing for feature selection, but i can't understand what is the difference between similarity and exit times in nature. I hope the author can give me a more detailed explanation to understa... |
NIPS_2022_1250 | NIPS_2022 | Lacking of discussions or motivations for the importance of the proposed idea
Empirical results: Can be on toy tasks
The paper pursues an interesting research direction, which tries to unify existing POMDP formalisms. The approach looks very promising. The proposed design of the critic is very interesting. It would bec... | - As the unified framework can now obtain provably efficient learning for most POMDP formalisms. Is there any limitations of its, e.g. can it do the same for any general POMDP formulations (continuous or infinite spaces)? |
ICLR_2023_4411 | ICLR_2023 | Weakness • The reviewer thinks the authors need to elaborate how the output labels are defined for density assessment. In section 3. Datasets, it seems the authors gives confusing definitions of density and BIRADS findings like “we categorized BI-RADS density scores into two separate categories: BI-RADS 2 and 3 as beni... | • How did you calculate precision/recall/F1-score for 4-class classification of breast density? Also, for breast cancer detection, researchers usually report AUC with sensitivity and specificity at different operating points to compare model performance. It might be more informative to provide AUC results for compariso... |
bpArUWbkUF | EMNLP_2023 | - There are some minor issues with the papers, but still, no strong reasons to reject them:
- I found that the creation of the dataset is optional. The Kialo dataset, well-studied in the community, provides exactly what the authors need, pairs of short claims and their counters. It is even cleaner than the dataset the ... | - I found that the creation of the dataset is optional. The Kialo dataset, well-studied in the community, provides exactly what the authors need, pairs of short claims and their counters. It is even cleaner than the dataset the authors created since no automatic processes exist to construct it. Still, what has been cre... |
NIPS_2021_2050 | NIPS_2021 | 1. Transformer has been adopted for lots of NLP and vision tasks, and it is no longer novel in this field. Although the authors made a modification on the transformer, i.e. cross-layer, it does not bring much insight in aspect of machine learning. Besides, in ablation study (table4 and 5), the self-cross attention brin... | 1. Transformer has been adopted for lots of NLP and vision tasks, and it is no longer novel in this field. Although the authors made a modification on the transformer, i.e. cross-layer, it does not bring much insight in aspect of machine learning. Besides, in ablation study (table4 and 5), the self-cross attention brin... |
ICLR_2021_1849 | ICLR_2021 | I see in this paper are: - Although there is a clear and formal explanation of why it is not possible to discriminate among classes from different task when there is no access to data from those previous classes, I am not fully convinced that the set of parameters kept from previous classes, and used in regularization-... | - In terms of the experiments, I consider the number of tasks quite limited. To be convinced I would like to see several tasks (at least 10) and sequential results in terms of tasks learned rather than epochs. Questions for authors: Please address my comments on the weaknesses above. |
ICLR_2022_212 | ICLR_2022 | Weakness: 1. The introduction of the motivation (the concept of in-context bias) is not easy to understand at the very beginning. The paper said: “the pretrained NLM can model much stronger dependencies between text segments that appeared in the same training example, than it can between text segments that appeared in ... | 3. The experiments are limited. The authors only conduct the evaluation on sentence similarity tasks and open domain QA tasks. However, there are many other tasks that involve sentence pairs. For example, sentence inference tasks such as MNLI and RTE are common tasks in NLP field. The authors should conduct experiments... |
o3V7OuPxu4 | ICLR_2025 | Overall, the paper lacks clarity and depth in describing both the technical implementation and practical contributions.
### **Major comments**
1. Unclear contribution: The paper does not effectively justify why this benchmark must exist as a standalone contribution rather than an addition to existing Starcraft II resou... | 6. It's important to have the prompt included in the appendix or supplement. Was it possibly in a supplement that I cannot access? ### **Minor comments** (These did not affect my score) - Abstract: Lines 016-019 are a bit difficult to understand; consider rephrasing - Figure 2: It’s unclear what this Figure is meant to... |
NIPS_2020_153 | NIPS_2020 | * Both this paper and the Nasr et al paper use number detectors (number selective units) as an indicator of number sense. However, the presence of number selective units is not a necessary condition for number sense. There are potentially other distributed coding schemes (other than tuning curves) that could be employe... | * The motivation for analyzing only the last convolutional layer is not clear. Why would numerosity not appear in earlier layers? |
NIPS_2018_761 | NIPS_2018 | Weakness] * How to set the parameter S remains a problem. * Algorithm SMILE is interesting but their theoretical results on its performance is not easy to interpret. * No performance comparison with existing algorithms [Recommendation] I recommend this paper to be evaluated as "a good submission; an accept". Their prob... | * How to set the parameter S remains a problem. |
NIPS_2016_265 | NIPS_2016 | 1. For the captioning experiment, the paper compares to related work only on some not official test set or dev set, however the final results should be compared on the official COOC leader board on the blind test set: https://competitions.codalab.org/competitions/3221#results e.g. [5,17] have won this challenge and hav... | 2. A human evaluation for caption generation would be more convincing as the automatic evaluation metrics can be misleading. |
NIPS_2018_630 | NIPS_2018 | - While there is not much related work, I am wondering whether more experimental comparisons would be appropriate, e.g. with min-max networks, or Dugas et al., at least on some dataset where such models can express the desired constraints. - The technical delta from monotonic models (existing) to monotonic and convex/c... | - The introduction claims that "these shape constraints do not require tuning a free parameter". While technically true, the *choice* of employing a convex or concave constraint, and an increasing/decreasing constraint, can be seen as a hyperparameter that needs to be chosen or tuned. |
wRbSdbGyfj | ICLR_2025 | 1. **Triviality of Convergence Proof**: The theoretical proof for convergence appears trivial. Although the paper claims that $Z$ is non-i.i.d., Assumption 4.1 indicates that $X$ is i.i.d., leading to a clear covariance matrix for $Z$ as $A^\top A / np$. Following Modification 1 in Appendix C, previous theorems can be ... | 1. **Triviality of Convergence Proof**: The theoretical proof for convergence appears trivial. Although the paper claims that $Z$ is non-i.i.d., Assumption 4.1 indicates that $X$ is i.i.d., leading to a clear covariance matrix for $Z$ as $A^\top A / np$. Following Modification 1 in Appendix C, previous theorems can be ... |
NIPS_2016_232 | NIPS_2016 | weakness of the suggested method. 5) The literature contains other improper methods for influence estimation, e.g. 'Discriminative Learning of Infection Models' [WSDM 16], which can probably be modified to handle noisy observations. 6) The authors discuss the misestimation of mu, but as it is the proportion of missing ... | 5) The experimental setup borrowed from [2] is only semi-real, as multi-node seed cascades are artificially created by merging single-node seed cascades. This should be mentioned clearly. |
pO7YD7PADN | EMNLP_2023 | 1. Limited technical contributions. The compression techniques evaluated are standard existing methods like quantization and distillation. The debiasing baselines are also from prior work. There is little technical innovation.
2. Limited datasets and models. The bias benchmarks only assess gender, race, and religion. O... | 2. Limited datasets and models. The bias benchmarks only assess gender, race, and religion. Other important biases and datasets are not measured. Also missing are assessments on state-of-the-art generative models like GPT. |
NIPS_2020_342 | NIPS_2020 | 1. The primary motivation for the work is not well supported. Certainly, cities do manage thousands of intersections. While unquantified, it is not clear that the cost of training individually would surpass that of the degradation seen in the multi-env setting. 2. It is stated both that the multi-env model has an inevi... | 2. It is stated both that the multi-env model has an inevitable performance loss and that the multi-env model outperforms the single-env model due to knowledge sharing. These two statements seem to be conflicting. Please clarify. |
oqDoAMYbgA | ICLR_2024 | 1. The experimental study is limited: the comparisons with other methods are provided only on a single Wiki-small dataset. From that, it’s not enough to judge on the comparison with other baselines.
2. The training time seems to be the main bottleneck of the method, its training is slower than for almost any other tree... | 3. The method seems to be quite sensitive to hyperparameters, so in order to apply it method for a new problem, one has to perform some careful hyperparameter search to find a proper $\alpha$. |
haPIkA8aOk | EMNLP_2023 | 1. The description of the metrics is limited. it would be desirable to have an explanation of the metrics used in the paper. Or at least a citation to the metrics would have been good.
2. The training objective in Equation 7 would increase the likelihood of negative cases as well resulting in unwanted behavior. Should ... | 1. The description of the metrics is limited. it would be desirable to have an explanation of the metrics used in the paper. Or at least a citation to the metrics would have been good. |
30kbnyD9hF | EMNLP_2023 | - Lack of reference explaining communication in this context.
- The paper introduces four communication modes (debate, report, relay, and memory) without sufficient support from literature, despite existing relevant work in argumentation theory. Section 4.2 provides inadequate details and lacks illustrative examples.
-... | - Figure 3 is challenging to understand. The workflow and captions are unclear, and the representation of communication modes on the left side is confusing. |
ICLR_2023_2630 | ICLR_2023 | - The technical novelty and contributions are a bit limited. The overall idea of using a transformer to process time series data is not new, as also acknowledged by the authors. The masked prediction was also used in prior works e.g. MAE (He et al., 2022). The main contribution, in this case, is the data pre-processing... | - It is unclear what is the "learned [MASK] embedding" mean in the SSL pre-training stage of the proposed method. |
NIPS_2020_1108 | NIPS_2020 | - The reported results seem to be partially derivative: extension to hyper-networks of results already presented in the literature for standard networks. - The case with finite width for f and infinite width for g is not discussed: it would have provided a complete treatment of the topic. - Presentation could be improv... | - The reported results seem to be partially derivative: extension to hyper-networks of results already presented in the literature for standard networks. |
NIPS_2019_1145 | NIPS_2019 | The paper has the following main weaknesses: 1. The paper starts with the objective of designing fast label aggregation algorithms for a streaming setting. But it doesnât spend any time motivating the applications in which such algorithms are needed. All the datasets used in the empirical analysis are static datasets... | 1. The paper starts with the objective of designing fast label aggregation algorithms for a streaming setting. But it doesnât spend any time motivating the applications in which such algorithms are needed. All the datasets used in the empirical analysis are static datasets. For the paper to be useful, the problem con... |
43SOcneD8W | EMNLP_2023 | 1. The reported performance gain of the proposed framework is marginal when compared to the improvements introduced by simple Prompt Tuning approaches. For instance,for Table 3, out of 2.7% gain over Roberta backbone on ReTACRED, prompting tuning (i.e. HardPrompt) already achieves the gain of 1.7%.
2. The scope of the ... | 2. The scope of the study is under-specified. It seems that the work focuses on injecting CoT- based approach to small-scale Language Models. If that is not the case, additional relevant CoT baselines for in-context learning of Large Language Models (for text-003 and ChatGPT) are missing in Table 2 and 3 (See Question ... |
NIPS_2016_117 | NIPS_2016 | weakness of this work is impact. The idea of "direct feedback alignment" follows fairly straightforwardly from the original FA alignment work. Its notable that it is useful in training very deep networks (e.g. 100 layers) but its not clear that this results in an advantage for function approximation (the error rate is ... | - Figure 3 is very hard to read anything on the figure. |
NIPS_2018_756 | NIPS_2018 | It looks complicated to assess the practical impact of the paper. On the one hand, the thermodynamic limit and the Gaussianity assumption may be hard to check in practice and it is not straightforward to extrapolate what happens in the finite dimensional case. The idea of identifying the problem's phase transitions is ... | - In the introduction the authors mention the fact that tensor decomposition is in general harder in the symmetric than in the non-symmetric case. How is this connected with recent findings about the `nice' landscape of the objective function associated with the decomposition of symmetric (orthogonal) order-4 tensors [... |
ARR_2022_358_review | ARR_2022 | - Some definitions and statements are not clear or well justified.
- Lack of clarity in the definition of the input/outputs for each subtask
063-065 Though most of the existing studies consider the expansion a regression problem ... -> Missing a reference to support this statement 081-082 TEAM that performs both the At... | 245 The GAT is trained with the whole model? Needs to be reviewed by a English native speaker and some sentences need to be rewriting for improving the clarity. |
NIPS_2020_1524 | NIPS_2020 | * The paper makes several “hand-wavy” arguments, which are suitable for supporting the claims in the paper; but it is unclear if they would generalize for analyzing / developing other algorithms. For instance: 1. Replacing `n^2/(2*s^2)` with an arbitrary parameter `lambda` (lines 119-121) 2. Taking SGD learning rate ~ ... | 1. Replacing `n^2/(2*s^2)` with an arbitrary parameter `lambda` (lines 119-121) 2. Taking SGD learning rate ~ 0.1 (line 164) — unlike the Adam default value, it is unclear what the justification behind this value is. |
2z9o8bMQNd | EMNLP_2023 | - So difficult to follow the contribution of this paper. And it looks like an incremental engineering paper. The proposed method has been introduced in many papers, such as [1] Joshi, A., Bhat, A., Jain, A., Singh, A., & Modi, A. (2022, July). COGMEN: COntextualized GNN-based Multimodal Emotion Recognition. In Proceedi... | - Error analysis plays a crucial role in evaluating model performance and identifying potential issues. We encourage the authors to conduct error analysis in the paper and provide detailed explanations of the model's performance under different scenarios. Error analysis will aid in guiding subsequent improvements and e... |
NIPS_2022_742 | NIPS_2022 | It seems that the 6dof camera poses of panoramas are required to do the projection. Hence, precisely speaking, the method is not fully self-supervised but requires camera pose ground truth. This is usually accessible, easier compared to the ground truth layout, but may also cause error for the layout projection and thu... | 2) analyze the domain gap. It would be nice to add some discussions about the gap between datasets. Some datasets are closer to each other thus the adaption may not be a big issue. Also, if the method is able to finetune a pre-trained model on synthetic data, then the value of the approach would be much higher. |
NIPS_2021_2163 | NIPS_2021 | Weakness:
I have some concerns on identification mechanism based on identity bank. 1) Scalability. As shown in Table 3 (a), the performance is getting worse with growth of the maximum number of identities. It means that the capacity should be preset to some small number (e.g., 10). In real-world scenario, we can have m... | 1) Scalability. As shown in Table 3 (a), the performance is getting worse with growth of the maximum number of identities. It means that the capacity should be preset to some small number (e.g., 10). In real-world scenario, we can have more than 10 objects and most of the time we don't know how many objects we will nee... |
EtNebdSBpe | EMNLP_2023 | - The paper is hard to read and somewhat difficult to follow.
- The motivation is unclear. The authors argue that the LLP setup is relevant for (1) privacy and (2) weak supervision. (1) Privacy: the authors claim that the LLP paradigm is relevant for training on sensitive data as the labels for such datasets are not pu... | - The authors claim it to be one of the preliminary works discussing the application of LLP to NLP tasks. However, I don't see anything NLP-specific in their approach. |
NIPS_2020_1519 | NIPS_2020 | - The proposed gradient unrolling method requires N steps to unrolling the gradient, which is slow and perhaps difficult to scale up to learning large and complicated EBLVMs. Although corollary 3 indicates that the estimation accuracy can be asymptoticly arbitrarily small, that requires N to be sufficiently large that ... | - For comparison, at least one NCE-based method should be included. [1] shows that with a strong noise distribution, this line of work is possible to learn EBM on natural images. |
Akk5ep2gQx | EMNLP_2023 | 1. The experiment section could be improved. For example, it is better to carry significance test on the human evaluation results. It is also beneficial to compare the proposed method with some most recent LLM.
2. The classifier of determining attributes using only parts of the sentence may not perform well. Specifical... | 1. The experiment section could be improved. For example, it is better to carry significance test on the human evaluation results. It is also beneficial to compare the proposed method with some most recent LLM. |
ICLR_2021_2802 | ICLR_2021 | of this paper include the following aspects: 1. This paper is not well written and some parts are hard to follow. It lacks necessary logical transition and important figures. For example, it lacks explanations to support the connection between the proposed training objective and the Cross Margin Discrepancy. Also, it s... | 2. The authors claim that there is still no research focusing on the joint error for UDA. But this problem of arbitrarily increased joint error has already been studied in previous works like “Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment”, in ICML2019. The authors should discuss on that work and... |
ICLR_2023_1093 | ICLR_2023 | 1. I thought the novelty is questionable. The authors claimed that the proposed Uni-Mol is the first pure 3D molecular pretraining framework. However, there have been already a few similar works. For example,
a. The Graph Multi-View Pre-training (GraphMVP) framework leverages the correspondence and consistency between ... | 2. The comparison with the SOTA methods may be unfair. The performance of the paper is based on the newly collected 209M dataset. However, the existing methods use smaller datasets. For example, GEM employs only 20M unlabeled data. Because the scale of datasets has a significant impact on the accuracy, the superior of ... |
rs78DlnUB8 | EMNLP_2023 | 1. The paper lacks a clear motivation for considering text graphs. Except for the choice of complexity indices, which can easily be changed according to the domain, the proposed method is general and can be applied to other graphs or even other types of data. Moreover, formal formulations of text graphs and the researc... | 2. Several curriculum learning methods have been discussed in Section 1. However, the need for designing a new curriculum learning method for text graphs is not justified. The research gap, e.g., why existing methods can’t be applied, is not discussed. |
ICLR_2021_1181 | ICLR_2021 | 1.For domain adaptation in the NLP field, powerful pre-trained language models, e.g., BERT, XLNet, can overcome the domain-shift problem to some extent. Thus, the authors should be used as the base encoder for all methods and then compare the efficacy of the transfer parts instead of the simplest n-gram features.
2.The... | 1.For domain adaptation in the NLP field, powerful pre-trained language models, e.g., BERT, XLNet, can overcome the domain-shift problem to some extent. Thus, the authors should be used as the base encoder for all methods and then compare the efficacy of the transfer parts instead of the simplest n-gram features. |
zWGDn1AmRH | EMNLP_2023 | 1.This paper is challenging to follow, and the proposed method is highly complex, making it difficult to reproduce.
2.The proposed method comprises several complicated modules and has more parameters than the baselines. It remains unclear whether the main performance gain originates from a particular module or if the i... | 2.The proposed method comprises several complicated modules and has more parameters than the baselines. It remains unclear whether the main performance gain originates from a particular module or if the improvement is merely due to having more parameters. The current version of the ablation study does not provide defin... |
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 ... | 8) l 196-7: this requires more explanation. Why exactly are the two quantities different, and why does this capture the difference in learning settings? ---- I still lean toward acceptance. I think NIPS should have room for a few "pure theory" papers. |
NIPS_2017_130 | NIPS_2017 | weakness)?
4.] Can the authors discuss the sensitivity of any fixed tuning parameters in the model (both strengths and weakness)?
5.] What is the scalability of the model proposed and computational complexity? Will the authors be making the code publicly available with the data? Are all results reproducible using the c... | 4.] Can the authors discuss the sensitivity of any fixed tuning parameters in the model (both strengths and weakness)? |
NIPS_2019_1411 | NIPS_2019 | ] *assumption* - I am not sure if it is safe to assume any programmatic policy can be parameterized by a vector \theta and is differentiable in \theta. (for Theorem 4.2) *initial policy* - In all the experiments (TORCS, MountainCar, and Pendulum), the IPPG polices improve upon the PRIOR. It is not clear if IPPG can lea... | - It would be interesting to see if the proposed framework works with different policy gradient approaches. *experiment results* - How many random seeds are used for learning the policies (DDPO and IPPG)? |
mERmlOPxPY | EMNLP_2023 | - W1) The paper evaluates only on one dataset and on one task. Results and conclusions would be stronger if the analysis were applied to more datasets and more tasks.
- W2) Similarly, only one LLM model (GPT-3) is examined.
- W3) Some terms like "co-prediction" (line 278) and "in-context" (line 285) are not defined or ... | -W1) The paper evaluates only on one dataset and on one task. Results and conclusions would be stronger if the analysis were applied to more datasets and more tasks. |
VmqTuFMk68 | ICLR_2024 | 1. Writtings could be improved in some places. For two examples,
* In definition 2.1, what are the "relevant" auxiliary model weights? The current definition is a bit difficult for me to interpret.
* In definition 2.3, are $p_t$'s referring to positional embedding? Could you explain why there aren't positional embeddin... | 1. Writtings could be improved in some places. For two examples, * In definition 2.1, what are the "relevant" auxiliary model weights? The current definition is a bit difficult for me to interpret. |
7tpMhoPXrL | ICLR_2025 | • GDPR Compliance Concerns: The paper’s reliance on approximate unlearning without theoretical guarantees presents a significant shortfall. While approximate unlearning may be practical, it falls short in scenarios where data privacy and regulatory compliance are non-negotiable. Without provable guarantees, it is quest... | • Dependence on MIA (Membership Inference Attack) Testing via Ulira: While the paper uses MIA testing as a metric for unlearning effectiveness, the effectiveness of MIA testing itself is not sufficiently robust for privacy guarantees. Additionally the use of U-LiRA [1] is recommended. |
NIPS_2018_197 | NIPS_2018 | weakness of the paper: its clarity. From the presentation, it seems evident that the author is an expert in the field of computer algebra/algebraic geometry. It is my assumption that most members of the NIPS community will not have a strong background on this subject, me including. As a consequence, I found it very har... | - I am not familiar with the literature: all the considerations in this paper should also be applicable to kernel (ridge) regression, no? Maybe this could also be presented in the 'language of kernel interpolation/smoothing' as well? |
ICLR_2023_1500 | ICLR_2023 | 1. A mathematical formulation for the entire problem is missed. Though the problem is complex and difficult to be solved with an end-to-end framework, the original formulation is still needed at the beginning of the Methods section, followed by a brief introduction of the entire framework, i.e. how to split the entire ... | 2. Quantitive evaluation results in Figure 3 only reflect middle outputs rather than the final outputs. Figure 4 illustrates the comparison of final results with a single data sample. Thereby, current evaluations are not convincing enough to confirm ModelAngelo’s superiority to competitors. Is it possible for a quantit... |
NIPS_2017_28 | NIPS_2017 | - Most importantly, the explanations are very qualitative and whenever simulation or experiment-based evidence is given, the procedures are described very minimally or not at all, and some figures are confusing, e.g. what is "sample count" in fig. 2? It would really help adding more details to the paper and/or suppleme... | - Most importantly, the explanations are very qualitative and whenever simulation or experiment-based evidence is given, the procedures are described very minimally or not at all, and some figures are confusing, e.g. what is "sample count" in fig. 2? It would really help adding more details to the paper and/or suppleme... |
FGBEoz9WzI | EMNLP_2023 | 1. Some claims may be inspired from existing studies; thus, it is critical to add the supportive references. For example, Lines 55-64: "we identify four critical factors that affect the performance of chain-of-thought prompting and require large human effort to deal with: (1) order sensitivity: the order combination of... | 1. Some claims may be inspired from existing studies; thus, it is critical to add the supportive references. For example, Lines 55-64: "we identify four critical factors that affect the performance of chain-of-thought prompting and require large human effort to deal with: (1) order sensitivity: the order combination of... |
NIPS_2016_192 | NIPS_2016 | Weakness: (e.g., why I am recommending poster, and not oral) - Impact: This paper makes it easier to train models using learning to search, but it doesn't really advance state-of-the-art in terms of the kind of models we can build. - Impact: This paper could be improved by explicitly showing the settings for the variou... | - Impact: This paper could be improved by explicitly showing the settings for the various knobs of this algorithm to mimic prior work: Dagger, searn, etc...it would help the community by providing a single review of the various advances in this area. |
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... | - Another unclear point is the paper presents specific examples of biases of target statistics (section 3.2) and prediction shift of gradient values (Theorem 1), and we can know that the bias can happen, but on the other hand, we are not sure how general these situations are. |
End of preview. Expand in Data Studio
README.md exists but content is empty.
- Downloads last month
- 12