paper_id stringlengths 10 19 | venue stringclasses 14
values | focused_review stringlengths 232 10.2k | point stringlengths 64 662 |
|---|---|---|---|
gybvlVXT6z | EMNLP_2023 | 1. I feel that paper has insufficiant baseline. For example, CoCoOp (https://arxiv.org/abs/2203.05557) is a widely used baseline for prompt tuning research in CLIP. Moreover, it would be nice to include the natural data shift setting as in most other prompt tuning papers for CLIP.
2. It would be nice to include the har... | 2. It would be nice to include the hard prompt baseline in Table 1 to see the increase in performance of each method. |
NIPS_2022_670 | NIPS_2022 | 1. Lack of numerical results. The reviewer is curious about how to apply it to some popular algorithms and their performance compared with existing DP algorithms. 2. The presentation of this paper is hard to follow for the reviewer. | 1. Lack of numerical results. The reviewer is curious about how to apply it to some popular algorithms and their performance compared with existing DP algorithms. |
NIPS_2020_295 | NIPS_2020 | 1. The experimental comparisons are not enough. Some methods like MoCo and SimCLR also test the results with wider backbones like ResNet50 (2×) and ResNet50 (4×). It would be interesting to see the results of proposed InvP with these wider backbones. 2. Some methods use epochs and pretrain epochs as 200, while the repo... | 1. The experimental comparisons are not enough. Some methods like MoCo and SimCLR also test the results with wider backbones like ResNet50 (2×) and ResNet50 (4×). It would be interesting to see the results of proposed InvP with these wider backbones. |
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... | - I don't think the probabilistic connection is drawn very well. It doesn't seem to be made formally enough to take it as anything more than motivational which is fine, but I would suggest the authors either cement this connection more formally or adjust the language to clarify. |
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... | 3. It would be good to show some empirical evidence that the proposed algorithm works better for Column Subset Selection problem too, as claimed in the third contribution of the paper. |
NIPS_2020_916 | NIPS_2020 | My major complaints can be characterized into the following bullet points: - Robustness is argued only indirectly by way of Lipschitz constants. While the authors present a novel formulation (i.e., differing from the standard low-lipschitz=>robustness claims in the ML literature) of how controlling the lipschitz functi... | - The applicability of the robust training scheme seems unlikely to scale to practical datasets, particularly those supported on high-dimensional domains. It seems like the accuracy would scale unfavorably unless the size of V scales exponentially with the dimension. |
VnMfQuDSgG | EMNLP_2023 | - The paper is entirely statistical. For a task like this, it is important to show the linguistic nuance that is captured by the metrics.
- The choice of datasets: is 4 years sufficient period to study style shifts? what kind of style shifts happen in such a time? Without these answers, it is hard to appreciate what th... | - The choice of datasets: is 4 years sufficient period to study style shifts? what kind of style shifts happen in such a time? Without these answers, it is hard to appreciate what the model is capturing. |
NIPS_2021_442 | NIPS_2021 | of the paper:
Strengths: 1) To the best of my knowledge, the problem investigated in the paper is original in the sense that top-m identification has not been studied in the misspecified setting. 2) The paper provides some interesting results:
i) (Section 3.1) Knowing the level of misspecification ε
is a key ingredient... | 2) The paper provides some interesting results: i) (Section 3.1) Knowing the level of misspecification ε is a key ingredient, as not knowing the same would yield sample complexity bounds which are no better than the bound obtainable from unstructured ( ε = ∞ ) stochastic bandits. ii) A single no-regret learner is used ... |
ICLR_2023_977 | ICLR_2023 | the evaluation section has 2 experiments, but only 2 very insightful detailed examples. The paper can use a few more examples to illustrate more differences of the output sequences. This would allow the reader to internalize how the non-monotonicity in a deeper way.
Questions: In details, how does the decoding algorith... | 2: the callout to table 5 should go to table 3, instead. Page 7, section 5, last par.: figure 6 callout is not directing properly |
NIPS_2020_1371 | NIPS_2020 | When reading the paper, I've got the impression that the paper is not finished with couple of key experiments missing. Some parts of the paper lack motivation. Terminology is sometimes unclear and ambiguous. 1. Terminology. The paper uses terms "animation", "generative", "interpolation". See contribution 1 in L40-42. W... | 3. Experiments. Probably the biggest concern with the paper is with the experiments. The paper reports only self comparisons. The paper also doesn't explain why this is so, which adds to the poor motivation problem. In a generative setting comparisons with SketchRNN could be performed. |
ICLR_2022_1794 | ICLR_2022 | 1 Medical imaging are often obtained in 3D volumes, not only limited to 2D images. So experiments should include the 3D volume data as well for the general community, rather than all on 2D images. And the lesion detection is another important task for the medical community, which has not been studied in this work.
2 Mo... | 2 More analysis and comments are recommended on the performance trending of increasing the number of parameters for ViT (DeiT) in the Figure 3. I disagree with authors' viewpoint that "Both CNNs and ViTs seem to benefit similarly from increased model capacity". In the Figure 3, the DeiT-B models does not outperform Dei... |
ICLR_2022_3352 | ICLR_2022 | + The problem studied in this paper is definitely important in many real-world applications, such as robotics decision-making and autonomous driving. Discovering the underlying causation is important for agents to make reasonable decisions, especially in dynamic environments.
+ The method proposed in this paper is inte... | - The paper is not difficult to follow, but there are several places that are may cause confusion. (listed in point 3). |
ICLR_2021_1948 | ICLR_2021 | a. Anonymisation Failure in References
i. A reference uncited in the manuscript body contains a non-anonymised set of author names to a paper with the same title as the system presented in this paper. This was not detected during initial review. "Shuby Deshpande and Jeff Schneider. Vizarel: A System to Help Better Unde... | 1) "However, there is no corresponding set of tools for the reinforcement learning setting." - This is false. See references below (also some in the submitted paper). |
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 ... | 2. The results, while mostly based on "standard" techniques, are not obvious a priori, and require a fair degree of technical competency (i.e., the techniques are really only "standard" to a small group of experts). |
ARR_2022_223_review | ARR_2022 | The majority of the weaknesses in the paper seem to stem from confusion and inconsistencies between some of the prose and the results.
1. Figure 2, as it is, isn't totally convincing there is a gap in convergence times. The x-axis of the graph is time, when it would have been more convincing using steps. Without an eff... | 2. Line 148: I think it would make sense to make a distinction between hard prompt work updates the frozen model (Schick and Schütez, etc) from ones that don't. |
CEPkRTOlut | EMNLP_2023 | No ethics section, but there are ethical issues that deserve discussion (see the ethics section).
Also a few, mostly minor points:
- When the corpus was created, participants were told to speak in such a way to make the intent of the speech unambiguous. This may lead to over-emphasis compared with natural speech. There... | - The amount of data used to train the text disambiguation model was significantly lower than the data used for training the end-to-end system. Given that the difference between the two proposed systems is only a few percentage points, it brings into question the conclusion that the direct model is clearly the better o... |
pxclAomHat | ICLR_2025 | 1. The paper does not explicitly link the quality of the optimization landscape to convergence speed or generalization performance, which undermines its stated goal of theoretically explaining the performance of LoRA and GaLore. Many claims also lack this connection (e.g., lines 60-61, 289-291, and 301-304), making the... | 3. The paper does not clearly motivate GaRare; it lacks evidence or justification for GaRare's advantages over GaLore based on theoretical analysis. Additionally, a more detailed algorithmic presentation is needed, particularly to clarify the process of recovering updated parameters from projected gradients, which woul... |
NIPS_2017_356 | NIPS_2017 | ]
My major concerns about this paper is the experiment on visual dialog dataset. The authors only show the proposed model's performance on discriminative setting without any ablation studies. There is not enough experiment result to show how the proposed model works on the real dataset. If possible, please answer my fo... | 3: To further backup the proposed visual reference resolution model works in real dataset, please also conduct ablation study on visDial dataset. One experiment I'm really interested is the performance of ATT(+H) (in figure 4 left). What is the result if the proposed model didn't consider the relevant attention retriev... |
NIPS_2019_220 | NIPS_2019 | 1. Unclear experimental methodology. The paper states that 300W-LP is used to train the model, but later it is claimed same procedure is used as was used for baselines. Most baselines do not use 300W-LP dataset in their training. Is 300W-LP used in all experiments or just some? If it is used in all this would provide a... | 2. Missing link to similar work on Continuous Conditional Random Fields [Ristovski 2013] and Continuous Conditional Neural Fields [Baltrusaitis 2014] that has a similar structure of the CRF and ability to perform exact inference. |
ICLR_2022_2470 | ICLR_2022 | Weakness:
The idea is a bit simple -- which in of itself is not a true weakness. ResNet as an idea is not complicated at all. I find it disheartening that the paper did not really tell readers how to construct a white paper in section 3 (if I simply missed it, please let me know). However, the code in the supplementary... | 4. It would be interesting to try to explain why WPA works -- with np.ones input, what is the model predicting? Would any input serve as white paper? Figure 2 seems to suggest that Gaussian noise input does not work as well as WPA. Why? Authors spend lot of time showing WPA improves the test performance of the original... |
ICLR_2023_1979 | ICLR_2023 | Weakness: 1.It seems that the method part is very similar to the related work cited in the paper: Generating Adversarial Disturbances for Controller Verification. Could the author provide more clarification on this? 2.Experimental comparison to RRT* seems not good: Even though the RRT* baseline is an oracle without par... | 1.It seems that the method part is very similar to the related work cited in the paper: Generating Adversarial Disturbances for Controller Verification. Could the author provide more clarification on this? |
NIPS_2022_2513 | NIPS_2022 | Weakness:
The claim in Line 175~176 is not validated which it is valuable to see whether the proposed method could prevents potential classes from being incorrectly classified into historical classed.
In Tab. 1, for VOC 2-2 (10 tasks) and VOC 19-1 (2 tasks) MicroSeg gets inferior performance compared with SSUL, the rea... | 2. The proposed method adopts a proposal generator pretrained on MSCOCO which aggregates more information. Is it fair to compared with other methods? Besides, could the proposed technique propmotes existing Class incremental semantic segmentation methods. The authors adequately addressed the limitations and potential n... |
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 ... | 4. End of Sec.2., there are two important parameters/thresholds to set. One is the minimum cluster size and the other is the conductance threshold. However, the experimental section (Sec. 3) did not mention or discuss how these parameters are set and how sensitive the performance is with respect to these parameters. |
NIPS_2017_330 | NIPS_2017 | - Section 4 is very tersely written (maybe due to limitations in space) and could have benefitted with a slower development for an easier read.
- Issues of convergence, especially when applying gradient descent over a non-Euclidean space, is not addressed
In all, a rather thorough paper that derives an efficient way to... | - Section 4 is very tersely written (maybe due to limitations in space) and could have benefitted with a slower development for an easier read. |
NIPS_2020_257 | NIPS_2020 | * In terms of novelty, note that both the motivation for the model as well as the initial parts of it hold similarities to some prior works. See detailed description in the relation to prior work section. * I would be happy to see results about generalization not only for the CLEVR dataset, but also for natural images ... | * This can be open for debate but I personally believe that the need for reinforcement learning for a static VQA task may be a potential weakness making the approach less data efficient and harder to train the models that use gradient descent. |
BTr3PSlT0T | ICLR_2025 | - I express skepticism about whether the number of videos in the benchmark can achieve a robust assessment. The CVRR-ES benchmark includes only 214 videos, with the shortest video being just 2 seconds. Upon reviewing several videos from the anonymous link, I noticed a significant proportion of short videos. I question ... | - As mentioned in the previous question, the distribution of videos of different lengths within the benchmark is crucial for the assessment of reasoning ability and robustness, and the paper does not provide relevant explanations. The authors should include a table showing the distribution of video lengths across the d... |
NIPS_2017_53 | NIPS_2017 | Weakness
1. When discussing related work it is crucial to mention related work on modular networks for VQA such as [A], otherwise the introduction right now seems to paint a picture that no one does modular architectures for VQA.
2. Given that the paper uses a billinear layer to combine representations, it should menti... | 8.L290: it would be good to clarify how the implemented billinear layer is different from other approaches which do billinear pooling. Is the major difference the dimensionality of embeddings? How is the billinear layer swapped out with the hadarmard product and MCB approaches? Is the compression of the representations... |
OvoRkDRLVr | ICLR_2024 | 1. The paper proposes a multimodal framework built atop a frozen Large Language Model (LLM) aimed at seamlessly integrating and managing various modalities. However, this approach seems to be merely an extension of the existing InstructBLIP.
2. Additionally, the concept of extending to multiple modalities, such as the ... | 4. The promised dataset has not yet been made publicly available, so a cautious approach should be taken regarding this contribution until the dataset is openly accessible. |
ICLR_2023_3203 | ICLR_2023 | 1. The novelty is limited. The proposed method is too similar to other attentional modules proposed in previous works [1, 2, 3]. The group attention design seems to be related to ResNeSt [4] but it is not discussed in the paper. Although these works did not evaluate their performance on object detection and instance se... | 1. The novelty is limited. The proposed method is too similar to other attentional modules proposed in previous works [1, 2, 3]. The group attention design seems to be related to ResNeSt [4] but it is not discussed in the paper. Although these works did not evaluate their performance on object detection and instance se... |
ICLR_2023_2283 | ICLR_2023 | 1. 1. The symbols in Section 4.3 are not very clearly explained. 2. This paper only experiments on the very small time steps (e.g.1、2) and lack of some experiments on slightly larger time steps (e.g. 4、6) to make better comparisons with other methods. I think it is necessary to analyze the impact of the time step on th... | 1. Fig. 3 e. Since the preactivation values of two networks are the same membrane potentials, their output cosine similarity will be very high. Why not directly illustrate the results of the latter loss term of Eqn 13? |
eI6ajU2esa | ICLR_2024 | - This paper investigates the issue of robustness in video action recognition, but it lacks comparison with test-time adaptation (TTA) methods, such as [A-B]. These TTA methods also aim to adapt to out-of-distribution data when the input data is disturbed by noise. Although these TTA methods mainly focus on updating mo... | - This paper investigates the issue of robustness in video action recognition, but it lacks comparison with test-time adaptation (TTA) methods, such as [A-B]. These TTA methods also aim to adapt to out-of-distribution data when the input data is disturbed by noise. Although these TTA methods mainly focus on updating mo... |
ICLR_2021_863 | ICLR_2021 | Weakness 1. The presentation of the paper should be improved. Right now all the model details are placed in the appendix. This can cause confusion for readers reading the main text. 2. The necessity of using techniques includes Distributional RL and Deep Sets should be explained more thoroughly. From this paper, the il... | 4. Section 3.2.1: The first expression for J ( θ ) is incorrect, which should be Q ( s t 0 , π θ ( s t 0 ) ) . |
ICLR_2021_872 | ICLR_2021 | The authors push on the idea of scalable approximate inference, yet the largest experiment shown is on CIFAR-10. Given this focus on scalability, and the experiments in recent literature in this space, I think experiments on ImageNet would greatly strengthen the paper (though I sympathize with the idea that this can a ... | 8: s/expensive approaches2) allows/expensive approaches,2) allows/ p.8: s/estimates3) is/estimates, and3) is/ In the references: Various words in many of the references need capitalization, such as "ai" in Amodei et al. (2016), "bayesian" in many of the papers, and "Advances in neural information processing systems" in... |
NIPS_2016_339 | NIPS_2016 | weakness of the model. How would the values in table 1 change without this extra assumption? 3. I didn't find all parameter values. What are the model parameters for task 1? What lambda was chosen for the Boltzmann policy. But more importantly: How were the parameters chosen? Maximum likelihood estimates? 4. An answer ... | 3. I didn't find all parameter values. What are the model parameters for task 1? What lambda was chosen for the Boltzmann policy. But more importantly: How were the parameters chosen? Maximum likelihood estimates? |
NIPS_2016_313 | NIPS_2016 | Weakness: 1. The proposed method consists of two major components: generative shape model and the word parsing model. It is unclear which component contributes to the performance gain. Since the proposed approach follows detection-parsing paradigm, it is better to evaluate on baseline detection or parsing techniques sp... | 3. The authors claim to achieve state-of-the-art results on challenging scene text recognition tasks, even outperforms the deep-learning based approaches, which is not convincing. As claimed, the performance majorly come from the first step which makes it reasonable to conduct comparisons experiments with existing dete... |
NIPS_2017_631 | NIPS_2017 | 1. The main contribution of the paper is CBN. But the experimental results in the paper are not advancing the state-of-art in VQA (on the VQA dataset which has been out for a while and a lot of advancement has been made on this dataset), perhaps because the VQA model used in the paper on top of which CBN is applied is ... | 4. Table 2, applying Conditional Batch Norm to layer 2 in addition to layers 3 and 4 deteriorates performance for GuessWhat?! compared to when CBN is applied to layers 4 and 3 only. Could authors please throw some light on this? Why do they think this might be happening? |
yIv4SLzO3u | ICLR_2024 | - Lack of comparison with a highly relevant method. [1] also proposes to utilize the previous knowledge with ‘inter-task ensemble’, while enhancing the current task’s performance with ‘intra-task’ ensemble. Yet, the authors didn’t include the method comparison or performance comparison.
- Novelty is limited. From my pe... | - Lack of comparison with a highly relevant method. [1] also proposes to utilize the previous knowledge with ‘inter-task ensemble’, while enhancing the current task’s performance with ‘intra-task’ ensemble. Yet, the authors didn’t include the method comparison or performance comparison. |
NIPS_2018_914 | NIPS_2018 | of the paper are (i) the presentation of the proposed methodology to overcome that effect and (ii) the limitations of the proposed methods for large-scale problems, which is precisely when function approximation is required the most. While the intuition behind the two proposed algorithms is clear (to keep track of part... | - the required implicit call to the Witness oracle is confusing. |
NIPS_2022_532 | NIPS_2022 | 1. Imitation Learning: The proposed method needs to be trained by behavioral cloning, which means 1) it requires a carefully well-designed algorithm (e.g., ODA-T/B/K) to generate the supervised data set. 2) More importantly, the data generated by ODA with a time limit L is indeed not a perfect teacher for behavioral cl... | 2) it loses the guarantee for finding the whole Pareto front. They have been properly discussed in the paper (see remark at the end of Section 4.1 and Conclusion). I do not see any potential negative societal impact of this work. |
ICLR_2022_1420 | ICLR_2022 | Weakness:
Lack of novelty. The key idea, i.e., combining foreground masks to remove the artifacts from the background, is not new. Separate handling of foreground from background is a common practice for dynamic scene novel view synthesis, and many recent methods do not even require the foreground masks for modeling dy... | 1) The proposed method cannot handle the headpose. While this paper defers this problem to a future work, a previous work (e.g., Gafni et al. ICCV 2021) is already able to control both facial expression and headpose. Why is it not possible to condition the headpose parameters in the NeRF beyond the facial expression si... |
ICLR_2023_3449 | ICLR_2023 | 1.The spurious features in Section 3.1 and 3.2 are very similar to backdoor triggers. They both are some artificial patterns that only appear a few times in the training set. For example, Chen et al. (2017) use random noise patterns. Gu et al. (2019) [1] use single-pixel and simple patterns as triggers. It is well-know... | 1.The spurious features in Section 3.1 and 3.2 are very similar to backdoor triggers. They both are some artificial patterns that only appear a few times in the training set. For example, Chen et al. (2017) use random noise patterns. Gu et al. (2019) [1] use single-pixel and simple patterns as triggers. It is well-know... |
ICLR_2022_3332 | ICLR_2022 | Weakness: 1. The writing needs a lot of improvement. Many of the concepts or notations are not explained. For example, what do “g_\alpha” and “vol(\alpha)” mean? What is an “encoding tree”(I believe it is not a common terminology)? Why can the encoding tree be used a tree kernel? Other than complexity, what is the theo... | 2. Structural optimization seems one of the main components and it has been emphasized several times. However, it seems the optimization algorithm is directly from some previous works. That is a little bit confusing and reduces the contribution. |
4kuLaebvKx | EMNLP_2023 | - The chained impacts of image captioning and multilingual understanding model in the proposed pipeline. If the Image caption gives worse results and the final results could be worse. So The basic performance of the image caption model and multilingual language mode depends on the engineering model choice when it appli... | - This pipeline style method including two models does not give better average results for both XVNLI and MaRVL. Baseline models in the experiments are not well introduced. |
GeFFYOCkvS | EMNLP_2023 | - The authors have reproduced a well-known result in the literature--left political bias in ChatGPT and in LLMs in general--using the "coarse" (their description) methodology of passing a binary stance classifier over ChatGPT's output. The observation that language models reproduce the biases of the corpora on which th... | - The authors have reproduced a well-known result in the literature--left political bias in ChatGPT and in LLMs in general--using the "coarse" (their description) methodology of passing a binary stance classifier over ChatGPT's output. The observation that language models reproduce the biases of the corpora on which th... |
NIPS_2017_53 | NIPS_2017 | Weakness
1. When discussing related work it is crucial to mention related work on modular networks for VQA such as [A], otherwise the introduction right now seems to paint a picture that no one does modular architectures for VQA.
2. Given that the paper uses a billinear layer to combine representations, it should menti... | 1. When discussing related work it is crucial to mention related work on modular networks for VQA such as [A], otherwise the introduction right now seems to paint a picture that no one does modular architectures for VQA. |
NIPS_2017_645 | NIPS_2017 | - The main paper is dense. This is despite the commendable efforts by the authors to make their contributions as readable as possible. I believe it is due to NIPS page limit restrictions; the same set of ideas presented at their natural length would make for a more easily digestible paper.
- The authors do not quite di... | - The authors mainly seem to focus on SSC, and do not contrast their method with several other subsequent methods (thresholded subspace clustering (TSC), greedy subspace clustering by Park, etc) which are all computationally efficient as well as come with similar guarantees. |
4N97bz1sP6 | ICLR_2024 | 1. The authors should make clear the distinction of when the proposed method is trained using only weak supervision and when it is semi-supervised trained. For instance, in Table 1, I think the proposed framework row refers to the semi-supervised version of the method, thus the authors should rename the column to ‘Full... | 1. The authors should make clear the distinction of when the proposed method is trained using only weak supervision and when it is semi-supervised trained. For instance, in Table 1, I think the proposed framework row refers to the semi-supervised version of the method, thus the authors should rename the column to ‘Full... |
NIPS_2020_1454 | NIPS_2020 | - Small contributions over previous methods (NCNet [6] and Sparse NCNet [21]). Mostly (good) engineering. And despite that it seems hard to differentiate it from its predecessors, as it performs very similarly in practice. - Claims to be SOTA on three datasets, but this does not seem to be the case. Does not evaluate o... | - Small contributions over previous methods (NCNet [6] and Sparse NCNet [21]). Mostly (good) engineering. And despite that it seems hard to differentiate it from its predecessors, as it performs very similarly in practice. |
NIPS_2018_25 | NIPS_2018 | - My understanding is that R,t and K (the extrinsic and intrinsic parameters of the camera) are provided to the model at test time for the re-projection layer. Correct me in the rebuttal if I am wrong. If that is the case, the model will be very limited and it cannot be applied to general settings. If that is not the c... | - "semantic" segmentation is not low-level since the categories are specified for each pixel so the statements about semantic segmentation being a low-level cue should be removed from the paper. |
NIPS_2021_2168 | NIPS_2021 | 1.The motivation to investigate a graph structured model is to capture the global dependency structure in the sentence which different from existing sequence models that tend to focus on the dependency between each word and its close preceding words. However,the encoder and decoder is based on Transformer，which can dra... | 3.In the ablation experiment, the performance without reinforcement learning dropped lower than without dependency tree.The two tables do not list the cases where dependency tree and RL are not used. |
ICLR_2022_562 | ICLR_2022 | 1. The main weakness of this paper is the experiments section. The results are presented only on CIFAR-10 dataset and do not consider many other datasets from Federated learning benchmarks (e.g., LEAF https://leaf.cmu.edu/). The authors should see relevant works like (FedProx https://arxiv.org/abs/1812.06127) and (FedM... | 1. The main weakness of this paper is the experiments section. The results are presented only on CIFAR-10 dataset and do not consider many other datasets from Federated learning benchmarks (e.g., LEAF https://leaf.cmu.edu/). The authors should see relevant works like (FedProx https://arxiv.org/abs/1812.06127) and (FedM... |
NIPS_2022_738 | NIPS_2022 | W1) The paper states that "In order to introduce epipolar constraints into attention-based feature matching while maintaining robustness to camera pose and calibration inaccuracies, we develop a Window-based Epipolar Transformer (WET), which matches reference pixels and source windows near the epipolar lines." It claim... | 4. Does the claim "It can be seen from the table that our proposed modules improve in both accuracy and completeness" really hold? Why not use another dataset for the ablation study, e.g., the training set of Tanks & Temples or ETH3D? |
5EHI2FGf1D | EMNLP_2023 | - no comparison against baselines. The functionality similarity comparison study reports only accuracy across optimization levels of binaries, but no baselines are considered. This is a widely-understood binary analysis application and many papers have developed architecture-agnostic similarity comparison (or often rep... | - vulnerability discovery methodology is also questionable. The authors consider a single vulnerability at a time, and while they acknowledge and address the data imbalance issue, I am not sure about the ecological validity of such a study. Previous work has considered multiple CVEs or CWEs at a time, and report whethe... |
ICLR_2021_2506 | ICLR_2021 | Weakness
== Exposition ==
The exposition of the proposed method can be improved. For examples, - it’s unclear how the “Semantic Kernel Generation” is implemented. I can probably guess this step is essentially a 1 x 1 convolution, but it would be better to fill in the details. - In the introduction, the paper mentioned ... | - CARAFE: Content-Aware ReAssembly of Features. The paper is in ICCV 2019, not CVPR 2019 In sum, I think the idea of the spatially adaptive upsampling kernel is technically sound. I also like the extensive evaluation in this paper. However, I have concerns about the high degree of similarity with the prior method and t... |
NIPS_2022_655 | NIPS_2022 | 1. How to get a small degree of bias from a clear community structure needs more explanations. Theorem 1 and 2 prove that GCL conforms to a clearer community structure via intra-community concentration and inter-community scatter, but its relationship with degree bias is not intuitive enough. 2. There is some confusion... | 1. How to get a small degree of bias from a clear community structure needs more explanations. Theorem 1 and 2 prove that GCL conforms to a clearer community structure via intra-community concentration and inter-community scatter, but its relationship with degree bias is not intuitive enough. |
NIPS_2021_1222 | NIPS_2021 | Claims: 1.a) I think the paper falls short of the high-level contributions claimed in the last sentence of the abstract. As the authors note in the background section, there are a number of published works that demonstrate the tradeoffs between clean accuracy, training with noise perturbations, and adversarial robustne... | 2.b) On lines 182-183 the authors note measuring manifold capacity for unperturbed images, i.e. clean exemplar manifolds. Earlier they state that the exemplar manifolds are constructed using either adversarial perturbations or from stochasticity of the network. So I’m wondering how one constructs images for a clean exe... |
NIPS_2019_494 | NIPS_2019 | of the approach, it may be interesting to do that. Clarity: The paper is well written but clarity could be improved in several cases: - I found the notation / the explicit split between "static" and temporal features into two variables confusing, at least initially. In my view this requires more information than is pro... | - I found the notation / the explicit split between "static" and temporal features into two variables confusing, at least initially. In my view this requires more information than is provided in the paper (what is S and Xt). |
4A5D1nsdtj | ICLR_2024 | 1. The motivation for the choice of $\theta = \frac{\pi}{2}(1-h)$ from theorem 3, is not very straightforward and clear. The paper states that this choice is empirical, but there is very little given in terms of motivation for this exact form.
2. For this method, the knowledge of the homophily ratio seems to be importa... | 1. The motivation for the choice of $\theta = \frac{\pi}{2}(1-h)$ from theorem 3, is not very straightforward and clear. The paper states that this choice is empirical, but there is very little given in terms of motivation for this exact form. |
NIPS_2019_629 | NIPS_2019 | - To my opinion, the setting and the algorithm lack a bit of originality and might seem as incremental combinations of methods of graph labelings prediction and online learning in a switching environment. Yet, the algorithm for graph labelings is efficient, new and seem different from the existing ones. - Lower bounds ... | - This paper deals with many graph notions and it is a bit hard to get into it but the writing is generally good though more details could sometimes be provided (definition of the resistance distance, more explanations on Alg. 1 with brief sentences defining A_t, Y_t,...). |
NIPS_2018_476 | NIPS_2018 | Weakness] 1) Originality is limited because the main idea of variable splitting is not new and the algorithm is also not new. 2) Theoretical proofs of existing algorithm might be regarded as some incremental contributions. 3) Experiments are somewhat weak: 3-1) I was wondering why Authors conducted experiments with lam... | 1) Originality is limited because the main idea of variable splitting is not new and the algorithm is also not new. |
NIPS_2016_313 | NIPS_2016 | Weakness: 1. The proposed method consists of two major components: generative shape model and the word parsing model. It is unclear which component contributes to the performance gain. Since the proposed approach follows detection-parsing paradigm, it is better to evaluate on baseline detection or parsing techniques sp... | 5. For the shape model invariance study, evaluation on transformations of training images cannot fully prove the point. Are there any quantitative results on testing images? |
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 ... | 2. The AAAI15 paper titled "Spectral Clustering Using Multilinear SVD: Analysis, Approximations and Applications" by Ghoshdastidar and Dukkipati seems to be a related work missed by the authors. This AAAI15 paper deals with hypergraph data with tensors as well so it should be discussed and compared against to provide a... |
ICLR_2021_1944 | ICLR_2021 | I have several concerns regarding this paper.
• Novelty. The authors propose to use Ricci flow to compute the distance between nodes so that to sample edges with respect to that distance. Using Ricci flow for distance computation is a well-studied area (as indicated in related work). The only novel part is that each la... | • Approach. Computing optimal transport distance is generally an expensive procedure. While authors indicated that it takes seconds to compute it on 36 cores machine, it’s not clear how scalable this method is. I would like to see whether it scales on normal machines with a couple of cores. Moreover, how do you compute... |
NIPS_2019_1131 | NIPS_2019 | 1. There is no discussion on the choice of "proximity" and the nature of the task. On the proposed tasks, proximity on the fingertip Cartesian positions is strongly correlated with proximity in the solution space. However, this relationship doesn't hold for certain tasks. For example, in a complicated maze, two nearby ... | 2. Â Some ablation study is missing, which could cause confusion and extra experimentation for practitioners. For example, the \sigma in the RBF kernel seems to play a crucial role, but no analysis is given on it. Figure 4 analyzes how changing \lambda changes the performance, but it would be nice to see how \eta and \... |
5UW6Mivj9M | EMNLP_2023 | 1) The paper was extremely hard to follow. I read it multiple times and still had trouble following the exact experimental procedures and evaluations that the authors conducted.
2) Relatedly, it was hard to discern what was novel in the paper and what had already been tried by others.
3) Since the improvement in number... | 1) The paper was extremely hard to follow. I read it multiple times and still had trouble following the exact experimental procedures and evaluations that the authors conducted. |
FpElWzxzu4 | ICLR_2024 | 1. In the intro section, the claim that Transformers reply on regularly sampled time-series data is wrong. For example, [1] shows that the Transformer model handles irregularly-sampled time series well for imputation.
2. In section 2, "Finally, INRs operate on a per-data-instance basis, meaning that one time-series ins... | 2. In section 2, "Finally, INRs operate on a per-data-instance basis, meaning that one time-series instance is required to train an INR". This claim is true but I don't think it is an advantage. A model that can only handle a single time series data is almost useless. |
NIPS_2017_110 | NIPS_2017 | of this work include that it is a not-too-distant variation of prior work (see Schiratti et al, NIPS 2015), the search for hyperparameters for the prior distributions and sampling method do not seem to be performed on a separate test set, the simultion demonstrated that the parameters that are perhaps most critical to ... | - l132: Consider introducing the aspects of the specific model that are specific to this example model. For example, it should be clear from the beginning that we are not operating in a setting with infinite subdivisions for \gamma^1 and \gamma^m and that certain parameters are bounded on one side (acceleration and sca... |
zpayaLaUhL | EMNLP_2023 | - Limited Experiments
- Most of the experiments (excluding Section 4.1.1) are limited to RoBERTa-base only, and it is unclear if the results can be generalized to other models adopting learnable APEs. It is important to investigate whether the results can be generalized to differences in model size, objective function,... | - Limited Experiments - Most of the experiments (excluding Section 4.1.1) are limited to RoBERTa-base only, and it is unclear if the results can be generalized to other models adopting learnable APEs. It is important to investigate whether the results can be generalized to differences in model size, objective function,... |
ICLR_2023_2934 | ICLR_2023 | - Fig. 1 leaves me with some doubts. It would seem that the private task is solved by using only a head operating on the learned layer for the green task (devanagari). This is at least what I would expect for the claims of the method to still uphold, because if the private task head can alter the weights of the Transfo... | - The parameters in Table 1, the model and the experiments seem to be only good for image data and ViT. Did the authors try to apply the same principles to other areas research areas such as NLP or simpler models in the image domain (CNNs)? I understand the latter might be due to the focus about state of the art perfor... |
X4ATu1huMJ | ICLR_2024 | **Overall comment**
The paper discusses evaluating TTA methods across multiple settings, and how to choose the correct method during test-time. I would argue most of the methods/model selection strategies that are discussed in the paper are not novel and/or existed before, and the paper does not have a lot of algorithm... | 5. **(Performance of TTA methods)** This is an interesting observation, that using non-standard benchmarks breaks a lot of popular TTA methods. If the authors can evaluate TTA on more conditions of natural distribution shift, like WILDS [9], it could really strengthen the paper. |
MY8SBpUece | ICLR_2024 | Weakness:
1. Based on my understanding, the core advantage of the proposed analysis is from the Hermite expansion of the activation layer, which can characterize higher-order nonlinearity and explain more non-linear behaviors than the orthogonal decomposition used in Ba et al. 2022. Please clarify this.
2. The required... | 2. The required condition on the learning rate (scaling with the number of samples) is not scalable. I never see a step size grows with the sample size in practice, which will lead to unreasonably large learning rate when learning on large-scale dataset. I understand the authors need a way to precisely characterize the... |
NIPS_2019_134 | NIPS_2019 | Weakness: 1. Although these tensor networks can be used to represent PMF of discrete variables. How these results are useful to machine learning algorithms or analyze the algorithm is not clear. Hence, the significance of this paper is poor. 2. The two experiments are all based on very small dataset either generated or... | 1. Although these tensor networks can be used to represent PMF of discrete variables. How these results are useful to machine learning algorithms or analyze the algorithm is not clear. Hence, the significance of this paper is poor. |
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... | 4.The unlabeled data (2000) from the preprocessed Amazon review dataset (Blitzer version) is perfectly balanced, which is impractical in real-world applications. Since we cannot control the label distribution of unlabeled data during training, the author should also use a more convinced setting as did in Adaptive Semi-... |
NIPS_2019_1158 | NIPS_2019 | 1. The proposed method only gets convergence rate in expectation (i.e. only variance bound), not with high probability. Though Chebyshev's inequality gives bound in probability from the variance bound, this is still weaker than that of Bach [3]. 2. The method description lacks necessary details and intuition: - It's no... | - It's not clear how to sample from the DPP if the eigenfunctions e_n's are inaccessible (Eq (10) line 130). This seems to be the same problem with sampling from the leverage score in [3], so I'm not sure how sampling from the DPP is easier than sampling from the leverage score. |
ICLR_2021_1682 | ICLR_2021 | + The value of episodic training is increasingly being questioned, and the submission approaches the topic from a new and interesting perspective.
+ The connection between nearest-centroid few-shot learning approaches and NCA has not been made in the literature to my knowledge and has potential applications beyond the ... | - The extent to which the observations presented generalize to few-shot learners beyond Prototypical Networks is not evaluated, which may limit the scope of the submission’s contributions in terms of understanding the properties of episodic training. |
NIPS_2021_2152 | NIPS_2021 | Weakness: 1. This manuscript is more like an experimental discovery paper, and the proposed method is similar to the traditional removal method, i.e., traverse all the modal feature subsets and calculate the perceptual score, removing the last subset. The reviewer believes that the contribution of the manuscript still ... | 2. The contribution of different modalities of different instances may be different, e.g., we have modalities A and B, some instances with good performance of modality A which belongs to the strong modality, whereas some instances with good performance modality B which belongs to the strong modality. Equation 3 directl... |
NIPS_2018_232 | NIPS_2018 | - Strengths: the paper is well-written and well-organized. It clearly positions the main idea and proposed approach related to existing work and experimentally demonstrates the effectiveness of the proposed approach in comparison with the state-of-the-art. - Weaknesses: the research method is not very clearly described... | - The abstract does a good job explaining the proposed idea but lacks description of how the idea was evaluated and what was the outcome. Minor language issues p. |
NIPS_2018_639 | NIPS_2018 | Weakness: - I am quite not convinced by the experimental results of this paper. The paper sets to solve POMDP problem with non-convex value function. To motivate the case for their solution the examples of POMDP problem with non-convex value functions used are: (a) surveillance in museums with thresholded rewards; (b) ... | - I am quite not convinced by the experimental results of this paper. The paper sets to solve POMDP problem with non-convex value function. To motivate the case for their solution the examples of POMDP problem with non-convex value functions used are: (a) surveillance in museums with thresholded rewards; (b) privacy pr... |
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