paper_id stringlengths 10 19 | venue stringclasses 15
values | focused_review stringlengths 7 9.4k | point stringlengths 49 654 |
|---|---|---|---|
ARR_2022_92_review | ARR_2022 | - The hyperlink for footnote 3 and 4 do not seem to work. - Line 172: an argument level -> on argument level | - The hyperlink for footnote 3 and 4 do not seem to work. |
ARR_2022_298_review | ARR_2022 | The main weak point of the paper is that at it is not super clear. There are many parts in which I believe the authors should spend some time in providing either more explanations or re-structure a bit the discussion (see the comments section).
- I suggest to revise a bit the discussion, especially in the modeling sect... | - I suggest to revise a bit the discussion, especially in the modeling section, which in its current form is not clear enough. For example, in section 2 it would be nice to see a better formalization of the architecture. If I understood correctly, the Label Embeddings are external parameters; instead, the figure is a b... |
ACL_2017_501_review | ACL_2017 | The experiments are missing a key baseline: a state-of-the-art VQA model trained with only a yes/no label vocabulary. I would have liked more details on the human performance experiments. How many of the ~20% of incorrectly-predicted images are because the captions are genuinely ambiguous? Could the data be further cle... | 528 - this description of the neural network is hard to understand. The final paragraph of the section makes it clear, however. Consider starting the section with it. |
NIPS_2020_314 | NIPS_2020 | 1) It seems like the model really works well when there is a mixture of datasets i.e. single, dual and multi speaker. Would be interesting to see dependency on this? 2) It seems like the model is limited to CTC loss, would it be possible to train them towards attention based enc-dec training? | 2) It seems like the model is limited to CTC loss, would it be possible to train them towards attention based enc-dec training? |
ARR_2022_138_review | ARR_2022 | 1. The problem formulation is flawed. The authors argue that one major motivation of this paper is to learn schema in a data-driven way other than laborious manual schema engineering. However, on the proposed four datasets, I feel that the schemas are somehow easy to learn. For example, on E2E, WikiTableText and WikiBi... | 1. In Section 3 (line 247-252), I am wondering tables are divided into three types. For me, one type (the column header) should work. |
IAFLoDz6H5 | ICLR_2025 | - The experiment setting is problematic.
- - Only toy tasks and language models are used. Instead of evaluating LLMs in a few-shot/zero-shot way, the paper fine-tunes the Pythia family of models on some classification tasks. Pythia models are only pre-trained on general corpus without any RLHF and their performances ar... | - - The attack methods are naive. Two attack methods are considered. One is to randomly add some tokens as suffixes of the inputs, while the other one generates a universal adversarial suffix. Since the paper is only considering the toy setting with classification tasks, I don't see any reason why other classical attac... |
Qg0gtNkXIb | ICLR_2025 | 1. Critical Methodological Limitations:
- The paper relies heavily on pre-trained language models (specifically BERT) for prompt sampling but fails to acknowledge this as a fundamental limitation. This is particularly problematic as such models have a training cutoff date (pre-2018 for BERT), making it impossible to fi... | 2. The mitigation methods affect the image generation capabilities of diffusion models, which can lead to lower image quality... |
ExZ5gonvhs | ICLR_2024 | - The employment of prior knowledge, specifically in the form of a pretrained visual model and the target dataset, diverges from the fundamental principles of Self-Supervised Learning (SSL).
- The incorporation of such prior knowledge raises concerns about the fairness of comparisons with existing SSL methods. There is... | - The incorporation of such prior knowledge raises concerns about the fairness of comparisons with existing SSL methods. There is a potential risk that the pretrained visual model and target dataset might leak additional information into the model, thereby skewing results and leading to issues of unfairness. |
ACL_2017_49_review | ACL_2017 | There are some minor points, listed as follows: 1) Figure 1: I am a bit surprised that the function words dominate the content ones in a Japanese sentence. Sorry I may not understand Japanese. 2) In all equations, sequences/vectors (like matrices) should be represented as bold texts to distinguish from scalars, e.g., h... | 1) Figure 1: I am a bit surprised that the function words dominate the content ones in a Japanese sentence. Sorry I may not understand Japanese. |
ICLR_2022_74 | ICLR_2022 | Weakness
• The theorem applies when the label error is small, less than 1/7. However, it might be non-trivial to obtain a predictor with that quality in the first place. For example, in the experiments, the initial solution are derived from k
-means (Lloyd's) algorithm, which might require many initial seeds to attain ... | • Minor suggestion: the average of k -means objectives with multiple seeds are used as a baseline, I think the minimal k -means objective over multiple seeds is more reasonable. [1] Jin, Chi, et al. "Local maxima in the likelihood of gaussian mixture models: Structural results and algorithmic consequences." Advances in... |
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 ... | - I think this manuscript is not following the NIPS style. The citations are not by number and there are no line numbers or an "Anonymous Author" placeholder. |
pFTBsdZ1UM | EMNLP_2023 | (W1) (Task definition and novelty are not clear.) The notion of indicative summarization is not clear. According to Footnote 2, it is not clear what is different from extreme summarization (e.g., single-document such as XSum and multi-document such as TLDR). The TIFU Reddit dataset [Ref 1] is not cited or mentioned in ... | - To me, the task looks closer to Argument Mining rather than Summarization. In any case, the paper should further clarify the differences against Argument Mining/Discussion Summarization. |
NIPS_2018_185 | NIPS_2018 | Weakness: ##The clarity of this paper is medium. Some important parts are vague or missing. 1) Temperature calibration: 1.a) It was not clear what is the procedure for temperature calibration. The paper only describes an equation, without mentioning how to apply it. Could the authors list the steps they took? 1.b) I ha... | 2) Uncertainty Calibration From one point of view it looks like temperature calibration is independent of uncertainty calibration, with the regularization term H. However in lines 155-160 it appears that they are both are required to do uncertainty calibration. (2.a) This is confusing because the training regularizatio... |
NIPS_2017_226 | NIPS_2017 | - An important reference is missing
- Other less important references are missing
- Bare-bones evaluation
The paper provides an approach to solve linear inverse problems by reducing training requirements. While there is some prior work in this area (notably the reference below and reference [4] of the paper), the paper... | 2) Important reference missing. The paper is closely related to the idea of unrolling, first proposed in, âListaâ http://yann.lecun.com/exdb/publis/pdf/gregor-icml-10.pdf While there are important similarities and differences between the proposed work and Lista, it is important that the paper talks about them and p... |
NIPS_2020_25 | NIPS_2020 | 1. The proposed method is inapplicable to data from absolutely continuous probability distribution. The number of possible values of a data point in this case will be infinite. However, the paper relies on the vectorization of the probability distribution. For truly real world continuous data, huge matrices will have t... | 4. The linear program in Theorem 3 need to be explained intuitively. I understand that this is a main theorem but it would help the reader a lot if the authors can explain what are the objective and the constraints in (3). |
yIEKq72cTE | ICLR_2024 | 1. The writing is confusing.
1. Definition 3.1 is not a definition. I can't see any definition from it. It seems to be a proposition or a theorem about the vulnerability of FedAvg for me.
2. Definition 4.1 is not clear.
1. Eq (4) is confusing. According to the definition of $\mathbb{N}$, $\mathbb{R}$ and $\mathbb{X}$, ... | 4. FLOT cost matrix in Algorithm 1 is not defined. |
NIPS_2021_2307 | NIPS_2021 | 1. It is unclear what the exact setting the paper considers in the continuous domain and how prior work would fail in that setting (please see the Questions). 2. Even when the paper proposes new algorithms (EI2 and UCB2) for the theoretical analysis, but it still benefits if we can see some experimental results about h... | 3. Does the bound in Theorem 2, Eq. (30) converge to 0 when T goes to infinity? As the bound in [Grunewalder et al, 2010], Eq. (27) does converge to 0. The first term in Eq. (30) does converge to 0, but it is not trivial to derive that the 2nd term in Eq. (30) also converges to 0. Can the authors prove this? Note: I'm ... |
ICLR_2021_961 | ICLR_2021 | Weakness:
The number of graphs satisfying the property is very limited. It requires an r-regular graph. That is, the number of edges connected to one node is the same for all nodes. This condition is very difficult to satisfy in applications. Therefore, the application would be limited too.
The quantization part is lim... | 1. Whether is it possible to update one node based on the results from multiple connected nodes (i.e., one node is activated)? Algorithm 2 is unclear. 'avg' is computed but not used. What are j' and 'i''? Update The authors' response addresses some concerns, and I would like to keep the initial scores. |
lHtNW6xqCd | ICLR_2024 | 1. The specific definition of the sparsity of the residual term in this paper is unclear. Does it mean that the residual term includes many zero elements? Besides, could the authors provide some evidence to support the sparsity assumption across various noisy cases? I think it's necessary to show the advantages of the ... | 1. The specific definition of the sparsity of the residual term in this paper is unclear. Does it mean that the residual term includes many zero elements? Besides, could the authors provide some evidence to support the sparsity assumption across various noisy cases? I think it's necessary to show the advantages of the ... |
ICLR_2023_1914 | ICLR_2023 | (1) The framing of synergies and their neuroscientific context is somewhat lacking. The premise of the paper is that muscle synergies can be predicted from the cortical inputs, e.g. We applied our method to the corticomuscular system, which is made up of corticospinal pathways between the primary motor cortex and muscl... | - ‘connectivity’ is misleading, as it isn’t using the structural connections between the brain and body. |
ICLR_2023_1584 | ICLR_2023 | Weakness:
1. The proposed method relies on a pretrained object detection network that contains the sufficient semantic information for the in-distribution data. When the semantic of in-distribution data like medical images is not covered by the object detection network (pre-trained on natural image), the built semantic... | 2. The paper is not polished and not ready to publish, with missing details in related work / experiment / writing. See more in "Clarity, Quality, Novelty And Reproducibility". |
NIPS_2022_2617 | NIPS_2022 | The essentialness of using orthogonal matrix is not studied. The whole OSA process 1. connects tokens within local windows with local window token orthogonalization, is serves as MLP layer within local windows, except the weight matrix of MLP is naturally orthogonal. 2. Connects tokens beyond local windows by forming n... | 3. Token reverse as the inverse of orthogonal matrix is easy to get, just the transpose of the matrix. Step 2 can be done regardless of the weight matrix of this local window MLP is orthogonal or not. Step 3 is the vital part that only orthogonal matrix weight can perform, I believe this should be studied, which is not... |
NIPS_2019_1089 | NIPS_2019 | - The paper can be seen as incremental improvements on previous work that has used simple tensor products to representation multimodal data. This paper largely follows previous setups but instead proposes to use higher-order tensor products. ****************************Quality**************************** Strengths: - T... | - With respect to Figure 5, why do you think accuracy starts to drop after a certain order of around 4-5? Is it due to overfitting? |
tGEBnSQ0uE | ICLR_2024 | 1. The experimental comparison of methods seems not complete. What about methods in Table 1 and non-DP counterparts (including yours, i.e. sigma=0, which is usually a very insightful upper bound of any DP method)? Currently only 3 methods are compared, which seems strange given that the paragraph "Decentralized learnin... | 3. The models and datasets are too toy-like. I would at least expect to see CIFAR100, which is of the same size as CIFAR10 but more difficult. It would be desirable to see also ResNet 34 or 50 (more compute, but managable by your machines), and ViT-tiny or small (similar compute as ResNet 18). Is there a foreseeable ch... |
NIPS_2019_387 | NIPS_2019 | - The main weakness is empirical---scratchGAN appreciably underperforms an MLE model in terms of LM score and reverse LM score. Further, samples from Table 7 are ungrammatical and incoherent, especially when compared to the (relatively) coherent MLE samples. - I find this statement in the supplemental section D.4 quest... | - Some natural ablation studies are missing: e.g. how does scratchGAN do if you *do* pretrain? This seems like a crucial baseline to have, especially the central argument against pretraining is that MLE-pretraining ultimately results in models that are not too far from the original model. Minor comments and questions : |
NIPS_2018_464 | NIPS_2018 | of the approach is the definition of the behavior characterization, which is domain-dependent, and may be difficult to set in some environments; however, the authors make this point clear in the paper and I understand that finding methods to define good behavior characterization functions is out of the scope of the sub... | - In the equation between lines 282 and 283, authors should state how they handle comparisons between episodes with different lengths. I checked the provided code and it seems that the authors pad the shorter sequence by replicating its last state in order to compare both trajectories. Also, the lack of a normalization... |
ICLR_2022_1905 | ICLR_2022 | Weakness: 1.The author claim that ‘The observation consistently shows that only parts of subdivision splines are useful for decision boundary; and the goal of pruning is to remove those (redundant) subdivision splines and find winning tickets.’, however, in theoretical part, the author didn’t provide how the proposed a... | 3.In the experiment, the author didn’t consider Vision Transformer, which is an important SOTA model in image classification. And it is unsure if such technique is still working for larger image dataset such as ImageNet. Will the pruning strategy will be different in self attention layers? |
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... | - 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... |
NIPS_2018_464 | NIPS_2018 | of the approach is the definition of the behavior characterization, which is domain-dependent, and may be difficult to set in some environments; however, the authors make this point clear in the paper and I understand that finding methods to define good behavior characterization functions is out of the scope of the sub... | - In SI 6.5, the authors should mention that despite the preprocessing is identical to that in Mnih et al. [7], the evaluation is slightly different as no human starts are used. |
wyHCt1P7SR | ICLR_2024 | - The biggest issue of the paper is the writing quality, which makes the paper very hard to follow. Details are listed below.
1. The introduction is extremely long and poorly organized. Many points are made, but I cannot find a precise sentence that emphasizes the essential contribution of the paper. The two applicatio... | 3. Fig.1 to Fig.3 are very difficult to parse. The texts in the figures are too small. The inputs and outputs for each task are not clearly explained. The captions are not self-contained, and it is also very hard to link them to certain parts of the main text. |
NIPS_2022_2286 | NIPS_2022 | Weakness 1. It is hard to understand what the axes are for Figure 1. 2. It is unclear what the major contributions of the paper are. Analyzing previous work does not constitute as a contribution. 3. It is unclear how the proposed method enables better results. For instance, Table 1 reports similar accuracies for this w... | 4. The authors talk about advantages over the previous work in terms of efficiency however the paper does not report any metric that shows it is more efficient to train with this proposed method. |
NIPS_2020_633 | NIPS_2020 | 1. The contribution is not enough. This paper address overfitting problem of training GAN with limited data, and proposed the differentiable augmentation. I think it is important factor, but still limited. 2. Using the accuracy (of both training and validation data) is not convincing metric, since conditional GAN suffe... | 1. The contribution is not enough. This paper address overfitting problem of training GAN with limited data, and proposed the differentiable augmentation. I think it is important factor, but still limited. |
NIPS_2016_450 | NIPS_2016 | . First of all, the experimental results are quite interesting, especially that the algorithm outperforms DQN on Atari. The results on the synthetic experiment are also interesting. I have three main concerns about the paper. 1. There is significant difficulty in reconstructing what is precisely going on. For example, ... | * L81: Please give more details. The state-space is finite? Continuous? What about the actions? In what space does theta lie? I can guess the answers to all these questions, but why not be precise? |
s4xIeYimGQ | EMNLP_2023 | 1) The improvement over CoT baselines, especially Self-Consistency Decoding CoTs, is not very significant. With Self-Consistency Decoding, the average improvement is usually around 0.5%, which may limit the real application of the proposed method considering the higher computing usage of backward verification.
2) The m... | 2) The method does not work very effectively on general reasoning tasks compared with mathematic reasoning. |
aw2Jc5DFZC | ICLR_2025 | - **W1. SoftMoE Formulation**: The SoftMoE formulation presented in Section 2 of this paper differs from that of the original SoftMoE paper [1]. In the original formulation, each expert processes $p$ slots, making $ \Phi \in \mathbb{R}^{d \times (n \cdot p)}$ and $C(X) \in \mathbb{R}^{m \times (n \cdot p)}$. However, i... | - **W2.3. Proof Technique**: After reviewing Appendix A, I noticed that the proof relies on a special case where a contradiction arises as matrix norms approach infinity. This is acknowledged by the authors in Section 3, where they mention that normalizing the input makes the results from Theorem 1 inapplicable. |
RSincg5RBe | ICLR_2024 | - Although this work states that the (hierarchical) latent approach for graph generation provides a scalable solution for molecule generation, the provided experiments are limited to datasets (e.g., GuacaMol) in which previous diffusion models (e.g., GDSS and DiGress) are applicable. In order to justify the scalability... | - As the continuous diffusion model (e.g., GDSS) outperforms the discrete diffusion model (e.g., DiGress) in Table 2, the continuous diffusion model should be compared as a baseline in Table 3 (i.e., conditional generation task). Although GDSS does not explicitly present a conditional framework, recent work [2] propose... |
fB1iiH9xo7 | ICLR_2024 | - Authors use object detection as the downstream task, but I personally believe LiDAR-based segmentation is the best choice. Colorization-based pre-training mainly learns the semantics in my opinion, but object detection needs accurate locations and poses especially in the benchmark using IoU-based metrics such as KITT... | - Authors use object detection as the downstream task, but I personally believe LiDAR-based segmentation is the best choice. Colorization-based pre-training mainly learns the semantics in my opinion, but object detection needs accurate locations and poses especially in the benchmark using IoU-based metrics such as KITT... |
CbfsKHiWEn | ICLR_2025 | 1. The evaluation could be compared with more baselines, please refers to https://arxiv.org/pdf/2409.02795.
2. These evaluation tasks are too simple; some methods may be effective on benchmarks like IMDB but may not generalize well to more complex tasks.
3. Although the authors provide beautiful theory proof, the objec... | 3. Although the authors provide beautiful theory proof, the objective of Eq (12) seems to be in contradiction with IPO. |
NIPS_2016_182 | NIPS_2016 | weakness of the technique in my view is that the kerne values will be dependent on the dataset that is being used. Thus, the effectiveness of the kernel will require a rich enough dataset to work well. In this respect, the method should be compared to the basic trick that is used to allos non-PSD similarity metrics to ... | - In the histogram intersection kernel, it think for clarity, it would be good to replace "t" with the size of T; there is no added value to me in allowing "t" to be arbitrary. |
HoyKFRhwMS | ICLR_2025 | 1. One limitation of retrieval and search engines is that the cost of storage and inference increases and the accuracy of recognition decreases as the memory database size grows. Although computing budget and accuracy can be traded off, this remains a limitation. Additionally, regarding the computation cost, I wonder i... | 2. The adaptation capacity of the proposed visual memory to accommodate ever-growing concepts assumes that the image encoder can produce meaningful embeddings for new concepts. While for geometrically distinctive concepts, I believe this is less of a concern for DINO representations, as they are observed to contain ric... |
ICLR_2022_3218 | ICLR_2022 | Weakness: 1) Since this paper focuses on biometric verification learning, the comparison against the state-of-the-art loss functions widely used in face/iris verification should be added (e.g., Center-Loss, A-Softmax, AM-Softmax, ArcFace). 2) Cosine similarity score is more often used in biometric verification, so I wo... | 1) Since this paper focuses on biometric verification learning, the comparison against the state-of-the-art loss functions widely used in face/iris verification should be added (e.g., Center-Loss, A-Softmax, AM-Softmax, ArcFace). |
ACL_2017_130_review | ACL_2017 | The paper starts with a detailed introduction and review of relevant work. Some of the cited references are more or less NLP background so they can be omitted e.g. (Salton 1989) in section 4.2.3. Other references are not directly related to the topic e.g. “sentiment classification” and “pedestrian detection in images”,... | 338 EF and 331 D2 transcription norms can be given. Technical comments: Line 029: ‘… as it a lightweight …’ -> shouldn’t this be ‘… as in a lightweight …’ Line 188: PLN -> NLP Line 264: ‘out of cookie out of the cookie’ – some words are repeated twice Table 3, row 2, column 3: 72,0 -> 72.0 Lines 995-996: the DOI number... |
ICLR_2021_2330 | ICLR_2021 | Weakness
- Method on Fourier domain supervision lacks more analysis and intuition. It's unclear how the size of the grid is defined to perform FFT, from my understanding, the size is critical as local frequency will be changed using different grid size. Is it fixed throughout training? What is the effect of having diff... | - Notation is confusing. M and N are used without definition. Suggestion - Spell out F.L.T.R in figure 4 - Figure 1 text is too small to see - It is recommended to have notation and figure cross-referenced (e.g. M and N are not shown in the figure) |
j80yTpU7ni | ICLR_2024 | * The proposed method involves multiple gradient updates and connection strength calculations (either one at a given iteration), which seems very computationally demanding compared to existing gradient based MTL methods like MGDA (which are already computationally intensive).
* Although the authors mention the proposed... | * In Algorithm1, using $p$ to denote the phase mixing probability and the dummy variable in the inner loop in Phase 2 might be confusing |
ARR_2022_285_review | ARR_2022 | The main weakness of the paper is that it's very terse, partly due to the 4-page limit of a short submission. This leads to potentially important information being omitted, see detailed comments below.
Some of the mentioned points could be easily alleviated by utilising the additional page that is provided upon accepta... | - The high-level description helps to understand the approach intuitively, but a more detailed (e.g. mathematical) formulation, for example in the appendix, would be helpful as well. Similarly, the figure is supposed to help to understand the problem better, but I find it confusing in two ways: First, the figure is too... |
jR6YMxVG9i | ICLR_2025 | -"a around" should be "an around"
-The grammar here could be improved as the phrasing is awkward: "Apart from agent framework"
-"VLM - generated" should be "VLM-generated" in Line 173
- If a trajectory fails to execute the desired command and the system tries to start over, the initial state, $s_1$, may not be the same... | - It would have been helpful to include additional benchmarking tasks outside of AitW. |
ICLR_2021_1292 | ICLR_2021 | Weakness:
Comparison of Complexity: [1] presents the complexity of different efficient transformers. For linformer[2], the time and memory complexity is O(nk). Is there any justification of LSH sampling equipped YOSO with complexity more than O(nm\tau log(d)+nmd)? 2.Experiments: YOSO takes linformer as baselines. Howev... | 2.Experiments: YOSO takes linformer as baselines. However, the pre-training experiment part does not provide steps vs ppl of linformer with YOSO in Figure 4. What is the comparison result of YOSO with linformer on iteration wise convergence? Also, linformer demonstrates better accuracy in downstream tasks such as SST-2... |
ACL_2017_276_review | ACL_2017 | The novelty is fairly limited (essentially, another permutation of tasks in multitask learning), and only one way of combining the tasks is explored. E.g., it would have been interesting to see if pre-training is significantly worse than joint training; one could initialize the weights from an existing RNN LM trained o... | -General Discussion: I was hesitating between a 3 and a 4. While the experiments are quite reasonable and the combinations of tasks sometimes new, there's quite a bit of work on multitask learning in RNNs (much of it already cited), so it's hard to get excited about this work. I nevertheless recommend acceptance becaus... |
ICLR_2022_1678 | ICLR_2022 | 1 - The authors propose a relaxation of rejection sampling which is using an arbitrary parameter β
instead of the true upper bound of the ratio p q
when the latter cannot be computed. The reviewer fails to understand why the authors did not directly use Importance sampling in the first place.
2- In algorithm 1, the rev... | 4 - In the abstract the authors require the proposal distribution to upper bound the target everywhere which is not true as the authors themselves clarify in the text. |
NIPS_2019_962 | NIPS_2019 | for exceptions. + Experiments are convincing. + To the best of my knowledge, the idea of using unsupervised keypoints for reinforcement learning is novel and promising. One can expect a variety of follow-up work. + Using keypoints as input state of a Q function is reasonable and reduces the dimensionality of the proble... | - Figure 1: Referring to [15] as "PointNet" is confusing when this name doesn't appear anywhere in this paper ([15]) and there exists another paper with this name. See "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation", Charles R. Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas. |
NIPS_2021_1721 | NIPS_2021 | I was very confused at the beginning about the difference between this paper and Bayesian RL. Assuming that my understanding of this paper is correct, I think the writing could be improved. Here are my comments:
It might be helpful to have a clear paragraph about the problem definition of this paper. I have found it co... | 6: is the policy gradient in Eq. 6 solving the optimal problem? So after convergence, will we get the optimal solution to Eq. 5? It might be better to clarify. Minor Line 78: but also on learning - on is unnecessary. Line 132: dπ(s) = (1−γ)... |
NIPS_2022_1048 | NIPS_2022 | and comments: 1. This paper mainly focused on group sufficiency as the fairness metric. Is it possible to derive similar results under criteria of demographic parity or equalized odds? What are the potential challenges for other fair metrics? Under these settings, is it still possible to achieve both fairness and accur... | 3. Is it possible to assume the general gaussian distribution rather than isotropic gaussian in the proposed algorithm? What is the difference? |
NIPS_2017_480 | NIPS_2017 | and limitations.
Other comments:
* Section 2.1: maybe itâs not necessary to introduce discounts and rewards at all, given that neither are used in the paper?
* Section 3.1: the method for finding the factors seems very brittle, and to rely on disentangled feature representations that are not noisy. Please discuss the... | * Line 192: freezing the partitioning in the first iteration seems like a risky choice that makes strong assumptions about the coverage of the initial data. At least discuss the limitations of this. |
ACL_2017_371_review | ACL_2017 | - The description is hard to follow. Proof-reading by an English native speaker would benefit the understanding - The evaluation of the approach has several weaknesses - General discussion - In Equation 1 and 2 the authors mention a phrase representation give a fix-length word embedding vector. But this is not used in ... | - Section 5.2: What is the intent of this section |
ACL_2017_818_review | ACL_2017 | 1) Many aspects of the approach need to be clarified (see detailed comments below). What worries me the most is that I did not understand how the approach makes knowledge about objects interact with knowledge about verbs such that it allows us to overcome reporting bias. The paper gets very quickly into highly technica... | 1) Many aspects of the approach need to be clarified (see detailed comments below). What worries me the most is that I did not understand how the approach makes knowledge about objects interact with knowledge about verbs such that it allows us to overcome reporting bias. The paper gets very quickly into highly technica... |
EWP9BVRRbA | ICLR_2025 | - The method does not address adversarial attacks aimed at generating benign yet contextually altered text, which may not contain harmful content but still alters the original intent. Could the authors discuss how their approach might be extended or modified to handle such cases, where adversarial examples produce beni... | - The threat model needs further clarification. Could the authors define the assumed threat model more explicitly, specifying the attacker’s level of access, capabilities, and the defender's available resources? Including this in a dedicated section would enhance clarity, particularly around the assumed white-box acces... |
ACL_2017_483_review | ACL_2017 | - 071: This formulation of argumentation mining is just one of several proposed subtask divisions, and this should be mentioned. For example, in [1], claims are detected and classified before any supporting evidence is detected.
Furthermore, [2] applied neural networks to this task, so it is inaccurate to say (as is cl... | - 590: The decision to do early stopping only by link prediction accuracy should be explained (i.e. why not average with type accuracy, for example?). |
O3Mej5jlda | ICLR_2024 | Below are several concerns related to weaknesses.
- In Introduction, the paper explains few-shot learning is "facilitating downstream scenarios where labeled data can be expensive or difficult to obtain". While few-shot learning is somewhat established in the community, can authors motivate the study of few-shot learni... | - The paper writes "sampling class-imbalanced tasks". As the few-shot learning setting has only a few examples for each class, how to set a reasonable class-imbalanced task? Can authors explain with concrete details? |
xbnNgqGefc | EMNLP_2023 | 1) All of the empirical evaluation is performed on one dataset. This makes it hard to judge the generalizability of the approach. But, it is understandable given the difficulty in annotating data for such a task.
2) The chat-gpt baseline is very rudimentary. Few-shot approach isn’t tested. Also, including the discourse... | 2) The chat-gpt baseline is very rudimentary. Few-shot approach isn’t tested. Also, including the discourse relation information in the prompts (probably in a Chain-of-Thought style approach) might yield good results. This will only add to the paper’s evaluation. But, it is extraneous to their line of evaluation as pre... |
NIPS_2019_573 | NIPS_2019 | of the paper: - no theoretical guarantees for convergence/pruning - though experiments on the small networks (LeNet300 and LeNet5) are very promising: similar to DNS [16] on LeNet300, significantly better than DNS [16] on LeNet5, the ultimate goal of pruning is to reduce the compute needed for large networks. - on the ... | 2) Authors don't explain the detail on how the ground truth of sensitivity is achieved, lines 238-239 just say "we first estimate a layer's sensitivity by pruning ...", but no details on how actual pruning was done. comments: |
NIPS_2020_409 | NIPS_2020 | - As also the authors explain, the proposed solution for the intractable normalizing constant is inelegant and also rather unclear. It is not clear how the function is approximated, given that the more complex version is not possible to know. Is some variational approximation used to make sure that the obtained functio... | - Some parts in the text could be written more clearly. For instance, -- could the authors explicitly explain what is a proper rotation matrix in line 97? -- what exactly is meant in l. 105-106 regarding solving the problem of the matrix being non positive semidefinite? |
NIPS_2018_134 | NIPS_2018 | - Some parts of the work are harder to follow and it helps to have checked [Cohen and Shashua, 2016] for background information. # Typos and Presentation - The citation of Kraehenbuehl and Koltun: it seems that the first and last name of the first author, i.e. Philipp, are swapped. - The paper seems to be using a diffe... | - line 111: it might make sense to not call g activation function, but rather a binary operator; similar to Cohen and Shashua, 2016. They do introduce the activation-pooling operator though that fulfils the required conditions. |
NIPS_2022_431 | NIPS_2022 | Lack information of comparison with related work.
Experiments on SQuAD have no results of the competitor A3.
Improvement is limited.
UPDATE: The authors' responses address my main concerns, which should be included in the revised version.
Minor suggestions: 1. The writing can be further improved. There are typos in: a)... | 2. The captions of Fig. 1 and Fig. 2 have large overlaps with your content. You can consider shrinking the captions to leave more space to your methods or related work. |
ARR_2022_342_review | ARR_2022 | 1. The importance of context is well known and well-established in several prior work related to hate speech. While the paper cites works such as Gao and Huang, 2017 and Vidgen, et al., it just mentions that they don’t identify the role of context in annotation or modeling. The former definitely considers its role for ... | 2. Table 2 includes several work but drops out Vidgen et al, 2021, which might be really similar to the dataset presented in this work though the size varies significantly here. So, why is this dataset not used as a potential benchmark for evaluation (for investigating the role of context in detection of hate) as well? |
NIPS_2016_283 | NIPS_2016 | weakness of the paper are the empirical evaluation which lacks some rigor, and the presentation thereof: - First off: The plots are terrible. They are too small, the colors are hard to distinguish (e.g. pink vs red), the axis are poorly labeled (what "error"?), and the labels are visually too similar (s-dropout(tr) vs ... | - The results comparing standard- vs. evolutional dropout on shallow models should be presented as a mean over many runs (at least 10), ideally with error-bars. The plotted curves are obviously from single runs, and might be subject to significant fluctuations. Also the models are small, so there really is no excuse fo... |
wwJJUamHVp | ICLR_2024 | 1) The stated contributions seem to "oversell" the method:
- As discussed later, all physics-informed neural operators do not require any training data.
- Most neural operators can deal with any form of PDE data (forcing, coefficients, boundary conditions, initial conditions).
- In contrast, the proposed method appears... | - It seems that the proposed approach is merely learning a surrogate model for solving the linear/linearized system of equations arising in FEM. It still requires carefully choosing basis functions and meshes and assembling stiffness matrices (i.e., in the specific case of the present work, it is heavily relying on FEn... |
NIPS_2021_57 | NIPS_2021 | I have some suggestions that may help the author to further improve the quality of the article. 1.The author may consider adding some verifications for tasks that rely more heavily on throughput, such as real-time target detection, real-time depth estimation. 2.The authors need to describe the experimental environment ... | 2.The authors need to describe the experimental environment in more detail, such as the CUDA version and the PyTorch version. Because different versions of the experimental environment will have a certain impact on training speed and inference speed. |
NIPS_2021_616 | NIPS_2021 | The authors discuss two limitations: first, this paper focuses only on methods with explicit negatives. This is not a problem for me since it is okay for an analysis paper to focus on one type of methods. The second limitation is that the datasets used in the experiments are not fully realistic. This again is not an is... | 2) fully realistic datasets will make it hard to control multiple aspects of variation with precision. I agree with the authors' judgement that there is no immediate societal impact. |
NIPS_2016_450 | NIPS_2016 | . First of all, the experimental results are quite interesting, especially that the algorithm outperforms DQN on Atari. The results on the synthetic experiment are also interesting. I have three main concerns about the paper. 1. There is significant difficulty in reconstructing what is precisely going on. For example, ... | * L156-166: I can barely understand this paragraph, although I think I know what you want to say. First of all, there /are/ bandit algorithms that plan to explore. Notably the Gittins strategy, which treats the evolution of the posterior for each arm as a Markov chain. Besides this, the figure is hard to understand. "D... |
ARR_2022_125_review | ARR_2022 | 1. A more explicit definition / description / explanation of what is meant by “systematic” performance gains or improvements seems to be necessary since this is one of the proposed advantages of the proposed method, and it would also be better if you could explicitly describe the connection between "systematic performa... | 3. Lines 078-079 / Line 08: For clarity, it would be better if the evaluation metric is mentioned here to better understand the scale of the improvement; this would also be helpful to understand the results reported in this paper for comparability: the expression “labelled F-measure scores (LF1) (including ROOT arcs)” ... |
NIPS_2017_110 | NIPS_2017 | weakness of this paper in my opinion (and one that does not seem to be resolved in Schiratti et al., 2015 either), is that it makes no attempt to answer this question, either theoretically, or by comparing the model with a classical longitudinal approach.
If we take the advantage of the manifold approach on faith, then... | - In the simulation study, the authors state that the standard deviation of the noise is 3, but judging from the observations in the plot compared to the true trajectories, this is actually not a very high noise value. It would be good to study the behaviour of the model under higher noise. |
NIPS_2020_664 | NIPS_2020 | 1) The algorithms (although rigorously analyzed) are somewhat obvious modifications of the best known ones from the online literature. 2) The bounds have o(1) terms and start improving over the previously known results for arbitrarily long inputs. I am not sure how large these inputs needs to be, but it seems that this... | 2) The bounds have o(1) terms and start improving over the previously known results for arbitrarily long inputs. I am not sure how large these inputs needs to be, but it seems that this would seriously limit the applications of this approach. |
NIPS_2020_91 | NIPS_2020 | - DVP needs to perform training at test time (25-50 epochs) per testing sequence. - The reviewer understands that the Figure 8 provides some insights "when to stop", however, it is unclear how it will change or is it sensitive to the length of videos (longer videos). - It is interesting to see how DVP perform on video ... | - It is interesting to see how DVP perform on video with different length? |
ARR_2022_82_review | ARR_2022 | - In the “Updating Facts” section, although the results seem to show that modifying the neurons using the word embeddings is effective, the paper lacks a discussion on this. It is not intuitive to me that there is a connection between a neuron at a middle layer and the word embeddings (which are used at the input layer... | - I was confused that the paper targets single-token cloze queries or multi-token ones. I did not see a clear clarification until reading the conclusion. |
ICLR_2023_2086 | ICLR_2023 | . Section 2: The authors mentioned that the absorbing diffusion is the most promising generation method. Can you add some explanation on that?
. Section 3.1: The diffusion ordering network produces the probability of the node at time t via equation (1). Can you explain why such an ordering would reflect the topology/re... | . Section 3.3: The proposed training objective has ignored the KL-divergence term in equation (3). Can you evaluate such approximation error, ie. calculate the actual KL-divergence and check whether it indeed approaches zero? Experiments: |
JJH7m9v4tv | ICLR_2025 | 1. This paper has a strong connection with [1], as both employ additional discriminator guidance to improve sampling by rectifying sampling bias. The only significant difference is the generator utilized by the GAN in this paper. Therefore, the main contribution may be considered limited.
2. Is it really useful to use ... | 3. In my opinion, Section 2 shows limited connection with the methodology section. Furthermore, the theoretical analysis is somewhat simplistic and closely related to [1]. |
NIPS_2022_2074 | NIPS_2022 | 1.) BlendedMVS is not evaluated quantitatively, and I couldn't find an argument for this.
2.) It is not clear to me why the explicit SDF supervision (Sec. 3.2) is done with occlusion handling (L. 165) and view-aware (L. 173). It is only stated that "the introduced SDF loss is consistent with the process of color render... | 11.) Would be interesting to further discuss or which situations the losses help in particular, e.g. mostly for specular areas? |
79FVDdfoSR | ICLR_2024 | **Contribution.** I doubt that the paper in its present state is strong enough for ICLR.
1. The main result looks like a relatively simple add-on to the study of equivariant networks by Wood & Shawe-Taylor (1996). Most results found in the appendix are either simple facts from the group and representation theory or are... | **Contribution.** I doubt that the paper in its present state is strong enough for ICLR. |
NIPS_2021_812 | NIPS_2021 | I see two primary weaknesses in this paper: 1) numerous sweeping claims are made regarding their superiority over previously published results without sufficient support. 2) The empirical demonstration of the model compares against control models that are not well fit to the tasks. Both of these weaknesses are easily a... | 2) The empirical demonstration of the model compares against control models that are not well fit to the tasks. Both of these weaknesses are easily addressable, by |
NIPS_2022_2286 | NIPS_2022 | Weakness 1. It is hard to understand what the axes are for Figure 1. 2. It is unclear what the major contributions of the paper are. Analyzing previous work does not constitute as a contribution. 3. It is unclear how the proposed method enables better results. For instance, Table 1 reports similar accuracies for this w... | 2. It is unclear what the major contributions of the paper are. Analyzing previous work does not constitute as a contribution. |
NIPS_2018_103 | NIPS_2018 | Weakness (or more like suggestions for future improvements): Although paper tries to address the practical concerns (handling the noisy cases and only need to sample two parameters at a time), the method still has some practical issues. For example, QBC-based methods are vulnerable to noise. If we could quantify the le... | 2. In Algorithm 2, it does not say how to determine n_t. What does the "appropriate number" mean in line 225? It is hard to find the answer in [30]. |
NIPS_2020_1160 | NIPS_2020 | 1.The authors proposed to use RL based method, while the motivation is not very clear. 2. It's hard to reproduce the results. Will the code be public avaliable. | 2. It's hard to reproduce the results. Will the code be public avaliable. |
NIPS_2019_1364 | NIPS_2019 | weakness of the paper. Questions: * In which cases the assumptions of theorems 3,4 hold? In addition to SLC, they have some matroid related assumptions. Since these results intend to demonstrate the power of the SLC class, these should be discussed in more detail. * How the diversity related \alpha enters the mixing bo... | 2) The claims that "in practice the mixing time is even better" are not nearly sufficiently supported by the experiments, and therefore the evidence provided to practitioners is very limited. |
NIPS_2022_1048 | NIPS_2022 | and comments: 1. This paper mainly focused on group sufficiency as the fairness metric. Is it possible to derive similar results under criteria of demographic parity or equalized odds? What are the potential challenges for other fair metrics? Under these settings, is it still possible to achieve both fairness and accur... | 1. Could we extend the protected feature A to a vector form? For instance, A represents multiple attributes. |
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