title string | paper_decision string | review_1 string | rebuttals_1 string | review_2 string | rebuttals_2 string | review_3 string | rebuttals_3 string | review_4 string | rebuttals_4 string | global_rebuttals string | dataset_source string | conference_year int64 | review_5 string | rebuttals_5 string | review_6 string | rebuttals_6 string | review_7 string | rebuttals_7 string | review_8 string | rebuttals_8 string |
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AdvAD: Exploring Non-Parametric Diffusion for Imperceptible Adversarial Attacks | Accept (poster) | Summary: In this paper, a diffusion-based adversarial attack method is proposed and extensive experiments were conducted to show the effectiveness of the method.
Strengths: 1. the paper is well-written and easy to read.
2. both adversarial attack and diffusion model are hot topics.
3. both Linf and L2 results are give... | Rebuttal 1:
Rebuttal: Thanks for your time in processing our manuscript and the valuable feedback. Our point-by-point responses are as follows.
**Weakness 1: Pseudo code.**
**Re:** We appreciate your attention to this detail. We acknowledge that due to page limitations, we did not include the pseudo co... | Summary: This work proposes a novel adversarial attack framework called Adversarial Attacks in Diffusion (AdvAD). Unlike prior methods that rely on generative models or specific loss functions, AdvAD formulates attacking as a non-parametric diffusion process. This approach theoretically explores a fundamental modeling ... | Rebuttal 1:
Rebuttal: Thanks for your time in processing our manuscript and the valuable feedback. Our point-by-point responses are as follows.
**Question 1: Explanation and verification of AdvAD.**
**Re:**
1) **Intuitive explanation.** The superior imperceptibility of AdvAD first comes from its modeling philos... | Summary: This paper proposes a new method, AdvAD, for generating imperceptible adversarial attacks against deep neural networks (DNNs). AdvAD is based on a novel non-parametric diffusion process, which initializes a fixed diffusion noise and then manipulates it at each step using adversarial guidance crafted by two mod... | Rebuttal 1:
Rebuttal: Thanks for your time in processing our manuscript and the valuable feedback. Our point-by-point responses are as follows.
**Weakness 1 & Question 1: Clear and detailed comparison with existing diffusion-based attack methods.**
**Re:** Compared to other recent notable works explori... | Summary: In this paper, the authors propose the Adversarial Attack in Diffusion method called AdvAD, which crafts imperceptible perturbations from the model perspective without the need for additional networks.
During the non-parametric diffusion process, the proposed AdvAD method introduces Attacked Model Guidance (A... | Rebuttal 1:
Rebuttal: Thanks for your time in processing our manuscript and the valuable feedback. Our point-by-point responses are as follows.
**Weakness 1 & Question 1: Details of AdvAD-X: exact step with DGI strategy, definition of non-critical image regions.**
**Re:** Due to the page limitation of ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable feedback and recognition of our contributions. We also appreciate they find the proposed method is novel (**K1mG**, **ygiL**, **nUXD**, **ytMn**), theory is solid (**K1mG**, **ygiL**), paper is well-written (**K1mG**, **jCt2**, **nUXD**, **ytMn**), and... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper studies a task about generating the imperceptible adversarial noise using the diffusion model. The proposed method theoretically models the attack process as a non-parametric diffusion process. Extensive experiments demonstrate the effectives of the proposed method.
Strengths: (1) The writing is go... | Rebuttal 1:
Rebuttal: Thanks for your time in processing our manuscript and the valuable feedback. Our point-by-point responses are as follows.
**Weakness 1: Defense of random smoothing.**
**Re:** In Table 3 of the paper, we have evaluated the robustness of proposed AdvAD using numerous advanced defens... | null | null | null | null | null | null |
Understanding Information Storage and Transfer in Multi-Modal Large Language Models | Accept (poster) | Summary: This paper presents an approach to investigate the layer in which multi-modal large language models retrieve factual information and show several insights into their behavior. To investigate causal tracing, they propose replacing the input text tokens with different ones so that the model can respond different... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback. We are glad that they acknowledge the strength and importance of our proposed methodologies and findings for future MLLM development and interpretability. Below we address the specific points raised by the reviewer:
**They offer an interesti... | Summary: The paper introduces MULTIMODALCAUSALTRACE, an algorithm designed to identify hidden states in large language models (LLMs) that store factual information, specifically extending this capability to vision-language models where images are encoded as visual tokens. Building on previous work in information storag... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback, and for acknowledging the comprehensiveness of our findings and insights. We are glad that they also see the importance of being able to identify and correct factual information in MLLMs, which is currently an understudied problem. Below we ad... | Summary: This paper studies the information storage and transfer for the multi-modality large language model (MLLM). The author provides a comprehensive empirical study leveraging the causal tracking method, i.e., corrupting a clean model by perturbing the input prompt, to identify which layers are used to retrieve inf... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback, in particular acknowledging that this is the “first work that provides a comprehensive study of knowledge tracing on MLLM”. We are also glad that the reviewer finds our paper well-written and feels that the work can provide a solid foundation for ... | Summary: This manuscripts studies mechanistic interpretability in autoregressive vision-language models. Towards this, the authors propose `MultiModalCausalTrace`, an extension of the causal tracing technique for analyzing text-only LLMs, which is to perturb "visual constraint" tokens with a set of semantically coheren... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing constructive feedback and appreciating the paper’s presentation, techniques and the findings. We are glad that the reviewer feels that VQA-Constraint is a valuable contribution to the research community.
Below we address the weaknesses raised by the reviewer:
... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their constructive feedback and comments. We have individually addressed the comments in their respective sections.
We want to highlight that our paper proposes a package of novel contributions that work together to advance our understanding of how state-of-the-ar... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Carefully Blending Adversarial Training and Purification Improves Adversarial Robustness | Reject | Summary: To better defend against adversarial attacks, the paper proposes a novel adversarial defense mechanism for image classification – CARSO – blending the paradigms of adversarial training and adversarial purification in a synergistic robustness-enhancing way.
Strengths: The paper proposes a novel defense mechani... | Rebuttal 1:
Rebuttal: We thank the Reviewer for his/her observations, noticing however that part of such review is based on what we believe to be a mischaracterisation of our paper’s goal and contents. We will address the Reviewer’s concerns in a similar list-based format.
1. **Poor presentation.**
**a)** We ... | Summary: This study proposes a novel adversarial defense method called CARSO. CARSO consists of two models: a classifier and a purifier. The classifier is (pre)trained to correctly classify possibly perturbed data. The encoder of the purifier is trained to generate a latent space from the internal representation of the... | Rebuttal 1:
Rebuttal: We thank the Reviewer for his/her constructive observations, and for the care put into writing the review. We will gladly comment upon all points raised, in a similar list-based format.
1. **On the superiority of CARSO *w.r.t.* AT classifier baselines.** With respect to the claim that CARSO sur... | Summary: This paper integrates adversarial training and adversarial purification to enhance robustness. It specifically maps the internal representation of potentially perturbed inputs onto a distribution of tentative reconstructions. These reconstructions are then aggregated by the adversarially-trained classifier to ... | Rebuttal 1:
Rebuttal: We thank the Reviewer for his/her observations.
Firstly, we would start by pointing out an inaccuracy in the summary of our paper. The tentative reconstructions of input images generated by the *purifier* are not aggregated by the *classifier*. Indeed, the *classifier* processes them independentl... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers for their time and useful remarks.
We would like to use this space to clarify once more, in an explicit fashion, the goals of our work. As stated in the *Abstract*, *Section 1* (the *Introduction*) and *Section 6* (the *Conclusion*), our first and foremost aim was that of i... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Curses of Future and History in Future-dependent Value Functions for Off-policy Evaluation | Accept (poster) | Summary: This paper proposes new off-policy estimators for POMDPs without exponential variance with the horizon. Specifically, outcome coverage and belief coverage are assumed which capture the past and future information, respectively. This framework reduces the estimation guarantees from exponential to polynomial.
S... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation of our work and the valuable comments.
> **”Is it possible to extend the analysis to infinite horizon?”**
We believe the answer is yes. In fact, the work of Uehara et al. [2022a] (which we build on) is in the infinite-horizon discounted setting. Howev... | Summary: This paper addresses off-policy evaluation in the context of POMDP, aiming to develop estimators that avoid exponential dependence on the horizon. Paper introduces two novel coverage assumptions --- outcome coverage and belief coverage --- tailored to POMDPs to achieve polynomial bounds on estimation guarantee... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation of our work and the valuable comments.
> **”Why such `future-dependence’ is necessary to deal with partial observation?”**
As we mentioned in the paper, the most obvious thing to try is to use history-dependent value functions by converting the POMDP ... | Summary: This paper studied the finite sample guarantee of future-dependent value function (FDVF) based method for policy evaluation problem in POMDPs. The existing guarantee depends on the boundedness of the FDVF which can be exponential in horizon. The authors studied this quantity and proposed new coverage assumptio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments.
> **“For the boundedness of FDVF, there isn't a clear discussion on the strictness of the assumptions in all cases except in some intuitive examples. A claim that $C\_{F, V}$ is polynomially bounded in all cases is needed.”**
$C\_{F, V}$ is the... | Summary: This paper studies off-policy evaluation in POMDPs and introduces two novel coverage concepts: outcome coverage and belief coverage. Outcome coverage uses a weighted norm to ignore unimportant futures in future-dependent value functions. Belief coverage is related to the covariance matrix of belief states unde... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments.
> **“the paper … does not assume the learning agent ever has access to the set of latent states S. If I'm incorrect about this, I ask the authors to let me know ...”**
You are right. As in the standard POMDP setting, the latent states $s\_h$ are... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Treatment of Statistical Estimation Problems in Randomized Smoothing for Adversarial Robustness | Accept (poster) | Summary: This paper presents two statistical innovations on top of standard randomized smoothing. The first is employing a randomized Clopper-Pearson interval (instead of deterministic), which marginally increases the certified radius for a particular number of samples. The second involves improving sample efficiency b... | Rebuttal 1:
Rebuttal: Thanks a lot for your review. We appreciate the comments and questions that will help improve the clarity of the paper. We would like to emphasize that the resulting (theoretically grounded) methods are reasonably simple ($\sim10$ lines of code) and (a heuristic) SotA uses $50\\%$ more samples an... | Summary: This paper proposes sample-efficient methods for computing probabilistic robustness certificates for randomized smoothing. The proposed methods replace the standard Clopper-Pearson confidence interval on the classifier’s score, with a confidence sequence, thereby allowing the number of samples to be determined... | Rebuttal 1:
Rebuttal: Thanks a lot for your thorough review! We appreciate the insightful comments that will help with the paper.
If we satisfactorily answer your questions and reservations, we kindly ask you to consider increasing your score.
## Questions
* **Does it apply to more general formulations of smoothing?... | Summary: They study the task of certified robustness, i.e. they need to decide if a point is robust at a certain radius or not, using as few samples as possible while maintaining statistical guarantees. Their main contribution is utilizing confidence sequences (instead of confidence intervals) that allows them to draw ... | Rebuttal 1:
Rebuttal: Thanks for your review and typos! We will clarify the binomial case. We are ready to answer any questions if any arise!
---
Rebuttal Comment 1.1:
Comment: I went through other reviews and responses. Due to the computational cost of RS it is hard to adopt them, and they show an empirical method o... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their reviews! In general we agree with them. In our understanding, the reviewers agree on the fact that we successfully attacked a well-known limitation of randomized smoothing. Their perceived weaknesses are occasional writing problems - those are easily fixable and do... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation | Accept (poster) | Summary: This paper considers reinforcement learning with low switching cost. The authors design a new algorithm named MQL-UCB for RL with general function approximation. The key algorithmic design includes a general deterministic policy-switching strategy that achieves low switching cost, a monotonic value function st... | Rebuttal 1:
Rebuttal: # Response to Reviewer ZRKX
Thank you for your insightful comments and suggestions! We answer your questions as follows.
---
**Q1** Some of the assumptions look very strong. The completeness of all functions $V: \mathcal{S} \to [0, 1]$, the second-order completeness and the existence of bonus or... | Summary: This paper studies the reinforcement learning under general function approximation. This paper attempts to find an algorithm which achieves optimal regret and maintains low switching cost in the mean time.
In the algorithm, first they calculated the empirical value function using the weighted ERM, where the ... | Rebuttal 1:
Rebuttal: # Response to Reviewer Zbvr
Thank you for your positive feedback! We address your questions point-by-point.
---
**Q1** The assumptions made on completeness and eluder dimension are somehow stronger than the usual assumptions in related literatures. Is there a lower bound showing that the normal ... | Summary: The paper studies reinforcement learning with general functional approximation and proposes a near-optimal algorithm with low policy switching times.
Strengths: The paper is very well-written, and the main results of the paper are of high quality. The authors improved prior works on RL with general functional... | Rebuttal 1:
Rebuttal: # Response to Reviewer 6wYy
Thank you so much for your strong support! We address your questions as follows.
---
**Q1** It would be helpful to provide a bit more details for readers who are not very familiar with the literature on RL with policy switching cost on how to obtain the counting of sw... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Enhancing Diversity in Bayesian Deep Learning via Hyperspherical Energy Minimization of CKA | Accept (poster) | Summary: This paper introduces the hyperspherical energy as an objective to prompt the diversity of particles in BNNs. It claims that the hyperspherical energy approach can avoid permutation invariance in traditional diversity metrics.
Strengths: I agree that the diversity of particles in BNNs is an important problem.... | Rebuttal 1:
Rebuttal: ## Technical Contribution
We strongly disagree with the reviewer’s dismissal of our contribution as “merely a regularization term”. Comparing neural networks in particle-based variational inference (ParVI) has been a difficult problem and most proposed comparison kernels suffer from the lack of ch... | Summary: The paper “Minimizing Hyperspherical Energy for Diverse Deep Ensembling” explores the use of Centered Kernel Alignment (CKA) and Minimization of Hyperspherical Energy (MHE) in Bayesian deep learning to enhance the diversity of ensemble models and parameters generated by hypernetworks. By incorporating these te... | Rebuttal 1:
Rebuttal: ## Questions
>When running the LeNet comparisons against DDU, was the model trained with spectral normalization (a requirement for DDU to function well) and how was the regularization strength determined?
Yes, all DDU models were trained with spectral normalization. We picked the best regularizat... | Summary: The authors proposed to improve the quantification of particle diversity in deep ensemble with hyperspherical energy (HE) on top of the CKA kernel. They further integrate the HE kernel in particle-based variational inference (ParVI) and generative ensemble with hypernetwork frameworks. The methods are evaluate... | Rebuttal 1:
Rebuttal: ### CIFAR-10 Performance
We did evaluate our approach using a standard ResNet18, shown in Table 4 of the manuscript. The inlier performance of all approaches (The “Accuracy” column in Table 4.) is above 95%. We compared using the permutation invariant kernels $\text{CKA}_\text{pw}$, $\text{HE}$ wi... | null | null | Rebuttal 1:
Rebuttal: ## General Remarks
We are grateful to the reviewers for their constructive feedback. Below, we respond to questions and concerns shared by the reviewers regarding running on a larger dataset.
## Larger Dataset
We agree with the reviewers that the evaluation of BDL on a larger dataset is desirabl... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
EnsIR: An Ensemble Algorithm for Image Restoration via Gaussian Mixture Models | Accept (poster) | Summary: This paper proposes a post-training model ensenble method for image restorationn by leveraging Gaussian mixture models on split pixels and lookup table for fast innference. The pixels are split into various bin sets according to their value ranges and the ensemble problem of the pixels is reformulated into Gau... | Rebuttal 1:
Rebuttal: **Q-1.** ZZPM is a new and recently proposed method after 2022, and averaging is the most straightforward and traditional method. Why is its performance sometimes worse than the normal averaging?
**A-1.** Thank you for your insight. ZZPM was developed for the latest image restoration competition ... | Summary: A novel post-training ensemble learning method for image restoration is developed in this work by employing Gaussian mixture models and the EM algorithm to generate better restoration results. The authors reformulate the ensemble problem of image restoration into various gaussian mixture models, use the EM alg... | Rebuttal 1:
Rebuttal: **Q-1.** The experiments all show the cases of three base methods, and one of them may be worse than the other two methods. But if all the base models are comparably good, can the method surpass other methods? Experiments with 2 or 4 base models, instead of 3, would be informative.
**A-1.** Thank... | Summary: This paper proposes an ensemble algorithm called EnsIR for image restoration tasks using Gaussian mixture models (GMMs). The method partitions pixels into range-wise bins, formulates the ensemble as GMMs over these bins, and solves for ensemble weights using expectation-maximization. The weights are stored in ... | Rebuttal 1:
Rebuttal: **Q-1.** Marginal improvement over existing methods: The performance gains are minimal compared to simpler approaches. For example, on the Rain100H dataset (Table 5), the proposed method achieves a PSNR of 31.725, only marginally better than the Average (31.681) and ZZPM (31.679) baselines. In som... | Summary: This paper reformulate the ensemble problem of image restoration into Gaussian mixture models (GMMs) and employ an expectation maximization (EM)-based algorithm to estimate ensemble weights for aggregating prediction candidates. Importantly the authors' method achieves state-of-the-art performance without trai... | Rebuttal 1:
Rebuttal: **Q-1.** I have some confusion. How are the averages in Table III achieved? Why is averaging able to achieve higher results than every single method (non-ensemble method)? Isn't it the average of every single method?
**A-1.** Yes, the "Average" refers to the average of the results of all single r... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers, ACs, SACs, and PCs for their effort and attention. We have uploaded a rebuttal file in PDF format to illustrate the tables and figures.
Table 1 presents the experimental results of the ensemble for four tasks, i.e., low-light image enhancement (LLIE), dehazi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ADOPT: Modified Adam Can Converge with Any $\beta_2$ with the Optimal Rate | Accept (poster) | Summary: The submitted work analyzes the divergence of Adam and RMSprop in smooth nonconvex settings. The authors propose a new optimizer ADOPT, whose convergence does not depend on the second-moment coefficient $\beta_2$.
The proposed optimizer is evaluated in toy settings, image classification, language modeling (fin... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. We will answer your questions to address your concerns. Please also refer to the general response and the PDF attached with it.
> The non-convergence of Adam is not an issue in practice and I am unsure about the practical implications of ADOPT over Adam. T... | Summary: The paper titled "ADOPT: Modified Adam Can Converge with Any β2 with the Optimal Rate" introduces a new adaptive gradient method named ADOPT. This method aims to address the non-convergence issues of the Adam optimization algorithm. Adam, despite its popularity in deep learning, does not theoretically converge... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. We will answer your questions to address your concerns. Please also refer to the general response and the attached PDF.
> The analysis still relies on the assumption that the second moment of the stochastic gradient is uniformly bounded, which might not al... | Summary: Motivated by the counterexample due to Reddi et al., this work designs an adaptive optimizer that can converge for the choice of beta_2 that is independent of the problem instance. Their analysis works for a more general condition than the previous works.
Strengths: It has overall good presentation. The main ... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. We will answer your questions to address your concerns. Please also refer to the general response and the PDF attached with it.
> In Theorem 4.1, please specify the choice of hyperparameters (beta_1, beta_2, \eps). I could not find this even in the appendi... | Summary: The paper proposes a new adaptive gradient method called ADOPT, which addresses the non-convergence issue of popular methods like Adam and RMSprop. The method modifies the calculation of second moment estimates and the order of momentum calculation and scaling operations. Extensive numerical experiments demon... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. We will answer your questions to address your concerns. Please also refer to “Author Rebuttal by Authors” and the attached PDF.
> The convergence of a modified version of Adam is not significant from theoretical sense unless the ADOPT can beat the performan... | Rebuttal 1:
Rebuttal: We thank all reviewers for their comments. They are insightful and help us to make our paper better. We have added new experiments and explanations to address the reviewer's concerns. Please also refer to the individual responses to each reviewer.
## Additional experiments of pre-training GPT-2
S... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning to Balance Altruism and Self-interest Based on Empathy in Mixed-Motive Games | Accept (poster) | Summary: The paper introduces LASE, a novel distributed multi-agent reinforcement learning algorithm. LASE aims to foster altruistic cooperation through a gifting mechanism while avoiding exploitation in mixed-motive games. The algorithm dynamically adjusts the allocation of rewards based on social relationships inferr... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments on our work!
> The comparison to the original gifting methods and the individual contributions of the gifting mechanism.
Our primary contribution lies in designing an algorithm that can dynamically adjust the gifting strategy to different co-players, performi... | Summary: This work proposes LASE, a multi-agent reinforcement learning framework that aims to improve co-operation between agents in mixed-motive games using transfer of rewards between agents in a zero-sum manner. Specifically, each agent uses counterfactual reasoning to compute a social relationship metric that compu... | Rebuttal 1:
Rebuttal: > The complexity of the framework and the scalability of the method.
Thank you very much for your suggestions on LASE’s scalability! To test LASE’s scalability, we have extended Cleanup and Snowdrift as follows:
| | Map size | Player num | Obs size | Init Waste/ Snowdrift num | Episode length |... | Summary: The authors propose a novel algorithm, LASE, that employs a gifting mechanism in order to steer agents toward equilibria of high social welfare in mixed motive games. A novelty in LASE is that it estimates the "social relationship" between a player and its co-players through a counterfactual Q-value baseline. ... | Rebuttal 1:
Rebuttal: > Estimate the uncertainty of their social relationship.
>
We select the $w^{ij}$ data from the last $10^6$ timesteps of training to calculate their mean value $\overline{w}$ and standard deviation $s$, which estimates the uncertainty of social relationships. The calculation method is as follows... | Summary: This paper introduces LASE (Learning to balance Altruism and Self-interest based on Empathy), a multi-agent reinforcement learning algorithm designed for mixed-motive games. LASE uses a gifting mechanism where agents share a portion of their rewards with others based on inferred social relationships. Counterfa... | Rebuttal 1:
Rebuttal: > Clarify issues
Thanks for your valuable suggestions! The overall framework and flow of LASE as well as the relationship between each module can be seen in Figure 1 in the paper and Algorithm 1 in the appendix. For clarity, here we present the pseudocode for both Social Relationship Inference (S... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Face2QR: A Unified Framework for Aesthetic, Face-Preserving, and Scannable QR Code Generation | Accept (poster) | Summary: This paper is pioneering in its integration of face identity with QR codes, proposing a novel pipeline for generating customized QR codes with embedded face identity. The key idea is to leverage diffusion models and control networks to create visually appealing QR codes while preserving face identity. The pipe... | Rebuttal 1:
Rebuttal: Dear Reviewer jWT5,
Thank you for taking the time to review our paper and providing valuable feedback. Below, I will address the raised concerns:
> **Q1: [Can the authors provide anaylsis of bad cases caused by failure of generative models?]**
* We include some bad cases caused by failure of gen... | Summary: The article introduces a novel pipeline designed to create customized QR codes that integrate aesthetic appeal, facial identification (ID), and scannability. The proposed approach incorporates three key components: (1) ID-refined QR Integration (IDQR) seamlessly incorporates facial ID into the QR code backgrou... | Rebuttal 1:
Rebuttal: Dear Reviewer MYMo,
Thank you for taking the time to review our manuscript and providing valuable feedback. All raised concerns are addressed below point by point:
> **Q1: [Each module and its technology is a combination of previous works.]**
* We acknowledge that the components—Diffusion models... | Summary: This work proposed Face2QR, a pipeline for generating personalized QR codes that balance aesthetics, face identity, and scannability. It mainly introduces three components: ID-refined QR integration (IDQR) for seamless background styling with face ID, ID-aware QR ReShuffle (IDRS) to rectify conflicts between f... | Rebuttal 1:
Rebuttal: Dear Reviewer ebKp,
Thank you for taking the time to review our manuscript and providing valuable feedback. All raised concerns are addressed below point by point:
> **Q1: [Method is built upon existing work like Diffusion models, Identity Preserved Generative Models and latest QR generation meth... | Summary: The paper presents an interesting framework to generate face-preserving QR code, which is useful in social entertainment applications. To enable this application, the paper first encode the Face ID information into the QR generation process and a refining process is applied to improve the integrity of facial f... | Rebuttal 1:
Rebuttal: Dear Reviewer p8m4,
Thank you for taking the time to review our manuscript and providing valuable feedback. All raised concerns are addressed below point by point:
> **Q1: [The paper is more likely to be an application paper rather than a neurips submission.]**
* We believe that our Face2QR is no... | Rebuttal 1:
Rebuttal: Dear Reviewers and Area Chairs,
We appreciate the reviewers (**R1** p8m4, **R2** ebKp, **R3** MYMo, and **R4** jWT5) for their insightful feedback.
The reviewers agree that:
**Novel approach**:
* **R3**: "The article introduces a **novel** pipeline designed to create customized QR codes..."
* **... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Optimal Parallelization of Boosting | Accept (oral) | Summary: This paper studies parallelization in weak-to-strong boosting algorithms. Such algorithms are modeled by the number of sequential rounds $p$ that they run for, and the amount of work $t$ that can be done in parallel in each round. Each unit of work in a round is typically a query to a weak learning algorithm, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful evaluation of our work. It was a joy for us to read it. It is clear that the reviewer built a solid understanding of our work, grasping multiple of the subtleties in the argument. This even extends to related works to some extents, as evidenced by the revie... | Summary: The authors study parallelized boosting, a natural weak-to-strong learning model recently re-introduced by Larsen and Karbasi. Building on recent work of Lyu et al., this work gives new upper and lower bounds on the trade-off between number of rounds, and number of parallel calls per round to the weak learner,... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review. As for Reviewer GD3o, we see that the present reviewer got a solid understanding of our work and its contributions. The question posed by the reviewer attests to this and, once again, we share our opinion that such level of comprehension is suitable... | Summary: The authors offer the bound of algorithm 1 in paper in a very traditional learning theory.
Strengths: I think a theory understanding of the algorithm is more important than the experiment reports. This paper shows the bounds for a kind of parallel boosting algorithm. The proof sturcture of algorithms is cle... | Rebuttal 1:
Rebuttal: We thank the reviewer for the effort invested in evaluating our submission. We were happy to see that the reviewer found the presentation of our arguments clear and values the theoretical nature of our work, which is its entire focus.
We remark that with a lot of parallel computation, the time to... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Meta-Reinforcement Learning with Universal Policy Adaptation: Provable Near-Optimality under All-task Optimum Comparator | Accept (poster) | Summary: This paper presents a bilevel optimization framework for meta-reinforcement learning (Meta-RL) named BO-MRL, which aims to enhance policy adaptation through a universal policy optimization algorithm. The framework is designed to improve data efficiency by implementing multiple policy optimization steps on a si... | Rebuttal 1:
Rebuttal: Thanks very much for your time and effort in reviewing our work. Thanks for your suggestions to make our manuscript better. We address your concerns as follows.
>**Weakness 1. Complexity of Implementation The proposed framework, while theoretically sound and empirically validated, may be complex ... | Summary: This paper proposes an optimization framework to learn the meta-prior for task-specific policy adaptation.
Strengths: 1. The proposed method claims to be data-efficient in terms of data collection developed only using one-time data collection. Also, this paper aims to solve the RL problem as a bilevel optimiz... | Rebuttal 1:
Rebuttal: Thanks very much for your time and effort in reviewing our work. Thanks for your suggestions to make our manuscript better. We address your concerns as follows.
>**Weakness 1. The proposed method heavily depends on the minimization problems of eq. 1 or eq. 2, which minimizes the distance between ... | Summary: The paper proposes a bilevel optimization algorithm for Meta-RL, which unlike MAML, implements multiple-step policy optimization on one-time data collection. In addition, the paper provides an upper bound on the expected optimality gap over the task distribution, that quantifies the model’s generalizability to... | Rebuttal 1:
Rebuttal: Thanks very much for your time and effort in reviewing our work. Thanks for your suggestions to make our manuscript better.
>**Weakness 1. Comparison to state-of-the-art: the paper compares the proposed algorithm to MAML, EMAML, and ProMP, which were proposed 5 years ago. Since meta-RL is a very... | null | null | Rebuttal 1:
Rebuttal: We are grateful and indebted for the time and effort invested to evaluate our manuscript by all reviewers, and for all the suggestions and reference recommendations to make our manuscript a better and stronger contribution. Please find below our detailed replies to all the reviewers' comments.
In... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FFAM: Feature Factorization Activation Map for Explanation of 3D Detectors | Accept (poster) | Summary: This paper proposes a feature factorization activation map to explain 3D detectors. It uses non-maximum matrix factorization (NMF) to obtain a global concept activation map and then refine it with feature gradients of an object-specific loss. A voxel upsampling strategy is further proposed to upsample sparse v... | Rebuttal 1:
Rebuttal: We greatly appreciate your insightful feedback. Below please find our clarifications in response to your comments.
**Q1.** The only concern is about the application, or the value in applications, of this proposed method. I understand this is a good adaptation and attempt to apply such explanation... | Summary: The paper proposes a method called Feature Factorization Activation Map (FFAM) to provide visual explanations for 3D object detectors based on LiDAR data. This method addresses the interpretability issue in 3D detectors by using non-negative matrix factorization to generate concept activation maps and refining... | Rebuttal 1:
Rebuttal: We greatly appreciate your insightful feedback. Below please find our clarifications in response to your comments.
**Q1.** Real-Time Applicability: How does the computational overhead of FFAM compare to existing methods in real-time applications, especially in autonomous driving scenarios?
**Aut... | Summary: This paper addresses the challenge of explanation and interpretability in 3D detection methods. It introduces a Feature Factorization Activation Map (FFAM), which utilizes non-negative matrix factorization (NMF) and object-specific gradient weighting to generate global and object-specific activation maps at th... | Rebuttal 1:
Rebuttal: We greatly appreciate your insightful feedback. Below please find our clarifications in response to your comments.
**Q1.** The rationale for using NMF is unclear. It appears to be a learning-based, PCA-like method to extract saliency from voxel features. How does it compare to the proposed method... | Summary: The paper proposes a “FFAM” for visual visualization of 3D Detectors. It introduces a non-negative matrix factorization (NMF) to decomposing 3D features into the product of two non-negative matrices. Besides, an object-specific loss is utilized to generate the object-specific saliency maps. Finally, the voxel ... | Rebuttal 1:
Rebuttal: We greatly appreciate your insightful feedback. Below please find our clarifications in response to your comments.
**Q1.** The description of Non-negative Matrix Factorization (NMF) is unclear. When the point cloud is large in scale, NMF may become unstable and exhibit a long convergence time.
*... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FlashMask: Reducing the Complexity of Attention Computation through Sparse Mask Representation | Reject | Summary: This paper proposes a novel method to address the high computational and memory complexity of current large-scale transformers. By adopting a simple yet effective column-wise sparse representation of attention masks, the algorithm achieves reduced memory and computational complexity while maintaining the accur... | Rebuttal 1:
Rebuttal: Thanks for your review.
----------
**For each weakness you mentioned:**
1. It is crucial to highlight the advantages of this method over related work to help readers fully understand its significance. However, in the subsection "Attention Optimization Techniques," the authors only mention the d... | Summary: This paper proposes FlashMask, which accelerates the masked attention mechanism that can reduce the original attention from O(N^2) to O(N) and simultaneously reduces the memory cost. Experimental results show that the proposed FlashMask significantly reduces training time without accuracy degradation.
Strengt... | Rebuttal 1:
Rebuttal: Thanks for your review.
----------
1. Even though FlashMask achieves significant improvement in the memory efficiency of sparse attention, the key idea is similar to FlashAttention, but it is just for sparse attention mechanisms. Based on this fact, the novelty of this paper is not strong. I rec... | Summary: The paper introduces FlashMask, an innovative algorithm designed to address the computational and memory challenges associated with conventional attention mechanisms in large-scale Transformers. FlashMask employs a column-wise sparse representation for attention masks, significantly reducing the computational ... | Rebuttal 1:
Rebuttal: Thanks for your review.
----------
We were sorry that we had not clarified our key points previously. In the SFT/DPO/RM training scenarios, the sparsity of the attention mask is usually natural, but not intended to speed up training while sacrificing accuracy. For examples, as illustrated in Fig... | Summary: This paper proposes FlashMask, a modification of FlashAttention with fixed masks. The paper shows speedup of FlashAttention when using sparse masks in the attention matrix.
Strengths: FlashAttention is an important algorithm, and sparsity in the attention matrix is an important feature. Further study of these... | Rebuttal 1:
Rebuttal: Thanks for your review.
----------
1. The paper seems to make claims that are unsubstantiated by experiments. In the abstract and introduction, the paper claims speedup without sacrificing model quality. However, there is no experiment evaluating model quality in the experiments. This is a criti... | Rebuttal 1:
Rebuttal: 1. The core contribution of this paper lies in proposing FlashMask, an extension of FlashAttention with sparse mask attention speedup, for downstream NLP tasks such as SFT, DPO, and RM. FlashMask introduces a column-based sparse mask representation and develops an efficient CUDA kernel implementat... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Universality of AdaGrad Stepsizes for Stochastic Optimization: Inexact Oracle, Acceleration and Variance Reduction | Accept (poster) | Summary: The paper explores the adaptive gradient methods for solving convex composite optimization problems. The authors propose two main algorithms, UniSgd and UniFastSgd, equipped with AdaGrad stepsizes. They establish efficiency guarantees under various conditions, demonstrating implicit and explicit variance reduc... | Rebuttal 1:
Rebuttal: Thanks for your time and efforts spent on reviewing this manuscript. We
appreciate the feedback and would like to make some comments on the points you
raised in your review.
- [W1]:
We agree that this is a drawback and it was explicitly written in the paper.
Note, however, that $D$ is indeed ... | Summary: This paper proves new convergence rates for stochastic gradient methods with
AdaGrad step-sizes. The authors' build on the fact that AdaGrad step-sizes
adapt to both the smooth and non-smooth settings and extend these results to
show convergence with biased stochastic gradient oracles on functions which are
on... | Rebuttal 1:
Rebuttal: Thanks for the positive evaluation!
## Major remarks
1. It seems there is a certain misunderstanding of Ass. 6, which we hope to clarify.
Basically, Ass. 6 is satisfied for finite-sum problems with (Hölder) smooth components (see also lines 186-194 in the paper for a more general example)... | Summary: This paper demonstrates the universality of AdaGrad in stochastic optimization, presenting adaptive algorithms that converge efficiently without prior knowledge of problem-specific constants. The research contributes novel variance reduction techniques, theoretical proofs, and empirical evidence, showcasing r... | Rebuttal 1:
Rebuttal: Thank you for the positive evaluation of our work. Below you can find the
answers to your questions / comments.
> The prior knowledge is also not required in UniXGrad[28] and AcceleGrad[32].
> What is the difference between them and the proposed algorithm?
From the algorithmic perspective, all t... | Summary: This paper studied how to apply AdaGrad type stepsize for stochastic convex optimization in a unified way. It proposed different algorithms and a general rule for the stepsize. Later, the authors presented different convergence rates under different settings, all of which match the existing best results. Final... | Rebuttal 1:
Rebuttal: Thanks for your time and efforts spent on reviewing this manuscript. We
appreciate the feedback and would like to make some comments on the points you
raised in your review.
## Major remarks
1. Our assumption on the bounded feasible set is satisfied whenever one knows
some upper bound $R$ on ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable comments. We did our best to
answer all the questions and will be happy to continue the discussion if
needed.
After reading the reviews, we had the impression that several aspects of our
work were probably unnoticed or underappreciated (perhaps, due t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DAPE: Data-Adaptive Positional Encoding for Length Extrapolation | Accept (poster) | Summary: This paper proposes a simple learnable positional encoding called CAPE that boosts the length extrapolation performance of Transformer language models.
Strengths: The empirical performance of CAPE is substantially better than previous positional encodings. I also think Figure 1 nicely demonstrates the flexibi... | Rebuttal 1:
Rebuttal: Dear Reviewer QhvW,
Thank you very much for appreciating our work. We will address your concerns below.
**Q1: The speed of training**
A1: We will answer the question in three parts: 1) **The potential way to improve the speed of CAPE**; 2) **The additional training ratio will gradually decrease... | Summary: Considering the fixed parameters of RoPE may lead to the generalization issue, this paper introduce a dynamic position embedding method named CAPE, where position encoding is depend on the input context. Specifically, CAPE enables testing-time adaptation to input context by using a two-layer LeakyReLU neural n... | Rebuttal 1:
Rebuttal: Dear Reviewer NzBk,
Thank you very much for appreciating our work. We will address your concerns below.
**Q1: The efficiency of CAPE**
A1: **With the model size increase, the additional computing cost ratio will decrease, compared to baseline Kerple**. Moreover, the CAPE can even speed up the t... | Summary: The paper proposes context-adaptive positional encoding. The paper proposes a 2-layered MLP to non-linearly integrate positional bias information (can be computed using prior methods like Alibi, FIRE, and Kerple) and query-key content-based dot product values representing semantic relations across different he... | Rebuttal 1:
Rebuttal: Dear Reviewer CfZF,
Thank you very much for appreciating our work. We will address your concerns below.
**Q1: The performance of transformer-xl relative encoding**
A1: We have shown the performance of transformer-xl below, which also presents great length extrapolation performance. Also, the Re... | Summary: This paper offers a doable solution for long context tasks. The paper introduces the Context-Adaptive Positional Encoding (CAPE) method to enhance transformer model adaptability and flexibility in processing long input lengths and contexts. To overcome the limitations of static positional encodings such as Abs... | Rebuttal 1:
Rebuttal: Dear Reviewer tTna,
Thank you for the detailed review. We will address your concerns below.
**Q1: The major concern is in the experimental evaluation part.**.
A1: We have presented the experimental results besides PPL, as shown in Page 9 Section 4.7 and Appendix D Experiments on Chomsky Hierarc... | Rebuttal 1:
Rebuttal: Dear all reviewers:
We sincerely appreciate the reviewers for the time and efforts on the review. We first address some common questions, followed by detailed responses to each reviewer separately. We hope our responses clarify existing doubts. We will really appreciate it if Reviewer tTna can ki... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning | Accept (poster) | Summary: The paper introduces OwMatch, a novel method for open-world semi-supervised learning (SSL). This approach incorporates conditional self-labeling and open-world hierarchical thresholding. Additionally, the paper provides theoretical analyses that demonstrate the unbiasedness and reliability of the label assignm... | Rebuttal 1:
Rebuttal: Thank you for your detailed and valuable suggestions. They play a crucial role in improving our manuscript.
**[W1]** We acknowledge the ubiquity of self-labeling (SL) and adaptive thresholding (AT) techniques, and we drew inspiration from the seminal contributions of TRSSL [6], OpenLDN [7], and ... | Summary: This paper proposes OxMatch, a semi-supervised learning (SSL) algorithm for an open-world setup where unlabelled data might come from outside of the labeled class distribution. The authors combine consistency regularization and self-labeling techniques to address the main challenges in open-world SSL (OwSSL). ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. With your help, our revised manuscript is now clearer and more readable.
**[W1;4;5]** We accept your suggestions regarding improving the alignment between the chart explanations and the text and providing more detailed legends and scales in our figures. We wi... | Summary: This paper proposes OwMatch, a new approach for open-world semi-supervised learning (OwSSL). The key contributions are: (1) A conditional self-labeling method that incorporates labeled data into the clustering process to reduce confirmation bias and misalignment. (2) A hierarchical thresholding strategy to bal... | Rebuttal 1:
Rebuttal: We appreciate your insightful analysis, it has been instrumental in refining our research. We hope that the following responses can make our manuscript clearer and more persuasive.
**[W1]** GCD and its related works indeed focus on a setting similar to OwSSL. However, it's worth noting that GCD-... | Summary: The paper has a novel idea, clear problems, and rich experimental results. It is a good article. However, there are some problems that need to be further optimized.
Strengths: The idea is clear and the problem is prominent.
The experimental results prove the superior performance of the proposed method in open... | Rebuttal 1:
Rebuttal: Your comments are appreciated, and they have been beneficial in enhancing our work.
**[Q1;3;4]** Based on your suggestion, we will revise our manuscript to fit the appropriate writing requirements. First, we will correct the reference citations in detail to avoid them being sentence components. S... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' valuable feedback, which has significantly helped us improve our paper. Below, we provide detailed responses to each reviewer. Additionally, we've included all required tables and figures in a one-page Reference PDF.
Pdf: /pdf/787ed6b7dd7cda4df3f32188437350afbb1ffc55.p... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Distributed Least Squares in Small Space via Sketching and Bias Reduction | Accept (poster) | Summary: Sketching is a technique from randomized numerical linear algebra which compresses an input matrix $A$ by multiplication with a random matrix $S$. Recent works [18] have shown how to characterize the bias in the sketched inverse covariance $(\tilde A^\top\tilde A)^{-1}$, where $\tilde A = SA$ for an input matr... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and questions. We are glad that the reviewer appreciates the simplicity of our algorithmic approach, and that the reviewer finds our technical contributions interesting and useful. Below, we provide clarifications on the reviewer's remarks, including on the d... | Summary: Sketched least squares involve estimating the term $(X^TX)^{-1}$ which has a high bias when the sketch matrix $S$ is not sub-Gaussian. This paper gives a sparse sketching method using a LESS embedding which runs in optimal space and current matrix multiplication time, where $S$ is sparse, and constructed based... | Rebuttal 1:
Rebuttal: We thank the reviewer for a careful read and detailed comments. We will address all of the typos and presentation suggestions in the final version.
- **Additional experiments.** We include additional experiments with other sketching methods (all of which are more computationally expensive than the... | Summary: The paper studies the least squares regression task and improves the space and communication amount in a distributed setting to be independent of $\epsilon$. The key to achieve this is by sketching the data in blocks that have an $\epsilon$ dependence which can be reduced to only d-dependencies by aggregating ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback, as well as the comments and questions. We will address them in the final version.
- **Definition of bias.** At a high level, we rely on the statistical definition of the bias of an estimator, which is (informally) the difference between the expectat... | Summary: This paper presents new techniques for distributed least squares regression using matrix sketching. The key contributions are:
1. A sparse sketching method that produces a nearly-unbiased least squares estimator in two passes over the data, using optimal space and current matrix multiplication time.
2. Improv... | Rebuttal 1:
Rebuttal: We thank the reviewer for comments and feedback. We will revise the abstract, and also expand our discussion of applications beyond least squares as outlined below.
- **Extensions to other loss functions beyond least squares.** In addition to least squares, we provide a more broadly applicable res... | Rebuttal 1:
Rebuttal: Thanks to all reviewers for the positive feedback and comments. We responded to those comments in the individual responses to each reviewer. We also provided additional experimental results on four different sketching methods (included in the PDF), and discussed the implications of our theoretical... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Sequoia: Scalable and Robust Speculative Decoding | Accept (spotlight) | Summary: This paper introduces a speculative decoding method Sequoia, which uses a novel sampling and verification method that outperforms prior work across different decoding temperatures. The speedup of Sequoia is large.
Strengths: 1. This paper discuss the proposed method in a detailed way. The algorithm is novel a... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful review. We are glad that the reviewer found our algorithm novel and the evaluation thorough and sound. We have tried to carefully address your questions. We hope the reviewer may consider raising their score in light of our response.
### Q1: Batch serving s... | Summary: The paper proposes SEQUOIA, an algorithm designed to improve the efficiency of serving large language models (LLMs) through scalable and robust speculative decoding. The SEQUOIA algorithm introduces a dynamic programming method to construct optimal token trees for speculation, enhancing scalability. Additional... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful review. We are glad that the reviewer appreciates our dynamic programming based tree search algorithm as well as the robustness of sampling and verification algorithms. We have tried to carefully address your questions. We hope the reviewer can consider rais... | Summary: This paper proposed an improvement on the tree-based speculative decoding methods to make the accepted tokens scale roughly in logarithm to the number of tokens generated by the draft model.
The author provided theoretical and empirical justifications for the tree construction and verification procedures. Th... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful review. We are glad that the reviewer found our work to have strong theoretical guarantees for scalability and also have good empirical results. Also thanks for noticing that our dynamic programming algorithm can be pre-computed offline. We have tried to car... | Summary: In this paper, the authors propose a novel speculative decoding algorithm to accelerate LLM generation. By leveraging the positional acceptance assumption and dynamic programming, they can determine an optimal tree topology with the tree size and depth. The experiments demonstrate that the proposed method outp... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful review. We are glad that the reviewer found our work **easy-to-follow** and having **comprehensive empirical results as well as theoretical analysis**. We have tried to carefully address your questions. We hope the reviewer can consider raising your score in... | Rebuttal 1:
Rebuttal: We thank all the reviewers [**R1** (uMCB), **R2** (qoEg), **R3** (XEn9), **R4** (yRTd)] for their thoughtful and highly supportive feedback! We were glad that the reviewers found the work **novel and meaningful** [R1,R3,R4], believed our theoretical analysis was **detailed, robust and strong** [R1... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Moving Off-the-Grid: Scene-Grounded Video Representations | Accept (spotlight) | Summary: This work presents a video representation learning approach where tokens are decoupled from explicit grid locations in the video sequence. Rather than simply extract patches to construct tokens and apply self-attention across blocks of frames, this proposed model predicts sub-pixel motion given a history of f... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and constructive feedback. We were pleased to hear that you consider our contribution to be novel, general, and the ideas widely applicable. We appreciate that you recognize the strength of our qualitative experiments to help understand the method’s working.
**“... | Summary: This work discusses Scene-Grounded Video Representations. Compared with current vision models that make each layer consist of tokens organized in a grid-like fashion, the authors introduce Moving Off-the-Grid (MooG), a self-supervised video representation model that proposes an alternative approach. The novelt... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and constructive comments.
**“how to prove the effectiveness of the Corrector module?”**
Thank you for your comment. Note that the corrector is the only part of the model that has access to the observation at the current time-step, i.e. to “correct” the predict... | Summary: The authors present a self-supervised video representation learning strategy. A grid-structure free feature extractor is trained using a next frame prediction objective. A corrector module extracts per-frame features. A predictor module predicts the next frame features. A decoder (with suitable grid free archi... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and constructive comments. We are pleased to find that you consider MooG to be an interesting and novel idea for self-supervised representation learning from videos, and that the presentation was clear to you.
**“Please compare a) on generic tasks (evaluate lear... | Summary: This paper proposes a field-based method for video representation learning called MooG. Instead of propagating a discretized grid of features for every pixel location in the video, the method updates an arbitrary set of state tokens. These state tokens are used to parametrize a context dependent PerceiverIO-st... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and constructive comments. We are pleased to hear that you found MooG interesting and novel, and recognized its considerable improvement over baselines in the frozen setting. We are glad that you found the writing and visualizations of high quality.
**“One base... | Rebuttal 1:
Rebuttal: We thank the reviewers for their helpful comments.We are pleased to hear that the reviewers find our paper to be well-presented (MZrN13, RPGH11, ykZT08), interesting (MZrN13, r1sm12), and novel (MZrN13, r1sm12, ykZT08). Reviewers MZrN13 and ykZT08 positively highlight the quality of the experiment... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Reranking Laws for Language Generation: A Communication-Theoretic Perspective | Accept (spotlight) | Summary: This paper proposes a reranking principle for language generation from a communication-theoretic perspective. The paper conceptualizes the generator as a sender transmitting multiple descriptions of a message through parallel noisy channels. A receiver is designed to decode the message by ranking the descript... | Rebuttal 1:
Rebuttal: Thank you for your review and suggestions. We are happy that you found our paper well written and the motivation and theoretical analysis interesting. We understand that your main concerns about our paper are related to our empirical validation—we address them below. We hope that this clarifies an... | Summary: A number of recent works in language generation can be framed as proposing two step methods, with a method to generate proposal strings, and another to rank these strings before choosing the best one to be output (this includes, e.g., MBR decoding).
This paper analyses this practice with a communication-theore... | Rebuttal 1:
Rebuttal: Thank you for your positive review and suggestions. We are glad that you found our paper well written and easy to follow, and the theoretical analysis interesting. We address below your main concerns.
> “limited empirical evaluation, with only two tasks, one generator model, and two reranking met... | Summary: This paper proposes to regard generator-reranker LLMs, i.e., LLMs generating multiple outputs and then reranking them, as communication systems. The idea is to consider the outputs noisy with the objective for the reranker to find the less noisy one.
Strengths: - the approach is very flexible. It doesn’t depe... | Rebuttal 1:
Rebuttal: Thank you for your positive review and suggestions. We are happy that you found our approach to be well formalized and flexible, the parallel with communication theory useful, and the experiments relevant.
We agree that the paper would benefit from more discussion about how our results can be u... | Summary: The paper provides a framework for understanding the theoretical properties of generator-reranker systems for language generation. It relates the reranking process to error correction during the decoding of messages in noisy channels, a concept that has been well-studied in communication theory. Explicitly, th... | Rebuttal 1:
Rebuttal: Thank you for your positive review and suggestions. We are glad that you found our paper to be very well written, the math to be clear and sound, and our method to have practical use. We address below your concerns about our paper.
> “there is not the same binary notion of acceptable/unacceptable... | Rebuttal 1:
Rebuttal: Dear reviewers,
We appreciate the time and effort you have taken to review our paper and provide constructive feedback. We are pleased to see that our work has been positively received.
The main weakness pointed out by the reviewers is that we illustrate our reranking laws on two applications ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Super Consistency of Neural Network Landscapes and Learning Rate Transfer | Accept (poster) | Summary: The paper investigates the loss landscape of the model with scaling width or depth, through observing the largest eigenvalue of the Hessian marix and the NTK matrix. Authors show empirically that the loss Hessian evolve almost identically for different model sizes (which is named Super consistency), however, ... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the strong overall score and for marking “excellent” our paper across all the three evaluation axes.
On the weakness:
1. **Larger scale experiment**: We managed to scale the Hessian computation up to 300 million parameters in a Transformer model trained on Wikitext wit... | Summary: The authors argue that the top eigenvalues of the loss Hessian stabilize throughout training under width and depth muP scaling. This phenomenon is called the Super Consistency of the loss landscape. The authors provide theoretical convergence guarantees and empirical experiments supporting their claims. The le... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the importance and novelty of our results. Here we address the concerns:
1. **A more diverse set of experiments**: we would like to gently push back on this point. We have already performed experiments on CIFAR-10, Imagenet (vision), and wikitext (language)... | Summary: This paper proposes the concept of super consistency, which describes the stable properties of the loss landscape during training. By analyzing the maximum eigenvalue of the Hessian matrix, it is found that the sharpness under the μP and Depth-μP frameworks remains super-consistent and stable near the threshol... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the careful assessment of our paper, and for highlighting the extensive suite experiments that we run, including different scaling regimes ($\mu$P, Depth $\mu$P, SP, NTK, etc..) and architectures. On the weaknesses:
1. **Theoretical limitation**: please notice that our p... | Summary: The authors conduct a series of experiments in which they investigate for which attributes of the neural network the gap between its infinite-width (or infinite depth) value and the finite counterpart grows or shrinks during training. Among other attributes, they look at the training loss, largest loss Hessian... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the detailed feedback and for the further discussion that we anticipate. Also, we thank the reviewer for acknowledging certain strengths of the paper, such as the importance and validity of our findings in the context of understanding neural networks’ loss lands... | Rebuttal 1:
Rebuttal: We thank the reviewers for their initial reviews and interesting comments. In particular, we summarize that all the reviewers have acknowledged the validity and importance of Super Consistency as a novel and important property for understanding the loss landscape of neural networks at different sc... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Discovering Sparsity Allocation for Layer-wise Pruning of Large Language Models | Accept (poster) | Summary: This paper introduces DSA, an automated framework for determining layer-wise sparsity in large language models (LLMs). The approach aims to enhance pruning techniques by using an evolutionary algorithm to discover optimal sparsity allocation functions, thereby improving model performance on various tasks. The ... | Rebuttal 1:
Rebuttal: **Dear Reviewer VeXf ,**
Thanks for the valuable feedback. We have tried our best to address all concerns in the last few days. If the reviewer finds our response adequate, **we would appreciate it if the reviewer considers raising the score**. Please see our responses below one by one：
> **Q1... | Summary: This article introduces DSA (Discovering Sparsity Allocation) which is designed to automate the discovery of sparsity allocation schemes for layer-wise post-training pruning in large language models (LLMs).
Strengths: 1. This paper presents a framework for automatically discovering effective sparsity allocati... | Rebuttal 1:
Rebuttal: **Dear Reviewer gYq,**
Thanks for the valuable feedback. We have tried our best to address all concerns in the last few days. If the reviewer finds our response adequate, **we would appreciate it if the reviewer considers raising the score**. Please see our responses below one by one:
------
> ... | Summary: This paper presents DSA, which models layer importance to sparsity ratios, and integrates the allocation function discovered by evolutionary algorithms into various methods, resulting in significant performance improvements.
Strengths: This manuscript is a qualified paper, i.e,
The method seems technically so... | Rebuttal 1:
Rebuttal: **Dear Reviewer s93u,**
Thanks for constructive comments. We have tried our best to address all concerns in the last few days. If the reviewer finds our response adequate, **we would appreciate it if the reviewer considers raising the score**. Please see our responses below one by one：
------... | Summary: This paper introduces DSA, an automated framework for discovering optimal sparsity allocation schemes for layer-wise pruning in LLMs. The proposed framework uses per-layer importance statistics and an evolutionary algorithm to explore effective allocation functions, which are then integrated into various pruni... | Rebuttal 1:
Rebuttal: **Dear Reviewer of38,**
Thanks for constructive comments. We have tried our best to address all concerns in the last few days. If the reviewer finds our response adequate, **we would appreciate it if the reviewer considers raising the score**. Please see our responses below one by one：
------... | Rebuttal 1:
Rebuttal: # **General Response**
**Dear Reviewers, Area Chairs, Senior Area Chairs and Program Chairs,**
We sincerely thank all reviewers for their positive feedback and constructive comments. **In the initial review, 3 Positive ratings are given.** Reviewers positively acknowledge **the novelty of the i... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper introduce a novel model pruning algorithm, DSA, aiming to prune unimportant model weights to increase the sparsity of models. Unlike previous model pruning methods which assign the same sparsity ratio for all layers. DSA proposes a method to calculate the sparsity allocation for different layers, wh... | Rebuttal 1:
Rebuttal: **Dear Reviewer 1f5c,**
Thanks for the valuable feedback. We have tried our best to address all concerns in the last few days. If the reviewer finds our response adequate, **we would appreciate it if the reviewer considers raising the score**. Please see our responses below one by one：
------
>... | null | null | null | null | null | null |
Aligning to Thousands of Preferences via System Message Generalization | Accept (poster) | Summary: In this paper, the authors introduce a novel method to align LLMs with diverse user preferences without requiring continual retraining for each specific preference. The approach utilizes a unique system message protocol that guides LLMs to produce responses tailored to specific, nuanced user preferences.
Stre... | Rebuttal 1:
Rebuttal: Dear reviewer bEUV, we deeply appreciate your valuable feedback.
We provide responses to your comments in the weaknesses (W) and questions (Q) section.
---
**Details of human annotation/evaluation (W1)**
We employ 14 undergraduate students aged 20-24 from various majors, consisting of 6 males ... | Summary: This paper addresses the issue that humans inherently have diverse values, while current LLMs are primarily aligned with general public preferences such as helpfulness and harmlessness. Previous work has trained new reward models (RMs) and LLMs for individual preferences, which is time-consuming and costly.
T... | Rebuttal 1:
Rebuttal: Dear reviewer k2dN, thank you for the important remarks that will help us better shape our contributions.
We will address your concerns in weaknesses (W1, W2) and questions (Q1, Q2) below.
---
**Redefining the scalability of our approach (W1)**
Creating a synthetic dataset can indeed be costly,... | Summary: This paper aims to align Large Language Models (LLMs) with individual user preferences at scale. The authors propose a paradigm where users specify their values within system messages to guide the LLM's generation behavior. To address the challenge of generalizing to diverse system messages, the authors create... | Rebuttal 1:
Rebuttal: Dear reviewer uZmQ, we appreciate your thoughtful comments.
Our responses to the issues raised in the weaknesses (W1) and questions (Q1) sections are detailed in the global responses (G2: Diversity, G4: Bias).
---
**Diversity of User Preferences (Q1)**
We measured the ROUGE-L score among user ... | Summary: This paper introduces JANUS, a novel approach to aligning large language models (LLMs) with diverse individual user preferences without retraining. The key contributions are the MULTIFACETED COLLECTION: A dataset of 192k diverse system messages reflecting varied user preferences, paired with 65k instructions. ... | Rebuttal 1:
Rebuttal: Dear reviewer eXeU, we deeply appreciate your constructive feedback.
Here we address the weaknesses (W) and questions (Q) that you raised on our work.
---
**Discussing the potential risks of allowing flexible preference specification and available safeguards (W1,Q1)**
We agree that training a m... | Rebuttal 1:
Rebuttal: We provide extra analyses on Multifaceted Collection and Janus in this global response, which we believe will comprehensively address various concerns about our approach. Four aspects of our method are further investigated: quality (G1), diversity (G2), safety (G3), and bias (G4). We attach a **PD... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MIDGArD: Modular Interpretable Diffusion over Graphs for Articulated Designs | Accept (poster) | Summary: This paper presents a diffusion-based framework for generating articulated objects represented as part graphs. Different from prior work [35] that generates both the part shapes and graph structures simultaneously, this work introduces a two-stage strategy that first generates the structure with per-part condi... | Rebuttal 1:
Rebuttal: **Part-level generation**
Part-level generation requires conditioning on the articulation graph, since the appearance of a part depends on its role within the graph. In future work, we aim to test further possibilities to condition the part-generation on already existing parts, e.g. by framing it... | Summary: This work tackles the problem of generating articulated 3D assets that are animatible. The authors mention that their generated shapes are directly compatible with existing physics simulation tools, i.e. MuJoCo. To this end, they first propose a structure generator, which conditionally or unconditionally gener... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback that help us to improve the clarity and quality of our paper.
**Writing (comment: I would limit the contribution bullets to the technical contribution of this work rather than focusing on results and open sourcing)**:
Thank you for your suggestion. We full... | Summary: This paper proposes several interesting improvements over existing articulated object modeling and highlights higher-quality part generation.
Specifically, an articulated object is parameterized to a graph where parts are nodes and joints are edges. This paper proposes a multi-model part VAE for generating hi... | Rebuttal 1:
Rebuttal: Thank you for highlightning the strength of our approach. We are delighted that you find the MuJoCo simulations in the supplementary repository impressive and recognize the significance of our technical improvements, including shape representation, Plücker manifold encoding, and bounding box align... | Summary: This work addresses the task of 3D asset generation for articulated objects.
This work aims to enhance the prior approach by achieving three main objectives: 1) increasing the interpretability and controllability of the generation process; 2) generating more natural joint motions and 3) reducing violations of... | Rebuttal 1:
Rebuttal: **Weakness 1.1** While our structure generator builds up on NAP \cite{lei2023nap} and also applies a denoising diffusion process on the graph, the representation and generative process are fundamentally different:
1.) Our approach *modularizes* articulated asset generation. This fundamentally dive... | Rebuttal 1:
Rebuttal: We would like to thank the editors and reviewers for the time they devoted to reviewing our paper and for their valuable feedback and constructive criticism. We have endeavored to address every suggestion and additional comment to the best of our abilities. Below, we provide a summary of our appro... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Decision-Making Behavior Evaluation Framework for LLMs under Uncertain Context | Accept (poster) | Summary: This paper proposes a comprehensive framework to evaluate LLM's decision-making behavior under uncertain contexts. By leveraging computational models from behavioral economics, as well as the experiment paradigms, the authors investigated the risk preferences, probability weighting, and loss aversion of LLMs f... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful review and the detailed feedback provided. Below, we address each of the concerns and questions raised in the weaknesses section.
1. Lack of Human Data:
We agree that including more human data would significantly enhance our study. The bottom row in Table 6 shows hu... | Summary: This paper presents a novel framework for evaluating the decision-making behavior of large language models (LLMs) under uncertain contexts, grounded in behavioral economic theories. The authors conducted experiments to estimate risk preference, probability weighting, and loss aversion for three commercial LLMs... | Rebuttal 1:
Rebuttal: We appreciate your thorough review and insightful comments. Below, we address each of the questions raised in the weaknesses section.
1. Regarding the Analysis of Potential Causes for Observed Variations:
As mentioned in our discussion section (lines 308-313), we have identified several potentia... | Summary: In this paper, the paper the authors model the decision-making behavior of certain open-source LLMs on lottery selection tasks. They further extend this work, by repeating similar analysis after priming the models with demographic information. Results indicate that the different LLMs differ in their risk profi... | Rebuttal 1:
Rebuttal: We sincerely appreciate your careful review and valuable feedback. Your comments are very insightful and have provided us with the opportunity to clarify our work. Before addressing the specific questions, we would like to emphasize that we recognize that LLMs are machines, not humans. However, by... | Summary: This paper proposes a framework to evaluation the decision making behavior of LLMs. It focuses on ChatGPT4Turbo, Claude3Opus and Gemini1pro. The results show that LLMs exhibit patterns similar to humans. Impacts of socio-demographic features are also analyzed and the results show that different LLMs can vary f... | Rebuttal 1:
Rebuttal: We appreciate the time and effort you have taken to review our paper and provide constructive feedback. Below, we address each of your comments and concerns:
Clarification of Summary:
Our paper presents a framework for evaluating the decision-making behavior of LLMs under uncertainty grounded in ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models | Reject | Summary: The paper introduces generalized isntruction tuning (GLAN), an approach for synthesizing instruction tuning data using a taxonomy-based approach. GLAN generates synthetic instruction data from pre-curated taxonomy of human knowledge and capabilities and aims to create diverse and broad-ranging instruction data... | Rebuttal 1:
Rebuttal: Thanks for your thorough evaluation and insightful feedback on our submission.
---
> While the paper addresses generalization, there is a risk that the generated synthetic data might overfit to the taxonomy's structure, potentially missing out on more nuanced, real-world instructions
GLAN is cu... | Summary: This paper introduces GLAN, a general and scalable method for instruction tuning of Large Language Models (LLMs). GLAN employs a top-down approach to generate high-quality instruction tuning datasets. Experiments across various benchmarks demonstrate that GLAN performs comparably to other existing methods.
St... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our submission and providing constructive feedback.
---
> The novelty is limited as similar top-down designs have been utilized in many previous works.
Regarding "similar top-down designs in previous works", could you please share specific references or e... | Summary: This paper proposes a generalized way of creating instruction data. The high-level motivation is to take inspiration from how curriculum is designed for human learning into a taxonomy of subjects and use the same to prompt an off-the-shelf LLM to create data. GLAN does not need seed examples, or pre built-taxo... | Rebuttal 1:
Rebuttal: We appreciate your detailed review and the valuable suggestions provided for improving our work.
---
> No use of actual human curriculum
We did not use human curriculum explicitly to build our taxonomy because:
1) Initially, we intend to automate the whole generation process and found GPT-4 is... | Summary: ## Overall summary
- This paper introduces GLAN, a method for enhancing LLMs by generating synthetic instruction data using a taxonomy of human knowledge and capabilities. GLAN constructs this taxonomy by decomposing knowledge into fields and disciplines, leveraging LLMs for generating a comprehensive syllabus... | Rebuttal 1:
Rebuttal: Thank you for your careful review and the thoughtful comments on our submission.
---
> However, I am wondering if there are newer topics, for example (within the medical area, we have the new topic called "Covid-19".) Since GLAN is very dependent on LLMs, the main area of concern would be ensurin... | Rebuttal 1:
Rebuttal: We thank reviewers for your valuable feedback; in this general rebuttal, we address common concerns and questions raised.
**Regarding Computational Cost**
“8 days using 32 A100 GPUs” is the cost of fine-tuning Mistral on the 10 million instructions we generated. We do not know the computational ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DeMo: Decoupling Motion Forecasting into Directional Intentions and Dynamic States | Accept (poster) | Summary: In this paper, a decoupled decoding process integrating intention and future state querying is proposed for motion prediction task. Through hybrid design with attention and Mamba, the proposed DeMo framework achieved strong performance on AV2 and NuScenes benchmarks.
Strengths: 1. A novel decoupled query desi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review and the suggestions for improvement. Below are our responses to the reviewer’s comments:
**Q1: Claim for SOTA performance.**
Thank you for your attention. Regarding the Contemporaneous Work LOF [1], it was released on arXiv on June 20, 2024, after we... | Summary: The manuscript presents a novel decoupling method for motion forecasting tasks, where the directional intentions are predicted first and the dynamic states following the predicted direction are predicted accordingly. The proposed solution is easy-to-follow, the model size is small, and the experimental results... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review and the suggestions for improvement. Below are our responses to the reviewer’s comments:
**Q1: About loss function.**
Thank you for your careful consideration of our work. As indicated in the right part of Table 5 in our paper, each loss component in... | Summary: DeMo: Decoupling Motion Forecasting into Directional Intentions and Dynamic States introduces a state of the art model architecture for motion forecasting (predicting the future trajectories of road actors for the purpose of autonomous driving). The authors make two notable contributions. First they provide ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review and the suggestions for improvement. Below are our responses to the reviewer’s comments:
## _Response to Weaknesses._
**1. Results: Results on WOMD (Waymo).**
We provide results on WOMD using the settings in UniTraj [1], as shown below. The results ... | Summary: The paper presents DeMo, a novel framework for motion forecasting in autonomous driving systems. DeMo decouples the motion forecasting task into two distinct components: mode queries for capturing directional intentions and state queries for modeling dynamic states over time. This separation allows DeMo to sep... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review and the suggestions for improvement. Below are our responses to the reviewer’s comments:
## _Response to technical descriptions._
**Q1: About Eq. (2) and related symbols meaning.**
For the meaning of {$t_1,…,t_{T_s}$}: as in Line 135, these represen... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Local Anti-Concentration Class: Logarithmic Regret for Greedy Linear Contextual Bandit | Accept (poster) | Summary: This paper analyzes a Greedy bandit algorithm, in the context of a linear bandit problem where (1) a regression parameter is unknown and fixed for the experiment and (2) the $K$ arms from which the decision-maker can choose are sampled at the beginning of each step $t$, from a fixed context distribution. Recen... | Rebuttal 1:
Rebuttal: Thank the reviewer for taking the time to review our paper and for the comments. However, we believe there is a fundamental disagreement between the reviewer's comments and the focus of our study. We hope to remedy this through open-minded discussion. We strongly believe and remain very confident ... | Summary: This paper aims to expand the range of distributions that can be used efficiently in exploration-free greedy linear contextualized bandits. For this purpose, a new condition called Local-Anti Concentration is introduced. It is claimed that different distributions from the exponential family satisfy this proper... | Rebuttal 1:
Rebuttal: Thank you for your feedback and for the opportunity to discuss our work with you. Most of your feedback appears to be clarifications and suggestions for stylistic edits or minor typos, which we appreciate. However, none of your comments seem critical enough to warrant a rating of "Reject: For inst... | Summary: The paper proposes a novel condition for context distribution, called *Local Anti-Concentration (LAC)*. Under LAC, the authors prove the regret of greedy algorithms for stochastic contextual linear bandits is $\mathcal{O}(\mathrm{poly} \log T)$, without additional margin assumption. The efficacy of the greedy ... | Rebuttal 1:
Rebuttal: Thank you very much for recognizing the value of our results. We appreciate your feedback and are happy to provide our responses to your comments.
---
**[W1]**
We are happy to address your comment and assure you that this should not be considered a weakness. Context distributions with discrete s... | Summary: The paper addresses the problem of linear contextual bandits with randomly generated contexts from a distribution
$f$. The goal is to determine under which conditions on $f$ a greedy algorithm (outlined in Algorithm 1) achieves reasonable regret.
By introducing the notion of Local Anti-Concentration (LAC) in... | Rebuttal 1:
Rebuttal: Thank you very much for recognizing the value of our results. We appreciate your feedback and are happy to provide our responses to your comments.
---
**[W1]**
In order to validate optimality of contextual bandit algorithms under stochasticity, we need to derive proper lower bounds. However, exis... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DeBaRA: Denoising-Based 3D Room Arrangement Generation | Accept (poster) | Summary: The authors present DeBaRa, a diffusion-based framework for indoor scene generation, and a self-score evaluation strategy to select conditioning input. They demonstrate the effectiveness of their approach on scene synthesis and several downstream tasks.
Strengths: + The empirical results are promising.
Weakn... | Rebuttal 1:
Rebuttal: We thank reviewer `sfxx` for their time and feedback. We address the reported weaknesses and questions in the following response:
> Insignificant Architectural Difference: The architectural differences between DeBaRa and other diffusion-based baselines (e.g., DiffuScene) are not substantial.
Fir... | Summary: This paper studies 3D room arrangement/layout generation. It proposes DeBaRA, a diffusion-based generative model, which can generate layouts given the list of furniture and the floor map of the room. The proposed method provides good results and is able to work in various scenarios: layout generation, LLM-guid... | Rebuttal 1:
Rebuttal: We thank reviewer `Fsdh` for their time and positive feedback. We address the reported concerns in the following response:
> It would be better if the ablation results could be provided for the following designs: EDM v.s. DDPM/DDIM, designed 3D spatial objective v.s. simple MSE, different CFG sca... | Summary: This paper proposes a method for room layout generation given objects and a floor plan using score-based EDM. First, three encoders are used to encode objects, the floor plan and the noise respectively. These encoded latents are then given as input to a noise based scene encoder which decodes into the output l... | Rebuttal 1:
Rebuttal: We thank reviewer `xNz4` for their time and feedback. We address the reported weakness in the following response:
> While using a permutation invariant Chamfer loss is intuitive, I believe it should still be ablated w.r.t just the standard chamfer loss
Evaluating the impact of our semantic-aware... | null | null | Rebuttal 1:
Rebuttal: # Response to all reviewers
We would like to thank reviewers for their time and insightful feedbacks and are pleased that they recognized our submission to propose a **"novel diffusion-based generative model, which is effective in the aimed task**" with an "**alternative pipeline which adds more ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
EM Distillation for One-step Diffusion Models | Accept (poster) | Summary: This work proposes EM Distillation which uses the idea of expectation maximization (EM) to distill a pretrained diffusion model. Naive adaptation of EM algorithm can be computationally expensive as it requires sampling from the teacher diffusion model (which can be slow). This work proposes an alternate approa... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback! It is very encouraging to know the reviewer liked the idea of combining Langevin updates with noise reduction, as well as our experiment results.
**[Computational overhead]**
See Global Response
**[Typos]**
We sincerely apologize for the typos in the ... | Summary: The paper proposes EM Distillation (EMD), a maximum likelihood-based approach that distills a diffusion model to a one-step generator model with minimal loss of perceptual quality. Notably, in EMD, the generator parameters are updated using samples from the joint distribution of the diffusion teacher prior and... | Rebuttal 1:
Rebuttal: Thank you for the feedback! We are very glad to see the reviewer appreciated EMD’s flexibility to trade off training efficiency and final performance by adjusting the MCMC steps. Below we respond to questions and concerns:
**[Theoretical justification for noise cancellation]**
We agree that ther... | Summary: This paper introduces a novel distillation method for converting a diffusion process into a one-step generator. The theoretical foundation is closely tied to the Expectation Maximization (EM) algorithm. The authors aim to minimize the forward Kullback-Leibler (KL) divergence between the target generator distri... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. It is our honor to know the reviewer liked the framework, exposition and technical inventions in our paper. One small correction we want to make in the summary is that in the expectation step, Markov Chain Monte Carlo (MCMC) sampling is initialized with s... | Summary: This paper introduces the EM Distillation (EMD) method, which efficiently distills diffusion models into a one-step generator model. It utilizes a maximum likelihood approach grounded in Expectation-Maximization (EM) and maintains good image generation quality. The method incorporates a reparametrized sampling... | Rebuttal 1:
Rebuttal: Thank you for the feedback! We feel very encouraged to receive the recognition that EMD provides a novel perspective on the distillation problem. Below we respond to your questions and stated weaknesses:
**[Overhead for using MCMC in training]**
See Global Response
**[Comparison with SiD]**
Th... | Rebuttal 1:
Rebuttal: # Global Response #
We would like to thank all reviewers for your careful and helpful feedback! Specifically, we want to express our appreciation to reviewer eeRQ for recognizing the motivation of using forward KL, to reviewer 3oLm for identifying the novel perspective that EMD offers on the dist... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper distill the diffusion model into single step generator through forward KL (mode-coverage) divergence. Apart from previous reverse KL divergence, it requires the joint samples z, x from student distribution. To do this, this paper utilize MCMC method to achieve the samples (z, x).
Strengths: 1. Impl... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We appreciate the acknowledgment of our motivation in using forward KL and the corresponding validation in the ablation between EMD-16 and EMD-1. However, we would like to point out a misunderstanding in the summary “Apart from previous reverse KL divergen... | null | null | null | null | null | null |
Loki: Low-rank Keys for Efficient Sparse Attention | Accept (poster) | Summary: This method propose the PCA based attention score approximation for top-k attention. Perform PCA on offline dataset, and store the PCA vectors for inference.
Strengths: This work easily makes a QK approximator without gradient-based training but with only simple PCA for top-k attention selection.
Weaknesses:... | Rebuttal 1:
Title: I fixed the .gz
Comment: I found out that the `tar.gz` file seems forced to be renamed `.gz` in OpenReview. Don't worry about my question 1, it is resolved by myself right now...
If possible, I will look into it during the discussion period. Thank you for your great work, and sorry for my mistake.
... | Summary: This paper reveals that key vectors lie in a significantly lower-dimensional space. Inspired by this finding, the author approximates the computation of the original attention score using PCA, then selects the top-k keys based on the approximate attention scores. Experiments across different models and dataset... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and appreciate that they recognize the potential impact of our low-dimensional observation and the strength of our evaluation.
### **Weaknesses**
**There are some typos (lines 44, 150) in this article, and some figures are unclear with text overlaps (Figur... | Summary: The paper introduces a method for approximating attention in LLMs, with the benefit of improved inference efficiency. The insight is to focus on the dimensionality of the key vectors computed in the attention block. Principal component analysis reveals that the keys lie in a low dimensional space. This gives r... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and appreciate that the reviewer finds our observation of the low intrinsic dimensionality of keys insightful and supported by theoretical analysis and extensive evaluation.
### **Weaknesses**
**The method does not seem to reduce the memory usage**
We agr... | Summary: This paper proposes a sparse attention mechanism for large language models by leveraging the low-dimensionality of key vectors in the attention block. The approach ranks and selects tokens in the KV-cache based on attention scores computed in the reduced dimensional space, leading to significant speedups in at... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and appreciate that the reviewer recognizes the strength of our extensive empirical evaluation backed by robust theoretical analysis.
### **Weaknesses**
**Implementation Complexity**:
We agree that achieving speedups with our method requires custom Triton ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable feedback.
### **PCA-TopK's performance on tasks with longer contexts**
We ran the LongBench [1] long-sequence benchmark for the Llama2-7B-Chat model with PCA-TopK and compared its performance with Full Attention in Figure 1 of the rebuttal pdf. Figure 1... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs | Accept (poster) | Summary: The paper proposes a problem specification (OPTO) and a solution (optimizer OptoPrime) as well as a software framework (Trace) for agentic program optimization. The paper demonstrates high performance of the bundle above in solving 5 benchmarks. The paper demonstrates the ability to optimize 3 types of paramet... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and questions.
# Comparison with TextGrad
Please note that we submitted Trace to NeurIPS on May 15 2024, and TextGrad was uploaded to arXiv and Github on June 11 2024. So a direct comparison was impossible.
# Narrative
We considered the narrative you suggest... | Summary: This paper proposes an end-to-end optimization framework, Trace, for the automatic design and updating of artificial intelligence systems. Trace is based on Optimization with Trace Oracle (OPTO), treating the computational workflow of AI systems as a graph of neural networks, which can be updated via backpropa... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and questions.
# Limited Scalability
We agree that scalability is a limitation of OptoPrime, as discussed in Section 6 (Limitations) and Section 7 (Conclusion) where we note the current focus on textualizable problems. However, we wish to clarify that thi... | Summary: The paper introduces Trace, a novel optimization framework that instances the concept of Optimization with Trace Oracle (OPTO). In Trace, the computational workflows is treated as dynamic graphs and rich information, including intermediate results, processing details and computational graph, are used as feedba... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and question.
Yes, in principle, Trace can indeed be adapted for use with other programming languages. The core design of Trace is based on the primitives node and @bundle, which define the nodes and operators, respectively, for the directed acyclic graph (... | null | null | Rebuttal 1:
Rebuttal: Thank you for reviewing this paper. This PDF contains figures and codes of the new virtual home experiments and a correction on the submitted code for running DSpy baseline, which is for addressing Reviewer GPUE's questions.
Virtualhome is a collaborative, stateful environment that requires two... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
3-in-1: 2D Rotary Adaptation for Efficient Finetuning, Efficient Batching and Composability | Accept (poster) | Summary: This paper proposes a 2D rotation adaptation method called RoAd for efficiently fine-tuning large language models (LLMs). RoAd achieves parameter-efficient fine-tuning by rotating representations. Experimental results demonstrate that RoAd performs excellently across multiple benchmarks, reducing the number of... | Rebuttal 1:
Rebuttal: Thank you for your thorough review. We are really encouraged by the highlights:
1. Our proposed method, RoAd, is **simple but efficient**.
2. RoAd **performs excellently across multiple benchmarks, reducing trainable parameters and computational overhead**.
3. RoAd **performs well** on small-scal... | Summary: This paper introduces a novel method for parameter-efficient finetuning, RoAd. By employing a straightforward 2D rotation to adapt LLMs, this paper addresses the challenges of existing parameter-efficient finetuning method for LLMs. Experiments on downstream tasks demonstrate the effectiveness of the proposed ... | Rebuttal 1:
Rebuttal: Thank you for your time, effort, and thorough review. We appreciate the positive feedback and are encouraged by your highlights:
1. We propose a **novel** PEFT method that efficiently adapts LLMs with **a minimal number of trainable parameters**.
2. Our method, RoAd, **enhances both batching effi... | Summary: This paper proposes a parameter-efficient finetuning method named RoAd, to address two challenges of current methods. The first challenge is the efficient deployment of LLMs equipped with multiple task- or user-specific adapters. The second one is the interpretability of LLMs. RoAd employs a straightforward 2D... | Rebuttal 1:
Rebuttal: Thank you for your time, effort, and thorough review. We appreciate the positive feedback and are encouraged by your highlights:
1. Our work is **well-motivated** on two challenges of existing PEFT methods.
2. Our method, RoAd, achieves **impressive results, surpassing other PEFTs in various task... | Summary: This paper proposes a novel parameter-efficient fine-tuning method called RoAD, aimed at addressing the challenges of efficient deployment of LLMs that require multiple adapters for distinct needs and enhancing the interpretability of LLMs. The motivation behind this approach stems from the observation that th... | Rebuttal 1:
Rebuttal: Thank you for your time, effort, and thorough review. We appreciate the positive feedback and are encouraged by your highlights:
1. We develop **effective approaches with better scalability and performance** on an important problem. And the achieved **performance is promising** to you.
2. The pap... | Rebuttal 1:
Rebuttal: Here we summarize the new results in the uploaded PDF, you can selectively read them if you are interested. We highlight some for your easy choice.
| Table or Figure | Content | Where for details (i.e. response to which point of which reviewer) |
| :--- | :--- | :--- |
| Table A.1 | **Further b... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Everyday Object Meets Vision-and-Language Navigation Agent via Backdoor | Accept (poster) | Summary: The paper proposes a novel backdoor attack paradigm, termed IPR Backdoor, for Vision-and-Language Navigation (VLN) agents. The authors highlight the potential security risks posed by VLN agents in sensitive environments and pioneer an object-aware backdoor attack, embedding triggers into the agent during train... | Rebuttal 1:
Rebuttal: **Weaknesses#1: The author proposed conducting experiments in physical and digital spaces. However, the author's definition of physical seems to be "pasting a physical object into an image," while a more general understanding of physical is to sample in the physical world rather than simply pastin... | Summary: This paper explores the security risks of Vision-and-Language Navigation (VLN) agents, which can be integrated into daily life but may threaten privacy and property if compromised. The author addresses this overlooked issue by introducing an object-aware backdoored VLN. This involves implanting backdoors durin... | Rebuttal 1:
Rebuttal: **Weaknesses#1: Action space. At Line 166, the current action space is based on the current state. Why is it the case? Typical in RL, the action space is fixed and does not change when the states change. If the action space is not fixed, how is it trained in this paper?**
Response:
Thank you for... | Summary: This work proposed a backdoor attack for Vision and Language Navigation task. It works by embedding natural or digital image as trigger into scene representation during agent rollout, and train agent to stop while preserving normal navigation capability using novel loss choices. The method achieved good attack... | Rebuttal 1:
Rebuttal: **Weaknesses#1: Despite situated the backdoor attack in VLN task, the method is not too different from image classification based backdoor attack, and produce stop action is similar to label prediction.**
Response:
Thank you for your question.
Label Prediction Differences: Image classification... | Summary: The paper addresses the security threats posed by malicious behaviors in Vision-and-Language Navigation (VLN) agents. The authors introduce a novel object-aware backdoor attack paradigm, termed the IPR Backdoor, tailored specifically for VLN's cross-modality and continuous decision-making characteristics. This... | Rebuttal 1:
Rebuttal: **Weaknesses#1：The paper could explore more complex and customized abnormal behaviors beyond the STOP action.**
Response#1:
Thank you for your suggestion. Exploring more complex and customized abnormal behaviors is a meaningful task, included in our future research plans (as stated in the Limita... | Rebuttal 1:
Rebuttal: Dear Chairs and Reviewers,
We deeply appreciate your management of this paper and the valuable time you dedicated to offering insightful comments. Our sincere gratitude also goes to all the reviewers for recognizing the importance of our work:
1. The topic is novel, timely, and intriguing.
2. The... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Multi-turn Reinforcement Learning with Preference Human Feedback | Accept (poster) | Summary: The paper presents a novel mirror-descent-based policy optimization algorithm for multi-turn preference-based RL in the tabular setting. It proves the convergence to Nash equilibrium and evaluates the algorithm's performance in the Education Dialogue environment, where a teacher agent guides a student.
Streng... | Rebuttal 1:
Rebuttal: We thank the reviewer for evaluating our work.
We would like to point the reviewer to the general response regarding their concerns with the size of the models and using ChatGPT/Gemini for evaluations. In short, we conducted additional experiments with T5-XL (3B) that solidify the conclusions of ... | Summary: This paper views the problem of RLHF for LLMs fine-tuning from a multi-turn interaction perspective, which is natural and promising. This problem is important and interesting to study. A formulation of multi-turn preference-based RL is given. Based on the formulation of the task and existing methods for the si... | Rebuttal 1:
Rebuttal: We thank the reviewer for carefully assessing our work. We kindly point the reviewer to the general response regarding their concerns with the size of the models – we conducted additional experiments with T5-XL (3B) that solidify the conclusions of our original experiments. We plan to add addition... | Summary: This paper aims to propose a new reinforcement learning with preference data method for multi-turn conversations. The proposed method is based on the assumption that reaching the Nash equilibrium of the current and another policy can lead to good optimization. The proposed method is stated primarily to extend ... | Rebuttal 1:
Rebuttal: We thank the reviewer for carefully reviewing our work. In the following we answer your concerns:
1. Our definition of Nash equilibrium is correct and this is in fact the most natural definition that arises from our multi-turn preference model. Note that only a pure Nash equilibrium satisfies ”one... | Summary: The authors propose reinforcement-learning methods for multi-turn interactions using preference feedback in this paper. They present a mirror-descent-based policy optimization algorithm and prove its convergence to Nash equilibrium.
Strengths: - The authors extend the RLHF paradigm to the multi-turn setting f... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to evaluate our work. Here are the responses to your comments:
Human evaluation: We refer the reviewer to the discussion in the main response on human evaluation and alignment. We briefly repeat the main message there: The main goal of our experimental se... | Rebuttal 1:
Rebuttal: We thank the reviewers for the time and effort put into the reviews. The following address points that appeared in multiple reviews.
# Paper Scope:
It appears to us that the reviewers have dedicated most of their attention to the experimental results, potentially overlooking the paper’s main cont... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data Corruptions | Accept (poster) | Summary: The authors introduce TRACER, a new Bayesian methodology to capture the uncertainty via offline data for robustness against all types of data corruptions. An appealing feature of TRACER is that it can distinguish corrupted data from clean data using an entropy-based uncertainty measure. Experiments are provide... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s insightful and constructive feedback. We have carefully addressed these concerns and accordingly revised the manuscript.
These comments have not only facilitated significant improvements in our manuscript but have also inspired us for further in-depth studies in our fu... | Summary: This paper presents a novel approach called TRACER, aimed at addressing the challenge of learning robust policies from offline datasets that are subject to various data corruptions. The key contribution of this work lies in the integration of Bayesian inference to capture uncertainty within the offline data. T... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's insightful and constructive comments and suggestions. We respond to each comment as follows and sincerely hope that our responses could properly address your concerns. If so, we would deeply appreciate it if you could raise your score. If not, please let us know your f... | Summary: The paper introduces TRACER, a robust offline reinforcement learning (RL) algorithm designed to address the challenges posed by data corruptions in offline datasets. The corruptions can be
realized in form of states, actions, rewards, and dynamics corruption. The proposed methodology uses Bayesian inference to... | Rebuttal 1:
Rebuttal: We appreciate your insightful comments. We respond to each comment as follows and sincerely hope that our responses could properly address your concerns. If so, we would deeply appreciate it if you could raise your score. If not, please let us know your further concerns, and we will continue activ... | Summary: This paper seeks to conduct reinforcement learning from corrupted offline data. More specifically, they propose the TRACER algorithm, which uses bayesian inference to calculate the uncertainty in estimating the action-value function. The authors conduct experiments with diverse corruptions on CARLA and Mujoco ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's insightful and constructive comments and suggestions. We respond to each comment as follows and sincerely hope that our responses could properly address your concerns. If so, we would deeply appreciate it if you could raise your score. If not, please let us know your f... | Rebuttal 1:
Rebuttal: # Global Response
We would like to thank reviewers for their insightful comments. We respond to the collective feedback below and hope that our responses could adequately address these general concerns. If so, we would deeply appreciate it if reviewers could raise the score. If not, please let us ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning from Highly Sparse Spatio-temporal Data | Accept (poster) | Summary: This paper proposes to address the challenge of learning from incomplete spatio-temporal data, which is prevalent in various real-world applications. Accordingly, this paper proposes a method named OPCR to handle data sparsity more effectively. Specifically, the method first directly utilizes spatial and tempo... | Rebuttal 1:
Rebuttal: Thanks for your careful reviews and insightful suggestions. We greatly appreciate your feedback. Please see the below responses to your comments (see global response to your questions about more baselines and confused notations).
> **[Weakness 1.1]** The introduction section presents most of the ... | Summary: The paper addresses the issue of incomplete spatio-temporal data. It theoretically analyzes how existing iterative message-passing methods are susceptible to the impacts of data sparsity and graph sparsity. It proposes the One-step Propagation and Confidence-based Refinement (OPCR). In OPCR, the Sparse Spatial... | Rebuttal 1:
Rebuttal: Many thanks for your positive comments and constructive feedback. Please see the below responses to your comments.
> **[Weakness 1 & Question 1]** The paper uses PAC-learnability to analyze generalization risk. Have the author(s) considered using other mathematical tools, such as Rademacher comple... | Summary: This paper proposes a sparse attention-based one-step imputation and confidence-based refinement approach named One-step Propagation and Confidence-based Refinement (OPCR). The authors evaluate the proposed model across two downstream tasks involving highly sparse spatio-temporal data. The contributions of thi... | Rebuttal 1:
Rebuttal: Many thanks for your positive comments and constructive feedback. Please see the below responses to your comments.
> **[Weakness 1.1]** In Section 4.1.2, the construction details of the input to the temporal sparse attention module are not described.
Thanks for your question. For the temporal spa... | Summary: This paper leverages the Probably Approximately Correct (PAC) theory to study the message-passing mechanism of spatial-temporal imputation. Inspired by the results of PAC, this paper introduces a One-step Propagation and Confidence-based Refinement (OPCR) model for spatial-temporal imputation. OPCR is comprise... | Rebuttal 1:
Rebuttal: Thanks for your careful reviews and insightful comments. We greatly appreciate your feedback. Please see the below responses to your comments (see global response to your questions about more baselines and confused notations).
> **[Weakness 1]** In general, the proposed method is a little bit in... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for taking the time to review our work. We appreciate that you find the problem is interesting and important (**Reviewer #Ctvj, #aMi7**), theory-inspired method is proposed (**Reviewer #g4JR, #aMi7**), the method is novel and efficient (**Reviewer #fc8s, #g4JR**), ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Bounding Box is Worth One Token: Interleaving Layout and Text in a Large Language Model for Document Understanding | Reject | Summary: This paper introduces the LayTextLLM method for document understanding, which encodes text positional information in the embedding space of an LLM and trains for effective understanding of document data as interleaved OCR-detected text and bounding box information. The results show improved performance compare... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback and appreciate the recognition of our paper’s contributions and novelty. We are grateful for the opportunity to address the concerns raised.
**W1-Model backbone:** We implemented LayTexLLM using Llama2-7b, consistent with previous OCR-based methods like DocL... | Summary: This work presents an innovative method for integrating layout information into LLMs to enhance document understanding tasks. Instead of treating bounding box coordinates as input text tokens, the bounding box information is embedded into a single token and interleaved with text tokens. This approach addresses... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback and appreciate the recognition of our paper’s contributions, writing and novelty. We are grateful for the opportunity to address the concerns raised.
**W1-Compute loss of bounding box**:
- First of all, our objective is to understand the bound... | Summary: The paper introduces a novel approach, named LayTextLLM, for document understanding tasks, which efficiently integrates spatial layouts and textual data within LLM. It employs a Spatial Layout Projector and introduces two innovative training tasks: Layout-aware Next Token Prediction and Shuffled-OCR Supervised... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback and appreciate the recognition of our paper’s novelty and improved performance. We are grateful for the opportunity to address the concerns raised.
**W1-Lack visual modality**: We acknowledge that incorporating visual information can enhance perfo... | Summary: This paper presents LayTextLLM, a novel approach to document understanding that effectively integrates spatial layout information and text into a large language model. Existing methods that integrate spatial layout with text often produce excessively long text sequences. LayTextLLM addresses these problems by ... | Rebuttal 1:
Rebuttal: Thank you for taking time to review our paper. We thank the reviewer for the thoughtful feedback and are grateful for the opportunity to address the concerns raised.
**W1-Incomplete related work**: Our survey focuses on decoder-only architectures within LLMs to highlight their unique capabilitie... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Monomial Matrix Group Equivariant Neural Functional Networks | Accept (poster) | Summary: The paper explores the important field of learning over weight spaces, where neural networks process other neural networks. Previous research has highlighted the importance of designing equivariant architectures that account for the symmetries of the input neural network, with a primary focus on permutation sy... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable feedback. Below we address your concerns.
**W1: Concrete example.**
**Answer:** See an illustrative example in **A4** of General Response.
**W2: Each Monomial-NFN can process a specific input architecture. Building over or extending the work to ... | Summary: The present manuscript concerns the design of Neural Networks capable of processing the weights and biases of other Neural Networks, particularly Fully Connected and Convolutional NNs (known in the literature as *weight space networks, neural functional networks or metanetworks*). Previous works considered onl... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable feedback. Below we address your concerns.
-----
**W1: Limited Expressivity.**
**Answer:** We agree with the reviewer's discussion on limitations. However, we would also like to share our thoughts on these limitations.
- Although **large size s... | Summary: This paper studies the extension of permutation equivariant neural functionals to accommodate the monomial group, which is a generalization of the permutation group. This extension leads to a new class of NFN called monomial NFN that can also handle the scaling symmetry of positively homogenous activation (REL... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable feedback. Below we address your concerns.
**Weakness: I don't see major weaknesses in the paper. Perhaps one shortcoming is that the majority of performance improvement comes when the weights are scaled, which is not a very natural perturbation in... | Summary: - Paper improves neural functional networks by taking into account weight scaling properties of ReLU networks and weight sign flipping symmetries of sin or Tanh networks.
- Monomial matrices are used to represent these symmetries, both permutation and scale/sign-flipping transformations.
- Proposed model has... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable feedback. Below we address your concerns.
**W1: Discussion/comparison with previous works in [C1,C2,C3,C5].**
**Answer:** Based on the additional references you provided, we have added the following discussion to the revised version of the paper... | Rebuttal 1:
Rebuttal: **General Response:**
Dear AC and Reviewers,
Thanks for your thoughtful reviews and valuable comments, which have helped us improve the paper significantly. We are encouraged by the endorsements that: 1) Our Monomial-NFN is a novel contribution to the neural functional literature with sound theo... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Interaction-Force Transport Gradient Flows | Accept (poster) | Summary: The paper proposes a gradient flow in combined Wasserstein-MMD geometry w.r.t. certain functionals. The authors primarily consider MMD squared functional, but also have some theoretical results regarding KL divergence functional. The work is more about theory: the authors are concerned about some mathematical ... | Rebuttal 1:
Rebuttal: We thank the reviewer for writing a long and critical review. We must point out that the review is filled with misunderstandings, which we do our best to clarify below. We would appreciate it if the reviewer could please consider our clarification.
> This theory is full of remarkable, non-trivi... | Summary: This manuscript proposes a new gradient flow over probability and non-negative measures termed the interaction-force transport (IFT). The flow is based on the inf-convolution of the Wasserstein Riemannian metric tensor and the spherical maximum mean discrepancy (MMD) Riemannian metric tensor. The authors provi... | Rebuttal 1:
Rebuttal: Thank you for taking the time to carefully read and review the paper. We appreciate that you have noticed the many subtle features we put into the manuscript. Thank you for the kind summary in the "Strengths" section. Below, we respond to a few concerns and suggestions.
> the roughness of the sta... | Summary: This paper proposes a novel gradient flow geometry – interaction-force transport (IFT). It is theoretically shown that IFT gradient flow has global exponential convergence guarantees both for MMD and KL energies. The authors propose an algorithm based on the JKO-splitting scheme and test it on examples with 2D... | Rebuttal 1:
Rebuttal: Thank you for the constructive suggestions and the critical review. We have incorporated most of your suggestions, e.g., new experiments. We did find the one comparison you suggested with KL inference difficult, for which we explained the reason.
> including experiments in dimensions larger than ... | Summary: This paper proposes a novel gradient flow geometry (IFT), based on the infimal convolution of the Wasserstein tensor with the MMD tensor. For this geometry, the authors show global exponential convergence guarantees for both MMD and KL energies. They then develop an algorithm for the IFT gradient flow and test... | Rebuttal 1:
Rebuttal: We thank the reviewer for the fair assessment and constructive suggestions. We are glad that the exposition of the paper is easy to follow -- thank you for this feedback. We have incorporated some of your suggestions and left more challenging ones for future work.
> experiments run are relatively... | Rebuttal 1:
Rebuttal: Dear reviewers, dear AC,
We would like to thank all reviewers for their constructive feedback. We are glad that the majority of the reviewers found our paper easy to read and contains non-trivial contributions.
The major concern expressed by some reviewers is that more experiments would strength... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Regret Minimization in Stackelberg Games with Side Information | Accept (poster) | Summary: The paper examines online learning within Stackelberg games, incorporating additional contextual information. Specifically, at each round $t$, both the follower and the leader observe a shared context $z_t$, which impacts their respective utilities. The leader, who is also the learner in this online learning f... | Rebuttal 1:
Rebuttal: [*“The regret bounds for the bandit case are not tight. However I believe the paper provides interesting first results for an interesting problem.”*]
We would like to point out that it is actually unclear whether or not an algorithm exists for the bandit settings which achieves better than O(T^{2... | Summary: The paper studies an online Stackelberg game, where the leader plays with a different follower type in a different context in each time step. The paper takes an online learning approach to solving this problem. It first that given that the context space is infinite, it is not possible to achieve sublinear regr... | Rebuttal 1:
Rebuttal: [*“The main impossibility result appears to reply on the fact that the context space is infinite and the model is non-linear w.r.t. the context.” and “Would the impossiblity result change if the context space is finite, or if the players' utility functions are linear w.r.t. the context.”*]
We wou... | Summary: The paper presents a study of Stackelberg games with contextual side information, impacting their strategies in a game theoretic setting. The authors introduce a framework for analyzing online Stackelberg games, where a leader faces a sequence of followers, and both or either sequences—contexts and follower ty... | Rebuttal 1:
Rebuttal: [*“The paper would definitely benefit from the addition of numerical experiments or case studies.”*]
We hope our numerical simulations address your concerns regarding the lack of experimental results.
[*“Could the authors provide more clarity on how the adversarial model for context selection wa... | Summary: This paper studied the regret minimization in Stackelberg games with side information which consider the additional information available to each player. The paper found that achieving no-regret learning is impossible in fully adversarial settings. However, it demonstrated that no-regret learning is achievable... | Rebuttal 1:
Rebuttal: [*”This paper lacks experimental results to verify the theoretical analysis.”*]
We hope our numerical simulations address your concerns regarding the lack of experimental results.
---
Rebuttal Comment 1.1:
Comment: Thank you for your response and the numerical simulation. I will keep my score. | Rebuttal 1:
Rebuttal: Thanks for taking the time and effort to review our submission. To summarize, we initiate the study of Stackelberg games with side information, which despite the presence of side information in many Stackelberg game settings, has not received attention from the community so far. We provide algorit... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Semidefinite Relaxations of the Gromov-Wasserstein Distance | Accept (poster) | Summary: The paper explores a semi-definite programming (SDP) based relaxation of the popular Gromov Wasserstein (GW) problem. The GW problem is an instance of non-convex quadratic program (QP). Standard SDP relaxation of QPs has been explored in the literature. The present work leverages this SDP relaxation result. H... | Rebuttal 1:
Rebuttal: *Detailed discussion on related work*
Could we check if this comment was intended as a weakness? It does not sound like one. We assume this comment was misplaced. Could the Reviewer clarify?
*High run-time*
We agree with the Reviewer’s concerns about run-time. We address some of these concerns ... | Summary: The authors propose a new algorithm for measuring the Gromov-Wasserstein (GW) distance, an metric for assessing the similarity of point clouds in different spaces. Their algorithm formulates the computation of the GW distance as a quadratic programming problem, which is then solved by semidefinite relaxation. ... | Rebuttal 1:
Rebuttal: *Limited novelty*
We emphasize, the paper has multiple contributions. The first contribution is that we compute globally optimal solutions to the GW problem. We can prove that our solutions are globally optimal. There is no existing work (as far as we are aware of), published or unpublished, that... | Summary: The authors propose a semidefinite programming (SDP) relaxation of the Gromov-Wasserstein (GW) distance. While the GW problem is non-convex, the proposed SDP relaxation is convex and hence can be solved in polynomial time with any off-the-shelf convex solver. The authors also provide an accompanying proof of g... | Rebuttal 1:
Rebuttal: *Real data*
We use a publicly available database of triangular meshes (Sumner et al. 2004). We obtain 18 points and compute distance matrices using Dijkstra's algorithm. Each object's probability measure is chosen to be uniform. We apply (GW-SDP) to the corresponding metric-measure spaces to dete... | Summary: The authors provide SDP relaxations of the Gromov-Wasserstein distance, which turns out to provide global optimal in many cases.
Strengths: -- The SDP relaxations and proofs of their exactness are most elegant.
-- The authors suggest that their method does not make as strong assumptions on the loss as in th... | Rebuttal 1:
Rebuttal: *Run time comparisons*
We wish to clarify that the current manuscript compares run times of the Conditional Gradient (CG) method, the entropic Gromov-Wasserstein method and our method. The comparisons are found in Page 5 of the submitted manuscript. For a fixed number of samples, the SDP runtime ... | Rebuttal 1:
Rebuttal: We thank the Reviewers for taking time to provide valuable feedback. We have incorporated many of these suggestions with additional experiments which we believe improve the paper substantially. We outline some of these new experiments and contributions below, and describe them in further detail to... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Activating Self-Attention for Multi-Scene Absolute Pose Regression | Accept (poster) | Summary: In this paper, the authors focus on improving the performance of multi-scene absolute pose regression models based on transformers. From statical analysis, the authors assume that the distortion between Q and K features in self-attention and the learnable position encoders are the reasons. Therefore, a Q-K ali... | Rebuttal 1:
Rebuttal: **Q1. Distinctive contribution**
A1. We can understand why this might be a concern.
[19] and our research both address the underlying problem of attention collapse.
However, [19] and ours are **clearly different in terms of the analysis on the causes and the solution for the attention collapse.**... | Summary: The paper investigates multi-scene absolute pose regression from a new perspective: query-key embedding space. Focusing on distortion of queries and keys, the paper solutions to activate self-attention, which includes an auxiliary loss to align queries and keys and fixed sinusoidal positional encoding. Experim... | Rebuttal 1:
Rebuttal: **Q1. Speed & memory efficiency**
A1. We would like to bring to your attention that the advantages of MS-APR mentioned have already been claimed and proven by previous research, and are not new assertions from our side.
Firstly, it is known that APR methods are much faster and more memory-efficie... | Summary: The paper analyzes the collapse of self-attention map in Multi Scene Pose Transformer model and proposes two simple but effective methods: auxiliary loss and fixed 2D sinusoidal encoding to solve this problem. The improved method delivers SOTA performance on the Multi Scene Pose Regression task.
Strengths: 1... | Rebuttal 1:
Rebuttal: **Q1. Novelty**
A1. We would like to highlight the novelty of our unique analysis and simple but effective solution.
By focusing on a different aspect from previous studies [18-21, 31-34], we identified the problem in self-attention modules of APR models and solved it, which previous works could ... | Summary: This paper is about a improving self attention in the transformer architecture for multi-scene APR. The show that the self attention module in the SOTA transformer model for APR is actually not helping much and offer an potential explanation. The paper claims that the keys and queries end up in different space... | Rebuttal 1:
Rebuttal: **Q1. Histogram**
A1. We thought that using coarse bins was suitable for visualizing and showing the general trend differences in purity between the baseline and our method.
However, we agree with the reviewer's suggestion to provide a more specific analysis.
Accordingly, we present the baseline'... | Rebuttal 1:
Rebuttal: Thank you for reviewing.
Responses to the questions can be found under each individual review.
The global rebuttal page includes the relevant figures and tables for your reference.
Pdf: /pdf/2da9102b9ced4cf6568f9a6f793e4b15bef0a6c9.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
GenWarp: Single Image to Novel Views with Semantic-Preserving Generative Warping | Accept (poster) | Summary: The authors propose to achieve novel view synthesis from a single image. Contrary to prior work, which warps using a monocular depth estimate and then inpaints, a more flexible architecture is introduced. To do this, a diffusion model is conditioned on warped coordinates, based on the desired pose change and a... | Rebuttal 1:
Rebuttal: We thank your thoughtful review and suggestions! If any of our responses do not adequately address your concerns, please let us know and we will get back to you as soon as possible.
---
### Q: More evidence for “cross-attention attends to corresponding points” be presented?
Thank you for the in... | Summary: This paper proposes a semantic-preserving generative warping framework to generate high-quality novel views from a single image, which mainly consists of two components:
- Condition the novel view synthesis on the warped 2D coords embedding from the estimated depth map.
- Augmenting cross-view attention with... | Rebuttal 1:
Rebuttal: We thank your thoughtful review and suggestions! We give a detailed response to your comments below. If any of our responses do not adequately address your concerns, please let us know and we will get back to you as soon as possible.
---
### Q: The proposed embedding could not benefit the synthe... | Summary: The paper presents a novel framework called GenWarp, which aims to generate new views from a single input image while preserving the semantic content of the original view. This is achieved by leveraging a generative process that incorporates both self-attention and cross-view attention mechanisms conditioned o... | Rebuttal 1:
Rebuttal: We thank your thoughtful review and suggestions! If any of our responses do not adequately address your concerns, please let us know and we will get back to you.
---
### Q: Handling extremely distant viewpoint changes.
Thank you for pointing this out. Our key contribution is effectively using e... | Summary: This paper proposes a novel single-shot novel view synthesis framework based on a pretrained T2I diffusion model. Instead of directly warping pixels between input view and novel view, an implicit approach is proposed to conduct geometry warping operation. The cross-view attention is used to eliminates the ar... | Rebuttal 1:
Rebuttal: We thank your thoughtful review and suggestions! We give a detailed response to your comments below. If any of our responses do not adequately address your concerns, please let us know and we will get back to you as soon as possible.
---
### Q. Novelty regarding cross-view attention.
Thank you ... | Rebuttal 1:
Rebuttal: # General Response
We would like to first thank the reviewers for the helpful suggestions and constructive reviews. We are greatly encouraged by their positive assessment regarding soundness (1 excellent, 3 good), contribution (4 good), and presentation (2 excellent, 1 good) of our work.
They ac... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Episodic Future Thinking Mechanism for Multi-agent Reinforcement Learning | Accept (poster) | Summary: This paper introduces an Episodic Future Thinking (EFT) mechanism for reinforcement learning (RL) agents to enhance decision-making in multi-agent scenarios. The EFT mechanism allows an agent to predict the future actions of other agents by inferring their characters from observation-action trajectories. This ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive reviews and insightful feedback about this work. Below, we describe how we have revised the paper to address the reviewer's concerns and questions.
- **Computational complexity**
We agree that considering computational complexity is crucial for practical sol... | Summary: Introduce an episodic future thinking(EFT) mechanism, which, along with the mechanism of counterfactual, is a cognitive activity of human beings.
The proposed algorithm predicts future observation transitions and uses them to determine the next steps of action. Although the maximum likelihood method is also us... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's time and effort. Here are our answers to the reviewer's comments.
- **Our contribution and motivation**
We would like to clarify our contribution is not trivial. To emphasize our contribution, we summarize the novelty as follows. **We introduce a novel social decisio... | Summary: This paper introduces an Episodic Future Thinking (EFT) mechanism for reinforcement learning agents in multi-agent systems with heterogeneous characters. The authors propose a multi-character policy and a character inference module to enable agents to predict other agents' actions and simulate future scenarios... | Rebuttal 1:
Rebuttal: We are thankful for the reviewer’s detailed feedback and constructive suggestions for improving our work. In response, we outline the revisions made to address the reviewer’s concerns and questions. We have marked the weakness, question, and limitation numbers associated with each discussion secti... | Summary: This paper presents Episodic Future Thinking (EFT), an approach for RL in multi-agent environments. EFT involves learning a multi-character policy (where character is a parameter that modifies the reward), and then using this to infer characters of other agents and planning accordingly, using these characters ... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's thorough review and valuable suggestions about this work. Below, we outline how we have revised the paper to address the reviewer's concerns and questions.
- **Fairness for experience in character diversity**
We agree with the reviewer that maintaining a fair e... | Rebuttal 1:
Rebuttal: We express our gratitude to all five reviewers for their insightful feedback. We are pleased to present the updates we have made in response to valuable suggestions, as detailed below.
- We compared the performance with **two additional baselines** on the research about opponent modeling and the... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper introduces an Episodic Future Thinking (EFT) mechanism for reinforcement learning (RL) agents, inspired by cognitive processes observed in animals, to enhance social decision-making in multi-agent systems with diverse agent characteristics. The EFT mechanism uses a multi-character policy to infer the... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's detailed feedback and valuable suggestions for enhancing our work. In response, we describe how we have revised the paper to address the reviewer's concerns and questions.
- **Additional experiment results to prove generalizability**
To demonstrate the efficiency of ... | null | null | null | null | null | null |
No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices | Accept (poster) | Summary: This paper demonstrates that typical design choices in large language model (LLM) watermarking schemes result in significant trade-offs between robustness, utility, and usability. To navigate these challenges, this paper rigorously examines a series of straightforward yet effective attacks on prevalent LLM wat... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments. In the following, we respond to each question.
---
>**Q1: One of the biggest weaknesses of this paper is that the proposed attacks mainly explore the drawbacks of existing literature in [11,14,33], and some of the tradeoffs described in the p... | Summary: The paper details three different attacks on LLM watermarking, targeting watermark removal and spoofing:
A1: spoofing by taking advantage of the robustness of the watermark
A2: removal by taking advantage of multiple watermarking keys
A3: removal by taking advantage of a public detector
Attacks are followed b... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's constructive comments. Please refer to **C1** in the global response for our clarifications on the positioning and contributions of our work. Our work studies the feasibility and ramifications of potential attacks, with the goal of better informing the public and the L... | Summary: In this work, the authors reveal new attack vectors including watermark-removal attacks and spoofing attacks that exploit common features and design choices of LLM watermarks. Besides, the authors propose a defense utilizing the ideas of differential privacy, which increases the difficulty of spoofing attacks.... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments. In the following, we respond to each question.
---
>**Q1: In Sec 3.1, what are the differences between "piggyback" and "general" spoofing attacks? Specifically, what does "piggyback" refer to?**
**A1**: Piggybacking (Sec 4, L 142) is a class... | Summary: This paper explores the vulnerabilities and trade-offs in watermarking schemes for large language models (LLMs). It highlights how common design choices in these schemes, aimed at ensuring robustness, multiple key usage, and public detection, make them susceptible to simple yet effective attacks. The authors d... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments. In the following, we respond to each question.
---
>**Q1: The citation in Line 248 and Figure 2 is not correct. The authors are supposed to cite [1].**
**A1**: Thanks for pointing this out. We will fix this typo in the revision to avoid conf... | Rebuttal 1:
Rebuttal: We appreciate all reviewers’ constructive comments. Below we clarify our contributions, respond to common questions, and present new experimental results.
>**C1: Clarification on the contributions and positioning of our work.**
Our work explores attacks that exploit design choices of common LLM ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Graph Coarsening with Message-Passing Guarantees | Accept (poster) | Summary: This paper studies the theoretical guarantees of graph coarsening for GNNs. The authors propose a new and directed message-passing operation specific to coarsened graphs, which makes many theoretical results possible.
Strengths: S1: The results are very useful for the field of graph coarsening. Theorem 2 show... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback. We will fix the typos and clarify the description of the proposed propagation matrix.
**Q1)** *Can the authors give some intuitive explanation on $\overline{C}_{\Pi}$, e.g., what kind of coarsening method will make this term smaller?*
From a theoretical po... | Summary: This paper proposes a new message-passing matrix for a graph coarsening algorithm. The goal is to have some message-passing guarantees for the new message-passing matrix, which is not the case with the previous message-passing matrices based on this coarsening. They provide theoretical proofs for linear varian... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback.
**Datasets** See global comment (A): we have performed additional experiments on the Reddit Dataset which is significantly bigger than Cora and Citeseer. Concerning heterophilous datasets, we note that spectral-based coarsening itself is probably very inef... | Summary: This work presents a novel computation method for the message-passing matrix on coarsened graphs. This method does not require recalculating degree matrices and other information on the coarsened graph and has comprehensive theoretical guarantees. Overall, it addresses a significant problem in graph coarsening... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback.
**Q 1)** *How effective is this work on datasets such as Arxiv and Products*
See global comment (A): improving our spectral corsening algorithm, we have conducted experiments on a larger graph, Reddit. This graph has 1.5 times more nodes than ogb-arxiv. Fo... | Summary: The authors describe an alternative way to obtain the connectivity matrix of a coarsened graphs and provide some bounds on operations performed on such a matrix.
Strengths: Theoretical work on how to optimally compute the connectivity matrix of a coarsened graph is an interesting and potentially useful resear... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback. We have addressed your comments individually below. Before answering the questions, we would like to precise that Graph Pooling and Graph coarsening are two methods that are linked, but with different purposes. Graph Pooling is generally incorporated into th... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their reviews and questions. In this global comment, we address two questions that were mentioned in multiple reviews. Namely, we introduce new experiments on a larger dataset (Reddit) and comment on the multidimensionality of node features.
**A)** ***Larger Dataset... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes a novel message-passing guarantee for graph coarsening and a new message-passing operation with the message-passing guarantee. Experiments demonstrate that the prediction performance of the proposed method outperforms some baselines.
Strengths: 1. The proposed message-passing guarantee is ... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback. We address each comment below.
**Q 1)** *How to select the hyperparameters in experiments (e.g. the number of the SGC layers)? The selected coarsening ratio is significantly larger than existing works*
We chose classical values for the training of SGC mod... | null | null | null | null | null | null |
Model Based Inference of Synaptic Plasticity Rules | Accept (poster) | Summary: This paper proposes a novel method for inferring plasticity rules from neural and behavioral data. In contrast to previous approaches, the plasticity rule is directly optimized to maximize the similarity of the output of a model trained with the plasticity rule to a target (neural activity or behavior). This a... | Rebuttal 1:
Rebuttal: # Response to reviewer MyoH
Dear Reviewer,
Thank you for your feedback and your recognition of the interesting aspects of our paper. We have addressed your highlighted Weaknesses and Questions one by one below:
## Weaknesses
1. We acknowledge that when choosing problems to test our approach we ... | Summary: This paper studies how to learn local learning rules (like those plasticity rules thought to be used by real neurons) in a data-driven way from neural activity or behavioral timeseries. This is applied to simulated as well as fly behavioral data. The true learning rules can be learned from synthetic activity t... | Rebuttal 1:
Rebuttal: # Response to reviewer 5Cze
Dear Reviewer,
Thank you for your careful reading of our work and insightful comments. We are glad that you found the paper to be “interesting,” and “relevant.” We address each of the comments mentioned in your Weaknesses, Questions, and Limitations sections below.
#... | Summary: This paper presents a reward learning-based method for recovering biological synaptic plasticity rules in a model network. It is meant to be applied to both neural and behavioral data. The method consists of taking real learning trajectories collected in response to stimuli (of some observable metric modality)... | Rebuttal 1:
Rebuttal: # Response to reviewer 2Jtn
Thank you for your constructive feedback on our manuscript. We are glad that you found our work to be “creative,” and “clever and convincing.” Below, we address each of the concerns you expressed in your review, and hope that you will find our revised manuscript to be... | null | null | Rebuttal 1:
Rebuttal: # General response to reviewers
We appreciate the thoughtful and constructive feedback provided by all reviewers. We have revised our manuscript based on this. This rebuttal addresses two key points relevant to all reviewers.
## **1. Scalability of the method**
In response to Reviewer 2Jtn's in... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning Successor Features the Simple Way | Accept (poster) | Summary: This paper presents a method for learning Successor Features (SFs) from pixel-level observations in reinforcement learning (RL) by combining a Temporal-Difference (TD) loss with a reward prediction loss. This approach simplifies the learning process, improves performance, and speeds up learning compared to exi... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We appreciate the opportunity to clarify and enhance our manuscript based on your observations. Please let us know if there is further clarification we can provide.
# 1. Balancing Context and Review of Related Work
Thank you for your feedback on the structure of our i... | Summary: The paper proposes a simpler method to learn Successor Features that avoids representational collapse. For this, the authors decompose the loss function to learn the successor features and task encoding separately. This allows for keeping the basis features fixed while learning the successor features, thus avo... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We appreciate the opportunity to clarify and enhance our manuscript based on your observations. Please let us know if there is further clarification we can provide.
# 1. Is DQN better than the SFs in Continual RL setting?
Thank you for your observations regarding the ... | Summary: This work presents a model architecture to learn successor features in reinforcement learning. It consists of optimizing Eqs. (5) and (6), i.e., a loss for learning the features and a loss for learning the task specific weights. It claims to avoid representation collapse. Experiments are conducted in common 2D... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We appreciate the opportunity to clarify and enhance our manuscript based on your observations. Please let us know if there is further clarification we can provide.
# 1. Important differences between the Universal SFs and our approach
While both our study and [1] util... | Summary: This work introduces a new algorithm for training successor features in deep reinforcement learning. This is achieved by optimizing two separate metrics. The first requires the model to predict the cumulative reward following a full trajectory and optimizes the successor features and the basis feature. The sec... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We appreciate the opportunity to clarify and enhance our manuscript based on your observations. Please let us know if there is further clarification we can provide.
# 1. Layout of Figure 5 and Figure 6
Thank you for your feedback on the order of Figures 5 and 6 in our... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers once again for their valuable feedback, which has guided clarifications and improvements that we will include in the final revision of our manuscript. **We have attached a set of figures in this Author Rebuttal, which we denote as General Response (GR)**, to ad... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
F-OAL: Forward-only Online Analytic Learning with Fast Training and Low Memory Footprint in Class Incremental Learning | Accept (poster) | Summary: The author proposed a method called F-OAL for online class incremental learning, which does not rely on back-propagation and is forward-only, significantly reducing memory usage and computational time. In summarize, the contributions are as follows:
1) The paper presents theF-OAL, which is an exemplar-free met... | Rebuttal 1:
Rebuttal: # Response to Reviewer XB6P
Thank you for the thorough review. We provide more detailed response below. Hope this can help you with your concerns.
## W1&L1: The formula No.4 seems to be wrong.
Thank you for pointing out the typo. We revise $ϕ(X)Y$ to $ϕ(X)^⊤Y$ in equation 4.
## L2: Wh... | Summary: This paper presents an analytic class incremental learning method that does not need backpropogation. The main idea is to use a pre-trained mode to extract features followed by random projection to higher dimensional space, and then use recursive least squares to update the linear regression weights. By doing ... | Rebuttal 1:
Rebuttal: # Response to Reviewer UyXG
Thank you for your valuable time in reviewing. We provide detailed information for your concerns below
## W1: Give more precise definition for all notations, such as their dimensionality.
Thank you for your suggestion. We have included a following notation table.
| ... | Summary: The authors address the problem of online class incremental learning (OCIL), where new tasks arrive periodically in a data stream and the trainer seeks to learn these new tasks without catastrophic forgetting of past performance. The paper presents two modes of OCIL; replay-based methods and exemplar-free meth... | Rebuttal 1:
Rebuttal: # Response to Reviewer nHHU
Thank you for your positive reviews and helpful suggestions. We provide detailed responses to your concerns below.
## W1: Abuse the notion of "data privacy".
Thank you for raising this important concern. We agree that the use of “data privacy” is less appropr... | Summary: The paper introduces Foward-only Online Analytic Learning (F-OAL), an exemplar-free approach designed for Online Class Incremental Learning. The method addresses Catastrophic Forgetting by utilizing a pre-trained frozen encoder and a recursive least square updated linear classifier, which significantly reduces... | Rebuttal 1:
Rebuttal: # Replies to Reviewer Pgch
Thank you for your constructive and detailed feedbacks. We provide detailed responses to your concerns below.
## W1: Dependence on the quality of the pre-trained encoder.
Thank you for the suggestion. Indeed, our method relies on well pre-trained backbones such as... | Rebuttal 1:
Rebuttal: # General Response
We thank all the reviewers for their time, insightful suggestions and valuable comments. In summary, Reviewer nHHU appreciates that our work is **natural**, **intuitive**, and overall **quite strong**. The writing is **clear**, **logical**, **informative** and **useful** for th... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CLAP4CLIP: Continual Learning with Probabilistic Finetuning for Vision-Language Models | Accept (poster) | Summary: The authors present a novel method for CL called CLAP (Continual LeArning with Probabilistic finetuning) applied to the CLIP model. The technique employs probabilistic modeling to refine task-specific modules aligned with visual-guided text features, improving model adaptation to new tasks while mitigating the... | Rebuttal 1:
Rebuttal: Dear Reviewer ey1B,
Thank you for your comments.
- We assure you that we will enlarge Figure 1 in the next version of the paper to improve its readability and clarity.
- We will correct the reference order accordingly and discuss the relevant reference in the related works. We apologize for an... | Summary: This paper introduces CLAP4CLIP, a method designed to enhance continual learning (CL) using CLIP, a pre-trained vision-language model. The method leverages probabilistic fine-tuning with task-specific adapters to mitigate the issue of catastrophic forgetting commonly faced in CL. By incorporating visual-guided... | Rebuttal 1:
Rebuttal: Dear Reviewer KrxH,
Thank you for your comments and suggestions. In what follows, we have tried our best to address your concerns.
- **Our method stands out without replay, replay further boosts its performance:** Thank you for your comment. We would first like to mention that our additional reb... | Summary: The paper emphasizes on the existing limitations of deterministic approaches in fine-tuning and highlights the need for probabilistic fine-tuning approach. Following this, it proposes a probabilistic parameter efficient fine-tuning method for continually learning vision language models like CLIP.
Strengths: 1... | Rebuttal 1:
Rebuttal: Dear Reviewer bibK,
Thank you for your comments and suggestions. In what follows, we have tried our best to address your concerns.
- **Why do we do probabilistic modeling of text feature space?** We opt for probabilistic modeling of task-specific text feature space rather than image feature spac... | Summary: This paper proposes Continual Learning with Probabilistic Finetuning (CLAP) for class-incremental learning using CLIP. The key modules of the proposed idea are as follows. First, the authors introduce a CLIP-based probabilistic finetuning model using Bayesian Variational Inference to achieve better generalizat... | Rebuttal 1:
Rebuttal: Dear Reviewer giBN,
Thank you for your comments and suggestions. We have tried our best to address your concerns below.
1-1) **Fair comparison with L2P and DualPrompt:**
The scores we report for L2P and DualPrompt are fair and use a CLIP-based backbone as well as memory replay with the same num... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their comments and constructive suggestions on our manuscript. Here, we provide a pdf containing results for the experiments asked in the reviews. We also highlight the three major points raised in the reviews and how our rebuttal has addressed these:
- **... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge | Accept (poster) | Summary: This paper introduces KG-FIT, a general framework that enhances the expressiveness of existing Knowledge Graph Embedding (KGE) models by integrating LLMs. KG-FIT contains four key steps: First, it utilizes an LLM to generate descriptions for a set of given entities, forming an enriched entity representation by... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful comments and their recognition of our work's strengths. We address their concerns as follows:
---
> ### **[W1/W2]** (Dependency on LLM knowledge and potential issues with limited domain coverage)
We appreciate the reviewer's concern about KG-FIT's relianc... | Summary: This paper addresses the limitations of existing KGE models that focus either on graph structure or fine-tuning pre-trained language models. It introduces KG-FIT, which leverages LLM-guided refinement to incorporate hierarchical and textual knowledge, effectively capturing both global and local semantics. Expe... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We appreciate your recognition of our work's strengths and we address your concerns as follows.
---
> ### **[W1]** *"there is a lack of the sensitivity study for the hyperparameters in the loss function"*
We appreciate the reviewer's concern about the lack ... | Summary: Knowledge graphs (KGs) are essential for representing structured knowledge in various domains. They consist of entities and relations, forming a graph structure for efficient reasoning and knowledge discovery. Current knowledge graph embedding (KGE) methods create low-dimensional representations of these entit... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We appreciate your recognition of our work's strengths and we address your concerns as follows.
---
> ### **[W1/Q1]** (Reconsidering motivation)
If the retrieval process here refers to the "retriever" mentioned in KICGPT [1], it's important to note that:
-... | Summary: The paper introduces a framework called KG-FIT for enhancing knowledge graph embeddings by integrating knowledge from large language models (LLMs). KG-FIT enriches entity descriptions using LLMs and then constructs a semantically coherent hierarchical structure of entities. It finally fine-tunes KG embeddings ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We appreciate your recognition of our work's strengths and we address your concerns as follows.
---
> ### **[W1.1]** *"There are several related studies on using LLMs to enhance text information in KGs [1,2] ..."*
We acknowledge this oversight. In our lates... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We sincerely thank you for your thoughtful and constructive feedback on our submission "Knowledge Graph Fine-Tuning Upon Open-World Knowledge from Large Language Models". We deeply appreciate the time and effort you've invested in reviewing our work.
> **[Strengths of Our Work]**... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Neural Persistence Dynamics | Accept (poster) | Summary: This work presents a novel approach to infer the parameters of governing equations describing the collective behavior of systems like point clouds. It leverages persistent homology to capture the topological features of the system's state. These features are then modeled using a Latent ODE system, capturing th... | Rebuttal 1:
Rebuttal: For better readability, we restate your comments/questions in *italic*, our response(s) are marked by ▶
*This work resonates closely with inverse problems for dynamical systems, where several works have been conducted to infer the initial parameters ...*
▶ Currently, we discuss related wor... | Summary: The paper addresses the challenge of predicting the specific parameters of models yielding point cloud dynamical systems known only partially from a set of observations in different time steps. This is achieved by leveraging information about the evolution of persistent homology vectorizations of the observed ... | Rebuttal 1:
Rebuttal: For better readability, we restate your comments/questions in *italic*, our response(s) are marked by ▶
*I think that the sentence in line 32 "... due to missing correspondences between individuals..." is hard to read. Which missing correspondences?*
▶ By "correspondences" we mean th... | Summary: This paper considers the problem of learning some parameters $\theta$ in---roughly speaking---a dynamical system of the form $\dot X = \phi(X, \theta)$, where $X \in \mathbb{R}^{n \times d}$ (with typically $d=3$), from an observed time-discrete trajectory $X(\tau_0),\dots,X(\tau_N)$.
The idea conveyed by th... | Rebuttal 1:
Rebuttal: For better readability, we restate your comments/questions in *italic*, our response(s) are marked by ▶
**ad Clarity:** We tried to balance the presentation of our conceptual idea vs. the technical detail of its particular realization. We can provide more detail on the ELBO objective and th... | Summary: This work considers the problem of learning the latent, continuous-time dynamics underlying time-evolving point clouds. To solve it, it leverages previous work on the persistent homology of point clouds and their vectorization, as well as the PointNet++ network, to obtain static representations of the point cl... | Rebuttal 1:
Rebuttal: For better readability, we restate your comments/questions in *italic*, our response(s) are marked by ▶
**Ad contribution:** The reviewer is correct in that we use the latent ODE framework of (Rubanova et al., 2019). However, the latter is only one particular variant (of many; other options... | Rebuttal 1:
Rebuttal: # General Response
We like to thank **all** reviewers for their overall positive feedback, their time, and their valuable comments and suggestions!
While we address all issues point by point per reviewer, we first comment on our approach's *computational aspects* and present a detailed runtime a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Improving Deep Learning Optimization through Constrained Parameter Regularization | Accept (poster) | Summary: The authors propose a form of regularization which adjusts the regularization strength based on the weights being inside or outside of a certain norm bound. A violation of the bound results in an increasing penalty and coefficient, while conforming to the bound results in a decreasing or zero regularization pe... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable feedback. We appreciate your positive assessment of our method's intuition and potential for adaptive regularization. We have carefully considered your comments and would like to address them as follows:
> The statement on L123-124 is hard to acce... | Summary: This paper presents Constrained Parameter Regularization (CPR) as an alternative to traditional weight decay.
CPR enforces an upper bound on the L2-norm of individual parameter matrices. It frames learning as a constraint optimization problem solved with the augmented Lagrangian method and can be integrated se... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review. We appreciate your positive comments on CPR's straightforward implementation, effectiveness across tasks, and clear presentation. We'd like to address your questions and concerns:
> The general idea of using adaptive regularization does not seem very novel. Ho... | Summary: This paper illustrates a new training algorithm to improve the weight decay strategy. Instead of giving the same strength to all weights in weight decay, the proposed method penalizes only the elements that are larger than the threshold. Based on extensive evaluation, the proposed method performs better than A... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and feedback on our paper. We appreciate your positive assessment of the strength of our experiments and the intuitive nature of our method.
We would like to address the weaknesses you identified as follows:
> As mentioned in the paper, the computational cos... | Summary: The paper introduces Constrained Parameter Regularization (CPR), a regularization technique for deep learning by dynamically tailoring regularization to individual parameters. CPR sets upper bounds on statistical measures of parameter matrices and reduces the learning into a constraint optimization problem. Th... | Rebuttal 1:
Rebuttal: We thank you for your time and effort in reviewing our paper. We appreciate your positive feedback on the clarity of our presentation and the extensive empirical validation across multiple deep learning domains. In the following we carefully considered your concerns and questions:
> The optimizat... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your thorough and constructive reviews of our paper on Constrained Parameter Regularization (CPR). We greatly appreciate your thoughtful comments and the opportunity to address your concerns.
We have prepared a 1-page PDF with additional experimental results that dir... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes a new regularization technique, Constrained Parameter Regression (CPR), to replace weight decay for training deep learning models. Conventional weight decay penalizes significant weight deviation. However, this can be too restrictive since some layers may need a larger deviation. Instead of ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review of our paper on Constrained Parameter Regularization (CPR). We appreciate your positive feedback on the method's principled motivation, clear presentation, and strong empirical results across multiple tasks.
Regarding your summary, we would like to point out ... | null | null | null | null | null | null |
GeNIe: Generative Hard Negative Images Through Diffusion | Reject | Summary: The paper proposes to employ text-to-image latent diffusion models to augment images through a controlled modification such that the resultant class is different from the source class. Such augmented images are referred to as hard negative images. Building upon SDEdit style image modification, the paper contro... | Rebuttal 1:
Rebuttal: **[hT8y][W1]: seemingly comparable performance of Txt2Img and GeNIe:**
- Thanks for this comment. Indeed diffusion-based augmentation techniques offer a notable margin compared to traditional approaches (such as Cutmix and Mixup). We offer an enhancement of a diffusion based approach by proposin... | Summary: This paper introduces GeNIe, a data augmentation method for training vision models using synthetic images. GeNIe generates images by combining a source category image with a target category text prompt, selecting those that feature source characteristics but belong to the target category as negative samples. E... | Rebuttal 1:
Rebuttal: $\textbf{[3KAK][W1]}$: $\textbf{How does $\texttt{GeNIe}$ control which features are retained or changed}$:
We instruct the diffusion model to generate an image by combining the latent noise of the source image with the textual prompt of the target category. This combination is controlled b... | Summary: In this paper, the idea is to generate data for data augmentation by utilizing a pre-trained diffusion model. The method employs different text prompts and an adjusted noise scheduler to generate hard negative samples for the source distribution. "GeNIe" creates new augmentations using diffusion by leveraging ... | Rebuttal 1:
Rebuttal: $\textbf{[Q6Ew][W1]}$: $\textbf{slowness of \texttt{GeNIe-Ada}}$:
- Thanks for this remark. As we highlight in our limitations, we acknowledge that $\texttt{GeNIe}$ is comparatively slower than traditional augmentation methods, while standing on par with (or even faster in the case of barebone $... | Summary: This paper introduces a novel augmentation method based on diffusion models. A latent diffusion model conditioned on a text prompt generates hard negatives, by adjusting the noise level. The hard negatives can be used as challenging augmentations. The authors demonstrate the effectiveness of their approach on ... | Rebuttal 1:
Rebuttal: $\textbf{[qsxy][W1]}$: "$\textit{happy with the paper, experiments, and presentation}$", $\textbf{selection of noise ratio $r$ across different datasets}$:
- We are pleased with reviewer's positive feedback, also for finding our proposed ideas interesting.
- Thanks for the interesting remark.... | Rebuttal 1:
Rebuttal: We do appreciate reviewer's constructive feedback which helped to further improve the quality and clarity of the paper. We are please by the positive feedback from reviewers [$\textbf{qsxy}$ and $\textbf{Q6Ew}$] for finding our proposed ideas "interesting", "original" and "convincing"; we also tha... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DOPPLER: Differentially Private Optimizers with Low-pass Filter for Privacy Noise Reduction | Accept (poster) | Summary: The paper looks at the sequence of DP-SGD noisy gradients through a signal processing perspective, and argues that the exact gradients are likely a low-frequency signal, while the noise has higher frequencies. As a result, the paper proposes filtering the high-frequencies with a low-pass filter to improve the ... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and providing constructive feedback to us. Below, we answered your questions. Please take a look at them and let us know if there are any remaining questions, and we would be more than happy to continue the discussion.
$\quad$
> Many of the signal processing conc... | Summary: This paper augments DP-SGD with a low-pass filter-based postprocessing on the iterates of DP-SGD. This design is based on the intuition that the noise contributes more to the high frequencies, while the gradients (assuming sufficient smoothness of the objective) contribute more to the lower frequencies.
The ... | Rebuttal 1:
Rebuttal: Thank you for providing feedback to us. We are glad that you found our paper well-written, the results significant, and the theory meaningful. We are also very excited to hear that you really liked the paper. Below, we respond to your main comments:
> Error bars
The attached PDF provides the err... | Summary: This paper suggests the effects of a low-pass filter for private training with DP-SGD. After investigating the noise and true gradients during training, the authors propose using previous gradients and momentum to distinguish effective gradients from random noise. They empirically prove their idea across vario... | Rebuttal 1:
Rebuttal: Thank you for recognizing the contribution of our paper and providing detailed feedback to improve the paper. Our responses to your specific comments are listed below.
> The authors use assumptions of bounded variance and gradient norm. However, these assumptions might not be true in real DP-SGD ... | Summary: This paper proposes DOPPLER mechanism to post-process DP gradients and reduce noise in them, before updating the model. The work makes an observation that, in frequency domain, there is a clear distinction between distribution of SGD gradients and DP noise; the earlier lies in a small window of lower frequenci... | Rebuttal 1:
Rebuttal: Thank you for recognizing the contribution of our signal processing perspective, and the precious advice for improving the presentation. We would like to address the reviewer's concerns as follows.
$\quad$
> Can you provide some intuition about what does it mean to convert a series of gradients fr... | Rebuttal 1:
Rebuttal: # Response to all reviewers
We would like to thank the reviewers for their constructive and detailed feedback. We are glad that the reviewers found our approach novel and effective despite its simplicity. Before responding to the individual reviewers' questions, we would like to thank the reviewe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Dataset Decomposition: Faster LLM Training with Variable Sequence Length Curriculum | Accept (poster) | Summary: This paper proposes to carefully segment the training data so that different documents won't be mixed together. They also propose a Grow-P2 curriculum that increases training efficiency and stability.
Strengths: The proposed Grow-P2 curriculum is useful to practitioners if they want to pretrain large language... | Rebuttal 1:
Rebuttal: We would like to thank reviewer uip1 for their feedback. We respond to the reviewer’s concerns and questions below and kindly request that you let us know if further clarification is needed.
---
> This paper presents only empirical results.
We appreciate that our work is considered an empirical ... | Summary: The paper explores dataset decomposition for LLM pre-training.
The method decomposes documents into subsequences and organizes them into buckets. Sequences of similar lengths are grouped in the same bucket, and different buckets have different lengths. This amounts to more efficient training. The paper inves... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer H2jF for their time and feedback. We are glad that the reviewer finds our work to be reasonably motivated, and the results to be extensive and promising. We respond to the reviewer’s concerns and questions below, address all of them in the revised paper, and kin... | Summary: The paper introduces 'Dataset Decomposition' (DD), a method to enhance the pre-training of Large Language Models (LLMs). Contrary to the traditional 'concat-and-chunk' approach, which can lead to unwanted cross-document attention and computational inefficiency, DD organizes datasets into buckets with sequences... | Rebuttal 1:
Rebuttal: We would like to thank reviewer z2w6 for their time and feedback. We are happy that the reviewer finds our experiments comprehensive, analyses noteworthy, and the method simple and effective. We address the concerns and questions raised by the reviewer below and kindly request to be informed if fu... | Summary: The paper introduces a method called dataset decomposition for training large language models (LLMs) more efficiently. Traditional LLM training processes use fixed-length token sequences, leading to inefficiencies such as unnecessary computational costs from cross-document attention. The proposed method tackle... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer, bcif, for their time and feedback. We are glad the reviewer finds the paper well-organized and motivated, the method effective, and the experiments valuable to the community. In the following, we address the concerns and questions raised by the reviewer and kin... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their time and feedback. We are pleased with the positive comments from the reviewers: Reviewer *bcif* finds the paper **well-organized and motivated**, the **method effective**, and the **experiments valuable to the community**; Reviewer *z2w6* finds o... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SuperEncoder: Towards Iteration-Free Approximate Quantum State Preparation | Reject | Summary: This paper introduces SuperEncoder, a novel approach to Quantum State Preparation (QSP) that aims to combine the scalability of Approximate Amplitude Encoding (AAE) with the speed of traditional Amplitude Encoding (AE). SuperEncoder uses a pre-trained neural network to directly estimate the parameters of a Par... | Rebuttal 1:
Rebuttal: We thank the reviewer for constructive feedback.
We are elated that the reviewer found our idea a nice solution, the presentation clear, and the evaluation comprehensive.
Following are our responses to each individual comment (which are highlighted in italics).
> *The gradient evaluation of the l... | Summary: In this paper, the authors propose a model, namely SuperEncoder, to solve the quantum state preparation problem. Instead of evolving the parameterized gates to generate the target quantum state, they train a model to predict the rotation parameters from the target states.
Strengths: Solve the quantum state pr... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. Following are our responses to each individual comment (which are highlighted in italics).
> *Poor results. The results seem ok with four qubits but decrease way too fast when increasing the number of qubits. The proposed method is not comparable to previ... | Summary: The paper addresses the problem of Quantum State Preparation (QSP), which is critical for quantum computing but requires a circuit depth that scales exponentially with the number of qubits, making it impractical for large-scale problems. The authors propose SuperEncoder, a pre-trained classical neural network ... | Rebuttal 1:
Rebuttal: We thank the reviewer for constructive feedback. Following are our responses to each individual comment (which are highlighted in italics).
> *[Scalability Issue] The most significant drawback of this work is its poor scalability. Since the input to the SuperEncoder is 2^n
dimensional, the numbe... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and comments, which have helped improve our paper.
We are pleased that the reviewers acknowledged our contributions and found our idea a novel solution.
Following are some of the main critiques; afterwards, we address each reviewer's comments individually.... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Multi-Scale VMamba: Hierarchy in Hierarchy Visual State Space Model | Accept (poster) | Summary: This paper presents a multi-scale Vmamba model, which incorporates multi-scale information into the design of the Vmamba architecture. Additionally, the authors analyze how the attenuation coefficient between tokens increases as the modeled distance in Vmamba grows, whereas MSVmamba alleviates this attenuation... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and for the time you have dedicated to reviewing our manuscript. We greatly appreciate your feedback, which has been instrumental in enhancing the quality of our paper. Your concerns about the **scalability (Q1)** and the **ablation of our model in more fine-... | Summary: This paper introduces a Multi-Scale Vision Mamba (MSVMamba) for computer vision tasks. It uses a multi-scale 2D scan operation on both the original and sub-sampled features to preserve long-range information and reduce computational costs. In addition, they address the problem of channel mixing in Mamba-based ... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and for the time you have dedicated to reviewing our manuscript. We appreciate your feedback, which has helped us improve the quality of our paper. We address your concerns in a few points as described below:
### **Q1: Concerns About Novelty.**
Thank you f... | Summary: This paper presents a multi-scale vision mamba aimed at improving the performance of state space models (SSMs) in vision tasks while maintaining efficiency. The motivation stems from analyzing the multi-scan strategy in vision mamba, where the authors link its success to alleviating the long-range forgetting i... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and for the time you have dedicated to reviewing our manuscript. We greatly appreciate your feedback, which helps enhance the quality of our paper. Below, we address your concerns regarding the **baseline involving the reduction of scanning numbers(Q1)** and ... | Summary: The paper introduces a novel vision backbone model, MSVMamba, which incorporates State Space Models (SSMs) to address limitations in computational efficiency and long-range dependency capture in vision tasks. The model utilizes a multi-scale 2D scanning technique and a Convolutional Feed-Forward Network (ConvF... | Rebuttal 1:
Rebuttal: Thank you for your comments and the time you have dedicated to reviewing our manuscript. We appreciate your feedback, which helps us improve the quality of our paper. We address your concerns in a few points as described below:
### **Q1: Lack of novelty.**
We agree that multi-scale strategy, SE... | Rebuttal 1:
Rebuttal: Dear reviewers and ACs:
First and foremost, we wish to express our sincere gratitude for the time and effort you have dedicated to reviewing our manuscript. Your insightful suggestion and comments could further enhance the quality of this paper.
We have conducted additional experiments to addres... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper introduces Multi-Scale VMamba (MSVMamba), a novel vision backbone model that leverages State Space Models (SSMs) to address the challenges of quadratic complexity in Vision Transformers (ViTs). The proposed Multi-Scale 2D Scanning (MS2D) and Convolutional Feed-Forward Network (ConvFFN) contributes to... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and for the time you have dedicated to reviewing our manuscript. We appreciate your feedback, which helps us improve the quality of our paper. Below, we address your concerns regarding the **efficiency comparison(Q1)** and the **ablation of our model to a tin... | null | null | null | null | null | null |
Neural Signed Distance Function Inference through Splatting 3D Gaussians Pulled on Zero-Level Set | Accept (poster) | Summary: This paper aims to infer neural signed distance functions (SDF) for Gaussian Splatting. To this end, this paper introduce an MLP to represent the SDF. To learn SDF from sparse and non-uniform Gaussian points, this paper introduces a differentiable pulling operation from Neural-Pull to align Gaussians on the z... | Rebuttal 1:
Rebuttal: **1. Reconstructing details.** Since we use the marching cubes algorithm to extract the zero-level set as a mesh surface, our results is limited by the resolution used by the marching cubes. While 2DGS uses multi-view depth images to reconstruct meshes with Poisson surface reconstruction, which ac... | Summary: This paper combines 3D Gaussian Splatting and NeuralPull to extract surface. By this way, it can utilize the existing extracting method Marching Cubes algorithm to extract the zero-level set as a mesh surface.
Strengths: 1. With a neural SDF network, this paper can utilize the Marching Cubes algorithm to extr... | Rebuttal 1:
Rebuttal: **1. Explanation of the method.** When training 3DGS, 3D Gaussians are progressively approaching to the zero-level set. At the same time, we pull Gaussians to align with the zero-level set of SDF. Under the joint optimization of these two process, along with our novel pulling operation and constra... | Summary: This paper focuses on the challenge of inferring a signed distance function (SDF) for multi-view surface reconstruction from 3D Gaussian splatting (3DGS), which is hindered by the discreteness, sparseness, and off-surface drift of the 3D Gaussian. To overcome these challenges, the authors propose a method that... | Rebuttal 1:
Rebuttal: **1. Eikonal loss.** We believe pulling based methods may not need an Eikonal loss to guarantee the learned distance field is an SDF. This is because we use a normalized gradient and a predicted signed distance when pulling a query. It has been proved by NeuralPull[1] that adding the Eikonal loss ... | Summary: The paper proposes a extension to 3D Gaussian Splatting by making it consistent with a neural SDF that is learnt along with the 3D Gaussians. To make it consistent, the Gaussians are projected to the zero level set, and the neural SDF is optimized to represent the SDF of the surface implied by the Gaussians. A... | Rebuttal 1:
Rebuttal: **1. Comparison between flat loss and surfels.** Our method requires calculating the inverse of a three-dimensional covariance matrix (Eq.(5) in our paper) to deterimine the distribution probability of a query point within its nearest Gaussian ellipsoid. This allows us to maximize the probability ... | Rebuttal 1:
Rebuttal: We thank reviewers for comments and highlighting our ***simple and interesting idea*** (Reviewer CCgx, 5rKf, ZhFP), ***good performance and visualization*** (Reviewer CCgx, ZhFP), ***well-written manuscript*** (Reviewer 5rKf). We have provided detailed responses to each reviewer's comments. All th... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
GSDF: 3DGS Meets SDF for Improved Neural Rendering and Reconstruction | Accept (poster) | Summary: The paper introduces GSDF, a new approach for both novel view synthesis and surface reconstruction that relies on a dual-branch architecture combining 3D Gaussian Splatting (3DGS) with neural Signed Distance Functions (SDF).
**Details:**
The paper aims to improve the quality of both the rendering compared to... | Rebuttal 1:
Rebuttal: Thanks for your efforts and valuable comments. Below we address concerns for each question. Common concerns are detailedly responded in the global rebuttal. Additional Figures and Tables are provided in the attached PDF, in which the index is denoted as *Figure A-D* and *Table A-B*.
**Q1. Why usi... | Summary: This paper proposes to jointly optimize 3D Gaussian Splatting (3DGS) and SDF (like NeuS). During GS optimization, the Gaussians are aligned to the zero-level set (and normals) of SDF. During the NeuS-like optimization, Gaussians are used to limit the range of ray sampling, resulting in efficient optimization. ... | Rebuttal 1:
Rebuttal: Thanks for your efforts and valuable comments. Below we address concerns for each question. Common concerns are detailedly responded in the global rebuttal. Additional Figures and Tables are provided in the attached PDF, in which the index is denoted as *Figure A-D* and *Table A-B*.
**Q1. Compari... | Summary: This paper tackles the challenge of representing 3D scenes from multiview images by introducing a novel dual-branch architecture named GSDF, which combines 3D Gaussian Splatting and neural Signed Distance Fields. This architecture enhances both rendering and reconstruction through mutual guidance and joint sup... | Rebuttal 1:
Rebuttal: Thanks for your efforts and valuable comments. Below we address concerns for each question. Common concerns are detailedly responded in the global rebuttal. Additional Figures and Tables are provided in the attached PDF, in which the index is denoted as *Figure A-D* and *Table A-B*.
**Q1. Output ... | Summary: This paper introduces GSDF that utilizes a joint optimization of the GS-branch and SDF-branch to constrain the inherent geometric issues of the 3DGS. Furthermore, it proposes three mutual guidances to ensure satisfactory outcomes in both rendering and reconstruction. The extensive experiments on datasets such ... | Rebuttal 1:
Rebuttal: Thanks for your efforts and valuable comments. Below we address concerns for each question. Common concerns are detailedly responded in the global rebuttal. Additional Figures and Tables are provided in the attached PDF, in which the index is denoted as *Figure A-D* and *Table A-B*.
**Q1. Impreci... | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable feedback.
We are encouraged that reviewers find
- our two-branch design is novel and effective in boosting reconstruction and rendering quality simultaneously;
- our analysis and experiments are useful and comprehensive.
We will release our code for r... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
RG-SAN: Rule-Guided Spatial Awareness Network for End-to-End 3D Referring Expression Segmentation | Accept (oral) | Summary: This paper presents RG-SAN, a new method for 3D Referring Expression Segmentation. It combines spatial reasoning with textual cues to segment 3D objects accurately. RG-SAN uses a Text-driven Localization Module and a Rule-guided Weak Supervision strategy. It outperforms existing methods on the ScanRefer benchm... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and recognition of our contributions. We greatly appreciate your commendation of our clear articulation of motivation and quantitative analysis. We're pleased you acknowledged our method's effectiveness in spatial relation reasoning for 3D scenes, expressing in... | Summary: This paper presents a novel and high-performing 3D referring segmentation network. Specifically, it approaches the problem from both 3D spatial relationships and natural language spatial descriptions, innovatively using explicit spatial position modeling and multimodal interaction. This allows the query corres... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and recognition of our work. We appreciate your acknowledgment of our exploration of text-conditioned 3D spatial perception and the effectiveness of the TLM and RWS modules. We're also glad you found our video demo and Figure 3 visualizations clear and insightf... | Summary: This paper presents the Rule-Guided Spatial Awareness Network (RG-SAN) for 3D referring expression segmentation (3D-RES), offering a novel approach to understanding spatial relationships in the visual-language perception domain. It aligns 3D and linguistic features not only at the semantic level but also withi... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and acknowledgment of our paper's strengths. We're pleased you appreciate our approach of attributing 3D spatial properties to text for 3D multimodal spatial perception modeling, recognize the promising performance of our model on ScanRefer, and underscore the ... | Summary: The paper proposes a new framework for 3D referring expression segmentation. The main contributions include analyzing the spatial information among objects and rule-guided target selection. Extensive experiments validate the effectiveness of the proposed method.
Strengths: The authors develop the method to ac... | Rebuttal 1:
Rebuttal: > Q1: Using spatial information is not new. Sec. 3.2.2 and 3.2.3 and Eq. (7) to (12) resemble those in [22]. Spatial information in 3D visual grounding has been explored, like [6].
>
A1: Thank you for your valuable feedback. Indeed we acknowledge that our use of positional encoding is based on p... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to the reviewers for their valuable feedback and positive comments on our paper. Their insightful reviews have greatly contributed to improving the clarity and overall quality of our work.
We appreciate Reviewer **6dEW** $\color{red}{(Rating:\mathbf{3},\ Con... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
From Causal to Concept-Based Representation Learning | Accept (poster) | Summary: The paper focus on recover human-interpretable concepts from observation. It proposes a concept based representation learning method, which relax causal notions with a geometric notion of concepts. Experiments on synthetic data, multimodal CLIP models and large language models supplement their results and sho... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review. We address their comments below.
> This approach sacrifices causal semantics. This can be particularly problematic in situations where a deep understanding of causality is crucial
In this work, we focus on the many applications where a relaxation of causal... | Summary: This paper proposes a theory to identify latent concepts from model representations. In contrast to previous work in the concept-based models' field, concepts are expected to be linearly represented in the model representations, and a linear transformation A is associated with such concepts. The paper is theor... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful review and are glad that they appreciate our contributions towards the important open problem of bridging causal representation learning and concept based representation learning.
> The experiments on CLIP and LLMs seem to me unrelated to the theory...How i... | Summary: This work takes a step toward learning human-interpretable concepts while relaxing the restrictions of (interventional) causal representation learning, and they do so inspired by the linear representation hypothesis.
The authors claim that learning the generative process and the "true" causal factors $f^{-1},... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review and nice summary of the paper. We also appreciate their insightful comments on the importance of this work.
> In the synthetic experiments, I was expecting to see large $d_z$ and small $n$
Thanks for the suggestion, we ran additional experiments an... | Summary: The authors argue the shift from causal representation learning (CRL) to concept-based representation learning since the current CRL framework relies on strong requirements such as interventional datasets and stands far from realistic, practical use-cases. The paper formalizes the notion of concepts and establ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review and are glad they find the paper novel, well-written and rigorous. We address the weakness below.
> In other words, the requirement of data partition $X^0, \ldots, X^m$ goes against the motivation of the proposed framework of handling continuous con... | Rebuttal 1:
Rebuttal: We appreciate the thoughtful reviews and suggestions by the reviewers.
We are glad that the reviewers found our approach original and novel (reviewers KRMD, jSX2, kVe2), well-written and rigorous (reviewers ohha, KRMD) and significant (reviewers jSX2, ohha).
The reviewers appreciated our theoreti... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
How Control Information Influences Multilingual Text Image Generation and Editing? | Accept (poster) | Summary: This article investigates an intriguing issue: how controlling information affects performance in the process of text image editing and generation. Through a series of observations and experiments, the authors extract valuable insights. Building upon these findings, they craft a novel framework that demonstrat... | Rebuttal 1:
Rebuttal: We thank you for your valuable comments and positive attitude to our paper. Below are my rebuttal and discussion for these questions 😊.
**Question 1: Internal workings.** The pipeline contains two control models and a diffusion process. As shown in Figure 4, the early timestamps in diffusion pro... | Summary: This paper introduces a novel framework to enhance the quality of multilingual text image generation and editing by optimizing control information. The authors investigate the impact of control information from three perspectives: input encoding, the role at different stages, and output features. They find som... | Rebuttal 1:
Rebuttal: We thank the reviewers for their positive attitude towards our paper and for their valuable comments 😊. Below are our rebuttal and discussion of the questions.
**Question 1: The evaluation.** We conducted evaluations on the AnyWord benchmark, which is the highest quality benchmark. Your suggesti... | Summary: This study explores the advancement of visual text generation using diffusion models within a ControlNet-based framework, focusing on the impact of control information. It examines input encoding, the role of control information during different stages of the denoising process, and the resulting output feature... | Rebuttal 1:
Rebuttal: We appreciate your insightful comments and have carefully considered your questions and suggestions. Below is our rebuttal and discussion of these questions 😊. Due to rebuttal restrictions, we are unable to include images. We will provide the corresponding visual results in the Appendix of the fi... | Summary: This paper analyzes the issue of control in image generation models. Specifically, the article addresses three aspects: input control information, the impact of control information at different stages, and output control information. The model was optimized for two tasks: text-to-image and image editing. Using... | Rebuttal 1:
Rebuttal: We thank you for your valuable comments and some of the affirmations of our paper. These questions are insightful and deepen my thinking. Below are my rebuttal and discussion for these questions 😊.
**The writing.** We apologize for the writing errors in our paper. In this paper, we primarily stu... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
AsCAN: Asymmetric Convolution-Attention Networks for Efficient Recognition and Generation | Accept (poster) | Summary: The paper performs a thorough empirical analysis of design choices made in neural networks designed for computer vision tasks. Based on reasonable assumptions, they first narrow down the design space of the building block of such networks to combinations of Fused MBConv and vanilla transformer layers followed ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback on this work.
As already pointed out by the reviewer, ablations on large-scale text-to-image generation tasks are computationally very expensive. Hence, we performed small ablations on the ImageNet-1K dataset. We list these ablations below. We wil... | Summary: The authors propose a principled way to design hybrid architectures for a variety of tasks, including image classification, semantic segmentation, class-conditional generation, and text-to-image generation. The goal is the resulting models, called Asymmetric Convolution-Attention Networks (AsCAN), to have the ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for reading the paper thoroughly and providing invaluable feedback. Below, we have tried to answer their questions and concerns. While we answered some questions in the main rebuttal, we reiterate their highlights for completeness.
**Originality: Limited Novelty.**
- O... | Summary: Asymmetric Convolution-Attention Networks for Efficient Recognition and Generation
In this paper, AsCAN combines both convolutional and transformer blocks. The authors revisit the key design principles of hybrid architectures and propose a simple and effective asymmetric architecture, where the distribution of... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We provide additional clarifications in this response to the best of our understanding of the reviewer's concerns.
**(Q.1) Lack of theoretical analysis.**
We propose a new design for hybrid convolutional-transformer architectures and apply this new arch... | Summary: The authors present AsCAN, a hybrid architecture that combines convolutional and transformer blocks, that can applied to both visual recognition and generation. This architecture features an asymmetric distribution, with more convolutional blocks in early stages and more transformer blocks in later stages. It ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for reading the paper thoroughly and providing invaluable feedback. Below, we have tried to answer your questions and concerns. While we answered some questions in the main rebuttal, we reiterate their highlights for completeness.
**Explanation of higher throughput whil... | Rebuttal 1:
Rebuttal: We are grateful to all the reviewers for their constructive and detailed feedback. We included following additional experiments in the attached rebuttal pdf:
1. Figure 1 top-1 accuracy vs throughput trade-off figures for both A100 and V100 GPUs
2. Table 1 with additional details on class conditio... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper presents a hybrid neural network architecture that incorporates both convolution-based and vision transformer (ViT)-based building blocks for discriminative and generative modeling. The proposed convolutional block, labeled as (C), is identical to the EfficientNetV2's FusedMBConv, where a standard 3... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for reading the paper thoroughly and providing invaluable feedback. Below, we have tried to answer your questions and concerns. While we answered some questions in the main rebuttal, we reiterate their highlights for completeness.
**Stem Design Choice.** This is a popul... | null | null | null | null | null | null |
Bridging OOD Detection and Generalization: A Graph-Theoretic View | Accept (poster) | Summary: This work proposes a framework to address OOD detection and generalization jointly for image data, using a graph representation, where edges are constructed by both self-supervised data transformation probability and supervised labels. An example is provided with theoretical analysis, to show the advantages an... | Rebuttal 1:
Rebuttal: We thank you for the positive and constructive feedback on our work! Below we address your questions and comments in detail.
> Further refinement for the OOD generalization performance
We appreciate your insightful analysis and totally agree with your perspective. Guarantees for OOD generalizati... | Summary: The paper proposes a graph-theoretic framework to address out-of-distribution (OOD) generalization and detection. The framework models the data using a graph, where vertices represent data points and edges indicate similarities based on supervised and self-supervised signals. By leveraging spectral decompositi... | Rebuttal 1:
Rebuttal: We thank you for the positive and constructive feedback on our work! Below we address your questions and comments in detail.
> Practicality of the algorithm/framework
You raise an excellent point. Our graph-theoretic framework can be used practically, as detailed in Section 3.2. In particular, t... | Summary: The paper introduces a novel graph-theoretic framework to tackle both out-of-distribution (OOD) generalization and detection. By representing data points as vertices in a graph and using the adjacency matrix decomposition, the authors derive data representations that allow for quantifiable error rates in OOD t... | Rebuttal 1:
Rebuttal: We thank you for the positive and constructive feedback! Below we address your questions and comments in detail.
> Questions about the metrics
We are happy to clarify this. SCONE adopted four metrics in experiments, **which are identical to ours**. Following SCONE, we introduce the FPR as the pr... | Summary: This paper proposes a unified framework for OOD detection and generalization, which first constructs a graph that includes both labeled and unlabeled data and derives data representations by factorizing the graph’s adjacency matrix. These representations help quantify OOD generalization and detection performan... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback. We are glad to hear that you found our paper insightful, clearly presented, and performing well. We address each of your concerns below in detail:
> Clarification on the number of hyperparameters
Thank you for bringing up this point. To clarif... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and commitment to providing valuable feedback and suggestions on our work. We are encouraged that reviewers find our idea to be **novel**, **interesting**, and **effective** (DU8N, ureZ, 6JZj), that our theoretical insights are **sound and valuable** (DU8... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Parametric model reduction of mean-field and stochastic systems via higher-order action matching | Accept (poster) | Summary: The authors present a framework for learning a time-dependent gradient vector field (parametric model) that describes how the particle evolves in the state space. The idea is based on the action-matching framework (https://arxiv.org/abs/2210.06662), which learns a time-dependent gradient field that interpolate... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough and insightful comments:
"The objective used by the authors is based on the action-matching paper, now adapted to include the dependence on the parameters (\mu) [...] the authors' only important contribution is using a higher-order quadrature scheme to dis... | Summary: The authors focus on learning models for population dynamics of parameterized physical systems that exhibit stochastic and mean-field effects in time. To do so, the authors leverage the Benamou-Brenier formula to learn gradient fields that transport the probability density as time evolves, and that enable the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive and thorough review. We will address all the points brought up.
"In (6) it seems that the integrand should be evaluated on instead of for consistency with (4)."
- Thank you, yes, this should be $s_{t, \mu}$.
"In (7) there seems to be a parenthesis missing ... | Summary: This paper develops models of population dynamics in physical systems that exhibit stochastic and mean-field effects, influenced by physics parameters. The goal is to create models that can efficiently predict system behavior as alternatives to classical numerical methods. By utilizing the Benamou-Brenier form... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and respond to the following points:
"The model assumes access to a dense set of time points for the Gauss-Legendre quadrature [...] the practical benefit would be limited"
- Having data at a good amount of time points is a common situation in the setting of... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for taking the time to read the paper and very much appreciate their detailed comments. Below we provide a detailed response. We summarize here some of the main points and how we addressed them:
- We now stress that the higher-order quadrature scheme that is introduced by o... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fast samplers for Inverse Problems in Iterative Refinement models | Accept (poster) | Summary: This paper introduces a inverse problem approach to reconstruct low-resolution blurred or obfuscated images, from pre-trained generative models. The method leverages conjugate integrators to project the diffusion dynamics in an easier space, solve the inverse problem and then map back.
Strengths: - The integr... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We provide a detailed response to several concerns below:
> I would suggest moving sections 2.3 and 3.4, as well as the numerical details of the experiments, to the appendix.....
We thank the reviewer for their suggestion. We stress that the final versi... | Summary: Inverse problems like super-resolution and deblurring remain computationally inefficient with current diffusion/flow models. The paper introduces a plug-and-play framework with Conditional Conjugate Integrators that use pre-trained iterative refinement models to simplify sampling. The method generates high-qua... | Rebuttal 1:
Rebuttal: $\newcommand\dag\dagger$
We thank the reviewer for their feedback. We provide a detailed response to several concerns below:
> The reason why the paper is restricted to the $\Pi$GDM paradigm is unclear.
We chose to work with the $\Pi$GDM baseline due to the following reasons:
1. Firstly, $\Pi$G... | Summary: This work proposes a plug-and-play based method that leverages pretrained diffusion and flow models to solve inverse problems. The proposed method called Conditional Conjugate Integrators adapts previously proposed Conjugate Integrators framework for fast sampling of diffusion and flow models to solve linear i... | Rebuttal 1:
Rebuttal: $\newcommand\dag\dagger$
We thank the reviewer for their insightful comments and feedback. We provide a detailed response to several concerns below:
>The benefits of the proposed method for the settings of noisy linear inverse problem setting as well as non-linear inverse problems remain unclear.... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful comments and are glad that the reviewers found our paper well-written (Reviewers hLds, PpUR), well-supported by theoretical arguments (Reviewer PpUR), our proposed method efficient (Reviewer hLds), and rigorously defined (Reviewer X2Z6). Below we highlig... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Integrating GNN and Neural ODEs for Estimating Non-Reciprocal Two-Body Interactions in Mixed-Species Collective Motion | Accept (poster) | Summary: This work proposes a graph neural network-based deep learning model for two-body interactions, which has better efficiency based on two numerical experiments.
Strengths: Two-body interactions are not my research field. Thus, I don't have enough expertise to evaluate this work. I sent an email to AC to re-assi... | null | Summary: This paper presents a model combining GNN and Neural ODEs for the task of two-body interaction prediction. Two numerical experiments were conducted to evaluate the model's performance.
Strengths: - Using a GNN and Neural ODE to describe the dynamics of two-body interaction state changes is well-motivated and ... | Rebuttal 1:
Rebuttal: Thank you for bringing to our attention these papers. We will cite these papers in the final edition.
Re – Question and Weakness (Relationship to Graph ODE):
The pre-existing methods [1-5] solve and train ODEs on a graph with fixed edges and consider changes in the edge weight. These approaches... | Summary: The paper introduces a novel deep learning framework for estimating two-body interactions in mixed-species collective motion. The authors combine graph neural networks (GNNs) with neural ordinary differential equations (Neural ODEs) to predict interactions between pairs of entities based on their states. This ... | Rebuttal 1:
Rebuttal: Thank you for the feedback and comments. We are encouraged by your overall evaluation and many of the technical suggestions which are very helpful in future extension of the work.
Re – Question 1 and Weakness 4 (Comparative analysis with SINDy and Bayesian optimization methods):
Thank you for... | Summary: This paper seeks to model dynamic interactions of biological agents (primarily cellular slimes) by adapting the (continuous time) Langevin model, which models the collective motion of the system from considering individual force terms and the summation of pairwise interactions. Such pairwise interactions can b... | Rebuttal 1:
Rebuttal: Thank you for the feedback and comments. Your careful reading was very helpful in identifying places that we missed to explain in the draft.
Re – Question 1 and Weakness 2 (Why not use a Physics informed neural network):
This was briefly described in the Background section, however it was not fu... | Rebuttal 1:
Rebuttal: We thank reviewers for careful consideration of our work and are thankful for their astute criticism, which was very helpful in vastly improving the quality of the manuscript. The manuscript was reviewed by 4 reviewers, with two of them (Shpk and Sm49) recommending acceptance. The other two, KAZi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision | Accept (poster) | Summary: This paper attempts to address a forward-looking and challenging question: "Can we limit human supervision to easier tasks, yet enable the model to excel in harder tasks?" (referred to as Easy-to-Hard Generalization). Based on the observation that "evaluation is easier than generation", the authors propose to ... | Rebuttal 1:
Rebuttal: Thank you for your insightful review and positive feedback. We're glad to hear that you appreciate the easy-to-hard generalization problem we’re going on. Your recognition of our proposal and motivations is encouraging. We address your questions below.
**Weaknesses**
**W1 (a). The definition of... | Summary: This paper addresses the issue that humans cannot always provide helpful demonstrations or supervision on tasks beyond their expertise. Based on the observation that evaluation is easier than generation, the authors propose "easy-to-hard generalization," training a verifier on easy tasks and leveraging its gen... | Rebuttal 1:
Rebuttal: Thank you for your insightful review and constructive feedback. We appreciate your recognition of the paper’s clarity, the proposed OPRM methods, and the solid experiments analysis. We address your concerns and questions below.
**Weaknesses & Questions**
**W1&Q1. What is the difference between "... | Summary: In this paper, the authors propose *easy-to-hard* generalization, which is to train a reward model on simpler tasks and then use it to evaluate the solutions for more difficult tasks. They have conducted in-depth studies on MATH, and also demonstrated effectiveness on the coding benchmark APPS. This work serve... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback on our paper. We are glad that you appreciate the inspiring easy-to-hard generalization problem we’re working on, and thank you for acknowledging the thoroughness of our experiments. We address your questions below.
**Weaknesses**
**W1. The definition of "ea... | Summary: - This paper studies the question of how a system can be improved when the performance of a system has surpassed human performance on a task.
- As a testbed, the paper uses problems from the MATH dataset which have been sorted into 5 levels by difficulty.
- The authors first train process-supervised reward mod... | Rebuttal 1:
Rebuttal: Thank you for your insightful review and the positive feedback on our paper. We are pleased that you found our proposals interesting and our experiments thorough. It's particularly encouraging to hear that you recognize the novelty of our easy-to-hard generalization approach and our results useful... | Rebuttal 1:
Rebuttal: Dear Reviewers and AC,
Thank you all for your time and effort in reviewing our paper. We are grateful to 3nAE, dzZx, and bhN7 for recognizing **the adequacy and novelty of our experiments and motivations** and acknowledging **the importance of the problem we are exploring, easy-to-hard generaliza... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CLIPLoss and Norm-Based Data Selection Methods for Multimodal Contrastive Learning | Accept (spotlight) | Summary: This paper introduces a novel metric, negCLIPLoss, for selecting high-quality data. Additionally, the paper proposes a norm-based metric, Normsim, which offers an improved measure of data quality and is compatible with existing methods. Both negCLIPLoss and NormSim demonstrate significant performance improveme... | Rebuttal 1:
Rebuttal: Thank you for your recognition of our paper and your constructive feedback. We have responded to your concerns and will revise our paper based on the discussions. We would also appreciate it if you could let us know if our response addresses your concerns.
> **Q1**: In Line 86, the authors asser... | Summary: This work proposes two new approaches for data selection for vision-language pre-training. The first approach, negCLIPLoss, adds the contrastive loss as a normalization term on top of the existing CLIPScore metric. The second approach, NormSim, further improves the performance if examples from target task dist... | Rebuttal 1:
Rebuttal: Thank you for your recognition of our paper together with your valuable comments and suggestions. We will revise our paper according to your comments. We respond to your questions below and would appreciate it if you could let us know if our response addresses your concerns.
> **Q1**: this is a ... | Summary: Data selection is crucial in the pretraining stage to clean the web-crawled, large, and noisy pretraining dataset. Typically, existing methods use embeddings to compute CLIPscore in order to assess the data sample alignment quality. This paper introduces two new methods to enhance this measurement:
1. negCLIPL... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback to help us improve our paper. We will revise our paper based on your feedback. We detail our response below and please kindly let us know if our response addresses your concerns.
> **Q1**: I think we need more clarification on how to interpret the Top X% i... | null | null | Rebuttal 1:
Rebuttal: # Reply to all reviewers for the major concern
We sincerely appreciate all reviewers for their insightful and constructive feedback to make our paper better. We will revise our paper according to these comments. Here we will address the most common concerns of the reviewers and will put other res... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards Editing Time Series | Accept (poster) | Summary: This paper introduces Time Series Editing (TSE), a method for generating time series data with control over their attributes. TSE generates a sample by modifying an existing time series through manipulation of specific attributes while maintaining consistency in others. This approach leverages the hypothesis t... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback!
>**W1. Edited attribute set**
Thanks for your comments.
In the tables below, we show the average experimental results of editing different types and numbers of attributes on the Synthetic dataset. It can be seen that our TEdit outperforms baseline method o... | Summary: The paper discusses the challenges of synthesizing time series data influenced by intrinsic and extrinsic factors. It highlights the limitations of existing benchmarks and methods and suggests a new task, Time Series Editing (TSE), which modifies given time series based on specified properties while preserving... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments!
>**W1. Comparison with [1]**
Thanks for pointing it out, and we will include a discussion and comparison of [1] in the revised version. Time Series Editing (TSE) proposed in our work is essentially different to [1], though these two works share some similaritie... | Summary: This paper introduces time series editing (TSE), a novel approach for modifying existing time series to match specified attributes while preserving other attributes. Unlike traditional methods that generate time series from scratch, TSE employs a multi-resolution modeling and bootstrap tuning framework, enhanc... | Rebuttal 1:
Rebuttal: Thanks for your appreciation and feedbacks! Below are the responses.
>**W1. Handling of diverse attribute types and their interactions.**
Thanks for your comments.
The following tables 1~6 show the complete experimental results of editing different combination of attributes (trend type, trend ... | Summary: This work proposes a new task as time series editing (TSE), that edits existing time series samples based on given sets of attributes. The goal is to perform edits that are only relevant to the changed attributes while preserving other existing attributes. The authors propose a multi-resolution modeling and ge... | Rebuttal 1:
Rebuttal: Thanks for your appreciation and comments!
>**W1. Suggested new datasets.**
Thanks for your suggestion. The datasets you mentioned are indeed related and useful, revealing a wider application space for our work.
We have looked into these datasets, many of which are EEG time series with various ... | Rebuttal 1:
Rebuttal: # General Response
We thank all the reviewers' insightful and valuable feedback!
We are encouraged by the positive comments from the reviewers such as:
- The proposed Time Series Editing (TSE) is an interesting and important task with a wide range of applications (Reviewer tfFr, ZaGr, EFAk and ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator | Accept (poster) | Summary: This paper proposes piecewise rectified flow (PeRFlow) for accelerating pre-trained diffusion models. To overcome the requirement of synthetic data generation in rectified flow, the authors propose to prepare the training data by dividing the entire ODE trajectory into multiple time windows. The sampling traje... | Rebuttal 1:
Rebuttal: Thank you very much for the comments and advice.
1. *The contribution of this work is weakened by its similarity to Sequential Reflow [1], and the additional design to be compatible with different parameterization strategies is somewhat incremental.*
- Technically, PeRFlow shares a similar idea ... | Summary: This paper presents a novel approach to accelerating diffusion models by introducing the Piecewise Rectified Flow (PeRFlow). This method significantly enhances the efficiency of generating high-quality generative samples by dividing the flow trajectories of diffusion models into several time windows and straig... | Rebuttal 1:
Rebuttal: Thank you very much for the comments and advice.
1. *I think the multi-step consistency model [1] should be discussed since it has a strong correlation with this paper. In the experiments section, you only compare PeRFlow with LCM and InstaFlow, both of which are relatively early works. There are ... | Summary: In this paper, the author proposes a new paradigm of sampling process (Piecewise Rectified Flow-PeRFlow) with reflow operation in the diffusion model, straightening the trajectories of the origin PF-ODEs and achieving a better performance in a few-step generation.
Specifically, the PeRFlow divides the sampling... | Rebuttal 1:
Rebuttal: Thank you very much for the comments and advice.
1. *The evaluation metrics for most generative models include FID and IS. And this paper only adopts the FID as the evaluation metric. Although this paper is aimed to accelerate the diffusion model with better performance, it is better if author ca... | Summary: The paper introduces a new flow-based method designed to accelerate diffusion models by dividing the sampling process into several time windows. The sampling path within each time window is straightened by the reflow operator. This approach allows for fast training convergence and, transferability, compatibili... | Rebuttal 1:
Rebuttal: Thank you very much for the comments and advice.
1. *When dividing the sampling process into several time windows, the error of the previous windows immensely affects the later ones, potentially increasing the cumulative error of the whole sampling process.*
- In practice, we observe increasing ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FactorizePhys: Matrix Factorization for Multidimensional Attention in Remote Physiological Sensing | Accept (poster) | Summary: The paper introduces FactorizePhys that utilizes Non-negative Matrix Factorization (NMF) to decompose voxel embeddings. By integrating FSAM into both 3D-CNN and 2D-CNN architectures, FactorizePhys can estimate blood volume pulse signals from video frames. Through evaluations and comparisons with state-of-the-a... | Rebuttal 1:
Rebuttal: We thank you for valuable comments and insightful questions. We will revise manuscript to reflect our responses.
For your comments on visualization, we request you to refer our global response. We address rest of the comments here:
* W1: The motivation to use the non-negative matrix factorizati... | Summary: The paper presents a novel attention block FSAM devised for handling spatio-temporal data. It is benchmarked against a range of SOTA architectures on a suitable selection of different datasets, and found to perform strongly.
Strengths: The paper is well presented and clearly structured
The is a good degree o... | Rebuttal 1:
Rebuttal: We thank you for acknowledging the novelty of our contributions and significance of the reported results. We highly value your suggestions based upon which we will further revise manuscript.
For your comments on statistical significance, uncertainty estimates and scalability, we request you to re... | Summary: The paper proposed the Factorized Self-Attention Module (FSAM), which jointly computes multi-dimensional attention across spatial, temporal, and channel dimensions using non-negative matrix factorization (NMF). The FSAM is integrated into a new end-to-end 3D-CNN architecture called FactorizedPhys, designed to ... | Rebuttal 1:
Rebuttal: We thank you for acknowledging the strengths of our contributions, providing constructive feedback and suggestions. We will revise manuscript to reflect responses to your comments that we describe here.
For your comments on visualization, variance and scalability, we request you to refer our glob... | null | null | Rebuttal 1:
Rebuttal: We would like to sincerely thank all reviewers for their valuable feedback that helps stregthening our contributions. We would like to respond to common comments here, while responding individually for the rest.
* a4X8, ECso:
Visualization of the attention maps to demonstrate the effectiveness t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Conjugated Semantic Pool Improves OOD Detection with Pre-trained Vision-Language Models | Accept (poster) | Summary: This paper proposes a novel method utilizing pre-trained vision-language models (VLMs) to enhance out-of-distribution (OOD) detection. Their core idea lies in constructing a Conjugated Semantic Pool (CSP) to enrich the pool from which OOD labels are drawn. Unlike simply expanding lexicons with synonyms and unc... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and the positive rating! In the following, your comments are concisely mentioned (due to the length limitation) and then followed by our point-by-point responses.
> 1. Ablation Study & OOD Score Function: ...
Thank you for the insightful comment! The OOD... | Summary: This paper presents a novel zero-shot out-of-distribution (OOD) detection pipeline that enhances performance by expanding the semantic pool with a conjugated semantic pool (CSP), which consists of modified superclass names that cluster similar properties from different categories. The approach aims to increase... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments and positive rating! Below, we briefly restate your comments (due to length limitations) and then provide our point-by-point responses.
> 1. Enhancement of semantic pool expansion within NegLabel
Thanks for the valuable comment. Our method builds on the NegL... | Summary: This paper explores how to set potential OOD labels to facilitate OOD detection with vision-language models. The paper first conducts a theoretical analysis, revealing that in addition to increasing the negative label space, it is also important to increase the probability of negative labels being activated an... | Rebuttal 1:
Rebuttal: We express our sincere gratitude for your valuable comments. Below, we first reiterate your comments, subsequently providing our detailed responses to each point.
> 1. In Line 276, the authors claim that CSPs overlapping with ID classes will not be selected as negative labels. How is this implem... | Summary: This paper presents improvements to zero-shot OOD detection methods based on pre-trained vision-language models. The study first models factors that influence the performance of existing pipelines and theoretically derive two necessary conditions for enhancing performance: expanding the OOD label candidate poo... | Rebuttal 1:
Rebuttal: We express our sincere gratitude for your valuable suggestions and the positive rating. Below, we first reiterate your comments, subsequently providing our detailed responses to each point.
> 1. The description in the caption of Table 1 is unclear. In the experimental section, the author mention... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their thorough reading of our work and the high-quality feedback they provided. Their comments have been immensely beneficial in enhancing the quality of our manuscript and deepening our own understanding of this field. We have uploaded detailed, point-by-point respo... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes a method for zero-shot out-of-distribution (OOD) detection using an expanded semantic pool of modified superclass names. By leveraging a pre-trained vision-language model, the approach aims to improve OOD classification performance by ensuring low mutual dependence among selected OOD labels.... | Rebuttal 1:
Rebuttal: We express our sincere gratitude for your constructive comments. Below, we first reiterate your comments, subsequently providing our detailed responses to each point.
> 1. The method proposed in this paper lacks innovation; its main framework and basic performance are entirely derived from NegLab... | null | null | null | null | null | null |
Variational Multi-scale Representation for Estimating Uncertainty in 3D Gaussian Splatting | Accept (poster) | Summary: This work suggests a method to estimate uncertainty for the output of a 3D Gaussian Splatting (3DGS) model, by means of variational calculus.
Specifically, the authors split the 3DGS model to two hierarchical levels (or- two scales). The coarser "Base" level is very similar to vanilla 3DGS, but now each Gaus... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review and the positive feedback! We address your comments and suggestions below.
## W1: Comparison with FisherRF
We present the results of evaluating the quality of depth uncertainty maps using FisherRF. Note that our training view selection and implementation of A... | Summary: This paper proposes an uncertainty estimation method for the 3D Gaussian Splatting (3DGS) radiance field reconstruction algorithm. The proposed methods leverage the multi-scale properties that lie inherently in a vast amount of Gaussian ellipsoids to improve the performance of variational inference. The paper ... | Rebuttal 1:
Rebuttal: Thank you for your acknowledgment of our work! We provide illustrations for your concerns below.
## W1: Ambiguous values in the offset table for each spawned Gaussian
Since we only offset the position and scale in the offset table, and each offset table contains $K$ spawned Gaussians, then for ... | Summary: The paper aims to quantify uncertainty in the learning pipeline in 3DGS. To this end, the author(s) proposed to leverage explicit scale information to build variational multiscale 3D gaussians leading to the construction of diversified parameter space samples. This results in the proposition of a multiscale va... | Rebuttal 1:
Rebuttal: Thank you for the kind and helpful comments! We will address your concerns below.
## W1: The results are not as impressive as claimed
Firstly, we’d like to clarify that the ensemble method is a naive baseline that trains 10 vanilla 3DGS with different random seeding, which incurs an extreme com... | Summary: This paper proposed a novel multi-scale variational representation for 3D gaussian splatting to estimates its uncertainty. Specifically, this paper introduced a spawn strategy to split a base gaussian into multiple sub gaussians and apply variational inference to estimate the uncertainty.
Strengths: (1), Esti... | Rebuttal 1:
Rebuttal: Thank you for your insightful comment and positive feedback! We address your concerns as follows.
## W1: Discuss more about active data acquisition.
In active data acquisition of 3DGS, image collection and the 3DGS model training are performed alternately. At each image collection step, the mo... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their time and efforts in reviewing our paper and providing insightful comments. Here we address two common questions from the reviewers in this global response, and the individual questions from each reviewer are provided separately.
## Q1: To Reviewe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On Softmax Direct Preference Optimization for Recommendation | Accept (poster) | Summary: This paper extends DPO from pairwise (Bradley-Terry) to multi-way comparison (Plackett-Luce), where one positive example is considered better than multiple negative examples. The paper applies this approach in recommendation systems, showing promising results on several standard benchmarks.
From Rafailov et a... | Rebuttal 1:
Rebuttal: **For Reviewer 6tmb**
We appreciate your comments, some of which inspires us to greatly improve our paper. Below we provide the point-to-point responses to address your concerns and clarify the misunderstandings of our proposed method. If you have additional questions, we would be pleased to disc... | Summary: proposed a softmax DPO in recommendation domain.
Strengths: 1. The paper mainly focus on softmax DPO for recommendation
2. The paper is well wrriten with good experimental validation.
3. The source code is avaliable.
Weaknesses: 1. The novely of this work is not high, it seems that they mainly adapt DPO to t... | Rebuttal 1:
Rebuttal: **For Reviewer AMu8**
Thanks for your time and feedback. To address your concerns, we present the point-to-point responses as follows. Looking forward to more discussions with you.
>**Comment 1: Uncertain Novelty.**
Generative LM-based recommenders has recently been explored which aim to direct... | Summary: The authors propose a modification to the Direct Preference Optimization (DPO) by incorporating a softmax loss to enhance the training of language model (LM)-based recommender systems.
Strengths: - The paper is well-written.
- The mathematical formulations are clear and appreciated.
- The proposed method is f... | Rebuttal 1:
Comment: **For Reviewer iyxD**
I would like to express my gratitude for your detailed review and the valuable feedback provided.
>**Comment 1: Uncertain Novelty.**
We agree with you that introducing multiple negatives is important and has been widely explored in conventional recommenders, as we briefly d... | Summary: Inspired by advancements in Direct Preference Optimization (DPO) and the success of softmax loss in recommendations, the paper propose Softmax-DPO (S-DPO). S-DPO enhances LM-based sequential recommenders by incorporating multiple negative samples in preference data.The paper demonstrate the superiority of S-DP... | Rebuttal 1:
Rebuttal: **For Reviewer gGah**
Your main suggestions about considering additional base models help us substantiate wide applicability of S-DPO.
>**Comment1: Lack comparison with traditional preference-based losses.**
Thank you for your insightful question, which raises a profound and previously unexplor... | Rebuttal 1:
Rebuttal: We are delighted to see the contributions of our paper have been acknowledged by the majority of the Reviewers. Specifically, we appreciate the Reviewers' recognition of our clarity in presentation (iyxD, AMu8, 6tmb), well-founded theoretical analysis (gGah, iyxD, 6tmb) and effectiveness (iyxD, AM... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Symmetry Discovery Beyond Affine Transformations | Accept (poster) | Summary: This paper proposes a method for finding a transformation that is invariant to a given function. The transformation is restricted as governed by a single parameter, so it is written as a flow described by a specific vector field. The method estimates the vector field by solving the polynomial regression. The a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We have responded below.
**Terminology**: With no intent to confuse the reader, we used the term "machine learning function" to cover very general types of functions which may appear within the context of machine learning. Such a function could be a regre... | Summary: The paper presents a method for continuous symmetry detection under the manifold assumption.
Crucially, the symmetries that are discovered by this method can extend beyond the affine ones.
The method is tested and compared against the state of the art (LieGAN), and is found to outperform it in scarce data regi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We have responded below to the concerns raised.
**Use of Polynomials**: In general, level set estimation can be applied using linear combinations of any set of smooth functions, though our experiments do specialize to polynomial functions. However, smooth... | Summary: The paper uses standard ideas from differential geometry to find symmetries in datasets.
The procedure is the following:
1. Estimate a parameterization of the dataset (what the authors call machine learning functions). This step looks like manifold learning.
2. Find a vector field under which the machine lear... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We have responded below to the concerns raised.
**Assumptions**: The single chart assumption is not necessary. Consider $f(x,y,z) = -x^2-y^2+z^2-1$. The surface $f=0$ is a hyperboloid of two sheets. We can estimate a continuous symmetry of this hyperboloi... | Summary: The authors are trying to solve a challenging problem to discover symmetry for given data, regarding such symmetry may include non-affine transformations. They observes one-parameter family of symmetric transformations can be represented as a vector field. In the proposed method, they first find machine learni... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We have responded below to the concerns raised.
**Pre-determined models**: We believe that we are the first to detect continuous symmetries in the context of machine learning beyond the affine group, despite work on the subject spanning decades. Our metho... | Rebuttal 1:
Rebuttal: We thank all of the reviewers for their very helpful comments. We first wish to emphasize here our novel contribution to symmetry detection, particularly in light of current SOTA methods. To date, the most successful method of detecting continuous symmetry is LieGAN, which is a large neural networ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Identifiable Object-Centric Representation Learning via Probabilistic Slot Attention | Accept (poster) | Summary: This paper studies the theoretical identifiability of object-centric representations (slots) in object-centric learning (OCL). Prior works study the same problem, but are limited to OCL models with an additive decoder. This work relaxes the constraint to non-additive decoders, which have shown important to sca... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments and constructive feedback. We appreciate the fact that our paper was considered to be clearly written and well presented, and we are glad that our results are perceived to be solid.
> **"However, the paper does not really have experimental result... | Summary: This paper addresses the problem of identifiability of object-centric representations. In contrast to prior works which achieve identifiability via assumptions on the generator, this paper explores identifiability via assumptions on the latent distribution. To do this, the authors introduce a probabilistic var... | Rebuttal 1:
Rebuttal: We thank the reviewer for their very detailed comments and constructive feedback which helped improve our paper significantly!
We also appreciate the fact that our paper was perceived to be well-written, easy to understand, and of interest to the community.
> **"One of the main issues I have wit... | Summary: Solving the problem of identifiability is necessary to find consistent and interpretable factors of variation. There are two approaches to do so: a) place restrictions on the decoders, and b) impose distributional constraints on the latent space. This work takes the second approach and aims to impose a GMM on ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful, detailed comments and constructive feedback. We greatly appreciate the positive outlook and the fact that our work was found to be well-written, well-motivated and novel.
> **"The paper states that the framework allows for tractably sampling from the... | Summary: This paper propose a probabilistic slot attention method that can learn identifiable object-centric representation. Compared with former work on identifiable object centric representation methods, the proposed model can scaling slot-based methods to high-dimensional images. Both theory analysis and experiments... | Rebuttal 1:
Rebuttal: We thank the reviewer for their effort and overall positive outlook on our paper. We are encouraged to read that our work is perceived as solid, novel, and well-written. Please see our responses to the questions raised below.
> **"the experiments are only take on two toy datasets."**
We kindly r... | Rebuttal 1:
Rebuttal: We extend our thanks to all the reviewers for their time and constructive feedback which has undoubtedly helped improve the paper. We are pleased that the work was perceived to be well-written, well-presented, and novel, with solid results and of interest to the community.
In the following, we h... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Adaptive Proximal Gradient Method for Convex Optimization | Accept (poster) | Summary: The paper refines the analysis of AdGD to accommodate larger stepsizes, an approach that exploits local curvature instead of global smoothness. The technique is later extended to ProxAdGD. Experiments demonstrate the superiority of ProxAdGD over Armijo’s linesearh.
Strengths: 1. The paper unfolds by providing... | Rebuttal 1:
Rebuttal: 1. The improvements over [MM20] are multiple:
(i) We allow for larger steps, leading to improved final complexity. While a direct comparison is missing due to the less explicit final bound in [MM20], we have strived to make our bounds as explicit as possible in this work.
(ii) We have gain... | Summary: The paper introduces new algorithms for solving convex optimization problems, where a step size parameter adapts to the underlying objective function. Adaptation is achieved by making good use of the already available gradient information, and does not have a further computational cost. The authors propose mul... | Rebuttal 1:
Rebuttal: Thanks for the positive evaluation of our work!
1. Indeed, we tried to explain our derivations as thoroughly as possible. As we mentioned, equation (13) is just a substitution of the previous equation into (10). We may have forgotten to clarify that $\alpha^{2}_{k} \|\nabla f(x^{k})\|^{2} = \|x^{... | Summary: This paper considers optimization problems where the function is convex and possibly composite. Gradient Descent and the Proximal Gradient method are studied, and one of the main motivations of this work is the paper [MM20], which developed a locally adaptive step size and an associated algorithm 'adaptive gra... | Rebuttal 1:
Rebuttal: Thank you for the positive evaluation of our work and the high praise! We are especially pleased that you liked the way the paper is written; we put a lot of effort into making it readable and enjoyable.
We agree with tightening the statements and will make the necessary changes in the revision.
... | Summary: In the paper, the authors explore two adaptive first-order algorithms in convex optimization, namely gradient descent and proximal gradient methods, which are based on observed gradient differences. With a novel Lyapunov energy, the authors prove its convergences assuming only local Lipschitz condition of the ... | Rebuttal 1:
Rebuttal: Thanks for the positive evaluation of our work!
1. Indeed, the theory won't be applicable in the nonconvex case. Convexity was instrumental in deriving all the proofs, and in the weakly convex case it is not clear how to proceed. However, we don't agree that this is a weakness. First of all, conv... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SocraticLM: Exploring Socratic Personalized Teaching with Large Language Models | Accept (spotlight) | Summary: This paper aims to introduce a “Thought-provoking” paradigm into LLM-based personal teaching. The authors propose an innovative “Dean-Teacher-Student” pipeline with three LLM-based agents to collect Socratic teaching data. During this process, the authors also contribute a student cognitive system to simulate ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your affirmation of our motivation, the contribution of our dataset, the novelty and generalization ability of our pipeline, and our sufficient experiments and comprehensive assessment.
$\bf{Q1}$:If GPT-4 itself cannot correctly solve a problem, can it still serve as a te... | Summary: The authors propose a novel method based on Socratic teaching for improving LLM teaching abilities.
Strengths: - novel, interesting, and well-described method for improving LLM teaching ability
- creation and release of a useful, novel teaching dialogue dataset
- propose and validate novel ways of testing LLM... | Rebuttal 1:
Comment: Hello. This submission has been labeled by the reviewer for additional ethics review, I recommond the authors to priortize their response to the ethics concern so the ethics reviewers could get a clearer picture of the situation as early as possible. Please find above the ethics concerns raised by ... | Summary: In this paper, the authors propose a novel SocraticLM to address the limitations of existing personal teaching methods that follow a “Question-answering” paradigm. To do this, the authors first propose a novel “Dean-Teacher-Student” pipeline to collect multi-round Socratic teaching dialogues, where the authors... | Rebuttal 1:
Rebuttal: We sincerely appreciate your affirmation of the significance of our motivation, the innovation and generalizability of our pipeline, the contributions of our evaluation system, and the effectiveness of our SocraticLM.
$\bf{Q1}$:How to assess the importance of Dean agent?
$\bf{A1}$:Many thanks f... | Summary: In this paper, the authors fine-tuned a language model on synthetic data for Socratic Personalized Teaching and evaluated the performance of the proposed model in comparison with a number of baseline options.
The authors proposed a multi-agent data synthesis pipeline. Using GPT-4, the authors simulated respo... | Rebuttal 1:
Rebuttal: We sincerely appreciate your affirmation of the novelty of our pipeline, the contribution of our evaluation system, and the significance of our experiments.
$\bf{Q1}$:Compare with related works in evaluating LLMs for education?
$\bf{A1}$:Thanks for your valuable question. The related works can b... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers’ efforts in reviewing our paper. We would like to thank all of them for providing constructive and valuable feedback, which we will leverage to improve this work. We are encouraged by the positive comments from reviewers, including:
- **Motivation**: “The motivati... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Entity Alignment with Noisy Annotations from Large Language Models | Accept (poster) | Summary: The paper proposes LLM4EA for annotating entity alignment pairs using an LLM. It introduces an active learning policy and an unsupervised label refiner to efficiently collect pseudo-labels. Experiments on OpenEA benchmarks demonstrate the strong performance of LLM4EA.
Strengths: The paper is well-structured a... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback, which has greatly helped improve the draft. Below, we address your concerns and weaknesses (w1-w4).
# [w1] Technical Novelty (distinctions from bootEA)
We respectfully point out that the LLM4EA is significantly distinct from bootEA, we outline three majo... | Summary: This paper proposes a new setting that uses LLM to generate entity alignment training pairs, then use the generated pairs to train a matching model for entity alignment between two KGs. The proposed method is evaluated on the OpenEA dataset and achieves better performance than baseline methods on the setting.
... | Rebuttal 1:
Rebuttal: We respectfully thank you for the helpful feedback that helps improve the draft. We answer the concerns and address weaknesses (w1-w6) as below:
# [w1, w4] Reasonability of using LLMs in EA and comparison with recent methods
We sincerely thank for pointing out this subtle point, and we answer th... | Summary: This paper tackles the challenge of entity alignment (EA) in merging knowledge graphs (KGs) by leveraging Large Language Models (LLMs) to automate annotations, addressing the costly and impractical reliance on human-generated labels despite difficulties like large annotation space and noisy labels. They propos... | Rebuttal 1:
Rebuttal: Thank you for your helpful feedback that helps us improve the draft. We answer concerns and address the three weaknesses (w1, w2, w3) in the following.
**[w1] Ablation of LLM model**
The choice of LLM model can affect annotation accuracy and cost. We recognized this factor and employed both GPT-... | Summary: The paper proposes an active learning/weak supervision based approach for knowledge base alignment (aligning entities across KGs). The paper explores an LLM labeler for generating entity alignment labels but explores active selection of source nodes labeled by the LLM, a label refiner which refines the LLM ann... | Rebuttal 1:
Rebuttal: Thank you for your time and effort in reviewing our work. We appreciate your insights and will address these points. Below is a summary of the content of our work.
- **Motivations.** Our work is motivated by the fact that existing methods heavily rely on accurate seed alignment pairs for training... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Reinforcement Learning Under Latent Dynamics: Toward Statistical and Algorithmic Modularity | Accept (oral) | Summary: This work investigates representation learning for decision-making in episodic MDPs. They consider a problem setting in which the goal is to through interactive decision-making learn a good policy. The task involves learning a good "decoder", i.e., function that maps the observations to "state" representations... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read our paper and for their positive review. We address questions/weaknesses below.
> I found the notation and terminology used in this paper to be very dense. [...]I think I would prefer that the authors to have fewer theoretical results, but more t... | Summary: This paper provides a theoretical analysis of statistical and algorithmic modularity for RL with latent dynamics. Specifically, it offers conditions and theoretical analysis under which RL with latents is tractable. For statistical modularity, both lower and upper bounds are presented. For algorithmic modulari... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review and their thoughtful questions. We address each of the individual questions below.
> The authors mentioned that block MDP or factored MDP would be a special case of this general framework. Suppose we narrow down the problem to block MDP or factored ... | Summary: This paper considers theoretical aspects of reinforcement learning in a certain class of MDPs whose observations are governed by a separate, potentially smaller, MDP. They formalize this class of MDPs and denote them latent MDPs. The authors then consider when such MDPs are statistically learnable, beginning w... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review and their helpful comments! Please see responses to individual questions below.
> Assuming that latent states can be uniquely decoded from the observations is a rather strong observation.
It is true that this imposes stronger assumptions on the ob... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
einspace: Searching for Neural Architectures from Fundamental Operations | Accept (poster) | Summary: Neural Architecture Search (NAS) often produces incremental improvements due to limited diversity in traditional search spaces. To address this, the paper introduces "einspace," a versatile search space built from probabilistic context-free grammar (CFG). Einspace supports a wide range of architectures and ope... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback, and respond to each point below.
_Contribution:_ There are three key differences between einspace and the CFG-based spaces of Schrodi et al [1].
- Our space unifies multiple architectural families (ConvNets, Transformers, MLP-only) into one singl... | Summary: This paper proposes a new type of neural architecture search (NAS) search space that goes beyond standard, small NAS search spaces. Most popular search spaces in NAS are quite small, and include architectures that include known motifs because they are built around standard architectures. The authors point out ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback, and respond to each point below.
_Computational costs:_ We thank the reviewer for the useful suggestion. In the table below we include search time results for NAS methods DrNAS, PC-DARTS and RE(Mix), that were originally listed in Tab.1. Note tha... | Summary: This paper proposes a new search space named einspace based on a parameterized probabilistic context-free grammar for neural architecture search. In contrast to conventional search space composed of high-level operations and modules such as recurrent layer, convolutional layer, and activation layer, einspace c... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback, and respond to each point below.
_Novelty:_ We acknowledge previous CFG-based studies exist, however we would highlight that we are the first work to introduce the concept of pCFGs for search space design, which we show brings advantages in contr... | Summary: The manuscript presents "einspace," a novel neural architecture search (NAS) space based on a parameterized probabilistic context-free grammar (CFG). The authors aim to address the limitations of current NAS methods by proposing a highly expressive search space that supports diverse network operations and arch... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback, and respond to each point below.
_Size and scope of the search space:_ We thank the reviewer for the comment however believe this point to be largely a matter of semantics. By decomposing coarse grained building blocks into atomic operators we me... | Rebuttal 1:
Rebuttal: We thank the six (!) reviewers for their time and valuable comments that improve the quality of our work. We are encouraged by the positive feedback, namely:
- Multiple reviewers appreciate the novelty of our core idea, to move beyond conventional NAS spaces (duvy, wLBZ, ZVSn)
- That our experime... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper introduces einspace, a hierarchical space of neural architectures based on parametric probabilistic context free grammar, which is expressive enough to accommodate various state-of-the-art architectures including ResNets and Transformers.
The Authors further perform Regularized Evolution (RE) searc... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback, and respond to each point below.
_Limited evaluation:_ We present additional experimental evaluation in this rebuttal on multiple axes. Taking into account all new results, our experimental evaluation now covers 16 different datasets, with sizes ... | Summary: This paper introduces einspace, a search space that is designed to hierarchically encode architectures using probabilistic context-free grammars (PCFG). It can encode various architectural components, such as convolutions, attention mechanisms, etc. The authors demonstrate the efficacy of simple blackbox optim... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback, and respond to each point below.
_Novelty:_ We agree that our core contributions relate to the search space. However we respectfully argue that the introduction of a valuable new space forms a meaningful and valid contribution, independently of p... | null | null | null | null |
MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue Resolution | Accept (poster) | Summary: This paper designs a Multi-Agent framework, MAGIS. Following the form of team problem solving, it designs four agents: Manager, Repository Custodian, Developer, and QA Engineer, which are used to decompose tasks, locate problem codes, generate codes, and review codes, respectively. They help the LLM to better ... | Rebuttal 1:
Rebuttal: Thank you very much for taking the valuable time to review our manuscript and thanks for your positive comments (i.e., drawing on the software engineering idea well, drawing on the cooperation method of the human team, and the improved performance).
We are sorry for the confusion and unclear expre... | Summary: The paper studies the reasons behind LLMs' failures in resolving GitHub issues, identifying key factors such as locating code files and lines, and the complexity of code changes. The authors propose a novel multi-agent framework, MAGIS, comprising four specialized agents: *Manager* and *Repository Custodian* f... | Rebuttal 1:
Rebuttal: Thank you very much for taking the valuable time to review our manuscript and thanks for your positive comments (i.e., intuitive and effective method, massive experiments, and insights for the research community).
We are sorry for the confusion and unclear expression in the previous version.
We h... | Summary: This paper introduces MAGIS, a novel Large Language Model (LLM)-based multi-agent framework designed to address the challenge of resolving GitHub issues in software development. The authors conduct an empirical study to identify key factors affecting LLMs' performance in this task, including file and line loca... | Rebuttal 1:
Rebuttal: Thanks a lot for reviewing our manuscript and thanks for your positive comments (i.e., the in-depth empirical analysis, rigorous methodology, and the well-designed visualizations).
We are sorry for the confusion and unclear expression in the previous version.
We have addressed each of the comment... | Summary: This paper proposes MAGIS, a multi-agent coding framework for solving patch generation tasks. The roles consist of Manager, Repository Custodian, Developer and QA engineer, with the task of planning, file location, file editing, and review, respectively. Experiments on the SWE-bench benchmark show that perform... | Rebuttal 1:
Rebuttal: Thank you very much for taking the valuable time to review our manuscript and thanks for your positive comments (i.e., the simplicity and effectiveness of our method, and the effectiveness in various settings).
We are sorry for the confusion and unclear expression in the previous version.
We have... | Rebuttal 1:
Rebuttal: Dear reviewers,
**Thank you very much for your valuable time in providing these constructive comments and suggestions. We have addressed each of the comments and suggestions by adding more experiments or explanations:**
---
- **Experiments with Different LLMs (15zT, 1wgB)**: We added experiment... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Universal Sample Coding | Accept (poster) | Summary: The authors consider the problem of efficiently encoding a sequence of $n$ iid realisations $X_i \sim P$ using as few bits as possible. This scenario is different from one-shot channel simulation because it involves multiple rounds of communication. Between these rounds, the encoder and decoder adjust their co... | Rebuttal 1:
Rebuttal: **Computational Complexity:**
As pointed out by the reviewer, the complexity of all currently known methods for exact channel simulation is proportional to $|\frac{dP}{dQ}|_\infty$. Thus, the proposed method has computational complexity
$$\sum_{i=2}^{\lfloor\log_{1+c} (n)\rceil+1} \left(\left\Ve... | Summary: This paper introduces Universal Sample Coding. This is a simple but significant extension to channel simulation where the sender and receiver communicate $N$ ($N>>1$) samples. The authors prove that the expected codelength per sample will be negligible when $N\rightarrow \infty$, and verify with toy examples. ... | Rebuttal 1:
Rebuttal: **Complexity:**
To answer the reviewer's question about the occurrence of outliers in KL divergence between P and its estimate we have plotted it for every round of sending $n=2^{14}$ samples from $k\in\{3,8\}$-dimensional distribution $P$. In every run $P$ is sampled from a Dirichlet distribution... | Summary: This paper studies the problem of communicating multiple samples from an unknown distribution using as few bits as possible. The authors provide upper and lower bounds on its communication cost that are within a multiplicative factor from each other. The upper bound is derived from analysing the communication ... | Rebuttal 1:
Rebuttal: The gap between the upper and lower bounds quickly diminishes as the dimension increases. In our approach, we focus on minimizing the upper bound by choosing an appropriate constant $c$. However, a different $c$ might work better empirically, although lacking optimality in the derived upper bounds... | Summary: The paper proposes a new problem called "Universal Sample Coding". This is related to a problem called reverse channel coding (or channel simulation) where the receiver must generate samples from a target distribution (P) that is known to the sender but not the receiver. In addition, the receiver and sender ha... | Rebuttal 1:
Rebuttal: Our problem setting is indeed very similar to conventional channel simulation as described by the reviewer, with the following difference: instead of a single sample, the goal of the decoder is to generate multiple samples from the target distribution $P$, which is known only to the encoder. To be... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their feedback, most of our rebuttals are specific to the reviews, and thus we respond to each individually.
To answer the question by reviewer NNh9, we plot the empirical KL-divergence.
Pdf: /pdf/73082c4c26d4bcb695923c3ceb62b053af11e806.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
GRANOLA: Adaptive Normalization for Graph Neural Networks | Accept (poster) | Summary: The paper introduces a novel graph-adaptive normalization layer named GRANOLA for Graph Neural Networks (GNNs). The authors argue that existing normalization techniques, such as BatchNorm and InstanceNorm, are not well-suited for GNNs due to their design not considering the unique characteristics of graph-stru... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback, and we are glad to see they have recognized the significance of our contribution. They have nonetheless asked some questions, which we address below.
**Q1:** The work focuses on the normalization of GNNs for graph-level tasks. However, there is a type of ... | Summary: This paper introduces GRANOLA (Graph Adaptive Normalization Layer), a novel normalization technique designed specifically for Graph Neural Networks (GNNs). The authors identify a critical gap in existing normalization methods for GNNs, which often fail to capture the unique structural characteristics of graph ... | Rebuttal 1:
Rebuttal: We are happy to see that the reviewer has appreciated the novelty of our approach, while finding our theoretical analysis solid and our empirical evaluation extensive. We would like to thank them for raising interesting points of discussion, which we address in the following.
**Q1:** GRANOLA's pa... | Summary: The paper pertains to the problem of using normalisation techniques specifically designed for graph-structured data and Graph Neural Networks (GNNs). Using constructed illustrative examples, the authors claim that existing normalisation techniques (including others designed for graphs) may create expressivity ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback, and the recognition of the importance of this work for the GNN community. The reviewer also raised concerns, which we address below.
**Q1:** ..it seems to me that the main reason behind the empirical success is the random features.
**A1:** We found that t... | Summary: This paper proposes an adaptive normalization layer for GNNs. It first points out that the traditional normalization layer (BatchNorm, InstanceNorm) are not specifically designed for graphs and thus may limit the expressive power of GNNs, and single normalization technique can not always be the best for all g... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback. We are glad to see the reviewer has appreciated the presentation of our work, finding the paper well-structured and easy to follow. We proceed by answering each question in the following.
**W1:** GRANOLA involves an additional GNN module to l... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their valuable comments and for their efforts in providing actionable feedback to enhance the quality of the submission.
Overall, reviewers appreciated our novel adaptive normalization scheme for GNNs that maintains linear complexity and provides consiste... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Tighter Convergence Bounds for Shuffled SGD via Primal-Dual Perspective | Accept (poster) | Summary: This paper aims at improving existing bounds on random reshuffling. While lower bounds are tight in the worst case, refined smoothness definitions allow to take a larger step-size in favorable cases, which in turns allows faster convergence. These smoothness definitions involve a supremum over permutation-depe... | Rebuttal 1:
Rebuttal: **We thank the reviewer for the positive evaluation of our paper. We hope that the clarifications provided below address the reviewer’s questions. We are happy to answer further questions in the discussion phase.**
---
### Summary
> Typos
Thanks for pointing out. It should be $E_{i_t} [ \nabla ... | Summary: This paper studied the convergence of random shuffled SGD through the lens of dual coordinate descent. By leveraging the analysis in coordinate descent, the author(s) derived a rate that is $O(\sqrt{n})$ faster than existing rate.
Strengths: Pros:
- The paper is well written and easy to follow, contributions... | Rebuttal 1:
Rebuttal: **We thank the reviewer for their valuable feedback. We hope that the answers provided below address the reviewer’s concerns and that the reviewer would consider reevaluating our work. We appreciate the opportunity to answer further questions in the discussion phase.**
---
### Weaknesses
> The p... | Summary: This paper considers the primal-dual aspect of SGD using sampling without replacement (shuffled SGD). The authors present results for shuffled SGD on smooth and non-smooth convex settings with tight convergence bounds for several shuffling schemes (IG, SO, and RR). In some specific settings, the convergence ra... | Rebuttal 1:
Rebuttal: **We thank the reviewer for their feedback and kindly request them to consider our responses below when evaluating our work. We appreciate the opportunity to further engage in a discussion, as needed.**
---
### Weaknesses
> The improvement in the convergence is not significant as it is only chan... | Summary: This paper focuses on SGD with shuffling for finite-sum minimization problems. While there exists tight convergence upper and lower bounds for SGD assuming a global smoothness constant $L_{\max}$, the proposed results give a more fine-grained analysis in terms of the component-wise smoothness constants $L_i$ t... | Rebuttal 1:
Rebuttal: **We appreciate the questions the reviewer raised. We hope that the answers provided below address the reviewer’s concerns and that the reviewer would consider reevaluating our work. We will fix the typos, update statements in the main body to apply to batch size $b$, and add more discussions in t... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their precious time and valuable feedback. In this top-level rebuttal, we reiterate the contributions and strengths of our work.
---
We first summarize our main contribution by quoting the Review S4vW where they acutely pointed out
> This paper aims... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
IODA: Instance-Guided One-shot Domain Adaptation for Super-Resolution | Accept (poster) | Summary: The author uses CLIP to assist in extracting the representation of target domain samples and implements a one-shot domain adaptation framework.
Strengths: The introduction of CLIP improves the performance of one-shot domain adaptation.
Weaknesses: Although the method is effective, it does not appear to be th... | Rebuttal 1:
Rebuttal: **Question1:** Why can Alpha-CLIP enhance the performance of SR when other versions of CLIP cannot?
**Response:** Conventional CLIP-based SR network domain adaptation methods face limited target domain diversity when using a single target domain LR image. To address this problem, we propose an in... | Summary: This paper proposes a framework of efficient instance-guided one-shot domain adaptation (abbr. IODA) with only one unlabeled target domain LR image for addressing image super-resolution (SR) issues. On top of that, it designs an instance-guided target domain distribution expansion strategy to expand the divers... | Rebuttal 1:
Rebuttal: **Question1:** During model training, is the selection of LR images for unlabeled target domains is random? Or does it need to be based on some criteria that are not stated in this manuscript?
**Response:** To ensure the reliability of the experimental results, we repeated each experiment 5 times... | Summary: The paper presents a novel approach to one-shot domain adaptation for super resolution. The key idea is to use the CLIP directional vector between low resoultion source and target domain images to guide the SR image generation in the target domain. They further use occlusion masks to further increase the perfo... | Rebuttal 1:
Rebuttal: **Question1:** Can you compare the performance of your models against the other domain adaptation-based SR approaches like [12] and [13]?
**Response:** As shown in Table 1 of the Rebuttal materials (Rebuttal.pdf), we have additionally included domain adaptive methods for super-resolution, DADA [... | Summary: This paper addresses one-shot domain adaptation (OSDA) in the field of super-resolution (SR). It leverages the fact that the content remains unchanged during super-resolution to propose an image-guided domain adaptation method, ensuring consistency by aligning the direction between the source domain and the ta... | Rebuttal 1:
Rebuttal: **Question1**: There seems to be an insufficient survey of domain adaptation methods in the SR task, specifically those outlined in references [1-4]. Major revision is needed to emphasize the necessity and originality of the proposed method, based on an analysis of these existing methods. Particul... | Rebuttal 1:
Rebuttal: 1. ### **Additional visual demonstrations**
As shown in Figure 1 of the Rebuttal materials (Rebuttal.pdf), we provide additional visualizations to demonstrate the effectiveness of the proposed method.
2. ### **Additional comparisons with domain adaptive methods for Super-resolution**
As shown i... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Protected Test-Time Adaptation via Online Entropy Matching: A Betting Approach | Accept (poster) | Summary: This paper proposes a novel test-time adaptation method based on martingales and online learning. It detects whether testing samples need to be adapted based on the sequential entropy values. Then, if a sample needs to be adapted, a pseudo-entropy value is computed for the adaptation. Overall, the idea of this... | Rebuttal 1:
Rebuttal: We thank the reviewer for your positive feedback and valuable suggestions. We are pleased that the reviewer found that “online drift detection and online model adaptation are naturally proposed and make sense.” We appreciate the positive feedback regarding the clarity of our writing. Thank you!
>... | Summary: This paper introduces a novel method for test-time domain adaptation using online self-training. It combines a statistical framework for detecting distribution shifts with an online adaptation mechanism to dynamically update the classifier's parameters. The approach, grounded in concepts from betting martingal... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and the constructive review. We are pleased that the reviewer appreciates the novelty and clarity of our writing. We very much value the reviewer’s comment that “the experiments on two TTA settings (single domain and continual TTA) confirm its effect... | Summary: The paper addresses the problem of adapting a classifier to a new domain at test-time. It proposes a framework that first detects a distribution shift and, based on the detection results, adapts the classifier. The distribution shift detector employs a sequential test evaluating if the distribution of the test... | Rebuttal 1:
Rebuttal: We thank the reviewer for your valuable feedback and suggestions. We are glad that the reviewer found our approach to be novel and an interesting research direction. It is gratifying to see that the reviewer thinks that “the entropy matching could potentially be a good alternative to the dominant ... | null | null | Rebuttal 1:
Rebuttal: We appreciate the reviewers' engagement with our paper and their valuable comments and suggestions. We will integrate their feedback into the revised paper and have conducted a new set of experiments, detailed below.
The reviewers acknowledged that the paper is well-written and introduces a novel... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Evolution of Statistical Induction Heads: In-Context Learning Markov Chains | Accept (poster) | Summary: This work introduces a simple “in-context learning” Markov chain modelling task and studies how transformer models tackle it. The task consists of inferring the transition probability matrix of the Markov chain, which is sampled from a prior Dirichlet distribution. The authors consider the task to pertain to “... | Rebuttal 1:
Rebuttal: Thank you for critically reading our work and helping us improve its presentation. We greatly value your in-depth review!
We will first discuss your most significant concerns with our work:
## In-Context Learning
You're right that we don't spend much time in our paper discussing what "in-contex... | Summary: The paper studies in-context learning with transformer models in a simple Markov Chain sequence modelling task. The authors empirically show the formation of statistical induction heads which correctly compute the posterior probabilities given bigram statistics. Moreover, they observe that during training the ... | Rebuttal 1:
Rebuttal: Thank you for the in-depth review and feedback!
You point out that there is some misalignment between our experimental story and our proofs, and you see that as our paper's major weakness. We agree that there is misalignment, due to the difficulty of analyzing SGD on multi-layer Transformers. How... | Summary: This paper introduces a task to investigate how in-context learning capabilities are learnt by transformer models. They show that models trained on this task go through a phase transition from which they start by modeling unigram to then acting as a bigram model. The authors further extend their work to the ca... | Rebuttal 1:
Rebuttal: Thank you for your review and questions. Here we address the main weaknesses and questions raised by you.
## Impact and related works
>Questionable impact since this topic has been explored heavily in the past years, with other similar tasks and toy models existing, showing similar results... Wha... | Summary: In the paper, the authors investigate the phenomenon of in-context learning exhibited by Transformers with the help of a simplified architecture and Markovian synthetic data. The authors show that experimentally the attention layers form statistical induction heads that help the model to implement an add-const... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful review. Here we address the questions/concerns raised by the reviewer.
1. > Do you think that an SGD analysis similar to the one carried out for the minimal model would carry over to a more complex architecture, closer to the actual transformer? If not, wha... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards Safe Concept Transfer of Multi-Modal Diffusion via Causal Representation Editing | Accept (poster) | Summary: The paper addresses the potential misuse of VL2I diffusion models, such as copying artistic styles without permission, which could lead to legal and social issues. The paper introduces an early exploration of safe concept transfer in MLLM-enabled diffusion models using a novel framework called Causal Represent... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback! Below we address your concerns. Please feel free to post additional comments if you have further questions.
1. Thanks for your suggestion. We are applying our technology to more actual deployed generative models and considering inviting users to participate... | Summary: The authors studied an important problem about misuse of Text-to-image (T2I) diffusion model, leading to legal and social issues. They propose a causal representation editing (CRE) method, extends representation editing from large language models to diffusion-based models. CRE improves safe content generation ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback! Below we address your questions and concerns. Please feel free to post additional comments if you have further questions.
**For weakness 1:** We report the comparison of inference time on generating 100 images as follows:
| Kosmos-G | SLD | ProtoRe | CRE... | Summary: The paper introduces Causal Representation Editing (CRE) for vision-language-to-image (VL2I) models to prevent undesirable concept generation. CRE prevents unsafe image generation through the following process:
1. Using a discriminator, it detects whether an unsafe concept is present in the user prompt.
2. If ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback! Below we address your questions and concerns. Please feel free to post additional comments if you have further questions.
**For weakness 1:** We emphasize the necessity of the discriminator by illustrating the disadvantages of existing inference-time refusal ... | Summary: This paper proposes a framework called Causal Representation Editing (CRE) to address the ethical and copyright concerns in vision-language-to-image (VL2I) diffusion models. CRE enhances safe content generation by intervening at diffusion timesteps linked to unsafe concepts, effectively removing harmful conten... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback! Below we address your questions and concerns. Please feel free to post additional comments if you have further questions.
**For Weakness 1 and Question 1:**
As the reviewer mentioned, the discriminator may not always make accurate judgments. To assess the imp... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Kaleido Diffusion: Improving Conditional Diffusion Models with Autoregressive Latent Modeling | Accept (poster) | Summary: Diffusion models generate high-quality images from text but often lack diversity, especially with high classifier-free guidance. Kaleido addresses this by using autoregressive latent priors, which generate diverse latent variables from captions. It enriches input conditions, resulting in more diverse outputs w... | Rebuttal 1:
Rebuttal: ## **W1: Explanation of the formula in Sec. 3.1**
Thank you for your valuable feedback.
- In our practical implementation, we indeed adhere to the existing classifier-free guidance (CFG) formulation. Both text descriptions and our introduced autoregressive priors indeed serve as conditioning sign... | Summary: This paper introduced Kaleido Diffusion which leverages an autoregressive model to first model the latent mode and then generate latents based on the sampled mode. The proposed method is reasonable. The authors explain the insight from a classifier-free guidance perspective. Several experimental results can su... | Rebuttal 1:
Rebuttal: ## **W2: Use of MLLM/context extractor; Introduction of pseudo labels**
The context (latent) extractor is employed to extract different types of abstract latents given the condition-image pair (Sec. 3.2). In practice, for different types of latents, we utilize different methods as the context (la... | Summary: This paper improves the diversity of diffusion generation by incorporating autoregressive latent priors. It leverages the autoregressive model to generate specific discrete latent features, and then concat them with the original extracted text features to serve as the condition of diffusion model. Experiments ... | Rebuttal 1:
Rebuttal: ## **W1: Training and Inference details**
We appreciate your inquiry into the specifics of our training and inference methodologies.
- During training, we investigate both isolated and combined uses of different latent tokens, including text, bounding boxes (bbox), blobs, and vokens, to highlig... | Summary: In this paper, the authors propose a principled pipeline called Kaleido Diffusion for text-to-image generation with better mode coverage and diversity. The main intuition is that, conventional DM requires large CFG to make samples locate at high-likelihood modes, which would somehow constrain the diversity by ... | Rebuttal 1:
Title: Request for clarification for W3 "the diffusion decoder idea"
Comment: Dear Reviewer,
We are currently in the process of drafting our rebuttal response and would greatly appreciate your clarification on a point mentioned in Weakness 3. Specifically, we are seeking clarification on the "diffusion dec... | Rebuttal 1:
Rebuttal: ## **Quantitative Comparison with CADS:**
In response to the request for more quantitative results and comprehensive baseline comparisons, we have conducted additional experiments, specifically comparing our Kaleido diffusion model with CADS [1].
- **Condition Annealed Diffusion Sampler (CADS)**... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Uncovering, Explaining, and Mitigating the Superficial Safety of Backdoor Defense | Accept (spotlight) | Summary: This paper studies post robustness of backdoor purification. The authors show that backdoors purified by existing defenses can be recovered via Retuning Attacks and they propose the Query-based Reactivation Attack to recover the backdoor. The authors address such an vulnerability by proposing a Path-Aware Mini... | Rebuttal 1:
Rebuttal: **We are grateful to you for your time and effort in reviewing our work, as well as acknowledging our contributions.**
### **Response to Weakness 1**
Thanks for your suggestion! As you suggested, we test our method PAM on more backdoor attacks, Adaptive-Patch, Adaptive-Blend, and All-to-All atta... | Summary: This paper investigates the effectiveness of current purification-based backdoor defenses and tries to uncover whether purified DNNs are truly free from backdoor vulnerabilities. The authors identify the “post-purification robustness” of DNNs and propose Retuning Attack (RA) and Query-based Reactivation Attack... | Rebuttal 1:
Rebuttal: **We are grateful to you for your time and effort in reviewing our work, as well as acknowledging our contributions.**
### **Response to Weakness and Questions**:
Thanks for your kind question!
Due to the space limitation, we only simply discuss practical implications for real-world settings in ... | Summary: This paper reveals a phenomenon in backdoor defense: the purified backdoor can be reactivated by fast retuning on a few backdoor samples. Building upon this observation, the paper explores both attacks and defense measures for more reliable backdoor research. On the attack side, a Retuning Attack (RA) is propo... | Rebuttal 1:
Rebuttal: **Thanks for your time and effort in reviewing our work!**
### **Response to W1**:
1. For "surely relearn the backdoor":
Sorry for the possible confusion. First, we emphasize **the reviewer’s statement “...model will surely relearn the backdoor.” is not correct**. **As emphasized in Lines 169... | Summary: Backdoor attacks are a major threat to Deep Neural Networks (DNNs), as they allow attackers to manipulate model predictions with backdoor triggers. Existing purification methods reduce the Attack Success Rate (ASR) of these models, but it's unclear if they fully eliminate backdoor threats. This study investiga... | Rebuttal 1:
Rebuttal: **We are grateful to you for your time and effort in reviewing our work, as well as acknowledging our contributions.**
### **Response to Weakness 1**:
Thanks for your suggestion! Following your suggestion, we add evaluations on more poisoning attacks.
**For more attacks:** We test our PAM on mor... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude to all reviewers for their time and efforts in reviewing our work. We will carefully revise our manuscript by adding suggested experiments, more detailed explanations, and fixing the typos.
**Here, we provide a global response to questions from revi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Expert-level protocol translation for self-driving labs | Accept (poster) | Summary: This paper proposes an automated protocol translation framework, which takes natural language descriptions designed for human experimenters as input and outputs a structured representation that can be used for self-driving labs. The framework consists of a three-stage workflow. First, a domain-specific program... | Rebuttal 1:
Rebuttal: > The proposed solution is a portfolio of standard applications of existing tools or well-known algorithms, which is less interesting and novel from a machine learning perspective.
Thanks for the comment. In this work, we study the problem of translating experimental protocols designed for human ... | Summary: The paper presents a framework for translating experimental protocols from natural language (NL) to machine-interpretable formats, specifically designed for self-driving laboratories. The proposed framework automates the protocol translation process through a three-stage workflow that constructs Protocol Depen... | Rebuttal 1:
Rebuttal: > What specific optimizations could be applied to reduce the computational requirements of the proposed framework?
Thanks for the question. Computational efficiency is always a topic of interest when evaluating new computational frameworks. Let us consider a new coming protocol with $k$ steps, w... | Summary: The work identifies the problem of translating from natural language instructions for scientific experiments to machine usable formats and frames it as a program synthesis problem. The proposed approach uses language models (along with other parsing techniques) to extract a structured sequence of instructions ... | Rebuttal 1:
Rebuttal: > Why is BLEU/ROUGE used as a metric? Can a more semantically aligned mode of comparison be used here.
This is a very good question. The same concern was considered during the development of our evaluation methodology. Direct comparisons across entire sentences under BLEU/ROUGE scores would inde... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers for their time and valuable comments. The feedback is both substantial and helpful for improving our paper. In this work, we systematically study the problem of translating experimental protocols for human to those suitable for self-driving laboratories. Accordingly, we prop... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction Tuning | Accept (poster) | Summary: This paper implements multimodal emotion recognition and reasoning by fine-tuning the LLaMA model with instructions. It is trained on a large-scale dataset, fine-tuned, and tested on three datasets.
Strengths: The global, temporal and local features of the video modality are considered, and LoRA fine-tuning (... | Rebuttal 1:
Rebuttal: Thank you for your insightful review. We've addressed your points carefully and will incorporate these clarifications in our revision.
**Q1: The complex interaction relationship between modalities is not considered.**
We appreciate your attention to the interaction between modalities. However, ... | Summary: The paper introduces a multimodal large language model, named Emotion-LLaMA for emotional state understanding. The authors use open-source tools to collect and annotate a dataset, named MERR for model pre-training. Then they perform instruction-tuning on downstream datasets for emotion recognition and emotion ... | Rebuttal 1:
Rebuttal: We appreciate your insightful review. Your feedback has been valuable in improving our work. We've addressed each point below and will incorporate these enhancements in our revision.
**Q1: How do you pre-train Emotion-LLaMA? Is it supervised learning with coarse-grained emotion labels?**
A1: Ye... | Summary: The paper presents a new multi-modal instruction tuning dataset for emotion recognition. The authors also present results of training on this dataset with a multi-modal architecture based on LLaMa 2. They show evaluation results on DFEW and MER2023.
Strengths: Evaluating multi-modal emotion recognition approa... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We've addressed your concerns as follows:
**Q1: The argument presented in lines 31 and 33 is not convincing. Why does an inability in methods lead to a lack of datasets?**
A1: The *issues* in line 33 refer to challenges faced by current multimodal large language mode... | Summary: The paper presents the Emotion-LLaMA model, a multimodal emotion recognition and reasoning system that integrates audio, visual, and textual inputs.
- The authors constructed the MERR dataset, which includes 28,618 coarse-grained and 4,487 fine-grained annotated samples across diverse emotional categories, en... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback. We have addressed each of your points below and will incorporate these improvements in our revised manuscript.
**Q1: Are the experimental results sensitive to language? How does the choice of HuBERT-Chinese affect performance?**
A1: Yes, Emotion-LLaMA is s... | Rebuttal 1:
Rebuttal: We appreciate the thoughtful feedback and constructive criticism from all reviewers. Your insights have been instrumental in refining our work. Below, we summarize the key changes and improvements we have made in response to your comments.
### Key Changes and Improvements
1. **Clarification of D... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Prune and Repaint: Content-Aware Image Retargeting for any Ratio | Accept (poster) | Summary: The paper presents an innovative method for image retargeting. It addresses two core challenges in image retargeting (preserving the main information and avoiding artifacts on key objects) simultaneously by carefully devising a content-aware seam-carving method and an adaptive repainting method respectively. B... | Rebuttal 1:
Rebuttal: * **Weakness 1**:
_Some detailed concepts are missed, such as $x_0$ in line 142._
**Response**:
Thanks for pointing it out. The center coordinate $x_0$ represents the center of the entire image. The height coordinate $H_{x_0}$ can be calculated as the average of the heights $H_i$ of all the sal... | Summary: This paper proposes a new image retargeting framework that prunes background and repaints local connections. It improves the traditional seam-carving with semantic guidance to make the pruning content-aware, avoiding deformation and loss of important objects. Meanwhile, the authors introduce an adaptive repain... | Rebuttal 1:
Rebuttal: * **Weakness 1**:
_Inference speed to be improved._
**Response**:
Thanks for your suggestion, we will further employ accelerated diffusion models to improve the inference speed.
* **Weakness 2:**
_Results on ratio 1:1 and 4:3 are not visualized._
**Response**:
Thanks for pointing out this i... | Summary: This work contributes to a new image retargeting model named PrueRepaint, which is adaptive to work with any target ratio.
The authors first improve the traditional seam-carving method with saliency priors to achieve content-aware pruning and protect important semantic regions.
After that, they introduce a... | Rebuttal 1:
Rebuttal: * **Weakness 1**:
_Lack of comparison with more retargeting methods._
**Response**:
Thanks for your advice. We have added experiments on InGAN as well as full-image repainting (FR) in Fig. R1, R2 and Tab. R1 in the PDF. Both the quantitative and qualitative comparisons show the large superiori... | Summary: The work presents an addon using Diffusion models to Seam Carving to perform Content Aware resizing of images.
Strengths: + None
Weaknesses: - The work seems to be a rehash of Seam Carving, and some diffusion models were added to perform retargeting. There is no novelty in the method.
- One page of the pape... | Rebuttal 1:
Rebuttal: * **Weakness 1**:
_No novelty in the method._
**Response**:
1) Seam-carving is a semantic-agnostic approach that often results in severe foreground loss and distortion (see Fig. 4, 5 and 7). In contrast, our proposed content-aware seam-carving (CSC) incorporates semantic awareness to prese... | Rebuttal 1:
Rebuttal: We thank all for your efforts and are glad to achieve your high recognition of the work's innovation, suitability and importance for image retargeting, significant outperformance, as well as the written. We gratefully thank all the reviewers for their constructive remarks and useful suggestions, w... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Most Influential Subset Selection: Challenges, Promises, and Beyond | Accept (poster) | Summary: The paper presents a theoretical analysis of why existing greedy additive methods fail to solve the most influential subset selection (MISS) problem, which aims to find the subset of training data with the largest collective influence. Greedy additive methods (first assigning individual influence scores, order... | Rebuttal 1:
Rebuttal: We thank Reviewer Uyvf for taking the time to review our paper and their constructive feedback. Please find below our point-to-point response.
**Remark 4.3.** We note that starting from Section 3.2, we adopt the closed-form of individual influences ($A_{-{i}}$) instead of the influence estimate... | Summary: The authors investigate the Most Influential Subset Selection (MISS) problem, which aims to identify a subset of training samples with the greatest collective influence on machine learning model predictions.
They discuss limitations of prevailing approaches in MISS and highlight issues with influence-based gr... | Rebuttal 1:
Rebuttal: We thank Reviewer 3kwV for taking the time to review our paper. Before addressing the reviewer’s comments, we would like to clarify some misconceptions.
- **Thesis and contributions.** Our work falls under the category of learning theory, and our thesis is to advance the theoretical understanding... | Summary: The paper explores the challenge of understanding the collective influence of subsets of training data on machine learning models, referred to as the Most Influential Subset Selection (MISS) problem. Traditional influence functions, which focus on individual data points, often miss the more complex interaction... | Rebuttal 1:
Rebuttal: We thank Reviewer 2zFQ for taking the time to review our paper and their constructive feedback. Please find below our point-to-point response.
**Comparison to [1,2].**
- We believe the main and the most fundamental difference between our work and [1] is the topics of study — our work focuses on... | Summary: The paper explores the problem of most influential subset selection, that is, selecting a subset of training data points whose removal would change a machine learning model the most. The paper develops further on previous works (most notably of Chatterjee and Hadi, and Kuschnig et al) and shows why the greedy ... | Rebuttal 1:
Rebuttal: We thank Reviewer btdJ for taking the time to review our paper and their constructive feedback. Please find below our point-to-point response.
**Non-invertible $N$.** We assume $N$ to be invertible since influence functions rely on the uniqueness of the optimal solution. When this is violated, w... | Rebuttal 1:
Rebuttal: We express our sincere gratitude to the reviewers for their detailed review. It is encouraging to see that the reviewers acknowledged the significance of the topic of study: “Addresses a timely important problem”, and “The paper is therefore not only relevant for the area of data influence but als... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
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