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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CryoGEM: Physics-Informed Generative Cryo-Electron Microscopy | Accept (poster) | Summary: In this paper, the authors introduce a method to generate large annotated cryo-EM datasets from a small number (100) of real micrographs. The method combines a physics-based model of the image formation model with a contrastive learning strategy. The authors show that their method can be used to improve the qu... | Rebuttal 1:
Rebuttal: Thank you for your appreciation and insightful comments. We will improve the clarity of the paper based on them.
## Clarification of Notations
Thank you for thoroughly reading our paper and the supplementary material. We truly appreciate your suggestions.
For mutual information extraction (Equa... | Summary: The paper introduces CryoGEM, an innovative method combining physics-based cryo-EM simulation with unpaired noise translation via contrastive learning to generate high-quality synthetic cryo-EM datasets. The approach significantly improves the visual quality of generated images and enhances downstream tasks li... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful suggestions. We will improve the paper based on them.
## On the Relationship between Gaussian and Actual Physical Noise
We follow the common practice in the literature of using Gaussian noise to model the reconstruction problem in cryo-EM. For example, cryoDRGN [1] ... | Summary: In this paper, the authors introduce Physics-Informed Generative Cryo-Electron Microscopy (CryoGEM), a novel generative model for cryo-electron microscopy (Cryo-EM) micrographs. CryoGEM is trained to produce micrographs that accurately replicate the ice gradient, point spread function (PSF), and noise characte... | Rebuttal 1:
Rebuttal: Thank you for your valuable suggestions. We appreciate the opportunity to address these concerns.
## On the Pipeline Efficiency
We acknowledge that CryoGEM indeed takes longer than manual annotating, as shown in the following table. However, particle picking is a tedious, labor-intensive, and ti... | null | null | Rebuttal 1:
Rebuttal: # Global Response
We are grateful that all reviewers recognize that CryoGEM showcases the usefulness of generative AI in structural biology. We will soon release the code and the data for the community to experiment with and improve. The reviewers have provided many insightful suggestions that we... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Image-aware Evaluation of Generated Medical Reports | Accept (poster) | Summary: This paper propose a novel evaluation metric, VLScore, for automatic medical report generation, considering both textual and clinical aspects. It key idea is to map the reports and their corresponding image to the joint visual-textual space and measure the similarity. Experiments demonstrate that they gain bet... | Rebuttal 1:
Rebuttal: Thank you for your helpful comments and insights, appreciating the novelty, and the results.
**Q1:** _motivation: evaluating the quality of radiology report generation through image modality..._
An image contains a wealth of information that can be described textually in many forms. Directly mea... | Summary: This paper proposed a novel evaluation metric, VLScore, which can more accurately reflect the alignment of generated reports and radiologists’ judgments. It is achieved by measuring the similarity in visual-textual space. To demonstrate the effectiveness of the proposed metrics, a new dataset with specific per... | Rebuttal 1:
Rebuttal: Thank you for your helpful comments and insights, appreciating the novelty, the motivation, the method, and the evaluation.
**Q1:** _dependency on the performance of multi-modal embedding models_
Our evaluation metric requires the selection of an embedding model that maps images and reports to a... | Summary: In this work, the authors introduce a novel metric (VLScore) for evaluating the quality of generated medical reports. The key idea is to measure the similarity between radiology reports while also taking the image itself into account; this is a distinction from prior works, which generally only compare radiolo... | Rebuttal 1:
Rebuttal: Thank you for your helpful comments and insights, appreciating the novelty, the motivation, the method, and the results.
**Q1:** _Assuming access to multimodal models sensitive to local features_
Our method does not rely on multimodal models sensitive to local features; it only relies on an embe... | Summary: The paper introduces a new image and language-based metric for generated report evaluation of X-ray images.
Strengths: 1. The concept of utilizing both semantic and vision-based representations in the form of a similarity score is very interesting.
2. The supplementary material effectively reflects different ... | Rebuttal 1:
Rebuttal: Thank you for your helpful comments and insights, appreciating the novelty, the motivation, and the results.
*Q1:* _the primary contribution is the metric itself ... technical novelty_
The main contribution is indeed the metric itself. However, the approach to designing it is novel. Furthermore,... | Rebuttal 1:
Rebuttal: **General Response to All Reviewers**
We would like to thank all the reviewers for their hard work and enlightening comments, appreciating the
motivation (_“an important, high-impact problem”_ [R-EkCX], _“the advancement of this aspect is important”_ [R-6uVW]),
the novelty (_“a new image and lan... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Confidence Calibration of Classifiers with Many Classes | Accept (poster) | Summary: The paper proposes a confidence calibration method. It reformulates a multi-class problem as the binary task "is the prediction correct?". Then a given calibration method (e.g. Temperature Scaling) is applied to that binary task.
Strengths: The proposed method is very simple and can be efficiently applied ... | Rebuttal 1:
Rebuttal: Thank you for the review. We are glad that you find our approach simple and efficient. Please find our rebuttal below.
**Weaknesses**
> The main justification of a calibration model is its improved results (usually measured by ECE).
In the case of TS the paper reports a minor improvement. Howeve... | Summary: The paper addresses the issue of miscalibrated confidence scores in neural network classifiers, particularly for problems with many classes. Traditional methods often fail in these scenarios. The authors propose transforming the multiclass calibration problem into a single binary classification problem, termed... | Rebuttal 1:
Rebuttal: Thank you for the review. We appreciate that you find that our method is simple, scalable, and has good performance. We hope our response can address your concerns.
**Weaknesses**
> 1. Lack of novelty. I read a paper very similar to the idea previously, make top vs all, but I am sorry that I can... | Summary: This paper presents a top-versus-all (TvA) approach for the confidence calibration of classifiers with many classes. The authors categorizes the post-processing calibration methods into two groups: scaling and binary methods, and lists several problems with these approaches. The authors reformulate the problem... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback. We value that you praise our approach, its motivation, and significance and acknowledge the extensiveness of our experiments. We hope our response can address your concerns.
**Weaknesses**
> 1. The theoretical contribution of the TvA approach and its impa... | Summary: The paper relates to post-hoc confidence calibration i.e. the calibration of
the top-prediction of a classifier such that ``when the class l is predicted
with confidence q, the probability of the actual class being l is also q''.
The calibration does not update the classifier.
The paper tackles two issues of ... | Rebuttal 1:
Rebuttal: Thank you for spending the time and effort to write this thorough review, which demonstrates a deep understanding of our work. We are grateful that you praise our writing and recognize our approach's efficiency and the exhaustivity of our experiments. The points you raised are relevant, and we wil... | Rebuttal 1:
Rebuttal: We thank all the Reviewers for the time spent reviewing our paper and for the constructive comments. In this global response, we group, summarize, and discuss the main identified strengths and weaknesses.
**Strengths**
The Reviewers mostly agree on three strengths:
- *The method is simple and ef... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes a method to improve confidence calibration in neural network-based classification models. It transforms the multiclass calibration problem into a binary classification surrogate, demonstrating enhanced performance across various neural network applications in image and text classification.
... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper. We appreciate that you value our discussion on the limitations of existing approaches, the straightforwardness of our approach, and its scalability and generality. We have addressed each of your concerns below.
**Weaknesses**
> 1. The paper lacks rigorous theor... | Summary: This paper proposes a new learning objective for model calibration in multi-class classification tasks. The authors analyze the issues with the current softmax-based scaling method and argue that it only considers the confidence (the probability of the predicted class) without accounting for other remaining cl... | Rebuttal 1:
Rebuttal: We appreciate your careful consideration of our paper. We are glad that you appreciated our experiments and found our motivation and writing clear. We answer your questions below, and the weaknesses in an additional Comment.
**Questions**
> 1. The results in Table 3 are not aligned with the pape... | null | null | null | null |
EgoChoir: Capturing 3D Human-Object Interaction Regions from Egocentric Views | Accept (poster) | Summary: This paper describes an approach to estimate interaction between humans and objects from egocentric videos. To this end, head movement, 3D object meshes and 2D egocentric video are used as input and processed individually before being combined to predict contact regions in both object and subject. Experiments ... | Rebuttal 1:
Rebuttal: > **W1. Contributions, in terms of novelty over related work.**
We summarize the types of existing methods that estimate interaction regions. A distinct type involves estimating 2D regions [1]. For methods in 3D, the types include: 1) part-level contact [2]; 2) estimate human or object in isolati... | Summary: This paper deals with the problem of inferring human-object interaction regions from egocentric views. It tackles the challenge of incomplete observations of interacting parties in the egocentric view by integrating the information from the visual appearance, head motion, and 3D object. It jointly infers 3D hu... | Rebuttal 1:
Rebuttal: > **W1. The statistical information about the dataset.**
We follow the advice and provide statistical information about the dataset **in the newly added PDF**, including interaction categories distribution of video clips, the distribution of object affordance annotations, and the distribution of ... | Summary: This paper investigates inferring 3D human contact and object affordance from a combination of egocentric video, human head motion, and 3D object point cloud. Inspired by real human behavior, which is based on visual observations, self-movement, and conceptual understanding, the authors propose a framework cal... | Rebuttal 1:
Rebuttal: > **W1. Leverage the existing annotations in EgoExo4D, such as 3D hand pose and scene annotation.**
Thanks for the advice. Combining 3D hand and body poses with the scene could indeed improve the annotation accuracy, which enables the contact to be calculated through the spatial distance at first... | Summary: This egocentric paper, aims to capture 3D interactions such as human contact and objects affordance, to achieve this it uses head motion, 3d object and the visual appearance
Strengths: The add to Ego-Exo-4D the interaction data through a semi-automated process
The approach appears to beat other works on this ... | Rebuttal 1:
Rebuttal: > **W1. How accurate is the semi-automated annotation process?**
As described in Appendix B.2, during the semi-automated annotation process, we conduct a manual check and refinement after each round of model prediction to ensure the accuracy of contact annotations. This is because the model estim... | Rebuttal 1:
Rebuttal: Thanks to all the reviewers for their effort and constructive feedback. We are encouraged that the reviewers appreciate our work, including:
* The key idea of the method is ingenious for the proposed task [Reviewer paDU, fLMx]
* The superiority of performance over baselines [Reviewer PyYF, paDU, ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Formalising Anti-Discrimination Law in Automated Decision Systems | Reject | Summary: The paper presents a formalization of fairness metrics intended to ease analysis of discrimination by automated decision making systems in the UK. While there is a relatively applied angle, the bulk of the contribution is intended to be a generic and re-targetable mathematical formalism.
Strengths: This paper... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and highly positive feedback. We appreciate your recognition of our work’s **"significant strength in its understanding of nuance with the way law works"** and that you were **"very pleased overall by the mapping the authors performed between relevant legal concepts i... | Summary: The paper maps existing literature and law on algorithmic fairness onto a decision-theoretic framework. It describes various desiderata (e.g. statistical parity) and legal restrictions (e.g., legitimate aims) in terms of expectations, distributions, estimation error, etc.
Strengths: The paper is well-written ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and for recognising the potential impact of our work.
We're pleased you found our paper well-written, comprehensive in its literature survey, and careful in stating legal tests. We are encouraged by your high rating and assessment of our paper's potential imp... | Summary: - There is a gap between the definitions of fairness studied in the computer science literature, and the definitions of fairness operationalized by courts adjudicating discrimination claims. This limits the usefulness of the CS definitions.
- Amongst work attempting to reconcile legal and computational definit... | Rebuttal 1:
Rebuttal: Thank you for your comments and questions on our paper. We are encouraged that you found the paper’s focus interesting and acknowledged that **"the fairness literature is biased towards the US, and I imagine most fairness researchers would be unaware of subtle differences between UK and US anti-di... | Summary: This paper addresses the issues around existing fairness metrics and bias detection/mitigation methods not corresponding with legal notions of fairness, specifically under UK anti-discrimination law. The authors propose a theoretical framework for a data-generating process that aims to formalise the legitimacy... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive feedback. We appreciate that you found our paper **"translates potentially inaccessible legal scholarship and discussions clearly for a technical audience"** and **“addresses some big limitations in existing literature”**.
## Weaknesses
> **W1** “Th... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper provides a UK-and-European-law-based view of anti-discrimination law as it relates to fair machine learning and automated decision systems. It does a good job laying out the doctrine, arguing correctly that work in this area to-date is very centered on US legal concepts such as disparate treatment v... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback, detailed review, and suggestions for improving our paper. We appreciate your recognition that our paper **"does a good job laying out the doctrine"**, **"is well written and well situated in existing literature"** and that **"generalizing beyond the US legal ... | null | null | null | null | null | null |
Navigating the Effect of Parametrization for Dimensionality Reduction | Accept (poster) | Summary: The paper focuses on comparing parametric and non-parametric neighborhood embedding methods for dimensionality reduction. Non-parametric methods excel at preserving local data structures but struggle with large datasets. Parametric methods, utilizing neural networks, offer better scalability but often compromi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the very detailed and encouraging review, especially the suggestions made to enhance our paper. Here are our detailed response to each question or concern raised by the reviewer:
- **Rationale behind the choice for shallow neural network, and generalizability to deep neu... | Summary: Authors study the effect of parameterization in neighbor embedding algorithms. They aim to improve how these methods capture local structure in the data. To do so they propose a new algorithm building upon PaCMAP with slight modification of its loss with new weights in front of each term.
Strengths: * code is... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review. Here are our response to each question or concern:
- **Mid-near sampling is not new.**
While the sampling process of the hard negatives roots from the mid-near sampling proposed by PaCMAP, in our work, we utilize them for a completely different purp... | Summary: The paper tackles the problem of Dimensionality Reduction (DR). The authors flag a problem with the current existing parametric DR methods. It is shown with empirical evidence that parametric methods cannot capture all the local details. To mitigate this problem, the paper presents ParamRepulsor, a new paramet... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and encouraging review. Here are our detailed response to each question or concern raised by the reviewer:
- **What is the computational time of ParamRepulsor compared to other methods?**
We made a comparison of the computational time in Appendix Sec. F.2, ... | Summary: The paper addresses the problem of improving parametric neighborhood embedding (NE) methods -- i.e., techniques that optimize a neural network to project a higher dimensional dataset into a lower dimensional space. The main advantage of these being that they don't have to be recomputed for new samples, as it o... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review and suggestions. Here are our detailed response to each question or concern raised by the reviewer:
- **Are parametric methods comparable to non-parametric methods?**
See common response 1.
- **How does the weight on MN sampling affect the global st... | Rebuttal 1:
Rebuttal: We would like to thank reviewers for providing us with valuable feedback.
We have taken note of the concerns raised by each reviewer and addressed them in detail. Here, we provide responses to the most shared questions, as well as responses that require an additional PDF page. We then provide a d... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Active Learning of General Halfspaces: Label Queries vs Membership Queries | Accept (poster) | Summary: This paper studies the problem of actively learning non-homogeneous half-spaces under Gaussian distribution. It shows that in the pool based model, any active learner cannot do better than the passive learner in terms of label complexity, unless exponentially many unlabelled examples are drawn. On the other ha... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for appreciating our work and providing useful suggestions. We next respond to the comments from the reviewer as follows.
## The benefit of pool-based active learning:
We start by commenting on the statement of Theorem 1.1. In the statement, we show that to learn a $... | Summary: This paper first provides a lower bound on active learning using label queries. This lowerbound is nearly tight compared with upperbound for label queries. To get around the lowerbound, the authors study active learning using membership query, which means the algorithm can directly access the random function t... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for appreciating our work and providing constructive feedback. We will improve the writing in the revised version of this work. Below, we answer the question from the reviewer.
## Using the correct initialization algorithm:
This is a good question: how to estimate... | Summary: The paper studies the question of active learning halfspaces over $\mathbb{R}^d$, under the Gaussian distribution in two different models: Label queries and membership queries. In the label queries model, the authors prove a lower bound implying that the learner must have a pool of size $exp(d)$ in order to do... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for the feedback. We will revise the writing in the updated version of this manuscript. We will also include a section that discusses future directions. Below, we respond to the weaknesses and questions pointed out by the reviewer.
## Confusion in the introduction:
>... | Summary: This paper considers active learning of general (non-homogeneous) halfspaces under Gaussian distribution. Define p=P(Y=-1). On the one hand, it proves that, roughly speaking, with standard label queries, one cannot learn a classifier with O(opt+epsilon) error that requires polynomially many unlabeled samples a... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for appreciating our work and providing us with constructive feedback. We respond to the points made by the reviewer below.
## Strong marginal distributional assumption:
We start by pointing out that this is the first work that studies the label complexity of learnin... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and effort in providing feedback. We are encouraged by the positive comments, and that all the reviewers appreciated the paper for the following (i) theoretically interesting (**dMkP,amzj,goh3,88gR,vQ1c**), (ii) technically deep and novel (**dMkP,amzj,goh3,88g... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper studies the classical problem of distribution-dependent---here standard normal disitrbution---active learning of non-homogeneous halfspaces using label and membership queries in the realizable and agnostic setting. There are two main contributions.
1. A strong lower bound in the label query setting... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the constructive feedback. We respond to the comments and questions from the reviewer as follows.
We first want to make some comments on the reviewer’s summary.
## Remark on the summary:
We do not view our work as a simple extension of previous work. Prio... | null | null | null | null | null | null |
Exploring Token Pruning in Vision State Space Models | Accept (poster) | Summary: The paper introduces a pruning-aware hidden state alignment method, stabilizing token neighborhoods and maintaining model performance during token pruning. It also proposes an adapted token importance evaluation method tailored for SSM-based models, effectively guiding the pruning process. Extensive experimen... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer for recognizing the strengths of our papers and providing valuable feedback. We are happy to address the raised questions as below.
---
**W1. The proposed method is limited to plain, non-hierarchical SSM-based models.**
We would like to kindly point out that ... | Summary: This manuscript aims to propose an effective pruning method for SSMs to achieve great trade-off of computation overhead and accuracy. The authors find that directly applying pruning strategies designed for transformer structure would greatly impair the performance of SSMs and give related analysis, that is SSM... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer for recognizing the strengths of our papers and providing valuable feedback. We are happy to address the raised questions as below.
---
**W1. More comprehensive comparison.**
We agree that a more comprehensive comparison would help the audience better underst... | Summary: This paper introduces a token pruning method for vision SSMs to improve computational efficiency while maintaining performance. The authors identify that direct application of existing token pruning techniques designed for ViTs fails in SSMs due to disruption of sequential token positions. To address this, the... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer for recognizing the strengths of our papers and providing valuable feedback. We are happy to address the raised questions as below.
---
**Q1. How to handle the consecutive pruned tokens and longer sequences?**
Thank the reviewer for raising this valuable ques... | Summary: This paper explores the token pruning in vision state space models. It gives the observations that utilizing the token pruning techniques designed for ViTs leads to significant performance drop in SSMs. The main reason is that naive application disrupts the sequential token positions. To solve this, this paper... | Rebuttal 1:
Rebuttal: We sincerely appreciate the feedback from the reviewer. We address the raised questions as below.
---
**W1. More quantitative analysis result here to prove the impact of token computation patterns on performance of SSMs.**
We would like to thank the reviewer for this valuable suggestion, we cond... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to the reviewers for their positive comments and constructive feedback on the paper. We sincerely appreciate the reviewers for acknowledging **our motivation** is clear and reasonable (Reviewer TrFe), with interesting observations that give insights to better... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper presents a token pruning method for vision state space models. The goal of this paper is to expand the token pruning methods for ViTs to recent SSM-based vision backbones. The authors observed that token pruning will change the computational characteristics of SSMs and lead to significant accuracy d... | Rebuttal 1:
Rebuttal: We sincerely appreciate the feedback from the reviewer. We address the raised questions as below.
---
**W1. Accuracy drop after finetuning.**
We appreciate the detailed feedback and would like to address the concern regarding the observed accuracy drop. Our work represents a novel advancement i... | null | null | null | null | null | null |
Statistical Efficiency of Distributional Temporal Difference Learning | Accept (oral) | Summary: This paper studies the finite sample performance/non-asymptotic analysis of distributional temporal difference by providing a tighter (minimax optimal) bound than previous works. They propose the non-parametric distributional TD without incorporating any parameterization error. By leveraging the conclusion tha... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's time and effort in reviewing our paper.
We are glad to hear that the reviewer finds it reasonable to investigate the non-asymptotic convergence of distributional TD.
We are also happy to know the reviewer thinks our theoretical results are technically sound.
... | Summary: The paper investigates the statistical efficiency of distributional temporal difference (TD) algorithms in reinforcement learning, focusing on non-asymptotic results. The authors introduce a non-parametric distributional TD algorithm (NTD), analyze its sample complexity with respect to the p-Wasserstein metric... | Rebuttal 1:
Rebuttal: Thank you for your encouraging comments.
We are very glad to know that
you find our results solid and novel.
Regarding the weaknesses and
questions, we provide the following detailed responses:
> Weakness 1: As a minor ... understand the authors' contributions.
Thanks for your suggestion of pres... | Summary: This paper presents last-iterate error bounds for distributional
temporal difference learning in the $W_p$ and Cramér metrics. The
results apply to a nonparametric/intractable distributional TD algorithm
(where return distributions can be represented exactly) and a tractable
projected distributional TD algorit... | Rebuttal 1:
Rebuttal: Thank you for your valuable review and constructive suggestions.
We are very glad to know thatyou think our theoretical results are technically precise, and acknowledge the contribution of the proposed Freedman's inequality.
Regarding the weaknesses and questions, we provide the following detail... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Expressive Capacity of State Space Models: A Formal Language Perspective | Accept (poster) | Summary: 1. This paper studies the expressive capacity of state-space models from a formal language perspective.
2. It shows that for flip-flop, SSM can do well. And for parity, SSM cannot do well.
Strengths: 1. State-space models have the advantage of low inference cost, therefore using it to learn language models a... | Rebuttal 1:
Title: Related work, just a comment
Comment: Given the formal nature of the paper’s title, it may be beneficial to reference the related paper available at https://arxiv.org/abs/1606.06737, which discusses formal language and the hidden Markov model. As the state-space model is a specific type of hidden Mar... | Summary: The paper presents several results on the expressiveness of *state space models (SSMs)*, viewed as *language acceptors* or a weak form of *language predictors*, from the perspective of formal language theory. These results are parametrized to subsume currently common SSM architectures, including *non-negative ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive score and encouraging review. Regarding the issues pointed out:
### Response to Weaknesses:
> This first reason is minor compared to the others. There are many results in this paper, which is desirable. However, it is a bit difficult to get an overview of... | Summary: This paper presents a comprehensive theoretical and empirical analysis of the expressive capacity of modern State Space Models (SSMs) within the framework of formal languages and automata theory. The authors establish important theoretical results, demonstrating that SSMs can effectively model star-free langua... | Rebuttal 1:
Rebuttal: We thank the reviewer for their high score and positive comments about our paper. We appreciate the reviewer's thorough examination of our proofs and their constructive insights and suggestions. We address the points raised as follows:
### Response to Weaknesses:
> While the paper does an admira... | Summary: This study investigates the expressive power of SSMs compared to Transformers and RNNs from the perspective of formal language classes (or circuit complexities), and reports their distinct strengths. Previous studies have shown that both SSM and Transformers are in TC0, suggesting some state tracking problems ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments and questions. We address the raised points as follows:
### Response to Weaknesses:
- Equation 4 Notation: We acknowledge that the subscript $w_{1...t}$ in Equation 4 was intended to indicate that the prefix $w$ consists of a sequence of $t$ in... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' detailed feedback and constructive criticism. We are particularly encouraged by Reviewer tByQ's thorough verification of our proofs and confirmation of their validity. We also recognize the valuable input from all reviewers regarding typographical errors and structural... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CountGD: Multi-Modal Open-World Counting | Accept (poster) | Summary: This paper focuses on multi-modality open-world object counting, where the model can receive text or visual exemplars or both as input. To this end, the authors repurpose Grounding DINO, an open-vocabulary object detector, as an open-world object counting model. The main idea is to treat visual exemplars as ad... | Rebuttal 1:
Rebuttal: Thank you Reviewer 44bk for recognizing the promising real-world applications of our work and the strong performance of CountGD on object counting. We address all weaknesses (W1-W5), minor issues (M1-M2), and questions (Q1-Q3) below.
**W1. Inaccurate claim. The authors claim that they introduce t... | Summary: The authors propose a novel object counting method CountGD that is based on GroundingDINO. It uses the strong localization and multimodal capabilities of GroundingDINO and adapts it to the task of few-shot object counting. The main architectural change is the introduction of visual exemplars to the GroundingDI... | Rebuttal 1:
Rebuttal: Thank you Reviewer QCsU for recognizing our experiments are thorough, our results significantly improve the state-of-the-art, and our work is presented well. We address all weaknesses (W1-W4) and questions (Q1-Q4) below. We combine some weaknesses and questions if they make overlapping points.
**... | Summary: This paper aims to improve open-vocabulary object counting in images by repurposing an existing detection model (GroundingDINO) and introducing multi-modal prompts using text descriptions and visual exemplars. The contributions include the introduction of COUNTGD, the first open-world counting model, improved ... | Rebuttal 1:
Rebuttal: Thank you Reviewer Jn2T for recognizing the extensiveness of our experiments, the novelty of our multi-modal counting setting, and how our approach significantly improves the practicality and accuracy of object counting. We address all weaknesses (W1) and questions (Q1-Q3) below.
**W1. It would b... | null | null | Rebuttal 1:
Rebuttal: We provide the figures and tables for the rebuttal as a PDF attached here.
Pdf: /pdf/4ee04188a33c7580a4875c449c557f21befef09d.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Generating Origin-Destination Matrices in Neural Spatial Interaction Models | Accept (poster) | Summary: The paper introduces the GENSIT framework to generate ODMs in agent-based models. The framework addresses challenges in traditional methods that rely on continuous approximations and ad-hoc discretizations, proposing a more efficient approach that operates directly on the discrete combinatorial space. The meth... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback.
> No theoretical guarantee...
Theoretically analysing the full joint framework is challenging future work, however we offer some existing theoretical insight that we will briefly discuss in the main and expand in the App.
We leverage the universal approx... | Summary: The authors proposed a novel framework that can effectively generate the discrete origin-destination matrices (ODMs) in agent-based models. By using neural differential equations to embed the spatial interactions and operating directly on the discrete combinatorial space, the method overcomes the limitations o... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful comments and important questions that will significantly improve the paper's clarity.
> The author introduced two sampling schemes...
We propose adding the following to the Appendix:
"The Disjoint scheme consists only of loss terms that depend directly o... | Summary: This paper introduces a novel framework named Generating Neural Spatial Interaction Tables (GENSIT) for efficiently generating origin-destination (OD) matrices in neural spatial interaction models. The primary objective is to address the challenges of existing methods, such as continuous approximations and ad-... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and questions. These will improve the paper's clarity and accessibility to a wider audience.
> The paper does not sufficiently explain the practical importance of generating OD matrices efficiently. For readers unfamiliar with this line of literature, it w... | Summary: The authors propose a generative model for origin-destination matrices $I \times J$ with known marginals. The model assumes that the counts in the matrix are realizations of Poisson random variables conditioned on parameters determined by an unknown intensity $\Lambda$. The matrix entries must also respect the... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback.
> I found this...
Please see **Themes 1,3** in global rebuttal.
> To be more specific, consider...
The SIM's intensities in (3),(4) are embedded in the Harris-Wilson SDE. The goal is to solve the SDE's inverse problem by learning the parameters $\boldsym... | Rebuttal 1:
Rebuttal: We thank all reviewers. All agree on the paper's contributions: "novel contribution" [sr5H,qQL1,dVmA,h7J4,C8eo], "new approach" [C3cp]. The "paper appears high quality ... is significant" [sr5H], "provides a solid approach" [C3cp], is "well-structured and clearly presents the motivation" [dVmA]. A... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper considers the task of estimating discrete Origin-Destination Matrices (ODM) which will be useful for generating synthetic agent populations. Rather than the computationally inefficient approach of searching the huge space of such matrices using expensive Agent-based Simulations, the study takes the a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback.
> ..contributions seem to be too specific to the task of generating discrete OD matrices...
We point to the global rebuttal (**Theme 1**) where we elaborate on our framework's wider applicability, importance, connection to ABMs, and societal impact.
> ..... | Summary: The paper introduces a new framework, GENSIT, for calculating origin-destination matrices for Agent-Based Models (ABMs) representing the movement of individual agents, from partially observed summary statistics. The method uses a neural differential equation as a physics-based way of embedding spatial interact... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback.
> Despite being well written, the paper is often dense and ...
Please see global rebuttal (**Theme 2**), where we list improvements we now make to increase clarity throughout, improve notation, and provide a more gentle introduction to technical concepts... | null | null | null | null |
SpeAr: A Spectral Approach for Zero-Shot Node Classification | Accept (poster) | Summary: The manuscript proposes the SpeAr method, which leverages spectral analysis and class prototypes to uncover the implicit clustering structures within graphs, providing a comprehensive understanding of node categories. The proposed method establishes an approximate relationship between spectral contrastive loss... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our manuscript and for the valuable comments. Below is a point-by-point response to the comments.
**> Response to Q1: Discussion on $k$**
In Section 3.2 of the main body, the eigenvector matrix is obtained by spectral decomposition, $F^{\ast} =... | Summary: Zero-shot node classification is a vital task in the field of graph data processing. Prediction bias is one of the primary challenges in zero-shot node classification. This paper employs spectral analysis coupled with learnable class prototypes to discover the implicit cluster structures within the graph, prov... | Rebuttal 1:
Rebuttal: Thank the reviewer for carefully reading and for the valuable comments. We greatly appreciate the time and effort you have taken to provide such thoughtful feedback. Below is a point-by-point response to the comments.
**> Response to W1: High computational complexity**
Given the shared concerns ... | Summary: This paper proposes a spectral approach for zero-shot node classification (ZNC) that addresses prediction bias based on node representation learning technique. It optimizes node representations by a two-stage training method with spectral contrastive loss and class prototypes, in which the class prototypes are... | Rebuttal 1:
Rebuttal: We are immensely grateful to you for recognizing the reasonability and presentation of our work. Your valuable suggestions inspire us to improve our work further.
If you think the following response addresses your concerns, we would appreciate it if you could kindly consider raising the score.
... | null | null | Rebuttal 1:
Rebuttal: ## **General Response**
We sincerely appreciate the reviewers for their valuable and constructive comments.
We are honored to see that the reviewers recognized the novelty (HC2X, 7tvx), reasonability (rWPM, 7tvx), and significant contributions (HC2X, 7tvx) of our framework. Several reviewers app... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Only Strict Saddles in the Energy Landscape of Predictive Coding Networks? | Accept (poster) | Summary: This paper studies the properties of the loss function of predictive coding (PC) networks. In PC networks, the loss function that is optimized is not a typical loss such as the MSE, but the “equilibrated energy” (or “PC energy”). The paper argues that the PC energy has some better theoretical properties than t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and are glad that they share our excitement about the work. Below we address each point and question raised by the reviewer.
The reviewer points out that our analysis of deep linear networks (DLNs) is a weakness of our theory. We would like to highlight th... | Summary: This paper explores how the energy landscape in deep linear networks trained with predictive coding compares to the loss landscape of DLNs trained with backpropogation. The authors prove that the energy of PC is a rescaled version of the MSE loss. The authors then show that, unlike BP trained DLNs, multiple po... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. Below we address each point raised by the reviewer.
Given that the equilibrated energy turns out to be a rescaled version of the MSE loss, the reviewer asks if PC could be interpreted as having a different effective learning rate than BP. Though pointing ... | Summary: This work looks at the (equilibrated) loss landscape of predictive coding networks, and analyzes the nature of saddle points therein --- especially in comparison to that obtained for backpropagation based deep linear networks with MSE loss. They find that several non-strict saddle points in the latter turn to ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. Below we address the points made by the reviewer one by one.
We tried to be very careful to not overstate our claims based on the results. In the abstract, introduction and conclusion, we state that “we provided theoretical and empirical evidence that the... | null | null | Rebuttal 1:
Rebuttal: We thank all of the reviewers for taking the time to read our paper and their feedback, which we believe improved the clarity, scope and contributions of the paper. In this global response, we address points that were raised by more than one reviewer and outline other relatively minor changes we m... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Who's asking? User personas and the mechanics of latent misalignment | Accept (spotlight) | Summary: The paper focuses on the refusal behavior of LLMs, providing motivation for study using evidence that early layers still contain harmful responses, and then investigating how different methods could potentially lead the model to increase or decrease refusal: prompting, contrastive activation addition, and user... | Rebuttal 1:
Rebuttal: Thank you for the great questions and suggestions, as well as your positive feedback on originality, quality, and significance of our in-depth investigation and results. We are glad that our draft has conveyed our findings with clarity.
# Choice of personas
Recent studies (e.g., Chao et al., 2023... | Summary: This paper investigates the mechanics of response refusal in LLMs by probing the Llama 2 13B and Vicuna 13B models. The authors investigated two ways of manipulating the model’s response behavior - prompt prefix (PP), or prepending text with instructions to the prompt, and contrastive activation addition (CAA)... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback and the great suggestions.
# Generalizability
We have conducted additional experiments with the Gemma 7B model (Gemma team, 2024), and have included them in the attached PDF in the global response above. We see similar trends in Gemma.
# Implications and li... | Summary: This study aims to improve model safety by discovering the encoding of user persona, and understand its effect on model refusal. It develops a challenging version of AdvBench which more implicitly posing the same unsafe request, and shows that by modifying the user persona, the refusal rate changes significant... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and great questions!
# Choice of Llama 2 13B chat
In section 2, our goal was to start with an exploratory analysis to motivate the study of intermediate representations with respect to model refusal and harmful beliefs. For scaling up the main experiments, ... | Summary: The paper studies the notion of persona in LMs: a latent variable representing the supposed agent with which the LM is communicating. It is shown that LMs encode the persona and may or may not leak unsafe content as a function of the perceived persona. It is specifically shown that in a simple prompting settin... | Rebuttal 1:
Rebuttal: Thank you for your feedback! We are glad to hear that you found our results about anti-social personas and using Patchscope for a more in-depth interpretation particularly interesting.
# Causal intervention clarification
Contrastive activation addition (CAA) (Rimsky et al., 2023) which is one of... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful comments!
We are glad that they found the paper to be *“well-motivated”* (19wX) and addressing *“an important problem”* (zwX8).
We appreciate that they unanimously found our experiments to be *“thorough”* (AgYD) and *“in-depth”* (19wX), and our r... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Frustratingly Easy Test-Time Adaptation of Vision-Language Models | Accept (poster) | Summary: This paper presents a TTA strategy called ZERO, in which the author carefully and thoroughly explains the entire work, from motivation to method and experiments, including numerous appendices. However, the observations mentioned are not novel enough, and the proposed method is not sufficiently flexible.
Stren... | Rebuttal 1:
Rebuttal: **W1-Q1: Novelty and relationship to over-under-confidence.** To clarify our novelties: (1) we provide theoretical tools to understand the pitfalls of Marginal Entropy Minimization; (2) the proposed baseline only requires manual tweaking of a single parameter, a single forward pass of the vision e... | Summary: This work studies the test-time adaption (TTA) of Vision-Language Models (VLMs), where the goal is to adapt the trained VLMs to unseen datasets/distribution. To this end, this work first revisits the commonly used Marginal Entropy Minimization (MEM) by showing its effect on marginal probability distribution $\... | Rebuttal 1:
Rebuttal: **W1 - Q1: Invariance to Entropy Minimization.** Invariance to MEM is strongly related to the uncertainty of the marginal probability distribution pre-TTA. The lower the initial entropy, the lower the impact of MEM on the $\arg \max$.
**Theory**: this is related to the proof of Proposition 2.1... | Summary: This work shows that Marginal Entropy Maximization (MEM), a leading class of methods for Test Time Adaptation (TTA) which involves maximizing the entropy of the predictive distribution marginalized over different views of the input, regularly results in the same argmax (and thus same final class prediction) as... | Rebuttal 1:
Rebuttal: **W1 - Q1: Independence among views.** We agree and we thank the reviewer for this remark, which allows us to enrich our manuscript further.
The theoretical framework of Section 2.3 models an ideal scenario, where independence holds among different inputs. To clarify, this means that the model's ... | Summary: This work carefully reviews the popular test-time adaptation (TTA) method, MEM, and finds that MEM has largely no effect on
arg max(𝑝). Based on this understanding, this work further introduces a clean method called Zero, which shows decent performance for the TTA task.
Strengths: 1. Great motivation for br... | Rebuttal 1:
Rebuttal: **W1 - Performance on Fine-grained datasets vs Natural Distribution Shifts.**\
We thank the reviewer for raising this interesting point, allowing us to further investigate our method.
A possible explanation may be linked to Sections 2.3 and 3.1 of the manuscript. Specifically, Zero improves over... | Rebuttal 1:
Rebuttal: ### **General Comment**
We sincerely thank all reviewers for the time and effort devoted to reviewing our manuscript.
Above all, we profoundly appreciate that the simplicity of the proposed baseline has been praised almost unanimously among reviewers (`Wz3n`, `a5fT`, `7eTE`).
On theoretical r... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Disentangled Unsupervised Skill Discovery for Efficient Hierarchical Reinforcement Learning | Accept (poster) | Summary: This paper proposes a method for learning disentangled skills that can be efficiently reused to solve downstream tasks. The mutual information objective design is simple yet effective. Intensive empirical results show the superiority of the proposed method.
Strengths: The algorithm design, based on factored M... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed reading of our paper and very constructive suggestions!
> The algorithmic contribution is not significant compared with other MI-based unsupervised skill discovery methods.
We believe simple ideas that work well are very valuable for the community. As we d... | Summary: The paper presents a method (DUSDi) for learning reusable skills through unsupervised interactions. Unlike existing methods that produce entangled skills, DUSDi focuses on disentangling skills into components that each affect only one factor of the state space. This enables efficient chaining of skills via hie... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed reading of our paper and very constructive suggestions!
> there is little information on how the formulations generalize to continuous skill space
For continuous skills, we can simply define each skill component z^i as a m-dimensional continuous vector (and... | Summary: Grounding on previous work on mutual information for skill learning, DUSDi proposed an algorithm for disentangled skill learning, which introduces several advances. First, DUSDi proposes to map factorized state components to factorized skill components. Then, it proposes the adoption of Q decomposition and Cau... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed reading of our paper and very constructive suggestions!
> for POMDPs or high-dimensional environments, the algorithm may not work as expected.
We agree with the reviewer that extending our method to partially observable complex pixel environments is non-tri... | Summary: This paper proposed DUSDi, which learned disentangled skills in an unsupervised manner. Specifically, DUSDi learns each skill component only affects one factor of the state space. DUSDi is evaluated in a specialized tasks comparing with skill discovery methods.
Strengths: - The proposed concept of disentangle... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed reading of our paper and constructive suggestions.
> It is not convincing how the disentanglement is beneficial in real-world scenarios.
First, we would like to point out the realism of our experiments. We used iGibson, one of the benchmarks that best match... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Representation Noising: A Defence Mechanism Against Harmful Finetuning | Accept (poster) | Summary: This paper proposes RepNoise, an effective mitigation strategy for harmful finetuning issues. The core philosophy of representation loss is to make the hidden representation of the harmful input to a random Gaussian noise. In addition to representation loss, the authors add two loss terms, i.e., stability los... | Rebuttal 1:
Rebuttal: # **As this paper shares a very similar assumption and setting with Vaccine [24], it is suggested the authors compare with Vaccines in the rebuttal.**
Thank you for raising this. Vaccine is a white-box defense like Security Vectors and requires the defender to have control over the training pipel... | Summary: The paper introduces a novel defense mechanism, Representation Noising (RepNoise), designed to protect large language models (LLMs) from being fine-tuned for harmful purposes. RepNoise addresses this by removing harmful representations from the model's internal structure, making it difficult for these harmful ... | Rebuttal 1:
Rebuttal: Thank you for pointing out the writing inconsistencies. We fixed them.
### Is it possible to achieve Harmful Fine-tuning Attack by reversing the loss function of RepNoise?
No this is not possible. We have empirically validated it by running RepNoise with a minus sign in front of the loss funct... | Summary: This paper proposes a method for mitigating harmful fine-tuning attacks (HFAs) on large language models (LLMs). The main idea is to fine-tune a model in such a way that an attacker---who is assumed to have full access to the weights after the defense is run---cannot easily update the model so as to elicit har... | Rebuttal 1:
Rebuttal: First we thank the reviewer for their detailed and insightful comments, our experience trying to address these has really helped strengthen our paper.
Unfortunately due to space of the rebuttal and our desire to give each concern it's due consideration, we have to present a fragmented response us... | Summary: It is known that safety alignment in current LLMs can be easily removed by further harmful fine-tuning of these models. This paper aims to make models robust against such harmful fine-tuning. To achieve this goal, the paper proposes an approach called Representation Noising (RepNoise). In short, the approach b... | Rebuttal 1:
Rebuttal: Thank you for your concern. First, there are a few minor differences in this attack than the ones given in the paper. Generally, for harmful question answering we do not evaluate multiple epochs (for 100 HEX-PHI samples at a batch size of 64 that’s 25 epochs for 50 gradient steps). Aside from the ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Online Consistency of the Nearest Neighbor Rule | Accept (poster) | Summary: This paper studies the problem of non-uniform consistency of the nearest neighbor prediction rule when the instances are generated by a random process that has conditional marginals dominated by some underlying reference measure. In particular, it generalizes the well-known results of Cover and Hart (1967) for... | Rebuttal 1:
Rebuttal: Thank you for your careful reading of the paper and for your thoughtful
comments. To address your comments/questions:
0. On the decision to present the results in more general metric measure space
setting over Euclidean space: the main tradeoff we were making was between
the benefits of a r... | Summary: This paper considers the consistency (or mistake-bound) of the nearest neighbour rule in the realizable online setting when the instances are not necessarily i.i.d. but drawn from a well-behaved stochastic process. The authors prove that when the underlying stochastic process is uniformly dominated, the neares... | Rebuttal 1:
Rebuttal: Thank you for your careful reading of the paper and for your thoughtful
comments. To address your comments/questions:
0. On the technical contributions of this work: while we do owe a great deal to
prior work in online learning, nearest neighbor methods, and geometric
measure theory (see ou... | Summary: This paper studies the nearest neighbor rule in the realizable online setting and closely checks under what assumptions it can achieve online consistency, i.e. the mistake rate eventually vanishes as the number of samples increases. It proves that for all measurable functions in doubling metric spaces when the... | Rebuttal 1:
Rebuttal: Thank you for your careful reading of the paper and for your thoughtful
comments. To address your comments/questions:
0. On the technicality of the writing: thanks for this feedback. We will
continue to work on clarifying the exposition.
1. This is a very interesting question. Perhaps the eve... | null | null | Rebuttal 1:
Rebuttal: Thank you to all the reviewers for reading our paper and the considered feedback
and questions.
All reviewers thought that we study a fundamental question in learning theory,
and that our results are significant: we show that the 1-nearest neighbor rule
achieves online consistency in settings fa... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Temporal Sentence Grounding with Relevance Feedback in Videos | Accept (poster) | Summary: This paper proposes a new task called Temporal Sentence Grounding with Relevance Feedback (TSG-RF) in videos, which extends the traditional Temporal Sentence Grounding (TSG) by introducing cross-modal video-text semantic relevance prediction. Besides, a new Relation-aware Temporal Sentence Grounding (RaTSG) ne... | Rebuttal 1:
Rebuttal: Dear Reviewer P4nK,
Thank you for your comprehensive and positive review of our work. We appreciate your insights and suggestions for further improvement. Below, we address your concerns point by point.
**Q1: The paper should illustrate bad examples in the visualization section, and discuss the... | Summary: This paper presents a novel task named Temporal Sentence Grounding with Relevance Feedback (TSG-RF) to overcome the limitations of conventional Temporal Sentence Grounding (TSG), which presumes the existence of relevant segments in videos. This paper introduces the Relation-aware Temporal Sentence Grounding (R... | Rebuttal 1:
Rebuttal: Dear Reviewer eoKJ,
Thank you for your detailed and positive review of our work. We appreciate your insights and suggestions for further improvement. Below, we address your concerns point by point.
**Q1: The weaknesses of this paper include a lack of sufficient baselines for comparison with the... | Summary: This paper introduces Temporal Sentence Grounding with Relevance Feedback (TSG-RF) in videos, a new task that addresses the limitations of traditional Temporal Sentence Grounding (TSG), which assumes relevant segments always exist within a video. TSG-RF accounts for the possibility that a video may not include... | Rebuttal 1:
Rebuttal: Dear Reviewer dpih,
Thanks for your detailed review and constructive feedback. We appreciate your insights and would like to address your concerns point by point.
**Q1: discussion about highlight detection**
**A1**: We thank the reviewer for sharing the insight. Actually, our RF task is quit... | Summary: The work develops a model that localizes query-related segments when present and provides feedback on non-existence when absent.
It proposes a Relation-aware Temporal Sentence Grounding (RaTSG) network, which reformulates TSG-RF as a foreground-background detection problem.
Also, the work uses a multi-granular... | Rebuttal 1:
Rebuttal: Dear Reviewer 3Qyt,
Thank you for your thorough review and valuable feedback. We appreciate your insights and would like to address your concerns point by point.
**Q1: The work claimed to incorporate foreground/background info. However, it was simply adding a binary classification, such binary... | Rebuttal 1:
Rebuttal: We thank all reviewers for their encouragement and guidance to further improve this work. **Here, we would like to discuss the specific design of our RaTSG framework for TSG-RF task.**
Firstly, we would like to emphasize that the primary innovation of our paper is the introduction of the new and ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
AutoPSV: Automated Process-Supervised Verifier | Accept (poster) | Summary: This paper introduces AutoCV, a novel method to create process reward models (PRMs). The approach involves training an an outcome-supervised verification model based on (S_{(1:t)}, y) pairs where S_{(1:t)} is the 1st~t-th steps in the response and y is the final correctness label of the entire response. The ou... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and insightful comments. We hereby address your concerns below:
**R W1 Presentation of Section 4.2.1:**
Thanks for the suggestion. To avoid potential misunderstandings, we will change the term to "calculation error detection."
We would like to provide the ... | Summary: This paper proposes a new method (AutoCV) that bridges the gap in popular techniques for enhancing reasoning capabilities of LLMs. Prior work have proposed *verification models* (models that evaluate generated reasoning steps and rerank candidate responses) as a promising solution. Here the community has focus... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and insightful comments. We hereby address your concerns below:
**Response to W1 Presentation:**
**1.1 No mention of the #samples being reranked**
Our task involves selecting the correct candidate from **five** responses. Therefore, we compare our method to ... | Summary: The authors propose AutoCV, a method for solving multi-step reasoning tasks with chain-of-thought prompting that involving automatically labeling each step of the multi-step process based on its likelihood of leading to the correct outcome, and combining an outcome supervised classifier (OSV) and process super... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and insightful comments. We hereby address your concerns below:
**R W1 Novelty and Substance of Our Paper**
1. **Theoretical Contribution:** While reference [22] demonstrates that $f_\theta$ can indicate the probability of reaching the correct final outcome,... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
StreamingDialogue: Prolonged Dialogue Learning via Long Context Compression with Minimal Losses | Accept (poster) | Summary: Standard Large Language Models (LLMs) struggle with handling dialogues with long contexts due to efficiency and consistency issues. This paper finds that the structure of the dialogue context is consistent, and special tokens may aggregate information. Therefore, this paper aims to use special tokens to encode... | Rebuttal 1:
Rebuttal: Dear Reviewer wRZP,
We sincerely thank you for your constructive suggestions and valuable feedback! We hope our response can help resolve your concerns.
> The training method is complex, resulting in higher training costs.
Our method outperforms baselines in both training and non-training... | Summary: This paper tackles the challenge of long context dependencies of LLMs in dialogue settings. The authors first posit that end of utterance tokens like "\n" and </s> could conceivably summarize the information in the utterance, and propose to attend to such sinks rather than entire utterances to allow LLMs to pe... | Rebuttal 1:
Rebuttal: Dear Reviewer 4bTd,
We sincerely thank you for your constructive suggestions and valuable feedback! We hope our response can help resolve your concerns.
> More baselines are needed (Weakness 1).
Your kind advice has inspired us to conduct more comprehensive experiments by incorporating the reco... | Summary: The paper introduces a novel approach for encoding long conversations. Motivated by the results of StreamingLLM, the authors observe that the end-of-utterance (EOU) or separator tokens aggregate more attention than other tokens in a dialogue generation task. The authors refer to the EOU tokens as conv-att sink... | Rebuttal 1:
Rebuttal: Dear Reviewer 7W1D,
We sincerely thank you for your constructive suggestions and valuable feedback! We hope our response can help resolve your concerns.
> Table 1 does not show the results with USL-H and Dial-M. Also, in the case of MSC, StreamingDialogue performs better than StreamingLLM on t... | null | null | Rebuttal 1:
Rebuttal: We greatly appreciate the time and effort all reviewers devoted to reviewing our paper and providing detailed, constructive feedback. The reviewers' insights and queries have played a crucial role in helping us refine our research. We have thoughtfully considered feedback from all reviewers and ho... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Flatten Anything: Unsupervised Neural Surface Parameterization | Accept (poster) | Summary: The authors introduce the Flatten Anything Model (FAM), an unsupervised tool for UV mapping 3D geometries. FAM consists of four main components:
1. **Deform-Net**: Adjusts 2D points in an input grid.
2. **Warp-Net**: Converts these 2D points into 3D space.
3. **Cut-Net**: Predicts a seam along which the 3D me... | Rebuttal 1:
Rebuttal: ### **[Rebuttal to Reviewer FN2a]**
### **[W1]** *Inaccurate claim about the settings of global and local parameterization.*
**Response:** Thanks very much for pointing out our inaccurate claim. Indeed, multi-chart local parameterization is a more suitable choice when dealing with highly-complic... | Summary: This paper proposes an unsupervised neural surface parameterization method, named FAM, which maps 3D surface points to adaptively deformed UV coordinates in the 2D parameter domain. Inspired by the actual physical procedures, the neural architecture includes several sub-networks for surface cutting, UV deformi... | Rebuttal 1:
Rebuttal: ### **[Rebuttal to Reviewer Hnxu]**
### **[W1]** *Clarifying the working mechanism and necessity of Cut-Net.*
**Response:** The Cut-Net component is indeed necessary in the whole architecture, and we are sorry for not adequately analyzing its working mechanism and effectiveness in the paper.
A... | Summary: This paper introduces the Flatten Anything Model (FAM), an unsupervised neural architecture designed to achieve global free-boundary surface parameterization by learning point-wise mappings between 3D points on the target geometric surface and adaptively-deformed UV coordinates within the 2D parameter domain. ... | Rebuttal 1:
Rebuttal: ### **[Rebuttal to Reviewer 4cq8]**
### **[W1\&Q1]** *Evaluation of self-intersection.*
**Response:** As suggested, we supplemented quantitative evaluations of self-intersection in Figure R5 of the uploaded one-page PDF file and made comparisons with SLIM for open-surface cases. Generally, it is... | Summary: This paper proposes a novel neural-network based optimization framework for obtaining surface parameterization of arbitrary 3D meshes.
Comparing to traditional methods like SLIM, this work can work for possibly low quality meshes of arbitrary topology.
The core of the proposed pipeline are 4 simple MLPs:
Giv... | Rebuttal 1:
Rebuttal: ### **[Rebuttal to Reviewer eo5W]**
### **[W1\&Q1]** *Discussing the advantages of global parameterization over multi-chart parameterization (e.g., Nuvo).*
**Response:** Actually, over the years, global surface parameterization has continuously been the mainstream direction of research, since i... | Rebuttal 1:
Rebuttal: ### **[Global Response]**
We sincerely thank all reviewers for their time and efforts in reviewing our paper, providing constructive comments and valuable suggestions. We are very grateful to reviewers' positive acknowledgment of this work:
-- Reviewer eo5W thinks that our approach is interestin... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Get rich quick: exact solutions reveal how unbalanced initializations promote rapid feature learning | Accept (spotlight) | Summary: This paper analyses the lazy versus rich learning dynamics of minimal but finite neural networks by deriving exact solutions under arbitrary layerwise initialization and learning rates. The theoretical insights are successively extended to networks of increasing complexity and corroborated by several experimen... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback and constructive questions. We are grateful for your positive feedback on our work and are committed to improving our manuscript by addressing each of the weaknesses you identified.
**Connections to infinite width parametrizations.** We agree with the review... | Summary: The paper studies the dynamics of training in neural networks with the scope of identifying how the variance of weights' initialization together with layer-wise learning rates determines different learning regimes, encompassing lazy, rich, and the transition between them. The paper identifies a conserved quant... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and constructive suggestions. We appreciate your positive feedback and address the weaknesses you highlighted individually. We hope this will increase your confidence in the importance of our work.
**NTK dynamics.** This is a good point; we can in fact study th... | Summary: The paper studies the learning dynamics of a deep network by leveraging the conserved quantities due to symmetries
Strengths: The theoretical finding that unbalancedness in the layers drives feature learning is a novel and interesting insight
The technical tool of using symmetries and conserved quantities to... | Rebuttal 1:
Rebuttal: Thank you for taking the time to thoroughly review our paper and highlight areas needing further clarification. We appreciate your positive feedback on our work. We will address each of the weaknesses you mentioned individually, hoping this will enhance your confidence in the significance of our r... | Summary: The authors derive exact solutions to a minimal model that transitions between lazy and rich learning, elucidating how unbalanced layer-specific initialization variances and learning rates determine the degree of feature learning in a finite-width network.
They provide evidence that this unbalanced rich regim... | Rebuttal 1:
Rebuttal: We appreciate your comprehensive review and the time you have taken to suggest improvements for our study. We are also happy to hear you think our work is a timely and important contribution to the field. We will respond individually to the weaknesses and questions you raised regarding our paper:
... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their careful and detailed comments. We greatly appreciate the time and effort you put into reviewing our paper, which we believe has significantly improved our work. We have addressed each reviewer’s questions individually and provided linked responses... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
First-Order Methods for Linearly Constrained Bilevel Optimization | Accept (poster) | Summary: This paper studies fully first-order methods for bilevel optimization with strongly convex lower-level objectives and linear lower-level constraints, including both inequality constraints and equality constraints. For linear inequality constraints, the paper used a penalty methods to construct hypergradient es... | Rebuttal 1:
Rebuttal: We are extremely grateful to the reviewer for their thoughtful questions and are encouraged that they found numerous aspects of our contribution interesting and useful for follow-up papers.
---
# Response to Weaknesses
---
1. ## Complexity of Algorithms.
We agree with the reviewer that some ... | Summary: This works deals with constrained bilevel optimization problems where the lower-level has linear inequality or equality constraints. A set of algorithms is developed that does not require access to the Hessian, but only to zeroth and first-order information. In the case of inequality constraints, convergence i... | Rebuttal 1:
Rebuttal: We are extremely grateful to the reviewer for their thoughtful questions and are encouraged by their positive assessment of our contributions, soundness, and presentation.
---
# Response to Weaknesses
---
1. ## Limited experiments.
We acknowledge the importance of testing our algorithms with l... | Summary: The paper provides algorithms for bilevel optimization with linear equality and inequality constraints. The main contribution is that the algorithms are fully first order and do not require Hessian computations. This is achieved by reformulating the linearly constrained bilevel optimization problem using the p... | Rebuttal 1:
Rebuttal: We are very grateful to the reviewer for their effort in reviewing our submission and are encouraged by their positive assessment of the theory, presentation, and significance of our work.
---
# Response to Weaknesses
---
1. ## Implementing Algorithm 2
In our paper, we implemented the simpler ... | Summary: This paper studies the linearly constrained bilevel optimization problem and provides a fully first-order method with solid theoretical analysis. To approximate hypergradient, the penalty method seems novel to me and it is applied in two settings where the LL problem is linear inequality or equality constraint... | Rebuttal 1:
Rebuttal: We are very grateful to the reviewer for their effort in reviewing our submission and are encouraged by their positive assessment of our theory and presentation.
---
# Response to Questions
---
1. ## Intuition behind clipping
Intuitively, the clipping ensures that consecutive iterates of the a... | Rebuttal 1:
Rebuttal: We are grateful to all the reviewers for well-thought-out reviews of our submission, appreciation of our ideas, and constructive efforts in helping us strengthen our work. We address all questions and remarks by responding directly to each review (and will incorporate all suggestions). Here we bri... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
GeoLRM: Geometry-Aware Large Reconstruction Model for High-Quality 3D Gaussian Generation | Accept (poster) | Summary: The primary objective of this paper is to develop a geometry-aware large reconstruction model. Previous approaches either predict tri-planes or per-pixel Gaussians for reconstruction from multi-view images. However, these methods lack an explicit correspondence between 2D image features and 3D representations.... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments. We have addressed the specific concerns and provided additional clarification and results as follows:
**Response to Weaknesses:**
* **Number of input views:** We agree that the comparison with InstantMesh using different numbers of input views ... | Summary: This paper introduced a sparse reconstruction model based on the LRM. This paper try to use the projection between the 3D point with the pixel position to save computational consumption and this paper use the deformable attention to lift the 2D feature to 3D, and finally propose a two-stage pipeline to genera... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their constructive feedback. Below are our detailed responses to the specific points raised:
**Response to Weaknesses:**
**About the method and the experiment:**
* **Training stages:** The primary reason for training the stages separately is due to the ***... | Summary: The paper proposes a geometry aware large reconstruction model, that represents the scene as 3D Gaussians. The paper proposes a novel architecture for multi view reconstruction that first generates a proposal occupancy grid for 3D gaussians with a proposal transfomer, and then refines them with a reconstructio... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's positive feedback and valuable insights. Here are our detailed responses to the comments and questions:
**Response to Weaknesses:**
* **About deformable cross attention:** We agree that deformable cross attention plays a crucial role in our method. To furth... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their feedback and valuable comments on our work. As suggested by Reviewer hKRc, we have further compared the mesh extraction of our method with other baseline methods. The detailed comparison is provided in the attached one-page PDF file. Additionally, followi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning Variational Temporal Abstraction Embeddings in Option-Induced MDPs | Reject | Summary: The paper presents an off-policy hierarchical RL method, based on the HiT-MDP formulation of a Semi-MDP. The HiT-MDP formulation treats the option $o$ as an extension of the original state $s$ (which can be chosen by an extended action), and combines initialization-, termination- and option-policy in a single ... | null | Summary: This paper proposes the Variational Markovian Option Critic (VMOC), an off-policy algorithm for hierarchical reinforcement learning. VMOC aims to address exploration inefficiency and update instability in existing methods. Key contributions include: 1. Use of variational inference for update stabilization 2. L... | null | Summary: The paper introduces the Variational Markovian Option Critic (VMOC) which combines variation policy iteration and the option critic. VMOC also modifies HiT-MDPs, where options are represented as latent embeddings rather than triples of (init states, policy, termination condition), to the off-policy setting. Th... | null | Summary: This paper introduces e Variational Markovian Option Critic (VMOC), which learns actions and options simultatenously. They build upon the Hidden Temporal Markovian Decision Process (HiT-MDP) [1] to build a novel off-policy algorithm that utilizes entropy augmented rewards. Their method learns options’ embeddin... | null | Rebuttal 1:
Rebuttal: We deeply appreciate reviewers' efforts. We will incoporate reviewers suggestions in our next version. | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health Interventions | Accept (poster) | Summary: The paper introduces a robust contextual bandit algorithm to optimize personalized mobile health interventions. The proposed algorithm leverages Thompson sampling, mixed-effect models, debiased machine learning, and nearest-neighbor regularization techniques to address the problems of user and time heterogenei... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review. We address the listed weaknesses and questions below.
# Weaknesses
Yes, there are three primary challenges. We will include these descriptions below in the final revision.
The first is the methodological challenge of figuring out how to bring all of the piec... | Summary: The paper "A Robust Mixed-Effects Bandit Algorithm for Assessing Mobile Health Interventions" introduces the DML-TS-NNR (Debiased Machine Learning Thompson Sampling with Nearest-Neighbor Regularization) algorithm. This novel contextual bandit algorithm is designed to address challenges in mobile health (mHealt... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and questions. We address the latter below.
# Q 1
We included a wide variety of competing algorithms (see appendix for the full set) to (1) assess how well DML-TS-NNR performs relative to simple baselines and (2) understand which aspects of DML-TS-NNR contr... | Summary: The authors propose a novel contextual bandit algorithm that addresses individual heterogeneity, nonstationarity, and nonlinearity of the reward function. This algorithm involves three distinct steps to manage these challenges.
Strengths: The current paper is easy to follow and addresses a complicated scenari... | Rebuttal 1:
Rebuttal: Thank you for your helpful comments. We address the outlined weaknesses and questions below.
# Hyperparameter Tuning
Hyperparameter selection plays an important role in the performance of bandit algorithms. While virtually all such algorithms require specification of some hyperparameters (at a m... | Summary: The paper introduces a novel robust mixed-effects bandit algorithm, named "DML-TS-NNR", designed to optimize mobile health (mHealth) interventions. mHealth aims to deliver personalized and contextually tailored notifications to promote healthier behaviors. The proposed algorithm addresses key challenges in mHe... | Rebuttal 1:
Rebuttal: # Weaknesses & Hyperparameters
Thank you for raising these concerns. Other reviewers asked about them as well. Our main rebuttal contains a detailed discussion of all three. Below, we include a brief summary of the main points and a few additional details for your specific questions:
1. **Comput... | Rebuttal 1:
Rebuttal: We thank the reviewers for their helpful and positive comments, including “the paper is well-structured and clearly explains the problem, the proposed solution, and the results” and “The methodology is rigorously developed, with comprehensive theoretical analysis and detailed proofs.” Here we addr... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Structure Consistent Gaussian Splatting with Matching Prior for Few-shot Novel View Synthesis | Accept (poster) | Summary: The paper proposes SCGaussian, a few-shot 3D Gaussian Splatting model to address novel view degeneration in sparse input scenarios. SCGaussian leverages matching priors to enforce 3D consistency by optimizing the position of Gaussian primitives along rays, overcoming challenges in monocular depth-based methods... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer #42ua for recognizing our work and the valuable comments. Here, we will address the concerns point by point.
**Q: It heavily relies on precomputed camera poses. When using COLMAP to obtain camera poses, SFM points are generated effortlessly as a byproduct.**
- As the ... | Summary: The paper proposes a structure guided novel view synthesis method for Gaussian Splatting. Experiments show that the proposed method produces clearer rendering results than other existing works.
Strengths: * The proposed method is well evaluated with various methods in neural rendering. Experiments is sufficie... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer #42ua for recognizing our work and the valuable comments. As suggested, we conduct more ablation studies with different types of matching models and add more discussion of the failure cases.
**Q: A discussion / ablation study on the pre-trained matching model (mat... | Summary: This work presents a Structure Consistent 3DGS method using matching priors to learn 3D consistency. A hybrid Gaussian representation, including non-structure Gaussian primitives and ray-based Gaussian primitives, is introduced. Position Consistency loss between two views is adopted to achieve multi-view align... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer #nvzF for your time and valuable comments. We noticed that the main concerns are the differences with CorresNeRF and more explanation of the implementation details. Besides, we will address all concerns point by point.
**Q1: The differences with CorresNeRF.**
First of... | Summary: This paper proposes SCGaussian that utilizes a pre-trained image matcher to get the dense pixel-wise matching correspondences between sparse observing views, then regard these as the prior for 3DGS to achieve high-quality few-shot novel view synthesis. It innovatively binds Gaussians to the matched pixels and ... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer #k97m for recognizing our work and the valuable comments. We noticed that the main concerns are the performance of our designs and the robustness of different matching models and texture-poor scenes. Here, we will explain the specific questions individually:
**Q: The p... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for providing constructive feedback that helped us improve the paper. We are encouraged that reviewers appreciated the methodology, experiments, and writing of our paper, and acknowledged that:
- the proposed method is novel, interesting, well-motivated and efficie... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
TrackIME: Enhanced Video Point Tracking via Instance Motion Estimation | Accept (spotlight) | Summary: In this paper, the authors propose TrackIME, a framework to tracking points in video. The proposed TrackIME leverages a segmentation model to improve both its efficient and effectiveness. TrackIME achieves SOTA performance on TAP-VID dataset.
Strengths: 1. The proposed method achieves competitive results whi... | Rebuttal 1:
Rebuttal: Dear reviewer 7a51,
Thank you for your valuable feedback and comments. We appreciate your remarks on the strengths of our paper, including the competitive results, low computation cost, and the first introduction of the segmentation model in point tracking tasks. We will address your concerns and... | Summary: This paper presents a new framework for video point tracking from instance motion. By integrating existing segmentation and point tracking base models, the performance of point tracking is significantly improved from object-by-object optimization. Ablation experiments and extensions in zero-shot video object s... | Rebuttal 1:
Rebuttal: Dear reviewer BSTo,
Thank you for your valuable feedback and comments. We appreciate your remarks on the strengths of our paper, including the enabling high-resolution point tracking, the valid experiments for ablation, and the extended results in zero-shot video object segmentation. We will addr... | Summary: This paper tackles the problem of point tracking, where the task is to track the movement of a single point in a video. Point tracking has experienced a fairly recent deep learning revival starting with PIPs [22 in paper ref], which was inspired by a handcrafted method named Particle Video from Sand and Teller... | Rebuttal 1:
Rebuttal: Dear reviewer jQxW,
Thank you for your valuable feedback and very detailed comments. We appreciate your remarks on the strengths of our paper, including the significance of the method, exhaustive experimentation, and clear presentation. We will address your concerns and questions in the response ... | null | null | Rebuttal 1:
Rebuttal: Dear reviewers and AC,
We sincerely appreciate your valuable time and effort spent reviewing our manuscript.
As the reviewers highlighted, we believe our paper provides a novel approach that incorporates point tracking models with the pre-trained instance segmentation (all reviewers). This appro... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Transcendence: Generative Models Can Outperform The Experts That Train Them | Accept (poster) | Summary: 1. Definition of Transcendence: The paper defines transcendence as a phenomenon where a generative model, trained on data from human experts, achieves capabilities that surpass the abilities of those experts.
2. Theoretical Framework: The authors provide a theoretical analysis of the conditions under which tr... | Rebuttal 1:
Rebuttal: > We appreciate the reviewer's insightful questions and valuable critiques. To summarize, we have addressed concerns about a toy theoretical model, and typographical errors. We trust that our responses below will provide a more comprehensive understanding of our research and its implications.
---... | Summary: In this work the authors focus on formalizing and investigating the concept of *trascendence*, i.e. the capacity of a model to outperform the experts it was trained on. To do so, they train transformer-based models for chess limiting experts used for training to a certain maximal ranking. In this setup they po... | Rebuttal 1:
Rebuttal: > We thank the reviewer for their perceptive questions and feedback on the choice of chess as a setting and clarifications about the theory. In the responses following, we have sought to address each point in detail. We hope our clarifications will elucidate a better understanding of our approach ... | Summary: The paper explores the phenomenon where generative models, trained to mimic human behavior, can surpass the performance of the experts generating the training data. The study focuses on autoregressive transformer models trained on chess game transcripts, demonstrating that these models can outperform the best ... | Rebuttal 1:
Rebuttal: > We thank the reviewer for praising the novelty of the concept of transcendence and the rigor of our theoretical proofs while noting concerns about the limited scope of our experiments and the lack of exploration of the practical implications of transcendence.
---
Limited..
> To help address thi... | null | null | Rebuttal 1:
Rebuttal: # Global Note
**Thank You.** We thank the reviewers for their insightful feedback and comments. We are encouraged to find that the reviewers recognized the paper as novel, introducing "a new perspective on model performance" (mPZQ). We are also grateful that the reviewers appreciated the evaluat... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Group Robustness is in the Details: Revisiting Finetuning under Spurious Correlations | Accept (poster) | Summary: The subject of the work is over-reliance on spurious correlations, which can often lead to poor performance on minority groups. The authors identify two failure modes of common class-balancing techniques: (1) class-balanced mini-batch finetuning experiences catastrophic collapse with standard hyperparameters o... | Rebuttal 1:
Rebuttal: We graciously thank Reviewer ax3f for their detailed comments, questions, and references. We appreciate that the reviewer recognizes our contributions to the understanding of class-balancing methods, robustness impact of scaling pretrained models, and spectral analysis of model representations. Be... | Summary: The authors propose a class balancing scheme that both discards samples from the majority classes and upsamples majority classes as a way to improve worst group accuracy/robustness.
Strengths: The authors propose a simple yet effective method that provides good performance for all of the datasets tested. The ... | Rebuttal 1:
Rebuttal: We warmly thank Reviewer kRBT for their detailed comments and questions. We appreciate that the reviewer recognizes the improved performance of our mixture balancing method and the independent interest of our model scaling and spectral analysis experiments. Below, we provide responses to each of t... | Summary: The paper tries to study the fundamental properties of fine-tuning DNNs and worst group accuracy in the presence of spurious correlations. The effort focuses on revealing nuances that were not clear. They conducted experiments with both 2 vision and 2 NLP datasets with spurious correlations and subgroup labels... | Rebuttal 1:
Rebuttal: We graciously thank Reviewer E5Mf for their insightful comments and questions. We appreciate that the reviewer recognizes our comprehensive experiments which challenge existing notions in the literature and leverage previously unknown nuances to improve model performance. Below, we provide respons... | Summary: This paper experimentally analyzes the impact of class balancing on group robustness.
Strengths: This research contributes to the community by investigating spurious correlations in machine learning models' reliance.
Weaknesses: - Due to significant variations in experimental results across datasets, general... | Rebuttal 1:
Rebuttal: We warmly thank Reviewer Rwfv for their comments and suggestions. Below, we provide responses to each of the reviewer’s points, combining weaknesses and questions as appropriate.
## Weakness 1
We thank the reviewer for the relevant comments. While robustness performance indeed differs across dat... | Rebuttal 1:
Rebuttal: We graciously thank all reviewers for their time and insights. Here, we provide new comparisons and experiments of interest to multiple reviewers.
## Additional class-balancing technique (all reviewers)
In addition to subsetting, upsampling, and mixture balancing, we investigated another common cl... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fairness-Aware Meta-Learning via Nash Bargaining | Accept (poster) | Summary: This paper tried to address the problem of hypergradient conflicts in fairness-aware meta-learning, where the overall validation loss gradient is not aligned with per-group validation loss. They do this by using Nash Bargaining to allow the different groups to achieve consensus on the gradient update direction... | Rebuttal 1:
Rebuttal: Thank you for the reviewer’s constructive feedback and acknowledgment of our contribution. We appreciate the opportunity to clarify our approach and address your concerns.
____
**W1: Transitioning to Stage 2 and selection of $T_{bar}$.**
Thank you for your question! In practice, we determine $T... | Summary: This paper studies fair prediction tasks where fairness is defined on some partition of the data points into groups (by gender or race, for examples). The paper studies a meta-learning framework that only needs access to group labels for the validation set rather than the entire training dataset. An outer hype... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer’s thoughtful feedback and recognition of our contributions. Thank you for giving us the opportunity to clarify our approach based on your insightful comments.
---
**W1: Discussion on the scale of experiments.**
Thank you for your comment! We've updated Section 4... | Summary: This paper proposes a novel method to solve the unfairness issue in machine learning. In particular, the authors observed that, existing methods may cause "hypergradient conflict" during the optimization process. To resolve the hypergradient conflict, this paper applies the Nash bargaining solution (NBS), and ... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback and constructive comments. We are grateful for the chance to discuss these questions.
**W1: Technical explanations on the game theoretical model.**
Thank you for your comment! The concepts you mentioned lacking emphasis on Page 4 have their detailed defin... | Summary: The paper addresses group-level fairness in ML with two-stage meta learning. The modeler is given access to a sensitive-group-labeled *validation* dataset and must simultaneously design how to (linearly) weight each group loss in validation via bargaining to resolve gradient conflicts, and how to translate tho... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful comments and valuable feedback. We appreciate the opportunity to address these points and clarify our approach.
**W1: Computational cost.**
We appreciate this insightful question. We agree the bargaining phase introduces additional complexity... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ColJailBreak: Collaborative Generation and Editing for Jailbreaking Text-to-Image Deep Generation | Accept (poster) | Summary: This paper proposes an image editing pipeline to obtain NSFW images. Utilizing image segmentation and editing techniques, the attack transforms a safe images generated by proprietary models into an unsafe counterpart.
Strengths: 1. The attack strategy of post-hoc editing is rather novel.
2. The proposed attac... | Rebuttal 1:
Rebuttal: ### **Q1:Time and computational cost of the attack**
Thank you for the comments. we have conducted a detailed analysis of the time and computational resources required for each step of the attack pipeline. We specifically measured the time required for a single attack, including the segmentation m... | Summary: The paper introduces a method to bypass safety filters in commercial text-to-image (T2I) models like DALL·E and GPT-4.
Diffrent previous methods which directly do the adversarial attack, this paper proposes a noval approach to bypass the safety filter and generate harmful content by collaboration of multiple ... | Rebuttal 1:
Rebuttal: ### **Q1:Dataset and more unsafe content types**
Thank you for your comments. Our main purpose of building the dataset is to validate the effectiveness of the proposed method. The scale of the dataset is not a decisive factor. In fact, we refer to some previous studies. For example, DACA(Deng et ... | Summary: The paper introduces ColJailBreak, a framework that jailbreaks commercial text-to-image models by creating a safe image and modifying it to incorporate unsafe elements. It reveals the vulnerabilities of current safety filters in text-to-image models.
Strengths: 1. The paper is well-written. It studies an impo... | Rebuttal 1:
Rebuttal: ### **Q1: Explanation of our jailbreaking method**
Thank you for the comment. We appreciate your concerns and would like to address them by elaborating on the intuition and rationale behind our proposed method. Our goal is to demonstrate the potential for generating unsafe images through a macro-l... | Summary: This paper proposes a jailbreaking framework designed to bypass safety filters in commercial text-to-image (T2I) models. Specifically, it introduces three components for the jailbreak attack: adaptive normal safe substitution, inpainting-driven injection of unsafe content, and contrastive language-image-guided... | Rebuttal 1:
Rebuttal: Thank you for your recognition and support. We are very happy with your evaluation of our work. In order to further improve our work, we plan to release the dataset and code as soon as possible, while ensuring that the release process follows ethical guidelines and best practices. Thank you again ... | Rebuttal 1:
Rebuttal: We appreciate the thoughtful comments and helpful criticism from every reviewer. In this work, we introduce a jailbreaking framework designed to bypass safety filters in commercial text-to-image (T2I) models. Specifically, we present three components for the jailbreak attack: adaptive safe word su... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Transition Constrained Bayesian Optimization via Markov Decision Processes | Accept (poster) | Summary: This paper explores extending Bayesian optimization to incorporate transition constraints through state dynamics. The approach treats the problem as an optimal control problem. For planning purposes, known state dynamics model the transition constraints, while the unknown objective is represented using a Gauss... | Rebuttal 1:
Rebuttal: **W: Generally, I believe the paper’s presentation could be enhanced...**
Indeed, the reviewer is right we could start by presenting the problem as a control problem with an unknown objective, and then derive a surrogate for the unknown objective that is refined over episodes. Our main audience i... | Summary: The paper introduces a new Bayesian optimization problem that finds the optimal policy to optimize a black-box function subject to transition constraints on the query. The method works for both discrete and continuous Markov chains. The paper empirically demonstrates several practical applications in physical ... | Rebuttal 1:
Rebuttal: **Q: Section 2 is the background but there are no citations in section 2.1. Is this a new solution proposed by the paper, or is it based on existing work?**
Please see the general rebuttal.
**Q: Line 575: Please explain why the proportionality holds. Monotonicity does not imply proportionality (... | Summary:
The authors address the problem of transition-constrained Bayesian optimization, modeling the transition constraints as a Markov decision process. They take a novel approach in deriving their utility function for this problem setting based on maximum identification and bounding the probability of choos... | Rebuttal 1:
Rebuttal: **W: The main weakness of the work would appear to be the lack of compelling evidence for why the authors' method should be chosen over competitor methods such as LSR or SnAKe on practical problems...**
Please see the general rebuttal.
**W: The code would benefit from a README describing how to ... | Summary: The paper studies the best arm identification task in the transition-constrained setting where the MDP captures the transition. To solve the resulting planning problem defined as maximizing the acquisition propogated through the MDP, the paper studies using the RL solvers and heuritics in the discrete and cont... | Rebuttal 1:
Rebuttal: **W: The objective is non-convex and differs from conventional optimization in RL. This demands heuristics, which are not sufficiently discussed.**
This is true for the continuous case and common to most setups in control theory. However, special to our setting, for all discrete environments in t... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for the time spent reading and evaluating our paper. We are happy to have the strengths of our work recognized, and we are also pleased to have received so much constructive feedback. Here we address points common to multiple reviewers.
**Comparison with SnAKe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Accelerating Nash Equilibrium Convergence in Monte Carlo Settings Through Counterfactual Value Based Fictitious Play | Accept (poster) | Summary: The paper introduces a novel algorithm, Monte Carlo Counterfactual Value-Based Fictitious Play (MCCFVFP). This algorithm aims to accelerate the convergence of Nash Equilibria in extensive-form imperfect information games. MCCFVFP combines the counterfactual value calculations from Counterfactual Regret Minimiz... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and observations on our work. Below we address the questions raised and the discussed weaknesses:
## Provide a more detailed comparison:
We update the experimental evaluation part (in global response part 3 'Experiments'), we compared our algorithm with dif... | Summary: The paper proposes a new algorithm for solving extensive-form imperfect information games that relies on Monte Carlo (MC) simulations. The method abbreviated MCCFVFP combines MC settings with Counterfactual Regret Minimization (CFR) and the best response strategy of fictious play. Experimental evaluation demon... | Rebuttal 1:
Rebuttal: We are extremely thankful to the reviewer for their feedback and very insightful questions. We address them below.
## Weakness 1&3 Paper writing
The issues you pointed out are crucial for improving the paper's readability. We will carefully revise the paper. And I think it's necessary for me to ... | Summary: The paper introduces a new algorithm called Monte Carlo Counterfactual Value-Based Fictitious Play (MCCFVFP) for solving extensive-form imperfect information games. This algorithm combines the counterfactual value calculations of Counterfactual Regret Minimization (CFR) with the best response strategy of Ficti... | Rebuttal 1:
Rebuttal: Thank you very much for your meticulous reading of the paper and for raising very valuable new questions.
## Paper Writing
Regarding the typos in paper writing, we will thoroughly revise the paper.
1. **** At line 58, there is a hidden GitHub link. In the camera ready version, this link will be ... | null | null | Rebuttal 1:
Rebuttal: # Global Response to All Reviewers
We thank all the reviewers for the detailed comments and constructive feedback. We found that many reviewers pointed out the same issues, including the writing of the paper, the related problems of clear-game, and the experimental part. Here, we will explain the... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Neural Assets: 3D-Aware Multi-Object Scene Synthesis with Image Diffusion Models | Accept (spotlight) | Summary: This paper presents a novel method for 3D-aware editing of multi-object images. Given the original image and 2D bounding boxes to specify the objects to be edited, and the 3D pose as the target of the editing, the proposed method encodes the object appearance feature and their pose features, which are then tak... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and valuable comments.
**1. Discussion about prior work BlobGAN-3D [1].**
A: Thanks for bringing up this related work. We will include this paper and add the following discussions in the camera-ready version of the paper:
> There have been prior w... | Summary: This paper considers controlling the 3d poses of different objects in an image generated by a diffusion model. By conditioning the diffusion model on a sequence of per-object appearance and pose tokens instead of text tokens it is possible to finely control the object poses, and furthermore to e.g. transfer ob... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and valuable comments.
**1. Background modeling issue on Objectron. Any attempts to fix it?**
A: Objectron videos only have camera movement, while objects remain static throughout the video. Due to this data issue, the global camera motion and the ... | Summary: The paper introduces an object-centric representation for multi-object 3D pose control in image diffusion models. Instead of text token sequences, it utilizes Neural Assets, which are per-object representations learned by pooling visual features from reference images and reconstructing objects in target frames... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments. We are glad to see the positive assessment of our paper, and will include the below discussions in the final version of the paper.
**1. The use of absolute object pose for pose tokens.**
A: This is a great question. As discussed in Sec. 3.1, ... | Summary: This paper addresses the task of multi-object pose and scale control in image generation and editing using diffusion models. The authors introduce an object-centric representation with a disentangled pose and appearance called Neural Asset. The neural asset for each object in an image is estimated using pose a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback and encouraging comments.
**1. Small background changes in areas far from the edited objects.**
A: We encode object appearance tokens by applying RoIAlign on the image feature map. Even if we use the paired frame training strategy for feature dis... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their helpful feedback and insightful comments.
We are glad that the reviewers find our paper “*well written*” (h1dc, bNz4), our Neural Assets framework “*elegant*” (W8vg) and “*novel*” (h1dc, bNz4, jimW). Also, our experiments are considered “*sufficient*... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Unlock the Intermittent Control Ability of Model Free Reinforcement Learning | Accept (poster) | Summary: This paper proposes a new method for RL problems that needs to propose a sequence of actions at each step due to latency of the environment. The method is straightforward in that it uses VAE to encode the action space of a consecutive of actions. Empirical results show that this simple technique improves the p... | Rebuttal 1:
Rebuttal: We are grateful to you for recognizing the importance of our research. Your suggestions inspired us to improve our work, we analyzed the difference between our setting (intermittent MDP) and Delayed-MDP through demo examples and detailed explanations in the revised version. Additionally, we compar... | Summary: The paper introduces Multi-step Action RepreSentation (MARS) to address intermittent control problems in reinforcement learning. Intermittent control refers to situations where the interaction between the decision maker and the executor is discontinuous due to interruptions or communication issues. MARS encode... | Rebuttal 1:
Rebuttal: We are deeply thankful to you for recognizing the presentation and originality of our work; this positive feedback is greatly encouraging. Furthermore, your objective advice motivates us to further improve this work.
If you think the following response addresses your concerns, we would appreciate... | Summary: This paper addresses the issue of intermittent control problems, common in real-world scenarios where interactions between decision-makers and executors are disrupted due to unstable communication channels. These disruptions lead to bidirectional blockages, preventing agents from acquiring state information an... | Rebuttal 1:
Rebuttal: We are deeply grateful for your recognition of our paper's motivation, performance, and potential academic impact. Your positive feedback is highly encouraging. We improved our work with your valuable questions.
If you think the following response addresses your concerns, we would appreciate it i... | null | null | Rebuttal 1:
Rebuttal: ## General Response
We sincerely appreciate all reviewers for their meticulous assessment and valuable insights to our paper. Special thanks to all three reviewers for their thorough and meticulous review of our submission.
Please permit us to present the additional primary experiment results her... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Is the MMI Criterion Necessary for Interpretability? Degenerating Non-causal Features to Plain Noise for Self-Rationalization | Accept (poster) | Summary: This paper presents a novel approach to “rationalization,” that is finding explainable support for predictions made by black-box models such as deep neural networks. It proposes to change the focus from extracted rationalization features to focusing on residuals, and argues that doing so makes it easier to su... | Rebuttal 1:
Rebuttal: Thank you deeply for taking the time to thoroughly review our paper. We are truly grateful for the insights and recommendations you've provided.
**Weakness1 (The reasoning presented in Sections 4.1 and 4.2 is insufficient)**
For Section 4.1, we guess that you think $I(Y;S)$ can be as high as $I(Y... | Summary: Aurthors propose a way to obtain NLP explanations via novel criteria. Idea is instead of adding a regularization term to MMI loss they use tokens not selected.
Strengths: Very neat idea that has been well explained. Experimental results also support the hypothesis.
Weaknesses: - Now results Tables only F1 nu... | Rebuttal 1:
Rebuttal: We are grateful for your detailed review and the thoughtful suggestions you provided.
**Weakness 1**. Now results Tables only F1 numbers are bolded. Please bold best and underline 2nd best for the other columns also.
**A**. Thank you for your suggestion, we will do it in our revision.
**Quest... | Summary: This paper focuses on rationalization, especially on finding the most reasonable subset of a text sequence that can predict the assigned labels.
It proposes a novel criterion, MRD (maximizing the remaining discrepancy), which minimizes the negative KL divergence of the distribution of labels given the input ... | Rebuttal 1:
Rebuttal: We sincerely thank you for dedicating your time and expertise to review our paper. Your insightful comments and suggestions are highly valued and appreciated.
1. **Weakness1 (It would be beneficial to demonstrate the generalization of the criterion through more experiments on different types of d... | Summary: The paper proposes a novel conseptualization within the Rationalizing Neural Predictions (RNP) framework of explainable AI, namely the "Maximizing the Remaining Discrepancy" (MRD) training criterion for a system consisting of an Extractor and a Predictor network. Contrary to existing methods, the MRD criterion... | Rebuttal 1:
Rebuttal: Thank you for taking the time to carefully review our work and provide constructive feedback.
**Weakness1**. The datasets seem to be quite similar in terms of the task.
**A**. Thank you for your valuable suggestion. We now add a graph classification task. Different from the previous text classif... | Rebuttal 1:
Rebuttal: Since most reviewers are interested in the results with LLMs, here we present the results of the experiments conducted with the **llama-3.1-8b-instruct** model. We perform both 2-shot prompting and supervised fine-tuning.
For 2-shot prompting, we provide the model with a negative text with its co... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
AutoMix: Automatically Mixing Language Models | Accept (poster) | Summary: This paper investigates how to automatically and adaptively select the LLM with a smaller size but keep good performance so that it offers the potential to achieve a better cost-effectiveness trade-off. This work uses a smaller model to predict first and then uses the small model to do self-verification. If mo... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review the paper and provide feedback! We believe all your questions can be addressed within this discussion period, but we would love to provide further clarification if needed.
---
### It is good that this work provides a detailed description of their cost mode... | Summary: This paper presents AutoMix, a method that routes query to language models (LMs) of various size and capabilities to optimize the performance within a cost budget. AutoMix has two key technical features, a few-shot self-verification mechanism which estimates the reliability of its own outputs, and a POMDP-base... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review the paper and provide feedback! We believe all your questions can be addressed within this discussion period, but we would love to provide further clarification if needed.
---
### Although the arguments provided in Line 216-219 is legitimate, setting the c... | Summary: This work introduces a novel solution called AutoMix to achieve an optimal balance between performance and cost when using various scales of large language models (LLMs). The paper presents two variants of AutoMix: AutoMix-T, which employs a thresholding strategy, and AutoMix-P, which uses a POMDP-based strate... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review the paper and provide feedback! We believe all your questions can be addressed within this discussion period, but we would love to provide further clarification if needed.
---
### There is a lack of analysis regarding absolute performance decay.
We would ... | Summary: AutoMix is an approach designed to optimize the performance and computational cost of LLMs by selecting the appropriate model based on the difficulty of the task. This is achieved through a few-shot self-verification mechanism and a Partially Observable Markov Decision Process (POMDP) based router. The few-sho... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review the paper and provide feedback! We believe all your questions can be addressed within this discussion period, but we would love to provide further clarification if needed.
---
### Q: I think it would be interesting to just randomly pick up a LLM for each q... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable feedback! We are encouraged that they find our "approach sound and intuitive, our paper as easy to read and follow" (Reviewer zQB8), that "our approach to self-verification and POMDP-based routing is novel, our methodology is sound, and recognizing the... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper presents AutoMix, a method designed to optimize the use of large language models (LLMs) by strategically routing queries to different models based on performance and cost considerations. AutoMix relies on a few-shot self-verification mechanism which involves the estimation of correctness of a smaller... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review the paper and provide feedback! We address your concern here:
### Generalizability of POMDP router to other tasks/data
The POMDP router does not assume any specific task or dataset characteristics to work, as it relies only on self-verification confidence ... | null | null | null | null | null | null |
Attack-Resilient Image Watermarking Using Stable Diffusion | Accept (poster) | Summary: This paper presents a new image watermarking method, ZoDiac, which leverages pretrained stable-diffusion model to generate watermark in the latent space (derived by using DDIM to process the original image). The author claims that ZoDiac achieves better robustness than the state-of-the-art (SOTA) watermarking ... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and critical feedback. Below we provide one-to-one responses and detailed discussions.
**Q1: Are all watermarking methods compared fairly?**
**A1:** Please refer to the global response to all reviewers. Thank you!
**Q2: Has the definition of robustness c... | Summary: The paper introduces ZoDiac, an image watermarking framework leveraging pre-trained stable diffusion models to embed concentric ring-like zero-bit watermark into existing images. ZoDiac operates by injecting the watermark into the trainable latent space. The method is extensively evaluated on three benchmarks ... | Rebuttal 1:
Rebuttal: Thank you for your positive comments and rating! Below we provide one-to-one responses to the three questions.
**Q1: Since previous works use different types of watermark, such as StegaStamp uses hyperlink bitstring, there is a concern regarding comparison fairness.**
**A1:** Please refer to the... | Summary: This paper presents ZoDiac, a novel image watermarking technique leveraging a pre-trained stable diffusion model to inject watermarks into a trainable latent space, enhancing watermark robustness against image attacks. Extensive experiments on three modern benchmarks demonstrate ZoDiac’s state-of-the-art water... | Rebuttal 1:
Rebuttal: Thank you for your positive comments and rating! Below we provide one-to-one responses to the two mentioned weaknesses.
**W1: While ZoDiac demonstrates excellent robustness under various attacks, as shown in Table 1, it sacrifices image quality, achieving only slightly better visual quality than ... | null | null | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions. We first provide a general response to all reviewers with additional details.
**Q: Are all watermarking methods compared fairly? (Reviewer mbRv Q1, Reviewer AMWU Q1)**
**A:** Yes, our evaluation is designed to guarantee fair comparison with existin... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Large Language Model Unlearning via Embedding-Corrupted Prompts | Accept (poster) | Summary: This paper introduces a novel approach named Embedding-COrrupted (ECO) Prompts for efficient unlearning in LLMs. ECO maintains an unlearning state during inference by utilizing a prompt classifier, eliminating the need to modify LLMs. This classifier identifies, corrupts, and safeguards prompts to be forgotten... | Rebuttal 1:
Rebuttal: We appreciate your thorough feedback and constructive criticism of our paper. Below, we respond to the weaknesses and questions you raised in your review.
> Although using a prompt classifier during inference to identify the unlearning state is straightforward, it also makes it less applicable fo... | Summary: This paper proposes a straightforward but well-crafted and effective method to implement unlearning for LLMs through an inference-time intervention. The method first trains a classifier, to determine whether a prompt contains material to be forgotten. Then, it corrupts the prompt to the LLM so that its respons... | Rebuttal 1:
Rebuttal: Thank you for your exhaustive feedback and the constructive criticism of our paper. Below we address the weaknesses and questions provided in the review above.
> However, most (all?) of the results in the main text would seem to be equally well served by the simple classifier + template mechanis... | Summary: This paper proposes a lightweight method for unlearning in large language models (LLMs), based on corrupting the embedding of a query related to the forget data identified by an external classifier during LLM inference. The experimental results demonstrate the effectiveness of this method on various benchmarks... | Rebuttal 1:
Rebuttal: We are grateful for your comprehensive review and the valuable insights you provided. Below we address the weaknesses and questions provided in the review above.
> The optimization objective in Equations (8) and (9) for the corruption function incorporates the distance measure function between th... | Summary: The paper proposes Embedding-COrrupted (ECO) Prompts, a lightweight framework for unlearning in large language models (LLMs). It reduces computational inefficiency by using a prompt classifier to identify and corrupt prompts that should be forgotten. In their experiments, this approach, involving zeroth order ... | Rebuttal 1:
Rebuttal: We appreciate your recognition of our efforts and your thoughtful comments. Below we address the question about knowledge entanglement.
> Can you describe knowledge entanglement in detail or define it formally? How far is it from structural unlearning, where the removal of one entity might impact... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
What Rotary Position Embedding Can Tell Us: Identifying Query and Key Weights Corresponding to Basic Syntactic or High-level Semantic Information | Accept (poster) | Summary: This paper investigates how the Transformer models with RoPE embeddings change their weights during pre-training and fine-tuning. The authors theoretically prove that RoPE divides key and query pairs into two groups: near-orthogonal and non-orthogonal weight vector pairs, which have different sensitivities to ... | Rebuttal 1:
Rebuttal: Dear reviewer 2wVC,
Thank you for your valuable feedback. Below, we address each of your concerns.
> W1: The paper heavily relies on the link between the level of abstraction learned by the model and the angles of the weight pairs.
> Q1: Could you please define and provide examples of these tw... | Summary: The paper is a novel work that identifies how the angle between weight vector pairs in the query or the key affects RoPE.
The authors devise a simple yet effective method that is novel and orthogonal to existing fine-tuning efficiency techniques, such as LoRA. Experiments demonstrate that combining the propos... | Rebuttal 1:
Rebuttal: Dear reviewer cttx,
we sincerely appreciate your detailed and valuable feedback. We address each of your comments in the following.
> W1: The introduction should more clearly specify the types of LLMs on which the authors conducted empirical studies rather than generally referring to LLMs.
Than... | Summary: The paper makes a significant contribution to the understanding of RoPE in LLMs and presents the QK-IPM method that reduces the number of trainable parameters during fine-tuning by targeting orthogonal weight vector pairs.The paper conducts experiments on TruthfulQA, GSM8K, and Hellaswag datasets to show the m... | Rebuttal 1:
Rebuttal: Dear reviewer LVwG,
thank you for your valuable feedback. We address each point of your concerns in the comments below.
> W1: The conclusion that shallow layers of LLMs focus more on basic syntactic information and deep layers of LLMs focus more on high-level semantics is somewhat boring. Many ... | Summary: The authors propose a fascinating approach to optimizing transformer-based large language models (LLMs). They delve into the intricacies of position encoding, particularly focusing on the widely used rotary position embedding (RoPE) technique. By examining how the angle between weight vector pairs impacts atte... | Rebuttal 1:
Rebuttal: Dear Reviewer CgW8, we sincerely appreciate the time and effort you devote to the reviewing process. We address each point of your concerns in the comments below.
> W1: A broader evaluation across various LLM architectures and more diverse datasets would strengthen the generalizability of the fin... | Rebuttal 1:
Rebuttal: Dear AC and Reviewers,
We sincerely thank you for the time and effort you dedicated to the reviewing process. We are delighted to hear that reviewers find the paper well-written (LVwG, cttx, 2wVC), providing solid theoretical insights with extensive empirical evidence (CgW8, LVwG, cttx, 2wVC) and... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Exploring DCN-like architecture for fast image generation with arbitrary resolution | Accept (poster) | Summary: This paper presents a new convolution-based diffusion model that is able to generate images at arbitrary resolutions when trained on fixed resolutions. It also achieves comparable performance when trained and tested on the same resolution compared to transformer backbones such as U-VIT and DiT.
Strengths: - T... | Rebuttal 1:
Rebuttal: **Which part contributes most to arbitrary resolution generation?**
Sorry for the confusion. Basically, the fundamental contribution to the ability of arbitrary resolution generation is our MultiScale DCN Block, which equips our model with the flexibility to handle different resolutions. So, the s... | Summary: This paper introduces FlowDCN, a novel image generation model that efficiently generates high-quality images at various resolutions. The model's core innovation is a group-wise multiscale deformable convolution block, which enhances its adaptability to various resolutions. Built on flow-based generative models... | Rebuttal 1:
Rebuttal: **Presentation issues.**
Thanks for your suggestions. We will re-organize the structure of our paper in the final version. We will add a preliminary section to introduce the background on DCN and Flow Matching. We will also rewrite the related work section to comprehensively discuss the relevant l... | Summary: The paper proposes a novel convolutional-based generative model called FlowDCN. This model addresses the challenge of generation speed and arbitrary-resolution image generation, which remains difficult for transformer-based diffusion methods due to their quadratic computation cost and limited resolution extrap... | Rebuttal 1:
Rebuttal: **FID in Table 5 seems too high.** Yes, we follow SiT, FiT and DiT to train our FlowDCN under 400K budgets. We follow the evaluation pipeline of FiT to obtain the metrics of arbitrary resolution generation without using CFG.
**ImageNet $512\times512$ Experiments.**
Given the time constraint on t... | Summary: This paper presents a purely deformable convolution based architecture for flow-matching based diffusion models. Such a purely convolution-based models can easily generalize to different aspect ratio/resolution during testing, which is a pain point for transformer-based models. Evaluated on ImageNet 256x256 be... | Rebuttal 1:
Rebuttal: **The relationship between DCN and common CNN.**
As Eq.3 states, DCN introduces a deformable field $\Delta p(x)$ and a dynamic weight $w(x)$. When all channels share the same static weights instead of dynamic ones, and deformable field $\Delta p(x)$ degrades to zeros, DCN degenerates to common CNN... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
PLIP: Language-Image Pre-training for Person Representation Learning | Accept (poster) | Summary: This paper presents a language-image pre-training framework and a large-scale synthetic image-text dataset for person representation learning. The proposed framework contains three elaborately designed pretext tasks: 1) Text-guided Image Colorization (TIC); 2) Image-guided Attributes Prediction (IAP); and 3) I... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and appreciation of our work. The concerns are answered as follows.
**(i) Pre-training on commonly used ViT.**
Due to the need for handling multi-scale image features in the TIC task of PLIP, we only consider the variants of vision transformers with hierarchical... | Summary: A novel vision-language pre-training framework is proposed in this paper, termed PLIP, for person-centric downstream tasks: image-based Re-ID, text-based Re-ID, attributes recognition, human parsing and person search. To form PLIP, three pre-training tasks are designed: text-guided image colorization, image-gu... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and appreciation of our work. The concerns are answered as follows.
**(i) Somewhat complex designs.**
Considering that existing general-domain multimodal pre-training techniques do not take into account person-related characteristics, they are not well-suited fo... | Summary: This paper introduces a Language-Image Pre-training framework, termed PLIP, designed for enhancing person representation learning. In order to better adapt to downstream person-centric tasks, three pretext tasks are designed to pay more attention to critical person-related characteristics, including Text-guide... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. The concerns are answered as follows.
**(i) Core difference between PLIP and Lapscore.**
In fact, there are many differences between our PLIP and Lapscore.
(a) The different core motivation. The core motivation of Lapscore is to design some modules specificall... | Summary: This paper introduces a person pre-training framework PLIP, which consists of three pre-text tasks: text-guided image colorization (TIC), image-guided attributes prediction (IAP) and identity-based vision-language contrast (IVLC). Furthermore, a large-scale person dataset SYNTH-PEDES is constructed to facilita... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. The concerns are answered as follows.
**(i) Analysis of experimental comparison fairness.**
We believe that it might be due to some of our wording that led to your misunderstanding that our method would result in information leakage and issues of unfair compari... | Rebuttal 1:
Rebuttal: We sincerely appreciate all reviewers' time and efforts in reviewing our paper.
We are glad to find that reviewers generally recognized our contributions including the meaningful and novel pre-training framework (Reviewers cgBx, bKKs, RKFb), the significance of our proposed dataset (Reviewers cg... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning Structure-Aware Representations of Dependent Types | Accept (poster) | Summary: The article contributes to the field by detailing a method to capture and utilize the internal compilation states of Agda, a dependently typed programming language, through JSON files. These JSON files represent different compilation states, reflecting various stages of the coding process. Key contributions of... | Rebuttal 1:
Rebuttal: Hi, and thank you for taking the time to review our paper.
> `... visual results on representation?`
We provide a visualization of the empirical distributions of positive and negative lemmas in Figure 3, which indicates that positive lemmas are consistently ranked significantly higher than negat... | Summary: This paper is the first to extract data from Agda, a dependently typed programming language. The extracted data can be utilized by machine learning practitioners. Additionally, the paper proposes a novel neural structure designed to faithfully represent dependently typed programs. The experiments demonstrate t... | Rebuttal 1:
Rebuttal: Hi, and thanks for the review.
We’re very glad you appreciated our work. We acknowledge that the presentation can sometimes be dense. We aimed to keep the tone and language inclusive and to provide brief descriptions of any jargon where possible. However, the paper's topic sits at a narrow inters... | Summary: The paper reported the creation of a new dataset to predict a term for filling the hole of a proof in Agda. The paper also proposed an attention-based neural architecture, trained it on the made dataset, and shows it outperforms the ordinary Transformer.
Strengths: + Creating a new dataset for the problem of ... | Rebuttal 1:
Rebuttal: Hello, and thank you for your critical review. We will try to address some of your concerns below.
---
> ` ...why the dataset is necessary or valuable ...`
Thank you very much for this question. The dataset is radically different to existing datasets, and while do point the fact out (*e.g.*, in... | Summary: This paper introduces a learning algorithm for Agda, a functional language used in proof assistance that is known for its dependent types.
The paper includes the design of the algorithm, and an experimental evaluation,
Strengths: The Agda language has become very popular in the functional prog community, esp... | Rebuttal 1:
Rebuttal: Hello, and many thanks for your honest review.
---
We're glad you appreciated the value of the architecture and the potential in the results.
We acknowledge the weaknesses you spot. We tried our best to accommodate a reader that is not necessarily familiar with Agda, but does have prior exposit... | Rebuttal 1:
Rebuttal: We thank all reviewers for their time and effort. We have responded to each review individually.
Here, we attach a pdf containing visualizations of neural representations, as requested by reviewer xpMx. Pending AC approval, we would be happy to include an anonymized link to an interactive version... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fair Bilevel Neural Network (FairBiNN): On Balancing fairness and accuracy via Stackelberg Equilibrium | Accept (poster) | Summary: The authors propose a bi-level optimization approach to simultaneously optimize a performance and a fairness loss. They show that this approach is, in theory, superior to a typical approach that regularizes the performance objective with a fairness objective. Using tabular, graph and vision datasets, they demo... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. We have addressed your questions and concerns below. If you have any further questions, we would be glad to discuss them.
## Weaknesses
### W1
We appreciate the reviewer's feedback on the assumptions in our paper. In response, we have added a dedicated discu... | Summary: The paper proposes and justifies a bi-level optimization approach to optimizing empirical risk minimization objectives when additional fairness constraints need to be considered. Using assumptions of Lipschitz continuity and local convexity, they prove that a bi-criteria (accuracy + fairness) problem is equiva... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. We have addressed your questions and concerns below. If you have any further questions, we would be glad to discuss them.
## Weaknesses
### W1
We acknowledge that some important information was missing from our initial presentation. To address these concern... | Summary: A common approach to bias mitigating in machine learning is to add a regularization term to the loss function that penalizes deviation from fairness. This is what the current paper calls the Lagrangian approach. It has long been know that this is not an effective approach to multi-objective optimization. The p... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. We have addressed your questions and concerns below. If you have any further questions, we would be glad to discuss them.
## Weaknesses
### W1
In our approach, the separation of accuracy and fairness parameters is a key design choice that allows for the bil... | Summary: This paper proposes a novel bilevel optimization framework called FairBiNN for addressing bias and fairness issues in machine learning models while maintaining accuracy. The approach formulates the problem as a Stackelberg game between accuracy and fairness objectives, proving that it yields Pareto-optimal sol... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. We have addressed your questions and concerns below. If you have any further questions, we would be glad to discuss them.
## Weaknesses
### W1
While our approach may initially appear more complex than traditional methods, we believe its benefits significant... | Rebuttal 1:
Rebuttal: # Global Rebuttal
We sincerely thank the reviewers for their constructive comments, which have significantly contributed to the improvement of our work. We have addressed the reviewers’ concerns regarding our method’s assumptions, limitations, and ablation studies.
## Assumption Discussion
We ap... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Rethinking Reconstruction-based Graph-Level Anomaly Detection: Limitations and a Simple Remedy | Accept (poster) | Summary: This paper investigates the reconstruction flip phenomena in graph autoencoder which may be beneficial for graph-level anomaly detection.
Then the paper proposes a novel graph autoencoder model MUSE, which simply represents a graph as multifaceted summaries of its reconstruction errors and achieves appreciable... | Rebuttal 1:
Rebuttal: Dear Reviewer H5pG
We deeply appreciate your positive reviews of our research. Your suggestions have greatly inspired our future research direction. Below, we provide detailed responses to your questions.
**We provide detailed references in the Global References section of the global rebuttal.**... | Summary: This paper introduces a new GLAD method. The authors identify and analyze a phenomenon termed "reconstruction flip", where Graph-AEs sometimes reconstruct unseen graphs (with certain patterns more accurately than training graphs) better than the training graphs themselves. Based on this analysis, the authors p... | Rebuttal 1:
Rebuttal: Dear Reviewer SHGf,
We deeply appreciate the time the reviewer dedicated to reviewing our paper. Your questions have improved the presentation of the connection between our analysis and method. Below, we provide detailed responses to the reviewer’s questions.
**We provide detailed experimental r... | Summary: The authors propose a novel approach to enhance feature representation by leveraging multifaceted summaries beyond the mere average of reconstruction errors, addressing a limitation prevalent in current reconstruction-based methods. This motivation is timely and relevant, aiming to enrich the feature space for... | Rebuttal 1:
Rebuttal: Dear Reviewer EQox,
We sincerely appreciate the reviewer’s valuable time and efforts. The reviewer’s questions and suggestions have greatly enhanced the alignment between our theoretical analysis and our proposed method. Below, we provide detailed responses to the reviewer’s questions.
**We prov... | Summary: Graph autoencoders aim to learn graph representations by reconstructing them, with a key application in graph-level anomaly detection. GLAD identifies graphs with unusual structures or node features by considering those with high reconstruction errors as anomalies. However, the paper reports counter-examples w... | Rebuttal 1:
Rebuttal: Dear Reviewer jxne,
We deeply appreciate the reviewer’s dedication to the review process. Your questions helped us better evaluate MUSE from various perspectives. Below, we provide detailed responses to your questions.
**We provide detailed references in the G6 section of the global rebuttal.**
... | Rebuttal 1:
Rebuttal: # General responses
We thank the reviewers for the invaluable time and effort they spent reviewing our paper. Your questions and comments helped us clarify and improve our work, and we will incorporate your comments in our revised manuscript.
In this general response, we provide (1) a summary of... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Dynamic Tuning Towards Parameter and Inference Efficiency for ViT Adaptation | Accept (poster) | Summary: This paper proposes DyT, which is a parameter-efficient fine-tuning method that can also achieve inference efficiency. DyT contains token dispatchers, which let tokens dynamically skip the original block, and MoE-adapter which uses multiple adapters to compose a mixture-of-experts. The paper evaluates the prop... | Rebuttal 1:
Rebuttal: > #### W1 & Q1: Clarify the novelty of the proposed methods over Conditional Adapters and AdaMix
Thanks. We would like to clarify the novelty of our method over Conditional Adapters and AdaMix as follows:
**Conditional Adapters[1]:**
- The token selection strategy in the token dispatcher is nove... | Summary: This paper proposes Dynamic Tuning (DyT) to improve both parameter and inference efficiency for ViT adaptation. DyT inserts a token dispatcher for each transformer block to learn to dynamically determine whether a token should be activated or deactivated.
Strengths: - This paper focuses on **inference efficie... | Rebuttal 1:
Rebuttal: > #### W1: The core technique in Dynamic Tuning seems to be token pruning without explicitly reducing the number of tokens. This paper does not well demonstrate the difference between existing dynamic token pruning methods and the proposed method.
Thanks. We would like to kindly clarify that our ... | Summary: The paper introduces a method called Dynamic Tuning (DyT) designed to enhance both parameter and inference efficiency when adapting Vision Transformers (ViTs) for various visual tasks. DyT employs a token dispatcher to selectively process only the most informative tokens, reducing computational redundancy. Add... | Rebuttal 1:
Rebuttal: > #### W1: Does introduced additional loss functions affect the speed of convergence
Thanks. The introduced additional loss functions do not impact the speed of convergence. We plot the loss values throughout the fine-tuning process and record the test accuracy at every training epoch. Please fin... | Summary: This paper proposes Dynamic Tuning, an efficient tuning method to enhance both parameter and inference efficiency for ViT adaptation. The method improves efficiency by dropping some tokens entering pre-trained layers through a token dispatcher while forwarding all tokens to the adapter, maintaining performance... | Rebuttal 1:
Rebuttal: We will include all explanations and results in the revision.
> #### W1:MoE improves performance only in video classification, not image classification, and is absent from most experiments
We appreciate this comment. We would like to clarify that the MoE-adapter is primarily designed to enhance ... | Rebuttal 1:
Rebuttal: Dear ACs and reviewers,
We sincerely appreciate the time and effort provided by all reviewers and ACs in our work. In particular, we are encouraged to see that Reviewer fmWb finds that our method **"achieves higher efficiency and performance than existing methods"**. Reviewer uJS2 thinks the prop... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Robust Graph Neural Networks via Unbiased Aggregation | Accept (poster) | Summary: The paper addresses the adversarial robustness of Graph Neural Networks (GNNs) against adaptive attacks. The authors propose a unified framework for robust estimation in GNNs to understand and improve their robustness. Specifically,
1. The paper provides a unified view of $\ell_1$-based robust graph signal s... | Rebuttal 1:
Rebuttal: Thanks for your recognition of the novelty and effectiveness of our method. We are glad to solve
your concerns and answer your questions with the following illustrations.
**W1a**: The phrase "without the false sense of security" in Sections 1 and 2 is unclear. What does it mean, and why is it sig... | Summary: This paper uncovers that while $\ell_1$-based models exhibit robustness against moderate adaptive attacks, their performance significantly degrades under strong adaptive attacks. The authors investigate the root cause of this phenomenon, identifying estimation bias as the culprit. To address this, they propose... | Rebuttal 1:
Rebuttal: Thanks for your recognition of the novelty and effectiveness of our method. We are glad to solve
your concerns and answer your questions with the following illustrations.
**W1:** The datasets considered by the authors are limited to only three citation networks. While I acknowledge that implement... | Summary: The authors point out that some of the successful defenses for GNNs are related to l1-based graph smoothing. Motivated by the bias of l1-based estimators, the authors develop an unbiased variant (RUGE), rooted in high-dimensional statistics. To optimize under such a non-smooth objective, the authors devise a Q... | Rebuttal 1:
Rebuttal: Thanks for your recognition of the novelty and effectiveness of our method. We are glad to solve
your concerns and answer your questions with the following illustrations.
**W1: The paper only studies heterophilic datasets**
**Answer:** We want to point out that our RUNG (Robust Unbiased Aggregat... | null | null | Rebuttal 1:
Rebuttal: Thank you to all the reviewers for recognizing the novelty and effectiveness of our method. Since all reviewers share concerns about the effectiveness of RUNG on heterophilic graphs and other domain-specific datasets, we will include the experimental results for several datasets in our global resp... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Multi-Head Mixture-of-Experts | Accept (poster) | Summary: The work presents an advanced MoE model architecture to further improve the expert utilization. Authors conduct comprehensive experiments on three pre-training tasks, English-focused language modeling, multi-lingual language modeling and masked multi-modality modeling with models at different scales (300M to 7... | Rebuttal 1:
Rebuttal: Thank you for your feedback :)
---
>**Q1**: I only have one but important concern: the end-to-end training and inference throughput. I understand that the theoretical computation and communication cost is not high, but since we are conducting a more complicated/fine-grained routing decision, the... | Summary: This paper proposes Multi-Head Mixture-of-Experts (MH-MoE), a simple yet effective routing strategy that splits each input token into multiple sub-tokens for expert routing. This operation significantly enhances the ratio of activated experts for each token, enabling more fine-grained assigning of tokens. Thro... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback. We address your feedback point by point below.
---
>**Q1**: My major concern is about the experimental settings. Implementing MH-MoE based on the X-MoE instead of the vanilla SMoE is strange. The improvements shown by the experiments may not be generalizable... | Summary: This paper introduces Multi-Head Mixture-of-Experts (MH-MoE), a method for training MoE models with enhanced expert activation. In particular, MH-MoE splits each input token into multiple sub-tokens, which are processed by a set of experts in parallel, and then merges them back into their original token form. ... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We address your feedback point by point below.
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>**Q1**: The experiments are limited by a single model architecture. The authors apply their approach by modifying the previously released X-MoE model. The authors compare their performance with the mentioned X-MoE m... | Summary: The paper proposed a new architectural extension to large pre-trained models (e.g. LLMs, or Multi-modal models). Specifically, the focus is on Mixture of Experts (MoE) models and the paper proposes a new method by introducing subtokens and routing each subtoken to experts.
The resulting architectural change p... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for the extensive review. Due to space constraints, we address your questions in the **Rebuttal** below as well as at the top of this page in the **Author Rebuttal**:
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>**Q1**: What is the actual practical training or inference time of running MoE vs. MH-MoE.
*... | Rebuttal 1:
Rebuttal: ## Supplementary rebuttal for Reviewer Rrmq
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>**Q4**: The core claim that the problem with MoE is: "low experts activation issue" has a few problems: a. It is never clearly explained why this is an issue. b. the paper also shows that increasing the number of heads (and therefore activations) ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Introducing Spectral Attention for Long-Range Dependency in Time Series Forecasting | Accept (poster) | Summary: This paper focuses on the exploration of long-term dependency within a whole time series sequence, to address the challenge of the short-term look-back window, which is interesting. Based on the observation, this paper provides Batched Spectral Attention to enable parallel training across multiple timesteps.
... | Rebuttal 1:
Rebuttal: We thank you very much for the insightful comments and suggestions. We have addressed each of your questions below. Please also review the global comment and the attatched PDF since they are used for answering your question.
---
**W1** We sincerely apologize for not providing a clear enough pres... | Summary: The paper presents a Spectral Attention mechanism to address the challenge of capturing long-range dependencies in time series forecasting. By preserving temporal correlations among samples and leveraging frequency domain information, the proposed method enhances the forecasting performance of various baseline... | Rebuttal 1:
Rebuttal: We thank you very much for the insightful comments and suggestions. We have addressed your questions below.
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**W1-a & Q1** Thank you for introducing this significant work. We will add this research to the related work section (we reported a summary in the global comment). While this study is ... | Summary: This paper introduces a new mechanism called "Spectral Attention" designed to address the challenge of long-term dependencies in time series prediction. Traditional models such as linear and Transformer-based predictors face limitations in handling long-term dependencies due to fixed input sizes and the shuffl... | Rebuttal 1:
Rebuttal: We thank you very much for the insightful comments and suggestions. We have addressed each of your questions below. Also, please review the global comment and the attached PDF.
---
### **Weaknesses**
**W1** We apologize for not providing sufficient specific meanings for some of the formulas in ... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable comments and constructive suggestions to strengthen our work. Also, for the positive comments and encouraging remarks: The paper introduces a novel Spectral Attention mechanism, addressing the long-term dependence in time series prediction (A63Y). This... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fair Secretaries with Unfair Predictions | Accept (poster) | Summary: This work studies algorithms with untrusted predictions for secretary problems, considering fairness. In this paper, an algorithm is deemed fair if it can accept the best candidate with at least a constant probability. This fairness definition implies that a good candidate deserves a fair chance. The paper fir... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and effort in reviewing this paper.
- “Can you provide formal comments on the definition of fairness? Is the current algorithm fair for the second-best candidate, especially when the value of the second-best candidate is very close to that of the best candidat... | Summary: This paper considers the secretary problem with predictions. The decision maker is given predicted values for all the candidates in advance. The existing algorithm by Fujii and Yoshida (2023) hires the candidate with the **expected value** at least $\max ( \Omega(1), 1-O(\epsilon) )$ times the optimal candidat... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and effort in reviewing this paper.
- “Although I am not familiar with the existing literature on fairness, I do not fully understand why the authors adopt this terminology.”:
> The literature on fairness contains many different definitions of fairness. We pr... | Summary: This paper examines the secretary problem with predictions and identifies a key shortcoming in prior work. This problem is similar to the classic secretary problem in which a series of candidates with arbitrary unknown utilities $u_i$ arrive in a random order to be interviewed (revealing their utility upon ar... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and effort in reviewing this paper.
- “Upper bound (impossibility results) are not explored, making it unclear if these are the tightest results for this setting” and “If we require $1 - C\epsilon$ smoothness, does that imply an upper bound on the achievable $... | Summary: This paper proposes new algorithm for the classical secretary problem in the algorithms with predictions framework. In particular, the paper introduces and tackles a new notion of fairness for this problem: the best candidate must have some probability of being accepted. The authors demonstrate how existing al... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and effort in reviewing this paper.
- "Can you please define I_pegged formally?" and "clarify initial conditions for Algorithm 1?":
> $I^{pegged}$ should have been initialized to the empty set before the start of the while loop. We will fix that omission. With... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Divide-and-Conquer Predictive Coding: a structured Bayesian inference algorithm | Accept (poster) | Summary: This paper proposes a version of predictive coding that is biologically plausible (in the sense that all computations are local), and can perform both classification and generation (by operating in both directions). This version of predictive coding, dubbed Population Predictive Coding (PPC), overcomes the lim... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and valuable feedback.
> would have liked to see the classification accuracy of the model on MNIST (as a percent). Predictive coding networks tend not to perform well on classification tasks.
It is true that, when used as generative models, predictive coding n... | Summary: SETTING: *Biologically plausible* EM with variational inference in directed graphical models. In particular, the authors aim to implement predictive coding with local parameter updates for learning.
APPROACH: "Variational" EM with sampling (sequential Monte Carlo, SMC) in place of a separate inference/recog... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and valuable feedback.
> Novelty over previous work (Kuntz 2023;2024, Naesseth 2015)
Kuntz 2023 did not construct or evaluate importance weights, while Lindsten 2017/Kuntz 2024 and Naesseth 2015 did not propose a unique decomposition of a target model into Gibb... | Summary: Predictive coding (PC) is a speculative but attractive theory of how certain parts of the brain—especially the so-called 'canonical' cortical microcircuit—might implement Bayesian inference, and hence focus processing effort on the 'surprising' rather than the 'predictable' features of (e.g., sensory) stimuli.... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and valuable feedback.
> Does vanilla PC really require Gaussian densities and the Laplace approximation (Table 1)? My understanding is that the framework itself doesn't require this; rather, this is just a useful assumption people use to get something simple an... | Summary: The paper introduces a novel algorithm called Population Predictive Coding (PPC)
for structured generative models. The PPC algorithm aims to enhance the
performance and biological plausibility of predictive coding
approaches by respecting the correlation structure of generative models. The
paper provides theo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and valuable feedback.
> The technical sections (2 and 3) are complex.
Thank you for the pointer. We have heavily modified the explanations, restructuring the sections and adding sentences that lead to a more intuitive understanding of the algorithm. For more d... | Rebuttal 1:
Rebuttal: We sincerely thank the four reviewers for their close and detailed engagement with our manuscript, stretching across the computational neuroscience material and the core contributions on Bayesian inference.
We see the reviewers divided on score but in broad agreement on the contributions, and nee... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Free Lunch in Pathology Foundation Model: Task-specific Model Adaptation with Concept-Guided Feature Enhancement | Accept (poster) | Summary: The paper proposes a way to enhance image features from VLM pathology foundation models for downstream tasks by aligning them with task-specific text prompts. The authors use two modules -- one which extracts an image representation aligned with task concepts and other which measures similarity of representati... | Rebuttal 1:
Rebuttal: We sincerely appreciate your detailed and constructive comments. We will address your concerns as follows:
> **1. Aligning patches with tasks**
Thanks for your comments. We address your concerns from the following aspects.
- The motivation behind patch-level alignment in this work is to enhance... | Summary: The authors introduce CATE, a novel approach designed to enhance the generalizability of histopathology models by leveraging task-specific concepts derived from the text endoder of pathology vision language models(VLM). CATE includes two modules: CIB and CIF, which work synergistically to improve model robustn... | Rebuttal 1:
Rebuttal: We deeply appreciate your positive comments and valuable suggestions. We would like to address your concerns below one by one:
> **1. The quality of concept anchors**
Thank you very much for your understanding. As we discuss in the paper, we agree that CATE's performance highly relies on the qua... | Summary: The authors introduce a new tool called Concept Anchor-guided Task-specific Feature Enhancement (CATE) for analysis of whole slide images. The authors are taking advantage of the open source weight now available for the CONCH pathology vision language model trained on paired captions of images from journals a... | Rebuttal 1:
Rebuttal: We highly appreciate your positive feedback and constructive suggestions. We would like to address your concerns as follows:
> **1. More diversified downstream tasks**
Thank you very much for your valuable comments. As we discussed in the paper, we agree that the performance of CATE relies on t... | Summary: The authors propose an approach to enhance the performance of Multiple Instance Learning (MIL) models by incorporating concept prompts within the context of pathological Vision-Language Models (VLMs). Two modules, i.e., the Concept-guided Information Bottleneck (CIB) module and the Concept-Feature Interference... | Rebuttal 1:
Rebuttal: We sincerely thank you for your insightful comments and constructive suggestions. We would like to address your concerns below:
> **1. Dependence on text prompts and principled ways to obtain prompts**
In our experiments, in addition to the expert-designed 'class-specific prompts' from the origi... | Rebuttal 1:
Rebuttal: We are sincerely grateful to the reviewers for your insightful comments, constructive suggestions, and acknowledging the clarity and quality of our paper, as well as our contributions in terms of novelty and effectiveness in feature enhancement for better WSI analysis.
We have carefully read and c... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
What matters when building vision-language models? | Accept (poster) | Summary: This work conducts comprehensive experiments for re-examing common choices in the VLM area, such as unimodal backbone, connector, number of visual tokens, data, etc. Based on their experiments, they observe several findings important to the VLM research community. Finally, they rely on their key findings to co... | Rebuttal 1:
Rebuttal: Thank you FVDr for your review. We appreciate your positive feedback regarding the strengths of our work.
Regarding the point mentioned about section 3.1, we can clarify our approach:
We wanted to start by showing some results with the cross-attention architecture that was the reference approach... | Summary: This paper empirically investigates several essential design choices of vision-language models, such as language backbones, visual encoder backbones, and different architectures. Through extensive experiments, they evaluate different model architectures, training methods, and data utilization strategies. Based... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. We appreciate your feedback and will address each point individually. We unfortunately cannot cite your questions without exceeding the maximum length.
1) It is true that our comparisons primarily involve open-source models. This is because state-of-the-art com... | Summary: The compares different methods and strategies involved in training VLMs
- impact on inference efficiency by model architecture (fusion module: cross-attention versus autoregressive) & on training stability by multimodal training procedure
- compare different design choices in a controlled environment and extra... | Rebuttal 1:
Rebuttal: Thank you reviewer Gtma for your interesting remarks, we will try to cover them one by one.
> While I fully appreciate the importance of the work in standardizing a recipe for training VLMs, I believe it lacks vigour to make such bold claims. For example, Finding 4 does contradict results in Tabl... | Summary: This work studies a question: what matters when building vision-language models? To this end, this work provides analysis from the following aspects, 1) Are all pre-trained backbones equivalent for VLMs? 2) How does the fully autoregressive architecture compare to the cross-attention architecture? 3) Where are... | Rebuttal 1:
Rebuttal: Thank you for your remarks that we’ll address one by one, unfortunately briefly and without citing your questions not to exceed the max length.
1) Optimization for chat scenarios depends on training data.\
We used YYYYYY, a compilation of 50 high-quality, mostly human-annotated datasets from lite... | Rebuttal 1:
Rebuttal: Dear reviewers, thank you for your detailed remarks. We have commented on each of your point individually.\
Before that, we would like to highlight a summary of our biggest contributions with this work.
- We demonstrate the effectiveness of LoRA training during pre-training for stable training an... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Generalization Analysis for Label-Specific Representation Learning | Accept (spotlight) | Summary: This paper proposes a new vector contraction inequality and derives a tighter label-specific representation learning (LSRL) generalization bound based on it.
This paper derives bounds for general function classes of LSRL with a tighter dependency on c than the SOTA, which provides a general theoretical guarant... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and active interest in helping us improve the quality of the paper.
The following are our responses to the Questions:
**1. Response to the Weakness.**
$\bullet$ We will add the proof sketch for Lemma 1 as follows:
First, the Rademacher complexity of the... | Summary: The article discusses the theory bounds of Label-Specific Representation Learning (LSRL) in multi-label learning. It highlights the need for a deeper understanding of LSRL's generalization properties and proposes novel bounds based on Rademacher complexity. This paper derives bounds for general function classe... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and active interest in helping us improve the quality of the paper.
**1. Response to the Weakness 1.**
We appreciate the reviewer's suggestion for a broader scope of applicability of the analysis method for DNN-based LSRL. The analysis of the precise stru... | Summary: This work makes one step towards the generalization analysis for label-specific representation learning. Compared with previous generalization studies on multi-label learning, this work provides a generalization bound with a much weaker dependency on the number of labels and decouple the relationship among com... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and active interest in helping us improve the quality of the paper.
**1. Response to Weakness 1.**
As the reviewer said, formally defining the loss function space can avoid any misunderstanding. This problem is mainly caused by the limitation of paper len... | Summary: The paper focuses on the theoretical analysis of Label-Specific Representation Learning (LSRL) within the context of multi-label learning. LSRL aims to improve multi-label learning by creating representations with distinct discriminative properties for each label. While LSRL has shown empirical success, its th... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and active interest in helping us improve the quality of the paper.
**1. Response to Weakness 1.**
a. The basic idea of LSRL is to decompose multi-label problem into c binary classification problems. This idea is effective for handling multi-label problem... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for your efforts in reviewing this paper, as well as your constructive comments and active interest in helping us improve the quality of the paper.
We provide detailed responses to the questions of each reviewer. Here we first make a brief summary of our resp... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Not so griddy: Internal representations of RNNs path integrating more than one agent | Accept (poster) | Summary: This paper studies RNNs trained to path-integrate the position of two agents. They show the network behaves differently to similar networks trained to path-integrate the position of a single agent, and make some neural level predictions.
Strengths: The paper was very nicely written and presented.
The questio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and helpful comments. We are encouraged that they found our work well written and our results carefully discussed. Below we respond to the specific questions the reviewer had.
**"One big choice that seems likely to have heavily influenced the learnt representa... | Summary: The authors study neural representations of space in artificial agents that perform path integration of the positions of two agents simultaneously. This is motivated by recent neuroscience studies showing place cells that respond to the location of nearby animals. The authors feed the velocity and head directi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and helpful comments. We are encouraged that they found our work novel and well written. Below we respond to the specific questions the reviewer had.
**"There is no attempt to analytically derive or explain the obtained result"**
We agree that the lack of an... | Summary: This paper studies the internal representations of recurrent neural networks that have been trained to path integrate two agents simultaneously, based on the hypothesis (and related experimental evidence) that individual agents account for the positions of others in multi-agent environments. The authors augmen... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and insightful comments. We are encouraged that they found our work well written and our analysis solid. Below we respond to the specific questions the reviewer had.
**"The RNNs used in the experiments are not noisy and are simple vanilla discrete-time RNNs."*... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and thoughtful comments. All reviewers found our work clear and novel, which we find encouraging. All reviewers identified similar weaknesses: this makes it clear that there are important ways our work can be strengthened. Here, we provide answers to the two m... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Nature-Inspired Local Propagation | Accept (poster) | Summary: The authors introduce a new model for describing the evolution of weights in a neural network. They do so by defining the problem as a directed graph and reformulating it to derive a Hamiltonian which they can then apply Hamilton's principle to to solve. This solution yields a set of differential equations fro... | Rebuttal 1:
Rebuttal: We thank the Reviewer for having appreciated our work. In what follows we will address the comments and questions raised.
*I appreciate that the author/s states in the conclusion that more work needs to be done to apply this to more applicable machine-learning problems, however, as a machine-lear... | Summary: The paper proposes a spatially and temporally local alternative to backprop for training RNN. The paper first proposed viewing learning as minimizing a functional of a variational problem using Hamiltonian equations. The standard backprop can be seen as a special case when the velocity in the functional is set... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the novelty of our approach. We will address the comments and questions raised.
*It would also be much better if the pseudo-code of the final algorithm were presented directly, [...]*
We agree that the presentation of the final algorithm that we are using ... | Summary: This paper, titled "Nature-Inspired Local Propagation," explores a novel learning framework that diverges from traditional machine learning methods, which heavily rely on large data collections and professional expertise. The authors propose a biologically inspired model emphasizing the critical role of tempor... | Rebuttal 1:
Rebuttal: **Explanation of the graphs and experimental setup**
We utilize a fully connected recurrent network with five neurons (see Figure 1 in the PDF rebuttal), each using $\tanh$ activation functions. One neuron, designated as the output unit, aimed to approximate a given reference signal through a quad... | null | null | Rebuttal 1:
Rebuttal: We are pleased to see the detailed analysis and the insightful list of comments and criticisms, which are greatly appreciated, along with the time dedicated to reviewing our work. We are confident that these will significantly improve the quality of our paper.
Since more than one reviewer comment... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LLMs Can Evolve Continually on Modality for $\mathbb{X}$-Modal Reasoning | Accept (poster) | Summary: The authors introduce continual learning into MLLMs to explore the ability of pre-trained LLMs to evolve continually on multiple modalities while keeping knowledge from being forgotten. A novel PathWeave with Adapter-in-Adapter (AnA) is proposed, in which uni-modal and cross-modal adapters are seamlessly integ... | Rebuttal 1:
Rebuttal: - **Weakness 1: The performance gap between the PEFT and fully finetuning method.**
$\quad$ Thanks for your comments. Our method does fall slightly behind in performance metrics compared to fully fine-tuning approaches. This is primarily because that the Parameter-Efficient Fine-Tuning (PEFT)... | Summary: Due to a serious illness I have been experiencing recently, I deeply regret to inform you that I am unable to complete the review as scheduled. I kindly request the Chair to consider the opinions of the other reviewers
Strengths: -
Weaknesses: -
Technical Quality: 3
Clarity: 3
Questions for Authors: -
Co... | null | Summary: This paper proposes a flexible and scalable framework, PathWeave, which enables MLLMs to allow MLLMs gradually to involve reasoning ability on diverse modalities. The introduction of the adapter-in-adapter structure effectively alleviates the heavy burdens of the joint training or data replay strategies in pre... | Rebuttal 1:
Rebuttal: Thank you for all your comments. We respond to each of the weaknesses and questions you raised to address your concerns and make the necessary revisions to improve the quality of the paper. Our point-by-point summary and responses to your comments are as follows. (Notes: the mentioned Tables 1-5 a... | null | null | Rebuttal 1:
Rebuttal: To all reviewers:
We are grateful to all the reviewers for their valuable comments. We hope that our responses have effectively addressed your previous concerns. We have revised our paper according to your comments. **The major changes are summarized as follows:**
- **According to Reviewer#PRBt ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
An Efficient Memory Module for Graph Few-Shot Class-Incremental Learning | Accept (poster) | Summary: This paper tackles the problem of graph few-shot class-incremental learning, proposes a novel framework named Mecoin to address the challenges of catastrophic forgetting and overfitting. Mecoin includes two key componets: the structured memory unit (SMU) for storing and updating prototypes and the memory repre... | Rebuttal 1:
Rebuttal: **Q1**: The abbreviations of the proposed method are confusing.
**A1**: We appreciate your attention to our work and the valuable feedback, and sincerely apologize for the confusion caused by the use of abbreviations. According to your suggestions, we have re-examined the entire text and decide... | Summary: The authors focus on graph few-shot class-incremental learning. The authors first introduce Mecoin to efficiently construct and preserve memory. To avoid extensive parameter finetuning and forgetting, the authors introduce a memory representation adaptive module called MRaM to separate the learning of prototyp... | Rebuttal 1:
Rebuttal: **Q1**: Lacks error bars, standard deviations.
**A1**: Thank you for the valuable feedback. In the initial version, we omitted detailed statistical information due to formatting and space constraints. However, these details are crucial for a comprehensive evaluation of model performance and res... | Summary: This paper mainly focuses on graph few-shot class-incremental learning. To alleviate the significant memory consumption and catastrophic forgetting of old knowledge, it proposes to store only old class prototypes and update class prototypes by considering the interaction among nodes and prototypes. Furthermore... | Rebuttal 1:
Rebuttal: **W1**：The paper needs further polish.
**A1**:Thank you for your thorough review and suggestions. We have carefully reviewed our paper and standardized the symbols and abbreviations. For example, we have unified MRaM and MRAM to MRaM. Additionally, we have conducted a comprehensive review and re... | null | null | Rebuttal 1:
Rebuttal: Thank you very much to the reviewers for their valuable feedback on our paper. The reviewers acknowledged our strong motivation (**R3**), efficient and low-cost method design (**R1, R2, R3**), and insightful analysis (**R2**), which greatly encouraged us. We are pleased that the reviewers found ou... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Approximately Equivariant Neural Processes | Accept (poster) | Summary: This paper implemented the approximately equivariant neural process (NP) by relaxing the equivariance of the NP decoder and the relaxation is conducted by adding several leanable parameters as fixed inputs fed with the data embeddings into the decoder.
Strengths: The proposed method is applicable regardless o... | Rebuttal 1:
Rebuttal: Thank you for your feedback and for recognising how easily our method can be applied to achieve approximate equivariance.
**Supplemental code** - As mentioned in the Paper Checklist, we intend to provide open access to the data and code prior to publication.
**Concept figure** - Please see the ... | Summary: The paper describes a new framework for soft/approximate equivariance, based on the functional analysis of compact on hilbert spaces, assuming unitary group actions.
This is then applied to equivariant neural processes.
Strengths: The proposed method is very general and conceptually well grounded, and to the... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the novelty of our approach, as well as its theoretical groundness, coupled with encouraging empirical evaluations. We address your concerns below.
**Focus on NPs** - Thank you for highlighting an important point about the general purpose of our approach. The reason wh... | Summary: This work considers approximately equivariant models --- which may better model or learn real-world tasks than exactly equivariant models --- especially in the context of neural processes. A new approximately equivariant method is developed, which uses an exactly equivariant model along with fixed inputs that ... | Rebuttal 1:
Rebuttal: Thank you for acknowledging our theoretical and empirical contributions in the field of approximate equivariance, with a focus on applications in neural processes. In particular, we are pleased that you appreciate the importance of setting the additional inputs to zero at test time outside the tra... | Summary: The work provides an alternative approach to obtain a loose equivariance constraint. The authors established an interesting relationship between equivariant and non-equivariant models, showing that equivariant models with enough fixed input can approximate any non-equivariant model. Subsequently, the authors s... | Rebuttal 1:
Rebuttal: Thank you for acknowledging our theoretical contributions, as well as the empirical evaluation on a variety of datasets. We hope that our work justifies the advantages of relaxing model symmetries and proves that the proposed framework is an effective approach for achieving this. We address your c... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for the time taken to review the paper, for their feedback and useful suggestions, and we hope to address all their concerns through our responses. We are pleased that, in general, our method was seen as a general way of achieving approximate equivariance, whil... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Noether's Razor: Learning Conserved Quantities | Accept (poster) | Summary: This work proposes a novel way to parametrise Hamiltonians that are approximately invariant to a set of learnable symmetries with the aim of improving existing methods of learning Hamiltonians of dynamical systems. It does so by forcing Hamiltonian neural nets (HNN) to adhere to symmetries, just as in reality,... | Rebuttal 1:
Rebuttal: Thank you for your feedback and help to improve the paper. We thank the reviewer for finding the paper well-structured and self-contained with a clear goal. The reviewer appreciated the empirical validation and experiments that demonstrate improved generalization of the proposed approach.
> Q1. N... | Summary: This paper proposes Noether's Lazor, a Bayesian framework incorporating learnable symmetries for Hamiltonian neural networks. It parametrizes a hidden symmetry as a flow (single-parameter group) derived from the system's conserved quantity. The flow is then applied to the Hamiltonian as it transforms the syste... | Rebuttal 1:
Rebuttal: Thank you for your feedback and help to improve the paper. We thank the reviewer for finding the paper 'intriguing' and 'conceptually very interesting' and that the empirical evaluation shows that the ideas can be promising
> Difference with prior work
We thank the reviewer for pointing this out... | Summary: This paper proposes a Bayesian framework to learn conserved quantities (and, implicitly, symmetries) in the context of Hamiltonian systems. The idea is that the Hamiltonian system has specific parametric conserved quantity (given by a quadratic function, line 136), in that case the conserved quantity can be le... | Rebuttal 1:
Rebuttal: Thank you for your feedback and help to improve the paper. We thank the reviewer for finding the paper 'intriguing' and 'conceptually very interesting' and that the empirical evaluation shows that the ideas can be promising
> Q1. Non-quadratic conserved quantity
First, we believe that the quadra... | Summary: The authors propose to use the parameterized symmetries for learning correct Hamiltonian dynamics from data. It is based on the Noether’s theorem, which states that the continuous symmetries generated by observables O for Hamiltonian H yields the conservation of O, and vice versa. To do this, the authors param... | Rebuttal 1:
Rebuttal: Thank you for your feedback and help to improve the paper. We thank the reviewer for finding the paper clear, well-written, easy to follow, and highly relevant to both the AI + Science and geometric machine learning communities. Further, we appreciate the reviewers deemed the paper very principled... | Rebuttal 1:
Rebuttal: We thank all reviewers for the feedback and help to improve the paper. We are excited to have received a "9: Very Strong Accept", and are confident that the concerns raised by the lower score reviews are sufficiently addressed in this rebuttal. Overall, most reviewers found the paper very 'clearly... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This work aims to model symmetries for strong inductive biases in machine learning models of dynamic systems. Instead of constraining models to certain symmetries, this work focuses on automatically learning them from data. It proposes to parameterize symmetries using conserved quantities by Noether's theorem.... | Rebuttal 1:
Rebuttal: Thank you for your feedback and help to improve the paper. We appreciate that the reviewer rated our paper with a "9: Very Strong Accept", found our approach a novel and significant contribution, well presented and recognized the new perspective on symmetry learning as well as convincing experimen... | null | null | null | null | null | null |
Dynamics of Supervised and Reinforcement Learning in the Non-Linear Perceptron | Accept (poster) | Summary: In this work, the authors derive a set of equations describing gradient flow in a non-linear finite-dimensional perceptron under the assumptions that the data is multinormally distributed, there is a small learning rate, and the task is binary classification. They develop the equations both for the case of a s... | Rebuttal 1:
Comment: Thanks to the reviewer for their overall positive assessment of our work, including its importance, soundness, and exposition. Thanks also for the numerous constructive comments, which we have addressed as described below.
The main weaknesses identified by the reviewer involve assumptions that we ... | Summary: This paper provides what seems to be the first derivation of equations for the dynamics of SGD-style learning in a nonlinear perceptron outside of the standard student-teacher paradigm. Specifically, a Fokker-Planck-like PDE is derived for the temporal dynamics of the weight distribution and from this ODEs are... | Rebuttal 1:
Comment: We appreciate the reviewer’s assessment of our work as impactful and novel. We also appreciate the reviewer’s constructive suggestions to improve our paper’s clarity, which was described as the primary weakness of the paper. In particular, the reviewer pointed out that our work’s soundness and pres... | Summary: This paper analyzes the weight dynamics of single layer neural networks with nonlinear output functions.
Strengths: The model of stochastic weight evolution dynamics is quite general.
Weaknesses: The theory is tested only in binary classification task.
Technical Quality: 3
Clarity: 2
Questions for Authors... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback, but we disagree with their conclusion that our paper should not be accepted based on the fact that our theory is applied only to binary classification in a single-layer system. Indeed, according to the other reviewers, our work “addresses an important open... | null | null | Rebuttal 1:
Rebuttal: We are grateful to the reviewers for their comments and suggestions on our work. We were encouraged by their recognition of the work’s novelty and impact, as well as its soundness and exposition. We have made numerous revisions to our paper in order to address the reviewers’ critiques, the three m... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Scalable DBSCAN with Random Projections | Accept (poster) | Summary: The authors presented a significant advancement in density-based clustering algorithms with the introduction of sDBSCAN. The scalability and speed improvements are particularly noteworthy, making it suitable for large datasets where traditional DBSCAN variants struggle. The theoretical underpinning provided co... | Rebuttal 1:
Rebuttal: Thanks for your reviews. However, we found that the raised weaknesses and questions have been addressed in the paper and Appendix (Section B).
We explain in detail below.
**W1) The algorithm’s performance may depend on the selection of parameters, and the sensitivity to these parameters is not de... | Summary: This paper proposes a scalable DBSCAN algorithm which facilitates random projection to quickly approximate the $\varepsilon$-neighborhood. The proposed algorithm speeds up conventional DBSCAN algorithms by orders of magnitude.
Strengths: S1. The proposed algorithm speeds up conventional DBSCAN algorithms by o... | Rebuttal 1:
Rebuttal: Thanks for your reviews. We will address the raised weakness below.
**W1. The main contribution may not be very high, because the core idea of approximating ε-neighborhood is borrowed from the previous work [22].**
sDBSCAN uses the recent result in [22], i.e. the property of random projections g... | Summary: The authors present an accelerated variant of DBSCAN based on random projections. Theoretical results are provided that indicate that this method will yield a similar clustering as the original DBSCAN. Experiments on real-world data show that this method indeed achieves similar performance at a fraction of the... | Rebuttal 1:
Rebuttal: Thanks for your reviews. We will address the raised weaknesses and questions below.
**W1: Regarding notation and proof of Lemma 1**
We agree with your comments regarding sloppy formulation and will fix them all.
Since we start sDBSCAN with cosine distance, we state Lemma 1 as a simplified versio... | Summary: DBSCAN is a popular density-based clustering algorithm. For a parameter $\epsilon$, a point $p \in X$ is core if its $\epsilon$-ball is large (over $minPts$ in size). Core points within each others' $\epsilon$-balls are then connected via an edge, non-core points within $\epsilon$-balls are considered cluster ... | Rebuttal 1:
Rebuttal: Thanks for your reviews. We will address the raised questions below.
**Q1) "We observe that such memory constraint is the primary hurdle limiting the current scikit-learn implementation on million-point data sets" -- This is presumably very dependent on hardware, input, etc, right? I feel like th... | Rebuttal 1:
Rebuttal: **1) Selecting $\epsilon$ for sDBSCAN by sOPTICS without ground truth**
We would like to use global rebuttal to explain further sOPTICS (details in Section A1), one of our contributions, to select the parameter $\epsilon$ of sDBSCAN.
Without sOPTICS, selecting a good value for $\epsilon$ will be... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: Paper studies scalable algorithms for DBSCAN, a popular clustering method.
In DBSCAN, each point considers a radius of epsilon, and is called "core" if it has at least m points in the epsilon ball.
Then, each core point is connected to all other points (core or non-core) in its epsilon ball.
Finally, connect... | Rebuttal 1:
Rebuttal: Thanks for your reviews. We will address the raised questions below.
**Q1) How do you extend this method to other metrics, non euclidean? I presume that one of the appeals of DBSCAN is the non-reliance of euclidean metric.**
sDBSCAN starts from cosine distance. To extend sDBSCAN on L1, L2, χ2, J... | null | null | null | null | null | null |
Semi-Supervised Sparse Gaussian Classification: Provable Benefits of Unlabeled Data | Accept (spotlight) | Summary: In this paper, the authors studied the problem of semi-supervised learning in a 2-class classification problem with a special distribution setting. They assumed that the samples in each class came from an isotropic Gaussian distribution with unknown mean vectors $\mu_1$ and $\mu_{-1}$. They also assumed that t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments. We address the issues raised one by one.
(0) We agree that our proposed LSPCA method is tailored to our specific case of a mixture of two Gaussians. However, we believe that the insights of our work, proposing a two step SSL scheme, using the l... | Summary: The authors identify a regime where semi-supervised learning has computational advantage over (purely) supervised or unsupervised learning. They propose an algorithm that achieves it and demonstrates the efficacy of the algorithms in simulations.
Strengths: - Extremely well-written paper
- The theoretical res... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments and encouraging feedback.
Regarding the question in L59
"On the computational side, is there a computational-statistical gap in SSL?"
The answer is yes. We will modify the text in lines L70-76 to explicitly mention that by adding too few labeled sam... | Summary: This paper studies the classical problem of clustering Gaussians with sparse means. While this problem was previously considered under the unsupervised (Sparse PCA) and supervised settings, the main innovation of the authors is to identify a phase where labeled and unlabeled data can be used together to estima... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments.
We agree with the referee regarding context of figure 1. We will add that the red and green regions follow from previous works, whereas the orange and blue are our novel contributions. We also reference the relevant theorems for these.
Regard... | Summary: The paper studies semi-supervised learning in a simple mixture of two Gauss(ians setting. Specifically, there is a uniform mixture of two Gaussians in $p$ dimensions with unknown means $N(\mu_1, I_p), N(\mu_{-1}, I_p)$. We assume that the difference between the means $\Delta\mu = \mu_1 - \mu_{-1}$ is a $k$-s... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review.
We agree that our work considers a limited case of two Gaussians. We remark that many previous authors also considered a same or similar setting. Indeed, it would be interesting to extend to larger number of Gaussians. However, as the SSL analysis... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models | Accept (poster) | Summary: This paper introduces AdvUnlearn, a robust unlearning framework that integrates adversarial training into diffusion models to enhance concept erasure. This method aims to prevent DMs from generating harmful or inappropriate content, such as nudity, even under adversarial prompt attacks. AdvUnlearn focuses on o... | Rebuttal 1:
Rebuttal: **Tables (referred to as Table Rx) can be found in the [attached PDF file](https://openreview.net/attachment?id=dQxPvBUICW&name=pdf).**
**W1 & Q2 & Limitations: More attacks for ASR measure.**
**A**: Thank you for the suggestions regarding two additional related works. We will cite them and ex... | Summary: The authors propose a method for robust (against adversarial attacks) concept-erasing for latent diffusion models. Specifically text-to-image diffusion models.
The main contributions are:
1. Integrating adversarial training into machine unlearning by modifying it as a bi-level optimization problem.
2. Contrar... | Rebuttal 1:
Rebuttal: **Tables can be found in the attached PDF file.**
**W1: Lack of Sufficient Evaluation across various attacks**
**A**: Thank you for your valuable feedback concerning the scope of our evaluation. We have included more attacks (including CCE and RAB) for evaluation. **Details can be found in Gene... | Summary: This paper proposes AdvUnlearn, a method aimed at enhancing the robustness of concept erasing. The approach integrates the principles of adversarial training into the unlearning process. Specifically, the text encoder is identified as the most suitable module for robustification. Experiments are conducted on e... | Rebuttal 1:
Rebuttal: **Tables (referred to as Table Rx) can be found in the [attached PDF file](https://openreview.net/attachment?id=dQxPvBUICW&name=pdf).**
**W1 & Q1: Lacks a sufficient number of attack methods.**
**A**: Thank you for your insightful suggestion regarding the range of attack methods in our study. ... | null | null | Rebuttal 1:
Rebuttal: Thank you all for the thoughtful reviews and suggestions provided. In response, we will meticulously address each raised question and concern sequentially. **We choose to add the additional experiments via tables (referred to as Table Rx) in the [attached PDF file](https://openreview.net/attachmen... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Hardness of Learning Neural Networks under the Manifold Hypothesis | Accept (spotlight) | Summary: It is widely believed that the low-dimensional structure in natural data is the key to the success of modern machine learning methods. Characterizing this structure and designing algorithms that leverage it, and the complementary problem of understanding when learning under this structure is hard, are thus imp... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments. We answer their questions and comments below.
___
> There is no attempt for instance to check empirically whether the scaling of the sample complexities in the hardness result matches the experiment.
If we understand the reviewer's point corr... | Summary: The paper establishes bounds on the learning hardness for a class of low-dimensional manifolds in the statistical query (SQ) model. It extends existing hardness results for neural network training in Boolean and Gaussian input models to more general geometries. These theoretical results are validated through s... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments and feedback. We address their questions below.
___
> The synthetic experiment itself does not seem convincing enough. The authors only use hypersphere as the input data. It remains unclear whether the results hold for other geometries besides th... | Summary: The present work considers the hardness of learning under the manifold hypothesis, i.e., the identification of low-dimensional structure by reconstructing low-dimensional manifolds from data and how the latter impacts on the computational complexity of learning algorithms. The authors investigate the data geom... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful questions and positive feedback. We answer their questions below.
____
> Any of the following concerns can be assumed mediocre and the first two are also discussed transparently.
> (1) The sampleable approach restricts the class of included manifolds.
> ... | Summary: The paper relates the sample complexity required for learning a manifold to its curvature – the curvature is characterized by its “reach” that is defined as the minimum distance from a point on the manifold to some point that has multiple nearest neighbors on the manifold. For a manifold in a n-dimensional hyp... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments and feedback. We address their questions below.
___
> Do you really need the SQ and the cryptographic hardness assumptions for the exponential lower bound? Cant you just get unconditional hardness bounds as with a space filling curve that goes to... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Geometric Exploitation for Indoor Panoramic Semantic Segmentation | Accept (poster) | Summary: The goal of the paper is panoramic semantic segmentation. Given a panoramic image, produce a panoptic segmentation.
Main ideas:
1) Separate panoramic image into two sets of segments:
over-sampled, representing planar objects such as ceiling and floor, and
under-sampled, representing for other elements
2)... | Rebuttal 1:
Rebuttal: Dear Reviewer DACe, Thank you for appreciating our approach. We will address your comments below:
**About the reviewer's summary**
We would like to clarify that our work is about semantic segmentation of indoor panoramas, not panoptic segmentation as stated in the reviewer's summary. We also pro... | Summary: The method divides the indoor panorama semantic segmentation problem into the prediction of over-sampled segmentation (like ceiling, floor, and planar objects) and under-sampled segmentation (like objects in indoor scenery like furniture, windows, door, etc.) subtasks.
The paper utilizes over-sampled segment... | Rebuttal 1:
Rebuttal: Dear Reviewer irP4, Thank you for appreciating our approach. We will address your comments below.
**Problem 1: About the heavy computationally complexity**
In fact, our model is slightly heavier than the baseline (Trans4PASS+) with 53M and 39M parameters respectively. However, in terms of TFLOPS... | Summary: This paper introduces a novel approach to panoramic semantic segmentation. The work views panoramic segmentation from two perspectives including over-sampled segmentation and under-sampled segmentation. The rich geometric depth information is exploited using a transformer-driven context module. The experiments... | Rebuttal 1:
Rebuttal: Dear Reviewer GGrZ, Thank you for appreciating our approach. We will address your comments (both weaknesses and questions) below.
**W1:** About merging process, we agree with the reviewer that the merging processed should be described and visualized clearly for better understanding
**W2:** About... | Summary: The authors decompose the indoor panoramic semantic segmentation task into two sub-tasks: segmentation and depth estimation and design to enhance the geometric information. Specifically, the method first introduces the vertical relative distance to demonstrate the relationships between planar objects (ceiling ... | Rebuttal 1:
Rebuttal: Dear Reviewer QfCk,
Thank you for appreciating our approach. We will address your comments below.
**Q1: About the wrong claim**
We agree with the reviewer that this sentence should be changed, our meaning is 'to reduce the distortion gap between segments in each group'.
**Q2: About the unfair ... | 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 QfCk, GGrZ ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Unified Gradient-Based Machine Unlearning with Remain Geometry Enhancement | Accept (spotlight) | Summary: The paper tackles the problem of machine unlearning, i.e. forgetting the influence of some data points, with remain-preserving manifold geometry. Authors dig deep into the gradient based approximate unlearning methods from the perspective of steepest descent and come up with a method beyond Euclidean constrain... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments, valuable feedback on our work, and constructive suggestions to help improve our presentation. We will address your concerns sequentially. **Relevant tables and figures are included in the attached rebuttal PDF**.
**W1**: Including results on other modalitie... | Summary: This paper summarizes the previous gradient-based unlearning methods and proposes three essential components for unlearning: weighted forgetting gradient ascent, remaining gradient descent, and a weight saliency matrix. Then, this paper derives the steepest descent direction and proposes a fast-slow weight met... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and noting that our method is attractive. We will address your concerns sequentially. **Relevant tables and figures are included in the attached rebuttal PDF**.
**W1**: Addressing imbalance in layer-wise changes for enhanced information removal.
> There are s... | Summary: This work introduces a perspective to unify previous machine unlearning approaches by decomposing the gradient descent direction into three components including forgetting gradient ascent, remaining gradient descent, and weight saliency matrix. The steepest descent direction is derived on the remain-preserved ... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and valuable comments on our presentation and extensive experiments. In Appendix B, we have included **Related Works** to help readers understand the concept of Machine Unlearning and to introduce some previous unlearning methods. **The relevant Tab.R1 is inclu... | null | null | Rebuttal 1:
Rebuttal: # General Response
Dear Program Chairs, Area Chairs, and Reviewers,
We sincerely appreciate your time, constructive critiques, highly pertinent concerns, and valuable suggestions, all of which substantially help improve our work. We are also grateful to the reviewers' consistent acknowledgment o... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient | Accept (poster) | Summary: The paper develops a simple but novel deep learning framework to implicitly optimize a seed input design to iteratively improve upon a particular property $$ g(\cdot) $$, until it reaches a fixed-point. In this way, the paper develops a novel framework for design optimization and provides a theoretical derivat... | Rebuttal 1:
Rebuttal: **[Setting $\Delta_x$ and $\Delta_y$]**
The choice of parameters $\Delta_x$ and $\Delta_y$ should be informed by the specific application. For example, in antibody design, domain experts recommend not using thresholds above a Levenshtein distance of 8, as such differences are considered biologica... | Summary: The paper address design optimization, which is the process of optimizing over a "design" parameter space to optimize over one or more observable outcomes in many scientific and engineering problems. The proposed framework "PropEn" uses a three step process, first identifying a "matched dataset" that pairs eve... | Rebuttal 1:
Rebuttal: **[Benchmarking Against Bayesian Optimization (BO)]**
Please see our general response.
**[Connection to Diffusion Models]**
Please see our general response.
**[Neighborhoods in L2 Ball]**
The assumption regarding neighborhoods in L2 balls was made solely for theoretical purposes, to make the ... | Summary: This work proposes the method PropEn, which is inspired by the concept of matching techniques in econometrics. Using PropEn (specifically in scenarios with a lack of large datasets), the authors can expand the dataset, which will inherently help in design improvement, etc. To do this, they train a network to l... | Rebuttal 1:
Rebuttal: **[Extending Matching to Other Models]**
Please see our general response.
**[Comparison to Active Learning]**
Please see our general response.
**[Evaluating Target Properties on Neighbouring Samples**]
Could you please elaborate on what you mean by "evaluating the properties on neighbouring s... | Summary: The paper presents a generative framework for property enhancement. The proposed framework consists of only a generative model, and it's missing a discriminator that is usually found in other frameworks for guided design. This is achieved by training the generative model on a "matched" dataset that consists of... | Rebuttal 1:
Rebuttal: **[Details on Training and Matched Datasets]**
We add the following table in the supplement:
| Dataset | # Pairs | # Unique Samples in Train | Y Range (Control) | Y Range (Treatment) |
|-----------------|---------|---------------------------|-------------------|---------------------|
| 8 ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their time and thoughtful comments. Here we will summarize our response addressing common/essential concerns and then follow with point by point responses. In what follows we refer to the submited manuscript as ‘submission’ and the pdf accompanying our rebu... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Molecule Design by Latent Prompt Transformer | Accept (spotlight) | Summary: This paper presents LPT, a novel transformer model for conditional molecule sequence design and generation. LPT first generates latent vectors from a learnable prior distribution, then autoregressively generates molecule sequence taking the latent vector as prompt. Comprehensive experiments show that LPT achie... | Rebuttal 1:
Rebuttal: We sincerely thank you for your thorough review and positive assessment of our paper. We appreciate your recognition of our novel model LPT, the framework for property-conditioned molecule sequence generation, and the strong experimental results across multiple benchmarks. We are pleased that you ... | Summary: The paper introduces an approach for molecule design, by leveraging recent advancements in conditional generative models for language and image generation. It contains three steps: (1) learnable prior distribution, (2) molecule generation model and (3) property prediction model. The experiments are comprehensi... | Rebuttal 1:
Rebuttal: Thank you for your detailed and constructive review. We appreciate the time and effort you’ve put into evaluating our work. We will address your comments and questions point by point:
>W1: Motivation for framework:
Thank you for your question. We kindly refer you to the **Global Response Point 1... | Summary: The authors introduce a new conditional generative model for molecules. The model is called the Latent Prompt Transformer (LPT). A conditional model capable of generating new molecules with desired target properties is very useful in de-novo molecular design since we often want to design new molecules with som... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and comprehensive review of our paper. We greatly appreciate your positive feedback on the originality, quality, clarity, and significance of our work. We're particularly grateful for your recognition of LPT's novelty and significance in de-novo molecule design.
Rega... | Summary: This work proposes a novel molecular optimization framework Latent Prompt Transformer (LPT)，modeling latent distribution, molecule sequences and properties distributions conditioned on the latent distribution. They uses MCMC in MLE training and conditional generation. Additionally, they propose an online learn... | Rebuttal 1:
Rebuttal: Thank you for your thorough review. We appreciate your recognition of our work's alignment with real-world scenarios, comprehensive experiments, and efficiency improvements. We'll address your comments and questions point by point.
> W1. Clarity improvements:
We'll add a diagram illustrating the... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Thank you for your insightful and constructive comments on our submission. We have added derivation of Eqn. 5 for completeness and clarified our motivation for using MCMC-based methods in online molecule design.
1. **Derivation of Equation 5**
$$
\begin{align*}
\nabla_\theta... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Exploring Consistency in Graph Representations: from Graph Kernels to Graph Neural Networks | Accept (poster) | Summary: This work aims to bridge the gap between neural network methods and kernel methods by enabling GNNs to consistently capture relational structures in their learned representations. The authors propose a loss function that enforces the similarity of graph representations to remain consistent across different lay... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's insightful question regarding the extension of our method to graph clustering tasks and large-scale graph datasets, since complexity and scalability are significant concerns in practical applications.
**1.On Graph Clustering Tasks.**
While applying our consistency l... | Summary: The authors propose to improve the quality of graph embeddings in GNNs by encouraging a n motion of consistency in the representation obtained at the various layers.
Strengths: The work presented here offers interesting contributions: 1) a new perspective on understanding the graph classification performance ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's question about artificial experiments, which provides an opportunity to explore how this method performs across different data scenarios. Should there be any remaining issues or suggestions for improvement, we welcome further feedback on how to refine our approach.
**... | Summary: In this paper, the authors introduce the shortcomings of graph neural networks (GNNs) in capturing consistency and similarity relationships between graphs, and proposes a new loss function, aiming to enhance graph representation at different levels. Through theoretical analysis and experimental verification, t... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insights on efficiency and the relation of the proposed method to contrastive learning. We aim for the responses to be clear in addressing the concerns and questions. If any concerns remain or if further improvements are deemed necessary, please let us know how we ... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for recognizing the novelty and theoretical contributions of our paper and appreciate the valuable feedback that has significantly enhanced our work. We hope our responses are informative and helpful. Further feedback on any remaining points of concern and suggesti... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs | Accept (poster) | Summary: The paper focusses on improving post-training-quantization for LLMs. Specifically, they combine insights into incoherence processing from the two #QUIP papers and rotational invariance from SliceGPT to improve accuracy after quantization. The insight is that LLMs are, or can be made, invariant to rotations on ... | Rebuttal 1:
Rebuttal: Thanks for your comments.
> Emoji's in titles should get desk-rejected ;)
The emoji is there just because we wanted to give a hint on how QuaRot should be pronounced :-)
> Table 2 does not really compare to anything. Is it possible to put more comparisons with other methods into the tables?
We... | Summary: This paper applies the random Hadamard transform (RHT) in strategic places in the GPT architecture to improve the quality of weight and activation quantized models. The RHT is a fully invertible (up to machine epsilon) transformation that effectively concentrates matrices. The authors claim that applying the R... | Rebuttal 1:
Rebuttal: Thank you so much for your comments.
> this paper is that it combines methods from existing works
Here, we explain our contributions and the main differences between QuaRot and the existing work.
1. SliceGPT: SliceGPT focuses on compressing LLMs by slicing the weights. Although we use the same ... | Summary: The authors provide a framework for W4A4KV4 quantization of LLMs, leading to computing and peak memory improvement while maintaining good performance on language generation and zero-shot tasks. They achieve this by introducing randomised Hadamard transformations at both weight and activations of transformer bl... | Rebuttal 1:
Rebuttal: Thank you very much for your comments and encouragement. We agree that we can improve the readability of our results and include more details in our Tables. We have included these details in our manuscript.
>Figure 3. What does the (α) mean in the box? Is it supposed to be a diagonal?
Yes, this ... | Summary: The paper introduces QuaRot, a novel quantization approach for Large Language Models (LLMs) that utilizes Hadamard transformations to address the challenge of outlier features in activations, weights, and KV caches. By incorporating these transformations, QuaRot enables the entire model, including activations ... | Rebuttal 1:
Rebuttal: Thank you very much for your comments!
>The distinction between the proposed randomized Hadamard transformations and the Hadamard quantization method in Xi et al “Training Transformers with 4-bit Integers” should be elaborated.
We acknowledged the above work in our paper (lines 63-64) and descri... | Rebuttal 1:
Rebuttal: Hello Reviewers, we appreciate you taking the time to read and evaluate our paper. It is great to see that you liked our paper and found it impactful.
We would like to summarise the updates we made to the paper and extra experiments we have done to address your concerns:
1. **Changes to the pap... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LoFiT: Localized Fine-tuning on LLM Representations | Accept (poster) | Summary: The paper proposes LoFiT, a procedure for *localized* fine-tuning of LLMs. LoFiT chooses a task-specific subset of attention heads by tuning head-wise learnable scales and selecting the heads with largest scales (by absolute value). After that, the algorithm tunes the biases for the chosen attention heads to s... | Rebuttal 1:
Rebuttal: We appreciate your detailed comments and valuable feedback!
> My main concern about the paper is that the main evaluations are limited to 3 tasks (TruthfulQA, MQuAKE, CLUTRR). This makes it unclear if LoFiT is generally applicable in place of PEFT methods or if it is only competitive for a speci... | Summary: The paper introduces Localized Fine-Tuning (LoFiT) - a two step method that involves (1) localizing attention heads that are important for a given task, and (2) learning an additive intervention for each important attention head. The authors evaluate the method over various tasks, and show that LoFiT outperfor... | Rebuttal 1:
Rebuttal: We appreciate your detailed comments and valuable suggestions on our work.
> Could benefit from a more thorough comparison with ITI [...] It would be helpful if the authors could explore why LoFiT is so much more effective than ITI.
We think that the main performance gain of LoFiT over ITI comes... | Summary: This paper introduces a lightweight fine-tuning method that trains bias offsets for only a subset of attention heads, achieving significantly lighter adaptation compared to methods that fine-tune all layers, with minimal performance loss. The proposed method involves two steps. First, attention heads to fine-t... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful comments and feedback! Please find our answers to the question as well as clarification to some misunderstandings in the review.
> The comparison with representation steering may be somewhat unfair, as LoFiT requires labeled data and an explicit training stage, while rep... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers for their thoughtful comments on our work. We would like to present additional results corresponding to points that multiple reviewers raised.
### Evaluation on additional datasets
As reviewers 63K5 and Yw79 suggest, we extend our evaluation to a broader collection of data... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Domain Adaptation for Large-Vocabulary Object Detectors | Accept (poster) | Summary: This paper addresses the domain generalization problem of large-vocabulary object detectors. Without requiring additional annotations, it uses the Knowledge Graph Distillation technique to transfer knowledge from CLIP, enhancing the detector's generalization capability on downstream datasets.
Strengths: 1. St... | Rebuttal 1:
Rebuttal: **Response to Weaknesses-1**:
The Proposal Network of Faster R-CNN generates a large number of region proposals on the input image (i.e., thousands to tens of thousands of region proposals), which make VILD-like methods very slow. On the other hand, our KGD is performed only on the selected box ... | Summary: This paper highlights the problem that in domain adaptation, detectors often correctly localize but misclassify. To solve this problem, this paper proposes the Knowledge Graph Distillation method, which uses the pre-trained knowledge of VLM to supplement the relational knowledge of various categories in vision... | Rebuttal 1:
Rebuttal: **Response to Weakness-1:**
Thanks for your comments.
As suggested, we compare the memory usage and computational overhead with other methods in the table below, where $\star$ signifies that the methods employ WordNet to retrieve category descriptions given category names, and CLIP to predict clas... | Summary: This paper addresses the challenges faced by Large-vocabulary object detectors (LVDs) in recognizing objects across diverse categories, particularly due to domain discrepancies in data distribution and object vocabulary. The paper proposes a novel approach named Knowledge Graph Distillation (KGD) that leverage... | Rebuttal 1:
Rebuttal: **Response to Weaknesses-1**:
Thanks for your comments. We would clarify that the motivation for using knowledge graphs is to explicitly and comprehensively extract CLIP knowledge for effectively de-noising pseudo labels generated by LVDs when adapting LVDs.
On the other hand, directly utilizing ... | Summary: The authors propose a method for domain adaptation for large-vocabulary object detectors. To perform the adaptation, the authors first construct a Language and a Vision graph from the set of classes of the target dataset. The language graph is built with nodes as the description of the target class and the hyp... | Rebuttal 1:
Rebuttal: **Response to Weakness-1**: Thanks for your detailed comments. In our context, the edges in LKG and VKG represent affinity edges based on the distance between node representations. On the other hand, we model these distances as the semantic similarities measured by CLIP model, where the CLIP model... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Decision Mamba: A Multi-Grained State Space Model with Self-Evolution Regularization for Offline RL | Accept (poster) | Summary: This paper introduces a novel application of state space model (SSM) into the offline RL problem. Prior to this, transformer-based architectures were used heavily, but the authors claim that they did not process “historical information,” which is often an important requirement in real world scenarios. In this ... | Rebuttal 1:
Rebuttal: We thank reviewer qjYZ for the valuable comments. We now address the concerns raised in the review below.
> **Q1**: I find the lack of detailed reasoning of the manuscript why Conv1D module brings “fine-grained” control.
**A1**: Intra-step relationships among the **S**tate, **A**ction, and **R**... | Summary: This paper tackles the sequential decision-making problem in an offline RL setting. The authors propose Decision Mamba (DM), an extension of Mamba to adapt to the problem. There are 3 main technical contributions: (a) DM architecture (a mix of fine-grained and coarse-grained SSM modules), (b) progressive self-... | Rebuttal 1:
Rebuttal: We thank reviewer vt4f for the time in evaluating our work. We now answer the concerns raised in the comments below.
> **Q1**: It is somewhat unclear why that design choice on DM was made.
**A1**: Compared to the transformer architecture, the state space model (used in mamba) has advantages in c... | Summary: The paper proposes a robust method based on Mamba for Offline Reinforcement Learning. Additionally, the paper using the knowledge of the past policy to refine the noisy labels as supervision avoids the model fitting the noisy trajectories. To better train the model, the paper introduce the inverse training goa... | Rebuttal 1:
Rebuttal: We thank reviewer 2Wf9 for the efforts in reviewing our work. We have provided detailed explanations and additional experiments to address your concerns.
> **Q1**: Lack of explanation for extracting intra step relationships...
**A1**: Intra-step relationships mean the potential causal relationsh... | Summary: This paper introduces Decision Mamba, an offline RL backbone based on State Space Models. It enhances policy robustness by integrating a fine-grained SSM module alongside the original coarse-grained SSM in Mamba. Meanwhile, it adopts a progressive self-evolution regularization to prevent the policy from overfi... | Rebuttal 1:
Rebuttal: We thank reviewer K8tX for the thoughtful comments. We now address the concerns below.
> **Q1**: (1) The advantages of DM appear somewhat constrained when considering variance. (2) It appears that performance enhancements stem more from the PSER rather than the Mamba architecture. (3) Combining P... | Rebuttal 1:
Rebuttal: # "Global" Response
We thank all the Reviewers, ACs, SACs, and PCs for their efforts and valuable comments. In terms of the idea of this paper, all the reviewers have recognized that exploring mamba on the Offline RL is interesting, and we have also made some modifications to mamba specifically fo... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FUSE: Fast Unified Simulation and Estimation for PDEs | Accept (poster) | Summary: The authors propose a new approach, FUSE to the simultaneous learning of an emulator and statistical estimation of underlying "discrete" parameters in a joint training step.
The approach splits the problem into two: (i) the forward problem which is modelled through a FNO neural operator approach, effect... | Rebuttal 1:
Rebuttal: **W1.** **Concerns about the novelty/applicability:** We thank the reviewer for this important question. We will exemplify the applicability of the methodology by providing an example in PWP. The goal in this setting is to predict the output function $s$ given a finite-dimensional vector of parame... | Summary: This paper proposes a framework to tackle simultaneously forward (simulation of the system) and inverse (estimation of key parameters of the system) problems for PDEs. Namely, the authors suppose the existence of an underlying parameter $\xi$ that characterizes the input functions $u$ of the PDE, and therefore... | Rebuttal 1:
Rebuttal: **W1.** We thank the reviewer for pointing out possible difficulties in understanding our model and data. An updated version of the model illustration is provided in the 1-page pdf, including the requested details. We are happy to incorporate any further specific suggestions, should we have misse... | Summary: The authors study the problem of joint prediction of continuous fields and statistical estimation of parameters for physical systems governed by PDEs. Prior work had focused on operator learning and then inference to determine the statistical parameters. Here, the propose to solve for both jointly in their me... | Rebuttal 1:
Rebuttal: **W1.** The reviewer rightly points out that a reference and comparison to classical numerical methods is indispensable when justifying the use of ML-based methods. Since the application field "parametric PDEs" for our method is rather broad, we are happy to adopt the suggestion to reference a st... | Summary: The authors propose "FUSE", a combination of multiple neural operator models, which are trained to jointly solve PDE forward problems and perform parameter inference of given parametric PDE.
The main idea is to start from a range of PDE solutions obtained from various parameter values, and then train neural op... | Rebuttal 1:
Rebuttal: **W1.** We thank the reviewer for pointing this out. We include an updated Figure in the one page document, which we believe is much more explanatory.
**W2.** Even though referencing pre-prints is a common practice in ML, the reviewer is right in that, if available, the respective journal or c... | Rebuttal 1:
Rebuttal: At the outset, we would like to thank all reviewers for their valuable time and feedback. We believe this discussion will lead to meaningful improvements in the quality and presentation of our work, improving its accessibility to practitioners of scientific machine learning. Furthermore, we would ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper introduces a novel framework called FUSE that unifies surrogate modeling and parameter identification for parametric partial differential equations (PDEs). Traditionally, field prediction and statistical estimation of discrete parameters have been separately handled by using operator learning surroga... | Rebuttal 1:
Rebuttal: We would first like to thank the reviewer for acknowledging the novel framework, the rigorous problem definition, and the integrated approach we take constructing a robust framework.
**W1**
Regarding the concern with respect to the lack of novelty in the FMPE application, we would like to highlig... | null | null | null | null | null | null |
DeltaDock: A Unified Framework for Accurate, Efficient, and Physically Reliable Molecular Docking | Accept (poster) | Summary: This paper proposed a model named DeltaDock for molecular docking. DeltaDock integrates protein pocket prediction and protein-ligand binding refinement. The pocket prediction is modeled as a pocket-ligand alignment problem with candidates of pockets given by other pocket prediction methods. The docking is form... | Rebuttal 1:
Rebuttal: Dear Reviewer, thank you for your insightful comments on our dataset, figures, and methods. Below are our responses to your questions.
---
> **For Weaknesses 1**
Thank you for raising this important question. This work utilizes the established approach of training on the PDBbind time-split tra... | Summary: This manuscript introduces DeltaDock, a novel two-stage framework for molecular docking. Similar to previous works that use geometric deep learning methods, this work also takes the geometric dl network to do the modeling and the prediction is based on a regression problem. The main contribution is a contrasti... | Rebuttal 1:
Rebuttal: Dear Reviewer，thank you for your insightful comments on our methods. Below are our responses to your questions.
> **For Weakness 1:**
Thank you for your valuable comment. FABind is an effective method, particularly its inspiring framework that first predicts pockets and then performs docking. Re... | Summary: A new method for molecular docking based on neural networks, called DeltaDock, is introduced. DeltaDock uses a two-step procedure. The first step is finding the binding pocket for a given ligand, which is implemented by aligning the molecular structure with the pocket embedding. The alignment is conducted with... | Rebuttal 1:
Rebuttal: Dear Reviewer, thank you for your insightful comments on our papers and methods. Below are our responses to your questions.
---
> **For Originality 1**
Thank you for your insightful observation and suggestion. We acknowledge that Equations 7 and 8 in the manuscript may have been misleading rega... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers,
Thanks again for your insightful comments and valuable suggestions, which are of great help to improve our work.
In the appended PDF file, we present additional experiments conducted in accordance with the reviewers' suggestions. These experiments aim to further strengthen our f... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes | Accept (poster) | Summary: This paper considers active learning strategies for global sensitivity analysis of expensive black-box functions to efficiently learn the importance of different input variables. Novel active learning acquisition functions are proposed to target key quantities of derivative-based global sensitivity measures (D... | Rebuttal 1:
Rebuttal: Thank you for your review of our paper and for your questions, which we answer below.
**Weaknesses:**
1. It is correct that we do use two performance metrics in our paper, RMSEs and NDCGs. As discussed also in the response to reviewer oJrN above, RMSE is the most effective performance metric for ... | Summary: The authors propose eight acquisition functions for active learning of functions of the gradient of a Gaussian Process, which is motivated by the use of the gradient for global sensitivity analyses. They provide experimental evidence assessing the relative performance of these acquisition functions.
Strengths... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and helpful suggestions. Here are responses to the weaknesses and questions:
**Weaknesses :**
1. While the variance and IG criteria are known in the literature, formulating them in a computationally efficient and tractable fashion is not straightforward f... | Summary: In this paper, the authors study how to select observation data to improve the efficiency of sensitivity analysis. The focus is on measuring sensitivity through a function’s gradient variability. The authors provide a derivation on gradient variance formulae based on Gaussian process surrogates. Various acquis... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and for recognizing the strengths of our paper. Below we provide answers to your concerns.
**Weaknesses:**
1. Thank you for the suggestion to fill out the matrix of acquisition functions. For the baseline f_{V_r}, since our experiments used noiseless functio... | Summary: The authors develop and compare sequential design criteria for Gaussian-process-based estimation of gradient-based sensitivity metrics to assess the importance of individual variables.
Strengths: The problem of learning variable sensitivities is well-motivated and a matrix of criteria are proposed to address ... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and for supporting our paper. Below we provide answers to your concerns.
**Questions**
1. We agree that adding a general takeaway would be beneficial for practitioners. We will add the following paragraph to the discussion: “Our general recommendation is to... | Rebuttal 1:
Rebuttal: We thank all of the reviewers for their constructive reviews. We have included new results in the rebuttal to address the major questions from each review:
1. Running times (Reviewer uHUu): The table in that review response provides running times for all of the active learning methods, for 7 of t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Generalizing CNNs to graphs with learnable neighborhood quantization | Accept (poster) | Summary: In this paper, the authors introduce a Quantized Graph Convolution Network (QGCNs), which directly extends CNN on GCN by decomposing the convolution operation into non-overlapping sub-kernels. It shows that a QGCN is identical to a 2D CNN layer on a local neighborhood of pixels. Then, they generalize this appr... | Rebuttal 1:
Rebuttal: Responses to listed weaknesses (*W*) and question (*Q*):
*W1* and *Q1*. We agree that we have done an inadequate job at explaining our motivation for this work in the introduction. We will update the introduction and related work sections to clarify our motivation and why we desired to improve on... | Summary: The authors find a new a way to generalize CNN from Euclidean space to graph data. The proposed method has good analogy with CNN's computing pattern, and can be applied to data with/without positional descriptors.
On dataset with positional descriptors, the proposed method is equivalent to SGCN. On dataset wit... | Rebuttal 1:
Rebuttal: Responses to listed weaknesses (*W*) and question (*Q*):
*W1.* Tables 3 and 4 in the paper record the performance of QGRN as compared to similar sized models in the GNN literature. A singular simple architecture subsuming these layers were trained across different hyperparameter ranges, as docume... | Summary: In this paper, the authors present a novel Quantized Graph Convolution Layer (QGCL) that extends the benefits of CNNs’ strong local inductive bias to graphs. The authors have shown that embedding a QGCL within a residual network architecture give state-of-the-art results on benchmark graph datasets.
Strengths... | Rebuttal 1:
Rebuttal: Responses to listed weaknesses (*W*) and question (*Q*):
*W1.* Thank you for highlighting this. We kindly refer to the related response given in the general rebuttal section, as this was a repeated concern for most reviewers. Thank you.
*Q1.* Runtime efficiency is indeed determined partly by the... | Summary: In this paper, the authors propose Quantized Graph Convolution Networks (QGCN), a GCN framework that directly extends CNNs by decomposing convolution operations into non-overlapping sub-kernels. This paper demonstrate that QGCN is essentially the same as a 2D CNN layer in dealing with pixel local neighbourhood... | Rebuttal 1:
Rebuttal: Responses to listed weaknesses (*W*) and question (*Q*):
*W1*. Excellent point. To address other important graph tasks, in addition to adding the SVAE example above we now run several node classification tasks on multiple types of datasets, mostly citation networks (like Cora, PubMed), Wikipedia ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for dedicating their time and providing high quality feedback on our manuscript.
One concern noted in various ways by the reviewers was that the computational overhead of QGCNs and QGRNs is high. We agree that this is a limitation of our approach as currently ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On Weak Regret Analysis for Dueling Bandits | Accept (poster) | Summary: The paper presents an analysis of weak regret in the context of dueling bandits, addressing the challenges posed by the non-linearity of weak loss. The authors introduce two algorithms: WR-TINF, which employs a reduction scheme to multi-armed bandits (MAB) and improves upon state-of-the-art methods, and WR-EXP... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback.
We thank the reviewer for pointing out minor issues and typos, we will correct them accordingly.
## Questions:
1. Indeed, the SST assumption is unnecessarily strong on Line 122, the total order assumption is sufficient.
2. **On using a partial de... | Summary: The authors introduce two new algorithms for weak regret minimization in the setting of $K$-armed dueling bandits in order to demonstrate how the optimal strategy changes depending on how the victory probabilities of the Condorcet winner compare to the victory probabilities of the arm most likely to beat each ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback.
## Weaknesses:
**Quality:** We performed additional experiments using the MBTW algorithm (from [1] below) under the same scenarios outlined in our Numerical Simulations section. The simulation results are presented in Figure 1 of the global rebutt... | Summary: This paper addresses weak regret minimization. The authors demonstrate a lower bound result in terms of gaps between the Condorcet winner and the sub-optimal arms. Furthermore, they propose the WR-TINF algorithm, which achieves this optimal regret when the optimality gap is sufficiently large. Additionally, th... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback.
## Weaknesses:
1. We apologize for the typos and the out-of-range equations. We will correct the typos we have identified as well as those noted in the reviews.
## Questions:
1. **On the correctness of Equation 8:** Both the statement of Lemma B... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their important feedback. As suggested by reviewer xTXf, we conducted experiments including the Modified Beat The Winner (MBTW) algorithm from [1]. While its performance is good in some scenarios (comparable to WR-EXP3-IX and WR-TINF), it suffers from instability in othe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Breaking the curse of dimensionality in structured density estimation | Accept (poster) | Summary: This paper proposed a new summary of graph, called graph resilience, which measures the complexity of density estimation. This concept is different from more its well-known counterparts, including rank, sparsity, manifold dimension etc. Quite a few concrete examples of graph along with their resilience are giv... | Rebuttal 1:
Rebuttal: __The concept is beautiful, but I am curious how the authors come up with the definition. Is there is any clear intuition to define it, or the authors start from the proof of the density estimation rate and then identify this key quantity?__
At a high level, a disintegration outlines a method to ... | Summary: The paper studies the problem of estimating a multivariate density, assuming that the density is Markov w.r.t an underlying undirected graph $G$. It is shown that the sample complexity for estimating such a density scales with the resilience of the graph, as opposed to the dimension d. Several examples of G a... | Rebuttal 1:
Rebuttal: __”It would have been helpful to provide a proof-sketch of the main theorems in the main text…”__
We are happy to incorporate this into the final draft.
__”It would have also been useful to provide some simulation results on synthetic data as this would have helped empirically validate the theor... | Summary: The authors, through the formalism of graph models for probability density functions, identify the “graph resilience” as a key quantity to estimate the sample complexity of computing the density.
Strengths: The work is original and technically sound, claims are generally well supported (with one exception tha... | Rebuttal 1:
Rebuttal: __“The only claim that I consider not well supported is related to lines 160-161, more concretely to the lower dimensional manifold. In order to support this claim, the authors should prove this claim by finding an example where the resilience r is lower than the dimension d and that cannot be map... | null | null | Rebuttal 1:
Rebuttal: # General Author Response
We thank the reviewers for their thoughtful reviews.
Overall the response from the reviewers was quite positive:
__Presentation:__
* HdwX: "The paper is written very well with a clear exposition which makes it easy to follow."
* yaps: "The definition is clear, esp Fig... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ReMAP: Neural Model Reprogramming with Network Inversion and Retrieval-Augmented Mapping for Adaptive Motion Forecasting | Accept (poster) | Summary: This paper present a model reprogramming-based strategy, ReMAP, to repurpose a foundation model, tasked with forecasting joint motion, pretrained with able-bodied (source) subjects for amputee (target) subjects. The proposed method incorporates network inversion and retrieval-augmented mapping to identify the... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the impact of our work. Your questions and constructive suggestions have helped us improve its clarity. Below, we provide detailed answers to your comments point-by-point. Due to the rebuttal limit of 6K characters, we move some answers under the "official comment" tab,... | Summary: ### Summary
The author(s) introduce a neural model reprogramming based approach for motion signals based time series forecasting, which is using input alignments method to match target low-resource patient data to the pre-trained models with better generalization.
The proposed alignment method mainly charac... | Rebuttal 1:
Rebuttal: Thank you very much for your positive and thoughtful review. We appreciate your recognition of the impact of our work, your acknowledgment of our work as interesting to the NeurIPS community, and your detailed feedback on areas for improvement. Below, we address each of your review comments. Due ... | Summary: This paper introduces ReMAP, a novel approach for adapting motion prediction models originally trained on able-bodied individuals to predict joint motion in limb-impaired patients, particularly those with below-knee amputations. The key innovation is the use of neural model reprogramming, which allows the adap... | Rebuttal 1:
Rebuttal: We wholeheartedly thank you for the positive evaluation of our work and the insightful feedback on our manuscript. We are happy that you recognize the value and motivation behind our proposed method, as well as the meaningful implementation variants and baselines. Your positive remarks are encoura... | Summary: The paper proposes a novel method, ReMAP, to address the challenge of motion prediction for individuals with limb loss, particularly in scenarios with limited data. The authors introduce a model reprogramming strategy that leverages deep learning's ability to adapt to new tasks without altering model parameter... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our manuscript and provide insightful feedback and suggestions to improve it. Below, we address your comments point-by-point. Please let us know if you have further questions and we will be happy to address them. Due to the rebuttal limit of 6K c... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Retrieval-Augmented Diffusion Models for Time Series Forecasting | Accept (poster) | Summary: This paper proposed Retrieval-Augmented Time series Diffusion (RATD) model for complex time series forecasting tasks, and designed an Extra Reference Modulated Attention (RMA) module to enable the guidance from the retrieved reference time series during the denoising process of the diffusion models. On five re... | Rebuttal 1:
Rebuttal: *Thank you very much to reviewer zh2V for the careful reading and consideration, as well as for acknowledging the innovative aspects of our approach* .
**Q1**: I am wondering whether there is any previous work on time series RAG?
**A1**: In the related work section, I mentioned that previous stu... | Summary: This paper introduces the Retrieval-Augmented Time series Diffusion model (RATD) to address the limitations of existing time series diffusion models, such as insufficient datasets and lack of guidance. RATD consists of two components: an embedding-based retrieval process and a reference-guided diffusion model.... | Rebuttal 1:
Rebuttal: *Thank you very much to reviewer FgUZ for recognizing our proposed method. Following your suggestion, we have conducted additional experiments and provided further explanations of the model's structure. We hope to receive your approval.*
**Q1**: The experimental setup and comparisons are insuffic... | Summary: This article proposes a retrieval-augmented diffusion model for time series prediction, featuring a simple and straightforward approach. It aims to address two key issues:
1. The lack of semantics and labels in time series data, leading to insufficient guidance during the diffusion model's generation process... | Rebuttal 1:
Rebuttal: *Thank you very much to reviewer uLZU for acknowledging some advantages of our proposed method. We understand you may have concerns regarding the overall novelty of the entire paper. We will address this point.*
**Q1**: The paper lacks innovation as the retrieval-enhanced diffusion method has alr... | Summary: Existing time series diffusion models are unstable due to insufficient datasets and lack of guidance. The RATD model combines an embedding-based retrieval process with a reference-guided diffusion model to improve stability and accuracy. RATD retrieves relevant time series from a database to guide the denoisin... | Rebuttal 1:
Rebuttal: *We thank Reviewer AGBG for the thorough and valuable feedback. We are glad that the reviewer found that the proposed model is effective and our paper is easy to read. The main concern of the reviewer is that the architecture of RATD lacks justification. Please see below for our responses to your ... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for the thorough reviews and valuable feedback. We are glad to hear that the idea is novel or innovative (Reviewer HJsX, AGBG, and FgUz ), this paper is well-written and easy to follow (Reviewer HJsX, AGBG, uLZU and hz2v), and provide clear explanations for the... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes a retrieval-augmented time series diffusion model that uses an embedding-based retrieval process and a reference-guided diffusion model. The proposed RATD retrieves relevant time series from an external database as references, which is later utilized to guide the denoising process of the di... | Rebuttal 1:
Rebuttal: *We thank Reviewer HjSX for the thorough and valuable feedback. We are glad that the reviewer found that the proposed idea is novel. The reviewer's main concern is the use of pre-trained models. Please see below for our responses to your concerns.*
**Q1**: The additional complexity and computati... | null | null | null | null | null | null |
Collision Cross-entropy for Soft Class Labels and Entropy-based Clustering | Reject | Summary: Soft labels are often used to represent ambiguous/noisy/uncertain targets in classification, particularly in self-labelled clustering, where pseudo-labels are estimated together with model parameters.
The authors propose an alternative to Shannon cross-entropy for a loss term, called the collision probability.... | Rebuttal 1:
Rebuttal: **Weakness 1: conceptually, the contributions are rather limited**\
To the best of our knowledge, collision cross entropy (9) is a new concept if the focus is on "collision", though collision entropy (6) is standard. Note that Renyi's work generalizes entropies and divergences, but not cross-entro... | Summary: The paper introduces the concept of collision cross-entropy (CCE) as an alternative to Shannon's cross-entropy (SCE) for self-labeling in the context of unsupervised and semi-supervised learning. The primary motivation is to address the limitations of SCE, especially its sensitivity to label noise and uncertai... | Rebuttal 1:
Rebuttal: **Weakness 1: it is not self-evident to me that the properties of the loss function translate into necessarily better properties for models, both as a function for training a classification model directly or for clustering.**\
Probably the clearest evidence that CCE is a better loss for learning f... | Summary: The paper focuses on the choice of the loss function in problems with soft distributions of the labels, in particular in the context of pseudo-labeling for unsupervised or self-supervised problems such as clustering. In sections 1-2 the paper gives a thorough review of existing practices and relevant theoretic... | Rebuttal 1:
Rebuttal: **Weakness 1: is it the new loss or EM algorithm**
Standard cross-entropy loss is used in [16] in the context of self-labeling (also using a specialized solver, only for efficiency due to convexity). Standard cross-entropy is used without self-labeling in [3], which is evaluated in [16] and some ... | Summary: This paper studies the loss function for soft class labels and entropy-based clustering. In particular, it introduces a new loss function called 'collision cross-entropy' as an alternative to Shannon's cross-entropy when class labels are represented by soft categorical distributions. The motivation for this ne... | Rebuttal 1:
Rebuttal: **Theory-1**\
We provide theoretical motivation for (9) on lines 184 - 192. It is a loss maximizing the probability of collision (equality) between two random variables: unknown true class (represented by solf-label distribution) and predicted class (represented by soft-max prediction/distribution... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Adversarial Representation Engineering: A General Model Editing Framework for Large Language Models | Accept (poster) | Summary: The paper proposes an Adversarial Representation Engineering (ARE) framework to address the challenge of editing Large Language Models (LLMs) while maintaining their performance.
The authors introduce the concept of Representation Engineering (RepE) and extend it by incorporating adversarial learning.
The ... | Rebuttal 1:
Rebuttal: Dear reviewer Ga6W,
We truly appreciate your valuable and constructive comments. We prepared a detailed response to address your concerns in the following.
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**W1**: The reliability of the concept discriminator should be evaluated. I think conducting some human annotations would be beneficial... | Summary: This paper addresses the challenge of understanding and controlling the internal mechanisms of Large Language Models. It proposes a novel Adversarial Representation Engineering (ARE) framework that leverages representation engineering and adversarial learning techniques. The proposed framework aims to provide ... | Rebuttal 1:
Rebuttal: Dear reviewer Nm4E,
We truly appreciate your valuable and constructive comments. We prepared a detailed response to address your concerns in the following.
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**W1**: The paper does not provide extensive discussion on the scalability of the proposed method for extremely large models (larger th... | Summary: This paper explores how to use representation engineering methods to guide the editing of LLMs by deploying a representation sensor as an oracle. The authors first identify the importance of a robust and reliable sensor during editing, then propose an Adversarial Representation Engineering (ARE) framework to ... | Rebuttal 1:
Rebuttal: Dear reviewer w6mA,
We truly appreciate your valuable and constructive comments. We prepared a detailed response to address your concerns in the following.
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**W1**: The technical novelty is somewhat incremental, as the proposed approach can be regarded as applying adversarial training to rep... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Weak-to-Strong Search: Align Large Language Models via Searching over Small Language Models | Accept (poster) | Summary: In this paper, they frame alignment as a decoding time problem, allowing the large language model to be frozen. They do this by parametrizing a reward function with the difference in the log-likelihood between small untuned and tuned language models. Interestingly, their approach does not require shared vocabu... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. To address your questions:
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> Ｗ1: Although there’s a related work section, I believe the comparison between the proposed method and other methods, e.g., the ones that rely on small language models to aid the alignment of LLMs, should be described in more ... | Summary: The paper introduces the "weak-to-strong search" method for aligning a stronger large language models (LLMs) by leveraging two weaker LLMs during test-time without requiring fine-tuning of the large models. This approach aims to improve alignment by maximizing the log-likelihood difference between tuned and un... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and we're glad you enjoyed the paper. To address your questions:
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> W1: Obtaining a tuned weaker model is still not a very trivial thing, and the performance of $\pi_{\text{base}}$ on downstream tasks could heavily depend on the tuned weaker model as well.... | Summary: The paper proposes a search/decoding method "weak-to-strong search" for improving LLM's performance at test time by leveraging the log likelihood from small language models. Specifically, it utilizes the log likelihood differences between tuned and untuned small models to guide the decoding of larger models. T... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. To address your questions:
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> W1: The paper does not explain clearly the overhead introduced during inference. How does weak-to-strong search impact memory usage and inference speed?
Throughout the paper, we select the hyperparameters for CBS (W, K, L) t... | Summary: This paper addresses the alignment of large language models without the need for fine-tuning. It conceptualizes the alignment process as a search problem, leveraging the log-likelihood difference between small tuned and untuned language models as both a reward and a critic. By transforming a sparse preference ... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. To address your questions:
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> W1: The paper does not include comparisons with existing decoding-based alignment baselines [1,2]
We would like to clarify that a key advantage of our work is its ability to **reuse off-the-shelf language models for test-tim... | Rebuttal 1:
Rebuttal: We are very thankful for the positive feedback from the reviewers. In this supplementary PDF, we include two additional empirical results: Figure 1: **inference costs analyses** [reviewer [mz4P](https://openreview.net/forum?id=dOJ6CqWDf1¬eId=aC7BMDbknK), W1] and Figure 2: **chunk-level PPO abla... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning Optimal Lattice Vector Quantizers for End-to-end Neural Image Compression | Accept (poster) | Summary: This paper proposed to optimize the codebooks of lattice vector quantization (LVQ) for improved rate-distortion performance in neural image compression. Unlike previous LVQ methods using pre-designed codebook structure, the proposed method is able to adaptively learn the optimal codebook structure for nonunifo... | Rebuttal 1:
Rebuttal: - **W1. [Omission of integral operation]**
Thanks for pointing out the lack of rigor in our math notation. Hope you can understand that it is only an innocent omission of an integral symbol.
We will write out, in the final version, the corrected probability mass function (PMF) over the lattic... | Summary: - This paper proposes a method of differential lattice vector quantization for DNN image compression.
- The authors apply BRT [5] to estimate a vector in the basis vectors B that is orthogonal, close to vector v.
- Experiments in Table 1 compare the proposed method with scalar quantizers, and experiments in Ta... | Rebuttal 1:
Rebuttal: - **W1. [Separated evaluation]**
We provide the results which are separately evaluated in the following table (BD-rate over uniform scalar quantizer). It can be observed that the performance trend is consistent across the separate and combined evaluations.
| Entropy Model | Bmshj2018 ... | Summary: In this paper, the use of lattice vector quantization (LVQ) with deep learning-based image compression has been studied. The authors proposed to learn the bases of the LVQ matrix, for changing the quantization cells to better capture feature correlations and thus better coding. The experiments demonstrated sig... | Rebuttal 1:
Rebuttal: - **W1. [Compared with other VQ based image compression methods]**
A good suggestion. We have addressed your concern by adding new comparison results with the following vector quantization-based image compression methods: SHVQ (NeurIPS ’17) [1] and McQUIC (CVPR ’22) [2]. From the provided table ... | Summary: In this paper, the authors proposed learning the lattice vector quantization for the end-to-end neural image compression. Their method is based on the learning the orthogonal basis function, and each vector is represented as the linear combination of the basis function during training, and they used orthogonal... | Rebuttal 1:
Rebuttal: - **W1. [General vector quantization]**
We build the general vector quantization model based on NVTC (CVPR'23) [1].
- **W2. [Entropy model]**
Sorry for the discrepancy. The entropy model is optimized over the lattice quantized vectors and used to code the same vectors into the bitstre... | Rebuttal 1:
Rebuttal: - We would like to thank the reviewers for their valuable feedback, and are encouraged by their positive reception of our work.
- We respond below to specific points raised by each reviewer. We hope we addressed all the reviewers' concerns, and we will be happy to provide additional clar... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
QUEST: Quality-Aware Metropolis-Hastings Sampling for Machine Translation | Accept (poster) | Summary: This paper proposes novel quality-aware sampling for neural machine translation, namely Quality-Aware Metropolis-Hastings Sampling. The main idea is that sampling from a model in proportion to a metric can be seen as sampling from a Gibbs distribution, and Metropolis-Hastings MCMC algorithm can be used for thi... | Rebuttal 1:
Rebuttal: Thank you for your positive and insightful comments. We address your questions below:
> “While you have a comparison on the sampling quality/diversity, I would also be interested in the performance of the translation model on common translation metrics.”
This is a good suggestion. We report add... | Summary: This paper proposes a novel approach called Quality-Aware Metropolis-Hastings (QUEST) Sampling, using a proposal distribution that is compatible with sentence-level metrics. The authors conducted experiments in the machine translation task with four directions En <> {Ru, De} and employed multiple decoder-only ... | Rebuttal 1:
Rebuttal: > “Since LLMs can handle longer text sequences, discussion on how to extend this idea to document-level metrics would be interesting. What kind of challenges would lie? From Section 5.1, QUEST might struggle more as sentence gets longer?”
This is a very good question. Please note that, in Appendi... | Summary: This paper presents a novel approach called QUEST Sampling, designed to generate high-quality and diverse translations in machine translation. The authors proposed methods to obtain high-quality and diverse parallel data and provide an effective way to avoid over-reliance on noisy quality estimates by using th... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback and insightful comments.
> “The authors may consider expanding their experiments to include a wider range of language pairs beyond just German and Russian.”
We agree that expanding the evaluation to more language pairs will strengthen our paper further. ... | Summary: This essay proposes one method to solve the challenge of balancing the generation quality and diversity of machine translation. This essay proposes this problem of sampling a set of high-quality and diverse translations. It is said that this proposed method can lead to high-quality and diverse outputs.
Stren... | Rebuttal 1:
Rebuttal: Thank you for your comments.
> “In fact, the quality of machine translation is good. So, can we induce that your method aims to improve the diversity of mt, which seems not very promising.”
The goal of our paper is to maintain or improve MT quality while increasing the diversity of the genera... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their constructive and helpful feedback. We have attached a pdf to support additional experiments performed during the rebuttal period.
We are glad that the reviewers found our approach to be novel (SYtr), the paper well-written and well-organized (SYtr, DVBo, L9No)... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Constrained Diffusion with Trust Sampling | Accept (poster) | Summary: This paper addresses this limitation by rethinking diffusion without training loss guidance from an optimization perspective. They formulate a series of constrained optimizations throughout the inference process of the diffusion model. In each optimization, they allow the sample to take multiple steps along th... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the performance of our method and raising valuable questions for discussion. Here are our responses to your questions:
1. Re: conditional probability in Eq. (12). The reviewer is correct that equation 12 should indeed take the form of $p(x_0|x’)$ instead of ... | Summary: The paper presents a method to enhance training-free loss-guided diffusion sampling. The key contributions are:
1. Introduction of Trust Sampling: A novel method called Trust Sampling is proposed, which diverges from the traditional approach of alternating between diffusion steps and loss-guided gradient step... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the novelty of our method, technical quality, structure of our paper, and significance of our work, and raising valuable questions for discussion.
Re: more evaluation tasks: the reviewer suggested several good tasks to further test our method: for example ... | Summary: This paper proposes a trust sampling scheme which incorporates given constraints loss function as guidance for constrained generation. This approach conducts early stop while detecting a mismatch between the predicted noises magnitude of sample and the noisy level at the current state manifold at each diffusio... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the significance of our work, noting that our paper is easy to follow, and raising valuable questions for discussion. Here are our responses to your questions:
1. Re: guarantee of constraint satisfaction: the reviewer is correct that our method cannot guaran... | Summary: This paper tackles the task of sampling from diffusion models with additional inference-time constraints. In this setting, synthesis needs to simultaneously follow the diffusion model-defined generative prior as well as a constraint objective. The paper proposes two techniques to achieve this in a robust fashi... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the clarity, technical quality, novelty, and significance of our work, and raising valuable questions for discussion. Here are our responses to the questions:
1. Re: heuristics in the method: our paper is heavily inspired by previous works DPS, DPS+DSG and L... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive feedback. We are encouraged by the reviewers’ recognition that our proposed method addresses the important task of Diffusion generation with inference-time constraints, which is widely applicable across many domains. Additionally, our experimental vali... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Neural Model Checking | Accept (poster) | Summary: The paper introduces a novel approach to hardware model checking using neural networks as proof certificates for linear temporal logic (LTL) specifications. Traditional model checking in electronic design automation (EDA) relies on symbolic techniques like SAT solvers, which are computationally intensive. In c... | Rebuttal 1:
Rebuttal: * Our method is as formally sound as traditional algorithms. We ensure this soundness through an SMT check, which validates that our neural ranking function is valid across the entire state space. Specifically, the SMT query in Eq. (4) represents the negation of the proof rules outlined in Eqs. (1... | Summary: This paper addresses the problem of hardware model checking with respect to LTL specifications. Although this a well-studied problem, hardware model checking can still suffer from scalability issues. A general automata-theoretic approach to model checking is to check if intersection of the formal languages cor... | Rebuttal 1:
Rebuttal: 1 Our work is inspired by neural certificates [25,44,2,58,87], including neural termination analysis. Previous results on neural certificates focus on reachability/termination and avoidance/safety and temporal logic is largely unexplored. We applied neural certificates to LTL model checking and co... | Summary: The paper presents a novel application of machine learning to hardware model checking. The model checking problem is given as a design written in SystemVerilog and a temporal logic property in linear-time temporal logic (LTL). Similar to many classical model checking approaches the authors first construct the ... | Rebuttal 1:
Rebuttal: * LTL model checking of hardware designs is indeed decidable and PSPACE-complete. While it is theoretically possible to achieve completeness by enumerating all transitions and employing a sufficiently large neural network as a look-up table for the entire state space, this approach is impractical ... | Summary: This work incorporates neural networks into the model checking process. Specifically, it learns a neural ranking function on random trajectories of the formal model. The ranking function first achieves zero loss on the training set and then is verified for soundness symbolically using SMT solvers. Evaluated on... | Rebuttal 1:
Rebuttal: 1. Our architecture comprises three main components. The first component is a non-trainable element-wise multiplication layer, designed to normalize the input. The second component is a trainable element-wise multiplication layer, whose purpose is to automatically focus attention on those inputs ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their comments, suggestions and questions, which we will address in the final version as we discuss in this rebuttal. We stress the novelty of our contribution, which introduces a new approach to model-checking temporal logic based on neural certificates. We demonstrated... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Dissecting Query-Key Interaction in Vision Transformers | Accept (spotlight) | Summary: The paper proposes SVD as a way for analyzing the interaction between the Key and Query vectors within the self-attention architecture. To this end, it measures the cosine similarity between the left and right eigenvectors of the attention score. The proposed approach evaluates the proposed mechanism on differ... | Rebuttal 1:
Rebuttal: Thank you for your review and questions. We are glad you found our approach and results interesting. We believe the points you raised are either about clarifying questions or comments that do not justify the score of 3. We reply to all your questions below.
> The paper seems to be written in a ha... | Summary: While previous studies on vision transformers focused on how self-attention groups relevant tokens, this paper analyzes how self-attention contextualizes tokens to understand comprehensive inter-token relationships across the entire image. To this end, this paper proposes using the Singular Value Decomposition... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and suggestions.
> W1. Familiar conclusion…
Thank you for the references. We will include them in the “Related work” section. Our emphasis on the explainability as you say, and also on analyzing feature interactions via the query and key modes, adds an impor... | Summary: The paper begins with the observation that self-attention in early layers of vision transformers tend to group similar objects while deeper layers focus more on gathering features from dissimilar objects or background. The paper then delves into the mathematical formulation of the attention mechanism, and reve... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and suggestions.
> The behaviour of deeper layers, i.e., tokens attending to dissimilar tokens, can use some more analysis…
Thank you for this excellent suggestion. During the short rebuttal time frame, we conducted a preliminary test on your suggested experi... | Summary: This paper proposes a new analysis framework to dissect the potential bottom mechanism of query-key interactions in Vision Transformers (ViTs) from the perspective of singular value decomposition. Several phenomena are presented via extensive quantitative and qualitative results, which leads to the basic conc... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and suggestions.
> 1.Analysis conducted in this work is mainly restricted to the ImageNet dataset…
We are interested in applying this approach to more complex visual scenes and other different datasets and domains in the future.
> 2.The ViT models investigat... | Rebuttal 1:
Rebuttal: Dear Reviewers and AC,
Thank you for your helpful reviews. We appreciate that reviewers thought our SVD approach for finding query-key interactions is novel/interesting, and that our methodology has applicability to other domains and applications.
We appreciate the thoughtful suggestions. In res... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MetaAligner: Towards Generalizable Multi-Objective Alignment of Language Models | Accept (poster) | Summary: Multi-objective alignment of language models is a significant topic for LLM community, while many prior work are either costly-to-compute or policy-dependent, restricting the further depolyment. This work is aimed at providing a retraining-free and policy-agnostic approach for multi-objective alignment, namely... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and recognizing its strengths. We hope the following responses can help address the concerns.
**Weakness 1**: MetaAligner has a simple methodology. It triggers 2 novel capabilities:
1. instance-level alternation of the alignment objectives without re-training;
... | Summary: The author proposes Meta-Objective Aligner (MetaAligner), the first policy-agnostic and generalizable method for multi-objective preference alignment.
Strengths: As a lightweight algorithm, this is particularly meaningful as model parameters continue to grow. Conditional weak-to-strong correction extends weak... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for reviewing our paper and recognizing the strengths of our work. We also thank the reviewer for willing to consider raising the scores. The following parts contain our point-to-point responses to the weaknesses and questions. We hope they can help address the revi... | Summary: This work proposes MetaAligner, a plug-and-play multi-objective alignment method that can generalize to unseen objectives.
Strengths: 1. This is a well-written paper that clearly expresses its core ideas.
2. MetaAligner is a lightweight alignment method that is easier to tune in multi-objective scenarios, wit... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for reviewing our paper, which provides careful assessments and valuable comments. We also thank the reviewer for recognizing the strengths of our work. The following parts contain our point-to-point responses to the weaknesses and questions. We hope they can help a... | Summary: This work extends Aligner to multi-objective alignment scenarios. The main contribution is adding a textual description to each sample in the existing preference datasets to indicate the reason for the preference between chosen and rejected samples. The authors found that MetaAligner is more efficient than pre... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for reviewing our paper and providing valuable comments. The following parts contain our point-to-point responses to the weaknesses and questions. We hope they can help address the reviewer's concerns.
**Response to Weakness 1**: We'd like to clarify our novelty an... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards the Dynamics of a DNN Learning Symbolic Interactions | Accept (poster) | Summary: This paper studies the training dynamics (underfitting to overfitting) of deep neural networks via the perspective of symbolic interactions. They formulate the learning of interactions as a linear regression problem on a set of interaction triggering functions. They show the two-stage dynamics. In the first st... | Rebuttal 1:
Rebuttal: Thank you for your comments. We are glad to answer all your questions. **If you have new questions, please let us know as soon as possible.**
**Q1: Ask for further clarification on the paper's contribution.**
> The contribution is not fully clear. Since this work heavily relies on [26] [27] [45]... | Summary: This study investigates the two-phase dynamics of DNNs learning interactions during training, demonstrating that DNNs initially focus on simpler, low-order interactions and progressively transition to more complex, high-order interactions. The learning process is reformulated as a linear regression problem, wh... | Rebuttal 1:
Rebuttal: Thank you for your comments. We are glad to answer all your questions. **If you have new questions, please let us know as soon as possible, so that we can try our best to answer any further questions in the discussion period.**
**Q1: Ask for experiments on more textual datasets.**
> The experime... | Summary: The paper investigates the two-phase dynamics of interactions during training by reformulating the learning of interactions as a linear regression problem. The authors provide an analytic solution to the minimization problem and use this solution to explain the two-phase dynamics of interactions.
Strengths: T... | Rebuttal 1:
Rebuttal: Thank you for your great efforts on the review. We will answer all your questions. **If you have new questions, please let us know as soon as possible. Thank you.**
**Q1:** "Presentation of the paper is difficult to follow ... particularly in summarizing relevant literature and explaining the mat... | null | null | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for the constructive comments and questions. We have carefully considered all your comments and answered all the questions, and will revise the paper to clarify all your concerns. In addition, we have followed your suggestions to conduct **new experiments**, pl... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
UMFC: Unsupervised Multi-Domain Feature Calibration for Vision-Language Models | Accept (poster) | Summary: To boost the transferability of CLIP across downstream domains, the paper proposed Multi-Domain Feature Calibration (UMFC). By mitigating CLIP biases in both visual and text encoders, UMFC significantly improves classification performance over existing methods across 3 downstream tasks.
Strengths: 1. The obse... | Rebuttal 1:
Rebuttal: **W1: The proposed methods rely on multi-domain data for calibration, which slightly weakens its application scenarios.**
(1) In fact, our method is not limited to multi-domain scenarios; it is also applicable to single-domain scenarios. In single-domain case, we employ the same calibration proce... | Summary: This paper proposes a training-free method "Unsupervised Multi-domain Feature Calibration ( UMFC)" to mitigate the model bias problem presented in CLIP models. The paper first observes the bias problem from the visual encoder and the text encoder perspective. Then it mitigates the problem by subtracting the bi... | Rebuttal 1:
Rebuttal: **W1: I think the improvements seem minor. In table 1, compared to the really easy approach CLIP-D, UMFC fails to improve it and UMFC + CLIP-E outperforms it by only 0.65% points. What about CLIP-D + CLIP-E? I think you should use this one to agast UMFC + CLIP-E.**
There are some misunderstandin... | Summary: This paper identifies the inherent model bias within CLIP, notably in visual and textual encoders. To mitigate the bias, authors propose a feature calibration method, termed Unsupervised Multi-domain Feature Calibration. Experiments in the setting of transductive learning and test-time adaptation show the effe... | Rebuttal 1:
Rebuttal: **W1: CLIP's ability to encode domain information is limited to specific domains.**
This is a valuable question. We would like to clarify that the feature bias of CLIP is **not** limited to some specific domains, but is a general issue.
- **Explanation of Figure 1(b):** We use t-SNE for visualiz... | Summary: **I am not an expert in this domain. So my review may not be informative.**
The paper introduces Unsupervised Multi-domain Feature Calibration (UMFC), designed to improve the adaptability of Vision-Language Foundation Models like CLIP to various downstream tasks across multiple domains using unlabeled data. T... | Rebuttal 1:
Rebuttal: **W1: While effective, the calibration process might introduce additional complexity in terms of understanding and implementing the recalibration mechanisms, particularly the calculation and subtraction of domain-specific biases.**
In fact, our calibration method is straightforward and computatio... | Rebuttal 1:
Rebuttal: ### **R1. Disadvantages of CLIP-D.**
CLIP-D serves as a comparison baseline by incorporating the domain names of test samples into its prompts. While CLIP-D demonstrates better performance compared to the CLIP model in Table 1, it has several notable drawbacks:
- **Dependency on Domain Labels and... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper attempts to address the problem of domain gap in existing VLMs. Based on an unlabelled mixed dataset, this paper first proposes to fit each different domain and remove the domain bias of image features by Gaussian mixture model. Furthermore, the domain bias of text features is similarly removed by t... | Rebuttal 1:
Rebuttal: **My major concern is whether the proposed methodology is a real improvement over CLIP-D (e.g., 61.68 vs. 61.03 in unsupervised calibration), considering the requirement for extra computational overhead and unlabeled dataset.**
Thanks for this question, and I would like to clarify this point in t... | null | null | null | null | null | null |
From Linear to Linearizable Optimization: A Novel Framework with Applications to Stationary and Non-stationary DR-submodular Optimization | Accept (poster) | Summary: This paper studies online optimization with upper linearizable/quadratizable functions which are a new class of objectives considered in this field. This class extends concavity and DR-submodularity in various settings, including monotone and non-monotone cases over different types of convex sets. A general me... | Rebuttal 1:
Rebuttal: Thank you for your review.
W1. Our notion of linearizable/quadratizable functions generalizes both DR-submodularity and convexity. There are many applications for DR-submodular maximization.
Example applications include experimental design [7], resource allocation [Designing smoothing functions ... | Summary: A new class of functions are introduced: upper linearizable/quadratizable functions, which extend concavity and DR-submodularity. It is shown how to apply algorithms for linear / quadratic maximization to this class, which offers a unified approach to DR-submodular optimiziation problems. The abstract framewor... | Rebuttal 1:
Rebuttal: Thank you for your review.
We will correct the typos in the final version.
Q1. Currently, there is no result showing linearizability of non-monotone functions over downward closed sets, with a better approximation coefficient/sample complexity than that can be specialized from the results of gen... | Summary: The paper introduces a novel notion of upper linearizable / quadratizable functions. Using this notion of functions, the paper proposes a general meta algorithm that extends certain online linear / quadratic maximization algorithms to handle upper linearizable / quadratizable function classes. This general met... | Rebuttal 1:
Rebuttal: Thank you for your review.
We will expand the explanations for each theorem to make the core ideas more clear.
We will also expand section 6 to include more explanations and intuition about the applications in the final version.
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Rebuttal Comment 1.1:
Comment: Thank you for your reply. I mai... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Thinking Forward: Memory-Efficient Federated Finetuning of Language Models | Accept (poster) | Summary: This paper introduces SPRY, a federated learning (FL) algorithm designed to finetune large language models (LLMs) on resource-constrained devices by addressing the excessive memory requirements of traditional backpropagation methods. SPRY tackles the challenge of high memory usage from intermediate activations... | Rebuttal 1:
Rebuttal: ### **W1. Scalability to a larger number of clients**
Please refer to our answer to reviewer KHem under “W2. Impact of the number of clients on performance”.
___
### **W1. Communication and computational overheads**
For communication and computational overheads, please see “1. Communicatio... | Summary: This manuscript is focused on the memory-efficient federated finetuning of LLMs. The author first uses Forward-mode Auto-Differentiation to reduce memory. Then, the author observes that merely substituting backpropagation with Forward-mode AD in FL scenarios often results in poor accuracy and computational ine... | Rebuttal 1:
Rebuttal: ### **W1. Computational load of Forward-mode AD and**
### **Q1. Computational overhead**
Compute cost of each client and the server is given in “2. Computation costs” under the global “Author Rebuttal”. We also state time per iteration cost here followed by the result analysis:
The computational... | Summary: This paper introduces a forward-mode AD federated learning algorithm (SPRY). They use SPRY to finetune LLMs and demonstrate a low memory footprint compared to backpropagation-based federated learning algorithms. The authors also derive SPRY’s convergence rate and provide theory behind why SPRY’s global gradien... | Rebuttal 1:
Rebuttal: # W1. Comparison to FwdLLM
FwdLLM[1] shows the equation of finite difference (which involves only function evaluation and no gradient computation) in their paper’s Eq1. And their experiment scripts[2] refer to the user of finite differences as well. Hence, we categorized FwdLLM as a zero-order me... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their suggestion on adding information on communication and computation costs, and we will update the manuscript with detailed explanations of the following:
____
### **1. Communication overhead**
Table 1 of the PDF attached to this response illustrates communication co... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making | Accept (poster) | Summary: The paper proposes a specialized multi-LLM-agent composition for (1) stock trading and (2) portfolio management. The authors collect historical multimodal data up to 10 years to evaluate their setup. Inspired by the real world financial institution structure, the authors instruct the 8 specialized agents to pe... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's feedback. In response to the identified weaknesses and limitations:
**[W1: Experimental results] We have updated the experimental results to include other three stocks.**
We have updated the experimental results to include other three stocks including AAPL, NIO and A... | Summary: The research introduces FINCON, an LLM-based multi-agent framework designed for a variety of financial tasks. FINCON is inspired by effective organizational structures in real-world investment firms and employs a manager-analyst communication hierarchy. Experimental evaluations show that FINCON’s risk control ... | Rebuttal 1:
Rebuttal: We thank the reviewer for your appreciation of our work, particularly regarding the innovative nature and efficacy of the FINCON framework. The reviewer's concerns about FINCON's weaknesses and limitations are carefully explained and addressed below.
**[Weakness: Transparency] The multi-agent int... | Summary: The study introduces FINCON, a large language model (LLM)-based multi-agent framework designed to improve financial decision-making, where it utilizes a manager-analyst communication hierarchy to enhance the synthesis of multi-source information and optimize decision-making outcomes through a risk-control com... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the novelty and soundness of our approach. To address your mentioned weakness and questions:
**[W: Ethical concerns and market impact] Clarification about Ethical Concerns & Impact on Market Dynamics**
1. **Regarding Data:**
We fully respect the copyright ... | null | null | Rebuttal 1:
Rebuttal: We sincerely appreciate all reviewers for their time and insightful feedback. We are glad that many reviewers found that:
**Our framework is novel and our design of risk-control component is unique.**
- [bNfR] The introduction of FINCON presents a novel approach ... for financial decision-makin... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Unified Framework for 3D Scene Understanding | Accept (poster) | Summary: The paper proposes a unified 3D segmentation frame work for six 3D segmentation tasks. It enhance the performance through building the inter-task connections. The model could achieve the state-of-the-art performance in individual tasks even comparing to the models specialized for individual tasks.
Strengths: ... | Rebuttal 1:
Rebuttal: Thanks for providing feedback and taking the time to review our work!
- **To Weakness 1**: “The writing needs improvement...”
**Reply:** Thank you for carefully reading this paper. We apologize for any confusion regarding notations. Specifically, $K_{v}$ refers to the length of the class name vo... | Summary: This work proposes UniSeg3D, a framework to unify 3D point cloud segmentation tasks. Compared to previous work that unifies 3 tasks, UniSeg3D additionally incorporates interactive segmentation, text-referring segmentation, and open-vocabulary segmentation. In total, six tasks are unified in a single Transforme... | Rebuttal 1:
Rebuttal: Thanks for providing feedback and taking the time to review our work!
- **To Weakness 1 & 2**: “Marginal performance gain...” / “Additionally, in Table 6, unifying…”
**Reply:** Good question! Unifying the tasks into a single model could save computation consumption and benefit real-world applica... | Summary: This paper proposes UniSeg3D, a unified framework for six 3D segmentation tasks that achieves SOTA results on the six tasks. The authors propose to use knowledge distillation and ranking-based contrastive learning to enhance inter-task knowledge sharing and the overall performance. Extensive experiments are do... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback!
- **To Weakness 1**: “Since the feature for visual prompt is sampled from the superpoints, the quality of the visual prompt significantly influence the overall performance of the model.”
**Reply:** Thanks. For the interactive segmentation task, it is intuitiv... | Summary: For the first time, this work proposes a unified model for several point cloud segmentation tasks, including panoptic, semantic, instance, OV, interactive, and referring segmentation. This work uses the typical query-based transformer perception architecture with the proposed knowledge distillation losses for ... | Rebuttal 1:
Rebuttal: Thanks for providing feedback and taking the time to review our work! **We promise that the training/inference codes, logs, and checkpoints will be released**.
**Weaknesses:**
- **To Weakness 1**: “The main weakness of this work is that the proposed architecture is widely used for multi-modal pe... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We sincerely thank the reviewers for their thoughtful comments and feedback. We are encouraged that the reviewers appreciate the simple, novel architecture and insightful module of UniSeg3D, solid experiments of the proposed method, well-written of the paper. We provide detailed r... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
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