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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion Models | Accept (spotlight) | Summary: This paper is concerned with estimating the local intrinsic dimension (LID) of a given data manifold. Intuitively, high intrinsic dimension indicates more complex data distribution and an accurate representation of the intrinsic dimension has useful applications in ML such as detecting outliers or adversarial ... | Rebuttal 1:
Rebuttal: We greatly appreciate your positive feedback and are delighted that you consider our work to be "an excellent and well-executed effort." In response to your questions and suggestions:
1. Thank you for the great suggestion! We will make sure to include a reference to the textbook “A Course in Metr... | Summary: This paper proposes a novel method for estimating local intrinsic dimension using an existing approximate likelihood estimator with diffusion models. The proposed approach is able to exploit a pre-train diffusion model to evaluate the log density function of the noisy version of the data (i.e., adding Gaussian... | Rebuttal 1:
Rebuttal: We appreciate your positive review and are pleased that you find our paper well-organized and clearly written. In response to the identified weaknesses and questions, we have addressed them as follows:
## Weakness: Including time-complexity comparisons
In the general rebuttal section, we provide... | Summary: The authors propose to employ the best available generative models, i.e., diffusion models, to the estimation of local intrinsic dimension. To this end, they build upon the LIDL estimator of Tempczyk et al. [ICML, 2022], but crucially resolve a number of limitations: (1) direct application of LIDL requires tra... | Rebuttal 1:
Rebuttal: We highly appreciate your comprehensive review, and we are grateful that you recognize the significant achievements of our work despite some concerns. Before addressing your points in detail, we would like to clarify a potential misunderstanding regarding the UNet vs. MLP issue. While certain patt... | Summary: The paper addresses the challenge of estimating the local intrinsic dimension (LID) of high-dimensional data, a measure reflecting the number of local factors of variation and data complexity. Traditional methods for LID estimation have limitations such as inaccuracy, high computational demand, and dependency ... | Rebuttal 1:
Rebuttal: We thank you for the positive feedback and the insightful comments. If the following responses satisfactorily address your concerns, we kindly request you consider raising your score!
## Weakness 1
Thank you for bringing this concern to our attention. We have provided a clarification for Section... | Rebuttal 1:
Rebuttal: We greatly appreciate the time reviewers have spent on our paper and are delighted to see that all four reviewers recommended acceptance. Reviewers found our method “well-principled” and “clearly written” (**MJca**, **9X9u**, **MdUJ**) and described it as “a really excellent and well-executed effo... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Neural P$^3$M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs | Accept (poster) | Summary: The accurate modeling of both short-range and long-range interactions in molecular systems is crucial for predicting properties like molecular energies and forces with high precision. However, traditional Geometric Graph Neural Networks (GNNs) fail to capture such interaction. The paper introduces Neural $\tex... | Rebuttal 1:
Rebuttal: We thank you for your recognition of our work’s contribution and clear organization. We will address your questions and concerns as follows.
**Weakness 1, Ablation Study of Atom2Mesh & Mesh2Atom and Efficiency of FFT**
We provide additional ablation studies on the impact of Atom2Mesh & Mesh2Atom... | Summary: This work introduces a long-range focused GNN that utilizes the combination of atom and mesh representations. The mesh framework in this work is trainable and unconstrained to the fragmentation algorithm. Results demonstrate superior performances across MD22, Ag, and OE62 datasets.
Strengths: - The need for l... | Rebuttal 1:
Rebuttal: We thank your recognition of our work’s novelty, contribution and clear organization. We will address your questions and concerns as follows.
**Weakness 1, Evaluation & Improvement**
We resolve the issues point-to-point in the **Questions** Section.
**Weakness 2, Anonymized Code**
We intend to... | Summary: The paper introduces Neural P3M, a framework designed to enhance geometric GNNs by incorporating mesh points alongside atoms and transforming traditional mathematical operations into trainable components. The mesh representations offers discrete resolutions necessary for formulating long-range terms. The Neura... | Rebuttal 1:
Rebuttal: We thank you for your recognition of our work’s novelty and contribution to the field of molecular modeling. We will address your questions and concerns as follows.
**Weakness 1, Mathematical Details**
Thank you for your suggestions. We have relocated some non-essential content from Sections 2 a... | Summary: This work proposes Neural P3M, a framework that enhances geometric GNNs by integrating mesh points and leveraging Fast Fourier Transform (FFT) for efficient computation of long-range interactions. The framework includes short-range and long-range interaction modeling and enables the exchange of information bet... | Rebuttal 1:
Rebuttal: We thank you for your recognition of our work’s superior performance and clear organization. We will address your questions and concerns as follows.
**Weakness 1, Novelty & Contribution of Neural P$^3$M**
While it's true that FFT is commonly employed in traditional chemical computations, as disc... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude to all the reviewers for dedicating their time to read our manuscript and for offering their valuable suggestions. We appreciate the recognition our manuscript has received from the reviewers. We have also addressed each concern raised on a point-by-p... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Chimera: Effectively Modeling Multivariate Time Series with 2-Dimensional State Space Models | Accept (poster) | Summary: The paper presents Chimera, a novel two-dimensional State Space Model (SSM) that can effectively model multivariate time series data. The model is designed to address several key challenges in multivariate time series modeling, including nonlinear dynamics along the temporal dimension, inter-variate dependenci... | Rebuttal 1:
Rebuttal: Thank you so much for your time and constructive review. We really appreciate it. Please see below for our response to your comments:
> *About several obscure writing …*
**Response:**
Thank you for bringing this to our attention. We agree with the reviewer that using a better notation can improv... | Summary: This paper proposed to use a state space model for time series modelling. Instead of using the SSM along the time dimension, the authors also have the space updated along the variables dimension, which makes the established both inter- and intra- variable dependencies.
Strengths: - The motivation is clear and... | Rebuttal 1:
Rebuttal: Thank you so much for your time and constructive review. We really appreciate it. Please see below for our response to your comments:
> *For forecasting tasks, the way the authors conducted evaluation metrics does not reflect the model performance for the target horizon …*
**Response:**
In our e... | Summary: The paper addresses the challenge of multivariate time series modeling using a neural architecture based on a variation of two-dimensional state-space models (SSMs), referred to as Chimera. This approach features a stack of 2D SSMs combined with nonlinearities, a decomposition of time series into trend and sea... | Rebuttal 1:
Rebuttal: Thank you so much for your time and constructive review. We really appreciate it. Please see below for our response to your comments:
**Motivation:**
> *2D-SSMs have been considered for multivariate time series ...*
**Response:** To the best of our knowledge, neither S4ND nor 2DSSM has been use... | Summary: The paper introduces Chimera, a novel 2-dimensional State Space Model (SSM) for multivariate time series modeling, addressing key challenges such as capturing complex temporal and inter-variate dependencies, and efficient training. Chimera uses two SSM heads with different discretization processes and time-var... | Rebuttal 1:
Rebuttal: Thank you so much for your time and constructive review. We really appreciate it. Please see below for our response to your comments:
> *the writing and expression may need correction …*
**Response:** Thank you for bringing this to our attention. Following your suggestion, we will make sure to f... | Rebuttal 1:
Rebuttal: Once again, we thank all the reviewers for their time and constructive reviews, which have helped us to improve the paper.
Following the reviewer suggestions, we have conducted additional experiments, and the results are attached to this comment.
1. In Figure 1, we visualize the found patterns... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Gradient-free Decoder Inversion in Latent Diffusion Models | Accept (poster) | Summary: The paper introduces a gradient-free method for LDM decoder inversion. Compared to traditional gradient-based methods, this method is computational and memory efficient which makes it suitable for large-scale tasks like video generation. They provide theoretical support for the method's convergence. Their empi... | Rebuttal 1:
Rebuttal: Thank you for your review, and we are encouraged that you found that our method is new [S1], faster and more memory-efficient than traditional grad-based methods [S1], theoretical analysis shows that our method converges under reasonable conditions [S2], effective in a practical application [S3]. ... | Summary: The paper proposes a gradient-free method for decoder inversion in latent diffusion models (LDMs), which significantly reduces computational complexity and memory usage compared to traditional gradient-based methods. The approach is theoretically proven to converge and is efficient in experiments with various ... | Rebuttal 1:
Rebuttal: Thank you for your encouraging review. We are pleased that you found our work well-written, clear, and easy to follow [S1], our method significantly saves memory and improves computational efficiency [S2], our theoretical analysis ensures convergence [S2], and the extensive experiments demonstrate... | Summary: The paper introduces a zero-order (gradient-free) inversion optimization algorithm for encoder-decoder based generative models, particularly focusing on latent diffusion models (LDM). The objective of the optimization problem is to find the latent vector $z$ for a given image $x$ such that $x=D(z)$ where D is ... | Rebuttal 1:
Rebuttal: Thanks for the review, and for finding [S1] our work novel, effective, and straightforward to implement for any encoder-decoder based architectures, [S2] our method demonstrates has significantly less runtime, [S3] technically motivated, and the assumptions verified computationally, [S4] and we pr... | Summary: This work provides a method for gradient-free decoding for latent diffusion models that can reduce the amount of required GPU memory and lessen the computation time as opposed to gradient-based methods. The method focusses on providing better invertibility in ldms that is based on a theoretical assumption that... | Rebuttal 1:
Rebuttal: Thank you for your valuable review. We are glad that you found our work new [S1], containing a detailed description of the method along with the reasonings behind the assumptions made [S2], experiments are detalied [S3], convergence analysis thorough [S3], and experiments and methods are well-writ... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Thank you for taking the time to provide such valuable feedback. We are delighted to learn that you found many strengths in our paper. All reviewers noted that our research offers advantages over existing gradient-based methods, particularly in terms of speed and memory. All revie... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Text-to-Model: Text-Conditioned Neural Network Diffusion for Train-Once-for-All Personalization | Reject | Summary: The paper introduces Tina, a text-conditioned neural network diffusion model designed for train-once-for-all personalization. Tina utilizes a diffusion transformer model conditioned on task descriptions embedded using a CLIP model. This innovative approach aims to generate personalized models for various end-u... | Rebuttal 1:
Rebuttal: # Response to Reviewer htFA
Thanks for your valuable comments and kind attention. We appreciate the opportunity to address your concerns and give detailed responses as follows.
> 1. Response to "The model parameter size in the experiments is too small; larger models are needed to evaluate effect... | Summary: To generate personalized models for a variety of end-users and tasks via text prompts, this paper introduces Tina, a text-conditioned neural network diffusion model. Tina employs a diffusion transformer model, complemented by a CLIP model to embed task descriptions. Remarkably, Tina demonstrates superior gener... | Rebuttal 1:
Rebuttal: # Response to Reviewer **Gsfd**
Thanks for your valuable comments and kind attention. We appreciate the opportunity to address your concerns and give detailed responses as follows.
> 1. Response to "I am wondering whether the experimental results excels or perform similarly to the SOTA performan... | Summary: This work introduces Tina, a text-conditioned neural network diffusion model designed for generating personalized models from text prompts. Tina aims to enable efficient personalization by training a generic model once and then customizing it for various end-user tasks using task descriptions. Leveraging a dif... | Rebuttal 1:
Rebuttal: # Response to Reviewer BHPs
Thanks for your valuable comments and kind attention. We appreciate the opportunity to address your concerns and give detailed responses as follows.
> 1. Response to "Some methodological details are sparse, such as the specific configurations and hyperparameters used ... | null | null | Rebuttal 1:
Rebuttal: # General Response
We thank the reviewers for their valuable comments and precious time.
We are deeply encouraged to receive recognition from the reviewers that the idea is *interesting and novel* (Reviewers Gsfd and htFA), the method is *practical* and *has excellent generalization and competi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Implicit Curriculum in Procgen Made Explicit | Accept (spotlight) | Summary: This paper introduces C-Procgen, an extension to the popular Procgen benchmark that includes many improvements over the original, most notably by adding "context parameters" that can control specific features of the procedural content generation. They thoroughly study the performance of agents in different con... | Rebuttal 1:
Rebuttal: We highly appreciate the feedback from the reviewer-2ABJ and will address each point in detail below.
---
**W1. Issues Regarding to Literature Comparison**
We appreciate the highlighting of Minigrid and Minihack as notable works similar to our C-Procgen, and regret the oversight of not mentionin... | Summary: After rebuttal
I think C-Procgen is a useful contribution in itself. Secondly, the author's rebuttal has persuaded me that their analysis is novel, and can be useful. I especially like the analysis of LPE and how this relates to PLR' relative performance to PPO.
I am really impressed with the authors running... | Rebuttal 1:
Rebuttal: We value the detailed feedback from the reviewer-7pFG and address each point in detail below.
---
**W1. Insights on Curriculum Design & Limitation**
Thanks for the reviewer-7pFG's feedback! While at first glance the implications for curriculum development might not be immediately apparent, our s... | Summary: This paper presents a benchmark called C-Procgen that builds on the existing Procgen benchmark by allowing access and control of the context parameters. Furthermore, this work investigates how learning progresses for an RL agent in the absence of a curriculum given a uniform distribution over levels. The exper... | Rebuttal 1:
Rebuttal: We truly appreciate the constructive feedback from the reviewer-h4d8 and will respond to the points raised as follows.
---
**W1. Termination Conditions**
We appreciate the insightful feedback from reviewer-h4d8.
While termination conditions are indeed an important aspect to consider, they do n... | null | null | Rebuttal 1:
Rebuttal: We would like express our sincere gratitude to all reviewers fortheir constructive comments!
We are particularly thankful for the following positive feedback:
- The proposed benchmark is a very worthwhile contribution. `Reviewer h4d8`, `Reviewer 7pFG`, `Reviewer 2ABJ`;
- The figures in the paper ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Graph Neural Networks Do Not Always Oversmooth | Accept (poster) | Summary: This paper studies the over-smoothing effect in Graph Convolution Networks in the infinite width limit using their Gaussian processes equivalence. The authors generalize the concept of deep information propagation to GCNs and identify the similarity between ordered and chaotic phases in deep neural networks to... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer’s insightful comments and the evaluation of our
work. The comments are very helpful; we will address them in the revision:
Re: Weaknesses
1. This is a good suggestion; indeed it is something we are currently looking
into. These shift operators are not column-stoch... | Summary: The paper investigates whether oversmoothing in GNNs is avoidable in theory. It investigates why oversmoothing occurs and derives an affirmative answer. In particular, depending on the variance of the initial weight matrices, GNNs can either enter a "regular" (oversmoothing) or "chaotic" phase (non-oversmoothi... | Rebuttal 1:
Rebuttal: We thank the reviewer for this thorough review and the helpful questions and
comments. We will address them in the revised version:
Re: Weaknesses
1. In the revision we will repeat the experiments shown in Fig. 3 on the Cora
and/or CiteSeer datasets. For preliminary results on the Cora dataset,
p... | Summary: This work aims to understand whether GNNs in large depths always suffer from oversmoothing. The research starts from the equivalance of gaussian process (GP) and infinitely-wide NNs, and utilize eigenvalues of the linearization of GCN GP to quantify whether the model is in the phase of oversmoothing or not. Af... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review and helpful questions and comments. We will address them in the revised version:
Re: Weaknesses
1. In the revision we will repeat the experiments shown in Fig. 3 on the Cora and/or CiteSeer datasets. This should provide readers with a better sense of the ge... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and valuable comments. A common point
across reviews was that the paper would be strengthened by adding results
which use a real world dataset instead of just the synthetic CSBM (contextual
stochastic block model). We agree with this assessment, so we will add... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Time-Reversal Provides Unsupervised Feedback to LLMs | Accept (spotlight) | Summary: The paper proposes a class of LLMs called Time Reversed Language Models, which are simply pretrained on an unlabeled corpus in reverse order, and finetuned on instruction-tuned datasets accordingly. The model is used to provide feedback to LM generations in four different tasks. For general question answering,... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their valuable feedback on our work. It is indeed encouraging that the reviewer finds our work to be of great significance to the NeurIPS community and worthy of a strong accept.
- **Clarity in some parts:** We thank the reviewer for this suggestion. We ackno... | Summary: This paper introduces Time Reversed Language Models (TRLMs), which operate in the response-to-query direction for scoring and generation. The key contribution is demonstrating that TRLMs can provide effective unsupervised feedback to improve language model performance on various tasks. Specifically, the author... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their feedback. We are happy that they find our work to be novel and versatile, our theory valuable, our experiments comprehensive and our improvements significant. We clarify the concerns in this rebuttal.
- **[Limitations section] Disclaimer on assumptions... | Summary: The paper explores the utilization of reverse/backward-trained causal LLM. This LLM can be used to score responses based on the probability of generating the queries given the scores (which can be combined with the probability of generating outputs from input). Then, they can be used for re-ranking. In general... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their valuable feedback and relevant references. We are happy that the reviewer finds this direction to be relatively unique, the toxicity filter to be an interesting idea, and the theoretical motivation to be helpful. We hope to address the reviewer's concerns ... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their valuable feedback on our work, which has helped improve our submission. The reviewers have appreciated the novel application of reverse scoring and generation through our proposed TRLM family of models to various tasks like retrieval, citation and amplify... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Calibrated Self-Rewarding Vision Language Models | Accept (poster) | Summary: This paper addresses an important and tough issue in LVLM – hallucination, which is usually caused by the misalignment of the image and text modalities. A new method CSR is proposed, by extending the language self-awarding approach to multimodality, considering both instruction-following (text) and image-text... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We've addressed your questions below and would appreciate it if you could let us know whether our responses meet your expectations.
> **Q1**: The ablation of the weight of instruction-following score and image-text alignment score is missing.
**A1**: We eval... | Summary: The paper generally follows self-rewarding language models and applies the idea to vision-language models. The method first ask a VLM to self-generate candidates, based on which they score the candidates with the VLM itself and CLIPScore, and then perform DPO on the generated candidates. Experiments on LLaVA d... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We have answered your questions below, and we would appreciate it if you could let us know whether our responses address your concerns.
> **Q1**: The methodological contribution is not sufficient as it basically follows self-rewarding language models [11] and... | Summary: The paper addresses the challenge of hallucination in Large Vision-Language Models (LVLMs), where generated text responses appear plausible but contradict the input image. This misalignment occurs because the models prioritize textual information over visual input, even with high-quality representations. Exist... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments. Below are our responses to your questions. Please let us know if they address your concerns.
> **Q1**: In the limitation section, the authors mention that they could only run three iterations due to computation issues. However, in Section 4.1, … supplemen... | Summary: The paper proposes a new approach to addressing the hallucination problem in Large Vision-Language Models (LVLMs). This phenomenon occurs when generated text responses appear linguistically plausible but contradict the visual input, indicating a misalignment between image and text pairs. The proposed solution,... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and providing valuable feedback. We address your concerns point by point below and would appreciate knowing if our responses address them.
> **Q1**: Technical Novelty: The primary distinction of the proposed method … differs from and improves upon existing method... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their constructive feedback. Below is a summary of the information covered in the attached PDF:
- **Figure R1**: Two cases selected from the CSR-generated datasets (Reviewer oVPD).
- **Figure R2**: Polished Figure 2 of the main paper (Reviewers oVPD, myph, 4v... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes a new method for preference alignment with LVLMs. Specifically, the reward is computed using its own LLM (text only) and an external CLIP model. The optimization is done with DPO. This process can be iterated for several times.
Strengths: S1. Preference optimization in LVLM is under explor... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback to help us improve our paper. We detail our response below and please kindly let us know if our response addresses your concerns.
> **Q1**: How did the performance improve over each preference data generation stage?
**A1**: In the first round of training, our... | null | null | null | null | null | null |
A hierarchical decomposition for explaining ML performance discrepancies | Accept (poster) | Summary: The paper proposes a hierarchical decomposition model for binary machine learning classifiers. The nonparametric model allows for a detailed decomposition of distributions shifts at aggregate or partial levels. Additionally, confidence intervals for the proposed estimators are presented.
Strengths: 1. Introdu... | Rebuttal 1:
Rebuttal: We thank the reviewer for a careful reading of the work. We are encouraged to hear that the reviewer found the manuscript well-organized and the method widely applicable.
1. **Binary classification**: While the manuscript focuses on binary classification, the framework can be readily extended to ... | Summary: The authors describe a novel method to detect root causes of distribution shift (performance variability) of ML classifiers across domains. The method estimates how much of the shift is due to covariate shift vs. outcome shift, and which input features contribute most to the said shifts.
Strengths: Very signi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and positive feedback. We appreciate the constructive criticism to improve the clarity of the paper, and have included a high-level description of the procedure in the global response. We will also revise the manuscript to focus more on the high-level explanati... | Summary: The problem of explaining and remedying a dropoff in the accuracy of a supervised learning model when applied to a new environment (i.e. a new joint distribution between inputs and output) is studied. The paper tackles these problems for a non-parametric setting and with the goal of identifying specific indivi... | Rebuttal 1:
Rebuttal: We thank the reviewer for a thorough reading of the work and the constructive feedback. We are excited to hear that you liked the work and hope that our response will convince you to raise the score.
* **Selecting $W$ and $Z$**: Thank you for asking this question on how to partition variables int... | Summary: This paper introduces a hierarchical decomposition framework aimed at explaining performance discrepancies of machine learning models across different domains. It proposes both aggregate and detailed decompositions to quantify the impact of shifts in feature distributions (marginal and conditional) on model pe... | Rebuttal 1:
Rebuttal: We thank the reviewer for helpful comments and are glad that they appreciated the novel aspects of the work. We summarize the framework in the global response for more clarity and respond to the reviewer's questions below.
* **Aggregate decomposition**: Each term in the aggregate decomposition $\... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their constructive feedback and the positive response.
To recap, the aim of the paper is to address a major **methodological gap**: there are currently no nonparametric methods that provide a detailed explanation for why the performance of an ML algorithm dif... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SEEV: Synthesis with Efficient Exact Verification for ReLU Neural Barrier Functions | Accept (poster) | Summary: The paper proposes for synthesizing (and verifying) Neural Barrier Certificates with ReLU activation functions.
The authors focus on continuous-time systems which are controlled via a control input u(t) and define a dynamical
system as safe if for every state there exists a control output u(t) such that some u... | Rebuttal 1:
Rebuttal: We thank the reviewer for suggesting R1 and R2. The branch-and-bound methods for neural network verification perform branch and bound on non-linear units, either in the propagation of spatial data structures as mentioned in R1, or bound propagation as done in R2. In these works, being able to veri... | Summary: This paper presents an efficient NCBF training and verification framework by leveraging activation regions along the safety boundary. Specifically, reducing piecewise-linear segments acts as a regularizer and nonlinear programs are solves for efficient verificaiton. Experiemnts validates the efficiency of the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing [1] and [2], and we will include a discussion about these works in the final submission. The reviewer is correct that verification-in-the-loop training is a known approach. The main contribution of this paper is to enhance the scalability of verification-in-the-... | Summary: The authors propose a novel approach to synthesize neural control barrier functions (NCBFs) for continuous-time deterministic dynamical systems. Their goal is to synthesize a NCBF to prove the set invariance of the system wrt a given set. In order to do that, the authors propose a verification algorithm to pro... | Rebuttal 1:
Rebuttal: A comparison of the present paper with [15] is as follows. We note that our paper considers both the synthesis and verification of neural CBFs (NCBFs), while [15] only considered the verification problem. Moreover, while our approach verifies the exact conditions developed in [15], we greatly enha... | Summary: This paper proposes a new training and verification method to synthesize control barrier functions to formally prove the safety of a neural network-controlled nonlinear system. The approach used by this paper involves a new verification procedure that systematically enumerates all linear pieces of ReLU neural ... | Rebuttal 1:
Rebuttal: The reviewer is correct that our result involves multiple parameters, including the regularizer coefficients $\lambda_B$, $\lambda_f$, and $\lambda_c$, as well as the parameter $k$ used by the regularizer $L_{B}$. We have included ablation studies of these parameters in the supplemental PDF.
We t... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for providing detailed comments that have helped improve the quality of our manuscript. We have provided rebuttals to the comments of each reviewer. We have also attached a PDF file containing additional simulations requested by the reviewers. In this general r... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Toward Real Ultra Image Segmentation: Leveraging Surrounding Context to Cultivate General Segmentation Model | Accept (poster) | Summary: The paper discusses the challenges of ultra image segmentation and proposes a Surrounding Guided Segmentation framework (SGNet) to address these challenges by leveraging surrounding context information.
Strengths: 1) Novelty: The paper identifies two specific issues to ultra image segmentation, namely general... | Rebuttal 1:
Rebuttal: > W1: I would like to see results on human subjects.
We aim to address the task of ultra image segmentation and common academic datasets are typically in remote sensing or medical imaging (L183-186).
To further verify the effectiveness of our method in large scale human subject segmentation, we c... | Summary: The paper introduces SGNet to address the limitations of existing UIS methods, i.e., generalization issues and architectural constraints. SGNet leverages a larger context around image patches to refine segmentation results, making it compatible with general segmentation models and enhancing their performance o... | Rebuttal 1:
Rebuttal: > W1: The challenges in Contribution 1 have been proposed in many segmentation tasks. Therefore, C1 is weak.
The contribution 1 is: "We excavate two essential but largely overlooked issues in UIS, which hold great value for the community. In addressing these challenges, we are the first to tackle... | Summary: To overcome the challenges in generalization and compatibility with real-world ultra images, SGNet revisits a classic sliding inference approach and incorporates a surrounding context module to refine local patch segmentation. SGNet is compatible with various segmentation models and achieves significant perfor... | Rebuttal 1:
Title: Request for clarification for W5 "reference [2]"
Comment: Dear Reviewer,
We are currently in the process of drafting our rebuttal response and would greatly appreciate your clarification on a point mentioned in Weakness 5.
Could you please provide the specific title of the reference [2] so that we ... | Summary: This paper focus on the generalization and architectural issues of the ultra image segmentation methods, and proposes SGNet which consists of two branches for processing surrounding patch and local patch, respectively. The motivation is to leverage the surrounding context for refining the segmentation results ... | Rebuttal 1:
Rebuttal: > W2: The evaluation of speed was insufficient (only included one of the lightest versions, i.e., ISDNet-Style). More adequate results could help to better understand the overhead of the method.
We have given the full-version (SGNet) speed comparison in Table 6 of the original manuscript (L281-29... | Rebuttal 1:
Rebuttal: We supplemented Table 1 of the original manuscript with specific inference modes for each methods (@Reviewer-MJRN).
And we have also presented the visualization results on the CelebAMask-HQ dataset (@Reviewer-xcUS).
Please refer to the attached pdf.
Pdf: /pdf/7e2917ed34b5da630453ef1a4154d6de44c3... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
RTify: Aligning Deep Neural Networks with Human Behavioral Decisions | Accept (poster) | Summary: The paper proposes a novel framework to align the temporal dynamics of RNNs and human reaction times for visual recognition tasks. The framework starts from a task-optimized RNNs and trains a function $f_w$ to transform the activity (hidden state) into a real-valued evidence measure $e(t)$ (learnable) that wi... | Rebuttal 1:
Rebuttal: **Methods of the current paper and the comparison between ACT**
We will definitely strive to make the paper clearer and simplify our writing. We think our writing may have caused some confusion and here are some points we would like to clarify.
First, our main goal is to align the computational... | Summary: Here the authors present a method for fitting neural network outputs to reaction times. The main technical step is setting up the calculation of a reaction time as an accumulation of a decision signal over time steps and creating a differentiable computation of the time when this accumulation crosses a thresho... | Rebuttal 1:
Rebuttal: **Psychophysics tasks**
First, for the natural image dataset, we are using the dataset already peer-reviewed and published in Nature Neuroscience (Kar et al., 2019). As the reviewer pointed out, this is done via the Amazon Mechanical Turk (Mturk) platform. The reviewer is correct that these onlin... | Summary: The paper introduces a new framework for training vision systems using human reaction times. The approach allows for dynamic integration of visual reasoning with decision making by incorporating human behavior over time. The approach is shown to be beneficial over a range of psychophysics tasks.
Strengths: * ... | Rebuttal 1:
Rebuttal: **Why is modeling RT important?**
Modeling RT is crucial for two main reasons. First, because of the so-called speed-accuracy trade-off (fast decisions come with high error rates, and vice versa), a model that explicitly accounts for behavioral decisions and RTs is expected to reflect human brain... | Summary: The authors present RTify, a novel approach that leverages Recurrent Neural Networks (RNNs) to model decision response times. RTify offers a dual benefit: it can align human and RNN response times, and it can self-supervise RNNs to optimize the speed-accuracy tradeoff. Through evaluations on both synthetic and... | Rebuttal 1:
Rebuttal: **Comparison between ACT**
We will add a comparison in the main text (see general rebuttal section).
**Mechanistic account of RT**
We will revise our wording to provide a more accurate claim about the scope of our work. Here, we emphasize how RTify helps our understanding of RTs. First, our res... | Rebuttal 1:
Rebuttal: We want to thank all the reviewers for providing valuable feedback. We appreciate the time and expertise shared with us, and we are confident that we have addressed all raised concerns. Our paper is now stronger than before. The converging concerns are listed and answered in our general rebuttal, ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD | Accept (poster) | Summary: This paper introduces a large vision-language model named InternLM-XComposer2-4KHD, which is designed to process images up to 4K HD resolution. The model incorporates a dynamic resolution adaptation mechanism that maintains the aspect ratios of input images, thus enhancing the model's ability to understand com... | Rebuttal 1:
Rebuttal: ### Q1: The paper frequently...
A1: Thanks for your valuable comments.
1. Enabling LVLM to understand high-resolution images remains a challenging and open problem in the field. The primary goal of our paper is to explore a general and effective strategy for high-resolution image understanding LV... | Summary: The paper presents InternLM-XComposer2-4KHD, a high-resolution MLLM that performs better than existing methods on various benchmarks.
Strengths: 1. The paper is well written and easy to understand.
2. The performance of InternLM-XComposer2-4KHD is good, which is valided on various benchmarks.
Weaknesses: 1.... | Rebuttal 1:
Rebuttal: ### Q1: Limited novelty...
A1: Thanks, we would like to highlight the novelty of our work in the following aspects:
1. As discussed in the related work (Lines 95-101), our approach is the first to address the challenges and propose solutions for handling variability in image feature patch layouts... | Summary: This paper aims to explore the high resolution scenes of multimodal large language models. The authors find that the performances are largely improved when the model is equipped with 4K resolution. This finding will greatly inspire subsequent research works, which is of great value to the research community. T... | Rebuttal 1:
Rebuttal: ### Q1: More training details
A1: Thanks for your valuable comments, here we show the training loss in the Pretrain/SFT stage. Please refer to Figure 1 of the rebuttal pdf.
### Q2: Is it reasonable to use so such many visual tokens for ONE image?
A2:
This is a great and interesting question, an... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank the efforts of all the reviewers and the AC. We are encouraged by the acknowledgment of our strong performance by Reviewer Lqsg, Reviewer kpFC, and Reviewer 339k, and the noteworthy and effective model design by Reviewer Lqsg and Reviewer 339k. We answered all the questions in ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fight Back Against Jailbreaking via Prompt Adversarial Tuning | Accept (poster) | Summary: In this paper, the authors introduce a new defense mechanism called Prompt Adversarial Tuning (PAT) designed to protect LLMs from jailbreak attacks. PAT enhances the robustness of these models by attaching a defensive prompt control to user inputs, optimized through a combination of adversarial and benign prom... | Rebuttal 1:
Rebuttal: Dear Reviewer jHn4:
Thank you for your recognition of the soundness and contribution of our paper. For your proposed weakness, here are our responses:
**Q1:** The optimization of the defense prompt incurs computational overhead, and the optimization process using greedy sampling is time-consumin... | Summary: This paper presents a prompt adversarial tuning (PAT) to protect large language models against jailbreaking attacks. PAT is a prompt tuning-based defense for jailbreaking attacks where a string and GCG attack string are jointly optimized to have an LLM generate benign outputs. Experiments on AdvBench and MT-Be... | Rebuttal 1:
Rebuttal: Dear Reviewer LA5t,
Thank you for your detailed and helpful reviews. Here are our responses to your concerns:
**Q1:** The evaluation does not cover many other attacks and defenses, such as SmoothLLM [1] and RPO [2] (which also has a similar methodology to PAT).
**A1:** Note that RPO [2] is a co... | Summary: This paper presents an in-context defense method against jailbreaking attacks against LLMs. The core idea is to tune the suffix to the system of a LLM with a multi-objective optimization framework: 1/ Benign prompts enhanced with this tuned suffix should activate normal response as if there were no suffix. 2/ ... | Rebuttal 1:
Title: Details on the comparison to reference [1] and [3]
Comment: Dear Reviewer 1UaM,
We sincerely appreciate your valuable and constructive suggestions. Before posting our rebuttal, we kindly note that reference [1] is a defense instead of an attack and reference [3] is an attack instead of a defense. M... | Summary: Inspired by prompt tuning and adversarial training, this paper proposed a new jailbreaking defense method named prompt adversarial tuning (PAT) which optimizes a defensive prefix by alternating between updating attack and defense controls with two opposite output targets.
Strengths: 1. This paper employs adve... | Rebuttal 1:
Rebuttal: Dear Reviewer DPyJ,
Thank you for your perceptive comments on our paper. Here are our responses to your concerns:
**Q1:** Another paper [1] proposed using an optimized soft system prompt to enhance model safety, which is highly relevant to this study. A corresponding comparison and discussion m... | Rebuttal 1:
Rebuttal: # General Response
**G1:** The adaptive attack setting only considers an adaptive GCG attack. The defense may be less effective when attackers implement other adaptive strategies.
**A1:** To investigate whether PAT can defend against more advanced adaptive attacks, we further perform experiments... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper focuses on defense against jailbreak attacks. More specifically, it first considers improving model robustness through prompt tuning. The proposed method, Prompt Adversarial Tuning (PAT), aims to design a prefix to input prompts that encourages LLMs to still provide correct responses for benign inpu... | Rebuttal 1:
Rebuttal: Dear Reviewer zrTY,
We really appreciate your positive comments on the strength of this paper. For your proposed weakness, our responses are as follows:
**Q1:** The proposed defense relies on obtaining harmful prompts through existing attack methods. This raises doubts about whether the proposed... | null | null | null | null | null | null |
Towards a "Universal Translator" for Neural Dynamics at Single-Cell, Single-Spike Resolution | Accept (poster) | Summary: This work develops a multi-task-masking (MtM) approach, based on a self-supervised Transformer, that masks and reconstructs activity across different dimensions for neural spiking data learning. Evaluated on the International Brain Laboratory dataset, the model improves tasks such as single-neuron and region-l... | Rebuttal 1:
Rebuttal: **Weaknesses:**
1. We agree with the reviewer that our submission was lacking in qualitative analysis. To partially address this, we provide visualizations of the rastermap reconstructions for all predictive tasks in Figure 1 of our one page pdf. For the final version of our paper, we will include... | Summary: This work proposes a transformer architecture (based on previously existing ones) and, more interestingly, a training procedure that, when applied to spiking neural data, should result in a foundation model for spiking neural data. The clever bit about the procedure is that the learning model is asked to recon... | Rebuttal 1:
Rebuttal: **Weaknesses:**
1. We agree with the reviewer that this is a important detail missing from our paper. To address this, we added schematics for NDT1 and NDT2 that demonstrate how tokenization and neural reconstruction works (please see Figure 3 in the 1 page PDF). We will add this figure to the su... | Summary: The authors propose a self-supervised approach to building a foundation model of single-trial neural population dynamics. The approach utilizes existing transformer architectures, which are trained using “multi-task-masking” (MTM), which alternates between several scales of prediction tasks, including co-smoot... | Rebuttal 1:
Rebuttal: **Weaknesses:**
1. We thank the reviewer for this suggestion to compare to a wider range of neural population models. We want to clarify that MtM can be used as a learning objective for any architecture (not just transformers). To demonstrate this, we ran a new experiment using the LFADS architect... | Summary: The paper presents a large-scale model pretrained on the International Brain Laboratory (IBL) corpus containing multi-region, multi-animal spiking activity of mice during a decision-making task. It introduces a self-supervised learning approach with a novel masking scheme called MtM which alternates between ma... | Rebuttal 1:
Rebuttal: **Weaknesses:**
1. We thank the reviewer for the suggestion to run more baseline architectures with the MtM objective. Our goal was to compare masked modeling approaches for neural population modeling which just includes temporal masking as a baseline. Based on this feedback, however, we ran a new... | Rebuttal 1:
Rebuttal: We thank the reviewers for the thoughtful and detailed feedback on our manuscript. We are excited to hear that the reviewers thought that our work "is original and forward thinking" (**JcgB**), represents "deep thinking about foundation models in neuroscience" (**P8rK**), and marks a "step in a ve... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On conditional diffusion models for PDE simulations | Accept (poster) | Summary: The paper studies forecastiong temporal dynamics associated with forward PDE problems using diffusion models. Different strategies for approximating the score function of trajectories and how to parameterize them are proposed, ranging from sampling the whole trajectory all at once to sample it step by step aut... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful consideration of our work. We have taken the time to address all the points raised in the review.
**Novelty** - While we acknowledge that the techniques per-se are not new, we were the first to:
1) quantitatively evaluate the decomposition on physics data... | Summary: The manuscript addresses the challenging problem of conditional diffusion modeling, towards accurate and efficient data assimilation in problems governed by PDE. The authors compare different diffusion modeling approaches in this setting, focusing on the auto-regressive setting, and also propose new approaches... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive consideration of our work and the useful feedback on it.
**Turbulent dynamics + metrics** - Thanks for your suggestion, we will add a mention about the turbulent dynamics of the data. For a discussion about metrics and spectrum, please refer to common answer... | Summary: This paper studies the application of diffusion models (DMs) to PDE forecasting and data assimilation, focusing on different approaches to condition the DMs on initial conditions (forecasting) or sparse observations (data assimilation). The conditioning can either occur explicitly during training by conditioni... | Rebuttal 1:
Rebuttal: Thank you for your review and your comments, we aim to address them below.
**Evaluation metrics** - Thank you for your suggestions, when we chose the evaluation metrics we followed [1], but we acknowledge that some other metrics can be used to assess the model. We provide some examples of energy... | Summary: The authors perform an extensive study of generative diffusion models applied to the task of PDE forecasting and data assimilation (DA). Further, they introduce an autoregressive (AR) sampling strategy, and a universal amortised model based with variable context size, based on masking.
Strengths: The authors ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time taken to review our paper and the positive consideration of it. We address the comments below.
**Alternative metrics**
Thanks for suggesting alternative metrics, such as the energy spectrum, as well as providing more qualitative examples. We agree that these a... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for the time taken to review our paper, the overall positive consideration of our work, and their feedback and useful suggestions. We are content that we managed to get the main conclusions of our investigation across, providing an “insightful comparison of the... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: - The paper tackles of the problem of ML-based PDE modelling. Specifically two sub-problems: (a) *forecasting*: to generate rollouts given initial observations; and (b) *data assimilation*: to refine a trajectory given partial and noisy observations.
- The proposed approach extends a score-based diffusion mode... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their positive consideration. We address the points raised in the Weaknesses and Questions sections below.
**Forecasting results**
It is true that in the plain forecasting task, our proposed models do not achieve SOTA performance, as we also highlight in t... | null | null | null | null | null | null |
UGC: Universal Graph Coarsening | Accept (poster) | Summary: Graph coarsening aims at scaling an original large graph into a small graph. This paper proposes a graph coarsening method which was designed to be equally suitable for homophilic and heterophilic datasets, specifically, aggregating node clusters identified by a hash function. This paper is one of the pioneeri... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments and insights and for taking the time to go through our paper.
**Ques 1)** Regarding .. *Line 4. vital insights’ *
**Ans 1)** We thank the reviewer for the suggestion. By *vital insights* and *vital information*, we mean retaining the basic stat... | Summary: This paper proposes a new Universal Graph Coarsening (UGC) framework designed to handle both homophilic and heterophilic graphs. The UGC framework is capable of retaining important spectral properties, including eigenvalue error, hyperbolic error, and 𝜖-similarity measure. Experimental results demonstrate sig... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments and insights and for taking the time to go through our paper.
**Ques 1)** *Although the LSH strategy is much faster than other methods, the memory space overhead is non-negligible. Could you please measure the space complexity and provide a furthe... | Summary: The authors propose a novel Universal Graph Coarsening (UGC) framework, which is suitable for both homophilic and heterophilic datasets. UGC integrates node attributes and adjacency information to leverage dataset heterogeneity effectively. The results demonstrate that UGC is significantly faster (4x to 15x), ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments and insights and for taking the time to go through our paper.
**Ques 1)** *For ScalableTrainingof Graph Neural Networks section, there are no detailed discussion on GNN models except GCN.*
**Ans 1)** Due to the limited space of the manuscript, we... | Summary: This paper present a framework UGC for graph coarsening to reduce a larger graph to a smaller graph. It uses Locality Sensitive Hashing (LSH) of augmented node features, and works on both homophily and heterophilic graphs. Experiments could verify its effectiveness in original graph property perservation and e... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments and insights and for taking the time to go through our paper.
**Ques 1)** *it claims that the augmented feature vector is calculated by dot product and concatenation, it is not very clear that how to concate A with node features X*
**Ans 1)** We... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insights and constructive suggestions. A comprehensive point-by-point response to the reviewers' comments is presented below. The major additional changes are listed below.
**Additional experiments**: We have incorporated all of the additional experiments requeste... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Rule Based Learning with Dynamic (Graph) Neural Networks | Reject | Summary: The paper proposes a method for neural network-based learning to incorporate expert knowledge in the neural network architecture by building rules and utilizing them in "rule-based" layers of the learned neural networks. It introduces RuleGNNs as a concrete application of the proposed method and evaluates its ... | Rebuttal 1:
Rebuttal: ### Weaknesses:
>W1: "The performance of RuleGNNs is expected to heavily rely on the quality of the rules generated from additional information or domain knowledge, however, the paper solely focuses on application of such rules without adequately discussing the challenges of building quality rule... | Summary: This paper proposes a novel model architecture rule-based layer, which induces different parameters given different inputs. Theoretical analysis demonstrates how the proposed architecture reduces back to classical feed-forward layers, and empirical results on both synthetic and real-world data sets demonstrate... | Rebuttal 1:
Rebuttal: ### Weaknesses:
>W1: "The implementation in this work may need further elaboration to make the proposed method easier to understand."
>
>> As stated by the reviewer hyNs there are some minor issues that will be corrected in a revised version of the paper to improve the readability.
>> Nevertheles... | Summary: This paper introduces rule-based (dynamic) neural network layers. The basic idea is to have a common set of parameters, i.e., weights and biases, where, depending on a certain rule, only a subset of these parameters are used in the forward pass. They show that certain fully connected and convolutional layers c... | Rebuttal 1:
Rebuttal: ### Weaknesses:
>W1: Theorem 1
>>Indeed, Theorem 1 in this generality is not proven in the appendix, but it is straightforward to extend Proposition 2 to the mentioned cases.
> As suggested, in a revised version of the paper, we will provide a full proof of Theorem 1 or restrict the statement of T... | Summary: The authors develop a broad framework for adding expert knowledge to Neural Networks. They formalize this by extending the learnable parameterized functions with an additional parameter consisting of the set of formal rules. In general, these rules maybe learnable as well. However, the authors focuses on these... | Rebuttal 1:
Rebuttal: ### Weaknesses:
> W1: "The author has used the notion of rules rather broadly. There is no formal language (logic or matrix language) for the rules. They are just arbitrary functions.
> This basically means that any existing NN model, in one way or another, can be seen as a special case of Rule ba... | Rebuttal 1:
Rebuttal: First of all, we would like to thank the reviewers for their valuable feedback and comments.
Regarding the reviewer specific comments, we have addressed point by point the reviewer's comments and answered the questions raised in the reviews in the "Rebuttal by Authors".
In the following, we will s... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
To Err Like Human: Affective Bias-Inspired Measures for Visual Emotion Recognition Evaluation | Accept (poster) | Summary: The principal subject of this paper is the definition of the concept of misclassification in the field of visual emotion recognition, and the proposal of a novel evaluation method based on Mikel's Wheel distance to assess the degree of misclassification in methods of visual emotion classification. The paper al... | Rebuttal 1:
Rebuttal: ## 1 Weakness 2，7:
As shown in Sec.3 of our rebuttal material，Emotional polarity is extremely important for emotion classification tasks. Therefore, we separate emotions of different polarities based on emotional polarity, but we do not shorten the distance between unrelated emotions. For example,... | Summary: This paper proposes a novel evaluation approach based on Mikel’s emotion wheel from psychology, which considers the "emotional distance" between different emotions. It is claimed that the measure design considering the granularity of the emotions can be a better metric to evaluate visual emotion recognition. E... | Rebuttal 1:
Rebuttal: ## Weakness:
- First, we have added more extensive semi-supervised comparative experiments in Tab.1 of our rebuttal material, as well as experiments in Tab.2 of our rebuttal material on image retrieval tasks and emotion binary classification tasks.
- Secondly, we strongly agree with your view tha... | Summary: The paper proposes a new measure for emotion recognition performance based on Mikel’s emotion wheel. The measure takes the distance between emotions into account. Experiments in semi-supervised learning on emotion recognition and user study demonstrate the effectiveness and superiority of the proposed metrics ... | Rebuttal 1:
Rebuttal: ## 1 Weakness1:
We have added comparative methods for the semi-supervised experiments in Tab.1 of our rebuttal material.
## 2 Weakness2:
In fact, it is a semi-supervised learning experiment setting. We divided the training set according to a predetermined number of labeled samples into labeled t... | Summary: This paper proposed new measures to evaluate the severity of misclassifications in visual emotion recognition. It addresses the limitations of traditional accuracy metrics by considering the psychological similarities between emotions. Utilizing Mikel's emotion wheel, the authors define an emotional distance m... | Rebuttal 1:
Rebuttal: ## 1 Weakness1: Contribution to visual emotion recognition
- Emotional ambiguity and emotional relevance have always been important issues in the field of emotions, and previous work has been dedicated to solving these problems.Previous works[1] extended single labels into label distributions, or ... | Rebuttal 1:
Rebuttal: ## 1 Semi-supervised learning supplementary experiment
We added comparison methods for semi-supervised experiments, including CoMatch[1], SimMatch[2], SoftMatch[3] and FreeMatch[4]. Additionally, we combined the two tables into a new one. Please note that comparing other methods with our method in... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Active learning of neural population dynamics using two-photon holographic optogenetics | Accept (poster) | Summary: The authors develop active learning techniques to design photostimulation experiments combined with 2p imaging to uncover dynamical systems model of brain activity. To this end, the authors employ low rank matrix recovery techniques. They demonstrate their approach on a dataset from mouse motor cortex.
Streng... | Rebuttal 1:
Rebuttal: > Fig. 2: It is unclear why the GRU is shown if it is not used later.
We include results for the GRU model to justify our use of the linear model. In particular, while similar GRU models have often appeared in the computational neuroscience literature (e.g., Pandarinath et al., Nature Methods, 20... | Summary: The advent of holographic optogenetics has brought about an unprecedented level of specificity in the way we stimulate and measure the activity across the neural population. The authors propose methods for efficiently determining effective photostimulation patterns to study neural population dynamics using two... | Rebuttal 1:
Rebuttal: > The dataset is very simple and does not contain anything to enrich the dynamics of the neural population except for random noise…
> The authors could potentially expand their repertoire of datasets to different modalities and more complex behavioral paradigms as this will help them infer whethe... | Summary: The paper proposes an active learning framework for choosing the next set of neurons to stimulate to best inform a dynamical model of the neural population activity. The active learning procedure takes advantage of the low-rank structure of the dynamical systems model. With synthetic and real datasets, the aut... | Rebuttal 1:
Rebuttal: > Experimental analysis: Although the paper has interesting experimental results, I would like further explanations of the results. For example, what is causing the discrepancy between the best and worst cases in Figure 4?
In Figure 4, the “Best” and “Worst” plots are the best performing and wors... | null | null | Rebuttal 1:
Rebuttal: We thank each of the reviewers for their helpful feedback, and will work to incorporate all suggestions in the final version. We have addressed specific questions in the following, and are also attaching a pdf with additional visualizations, as requested in some of the reviews.
Pdf: /pdf/a578c8703... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Text-Aware Diffusion for Policy Learning | Accept (poster) | Summary: The paper proposed a reward generating pipeline leveraging a text-conditioned diffusion model with a text prompt to master RL tasks described by the prompt.
The pipeline compares the difference of the generated image with/without the prompt and the original image to calculate a dense reward, which also makes s... | Rebuttal 1:
Rebuttal: We thank Reviewer KsL9 for their thoughtful comments and helpful feedback on our work. Below, we seek to both address the reviewer’s listed weaknesses and answer the posed questions:
**On hyperparameter tuning:** we concur with the reviewer that our approach benefits from initial tuning of the no... | Summary: The paper introduces Text-Aware Diffusion for Policy Learning (TADPoLe), a method for reinforcement learning that leverages pretrained text-conditioned diffusion models to compute dense reward signals. This approach allows agents to learn text-aligned behaviors without the need for expert demonstrations or han... | Rebuttal 1:
Rebuttal: We thank Reviewer TWnR for their detailed comments and thorough questions. We seek to address their concerns below:
**On pre-existing well-rendered videos:** we would like to clarify a potential misunderstanding - our method does not require any pre-existing well-rendered videos for environments ... | Summary: The paper presents Text-Aware Diffusion for Policy Learning (TADPoLe), a framework that leverages pretrained text-conditioned diffusion models to generate dense, zero-shot reward signals for policy learning in reinforcement learning tasks. The approach aims to address the limitations of manually designed rewar... | Rebuttal 1:
Rebuttal: We thank Reviewer iiYV for their helpful feedback and suggestions. We try to address their concerns below:
**Qualitative evaluation:** We report quantitative comparisons whenever available (Tables 1, 3, 4), and we agree that human evaluation may inevitably introduce subjectivity. However, they ar... | Summary: The paper introduces Text-Aware Diffusion for Policy Learning (TADPoLe), which uses a large-scale pretrained text-conditioned diffusion model to provide zero-shot reward signals for training agents without expert demonstrations or manually designed reward functions. TADPoLe enables agents to learn behaviors an... | Rebuttal 1:
Rebuttal: We thank Reviewer z3di for their comments and feedback on our work; we are happy to hear that the reviewer appreciates the novelty of our approach, and we seek to address their concerns below.
**On comparisons:** we performed *apple-to-apple* comparisons with three recent text-aware rewards, name... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive feedback, and are glad that TADPoLe was recognized as “novel”, “easy to follow”, and “demonstrates versatility across different environments and tasks”.
We have identified common points raised by the reviewers, which we summarize and respond to below:... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Shared Autonomy with IDA: Interventional Diffusion Assistance | Accept (poster) | Summary: In the Shared Autonomy context, the authors propose a value-based intervention assistance method that aims to only have a copilot intervene only when the value of the assistant’s actions exceed that of the human. The proposed method first trains an expert policy for the task using privileged goal information, ... | Rebuttal 1:
Rebuttal: Thank you for your effort to thoroughly review our paper and for your feedback. In response to your feedback, we have made meaningful improvements that have strengthened the study.
> While Section 2 (Related Works) currently covers more recent relevant work, it would benefit from also providing ... | Summary: This work presents the *intervention diffusion assistance* (IDA) framework for shared autonomy between a human "pilot" and an AI "co-pilot". The IDA framework is designed to be goal agnostic, and does not attempt to infer the pilot's current goal. This work extends Yoneda et al. (2023), using the same diffus... | Rebuttal 1:
Rebuttal: Thank you for taking the time to carefully read our paper and provide detailed feedback. We have performed additional experiments in response to your invaluable feedback, which we believe has further enhanced and strengthened our work.
> One potential weakness with this work is that the intervent... | Summary: This paper presents an intervention assistance (IA) method, IDA, that dynamically decides whether the co-pilot should take over the control. The decision is determined by comparing the expected values of the co-pilot’s action versus the pilot’s action. The experiments with human surrogates showed that the prop... | Rebuttal 1:
Rebuttal: Thank you for taking the time to carefully read through and understand our paper, and provide constructive feedback. We’ve made important changes in response to your feedback (including new experiments) that we believe have significantly improved the manuscript.
> The proposed method still seems ... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for their attention and thoughtful comments. We believe this has helped lead to a cleaner and improved manuscript.
We have responded to each of the reviewer's comments individually. Please see the attached pdf for tables and a figure that we refer to in reviewer-specifi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning the Latent Causal Structure for Modeling Label Noise | Accept (poster) | Summary: This paper proposes that traditional noise-labeled learning methods based on noise transition matrices have limitations. Specifically, only the noise transition matrices of certain special examples can be effectively estimated, while the transition matrices of other examples need to be estimated based on simil... | Rebuttal 1:
Rebuttal: ### Response to Reviewer 22VP
**Q1**: The structural causal model of the labeling process is not reasonable. The example feature $X$ should be the cause of the noisy label $\tilde{Y}$. If only the clean label y is the cause of the noisy label, then this structural causal model is still modeling c... | Summary: In learning with noisy labels, estimating an instance's noise transition matrix is crucial for inferring its clean label. Current studies assume previous relations between transition matrices and it may not hold in real world scenarios. Motivated by the relation between noise transition matrices are establishe... | Rebuttal 1:
Rebuttal: ### Response to Reviewer pfn2
**Q1**: The transition matrix is different sample by sample in instance-dependent transition matrix modeling. It means there is no similarity assumption for instance-dependent transition matrix.
**A1**: We believe that there is a misunderstanding. The transition mat... | Summary: The work tackles noisy labels learning under the context of classification problem. The proposal leverages a casual model to embed the relationship amongst features, labels and noisy labels. The authors show that the proposed causal model can be identified even under noisy data, hence enables learning the nois... | Rebuttal 1:
Rebuttal: ### Response to Reviewer afzG
**Q1**: I don't get why latent factors generating $\mathbf{X}$ and latent factors generating noisy label $\tilde{Y}$ have to be different? And how to enforce it?
**A1**: Thank you for your question. The latent factors for generating instance $X$ and noisy label $\ti... | Summary: The author addresses the problem of instance-dependent label noise. While previous research has used models that generate images from true labels and then predict noisy labels from the images and true labels, the author takes a different approach. The proposed model generates images from some latent factors de... | Rebuttal 1:
Rebuttal: ### Response to Reviewer S6tG
**Q1**: There is a lack of analysis on whether the model in Figure 2(b) is more effective than the model in Figure 2(a). To better demonstrate the effectiveness of the proposed model, I recommend either reporting the performance of InstanceGM or describing the resul... | Rebuttal 1:
Rebuttal: ## Global response
We sincerely appreciate the time and effort the reviewers invested in reviewing our manuscript. Your insightful comments and constructive advice have been instrumental in enhancing the quality and clarity of our work. We are grateful for your detailed feedback and guidance. In ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DiffSF: Diffusion Models for Scene Flow Estimation | Accept (spotlight) | Summary: This paper introduces a novel diffusion model designed for scene flow estimation, aiming to enhance both accuracy and robustness, particularly in the presence of noisy inputs or occlusions. The proposed denoising diffusion models effectively handle noisy data by modeling stochastic processes, filtering out sen... | Rebuttal 1:
Rebuttal: **Question 1: Additional visualizations.**
We propose additional qualitative results in the attached PDF in the common comment section above. Figure 3 shows the visualization comparison between GMSF and DiffSF on the KITTI dataset.
The results show that DiffSF is more robust than GMSF when the po... | Summary: This paper deals with scene flow estimation in 3D point clouds. It proposes a formulation based on diffusion model. The model takes the source and target frames of 3D point clouds as condition and turn the problem into a conditional generation problem. Different from naive conditional generation, the formulati... | Rebuttal 1:
Rebuttal: **Question 1: Consider providing more visualization of the reverse process other than the only one GIF attached.**
Thanks for the comment. We plan to add more visualizations of the diffusion process other than the visualization in Figure 1 in the main paper. However, due to the page limitation of... | Summary: This paper introduces DiffSF, which integrates transformer-based scene flow estimation with denoising diffusion models. The diffusion process involves progressively perturbing the ground truth scene flow vector field by adding Gaussian noise. Conversely, during the reverse process, the scene flow vector field ... | Rebuttal 1:
Rebuttal: **Question 1: Limited contribution.**
Optical flow and scene flow share similar ideas, i.e. estimating object movement from sensors' data. However, the sensors are completely different and the generated data has different modalities.
For RGB cameras, the output images are usually on a regular gri... | Summary: This paper proposes a scene flow estimation method that estimates scene flow from point clouds. The proposed method combines a previous scene flow method, GMSF, and diffusion model, where a denoising block is introduced based on GMSF. During training, the source point cloud is first warped via noisy scene flo... | Rebuttal 1:
Rebuttal: **Question 1: The organization of the technical part can be improved.**
The original idea of the organization was to decouple the introduction of the standard diffusion process and our contribution.
We first recap the basics of diffusion models for the audience in Section 3.1.
In Section 3.2 we ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable comments and questions.
We are happy that the reviewers find that using diffusion models in scene flow estimation is novel (qmev), interesting (gpDD), and elegant (yrBG), and that the motivation is well-justified (2g78).
Reviewer gpDD also finds the resul... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Identification of Analytic Nonlinear Dynamical Systems with Non-asymptotic Guarantees | Accept (poster) | Summary: This paper studies the problem of identifying an observable stochastic nonlinear dynamical system, in case the transition function is linearly parametrized and the noise is additive. The authors assume that the feature functions are analytic, and both the inputs and the noises are i.i.d., bounded, semi-continu... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments!
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`Q1`: **Bounded noises/inputs**
`A`: First, we politely point out that, though **linear** control usually considers unbounded noises/inputs, bounded noises/inputs are commonly studied in many **nonlinear** control literature (Mania et al. 2022, Shi et al. ... | Summary: The manuscript provides theoretical guarantees for nonlinear system identification using non-active i.i.d. noises, extending from linear systems to linearly parametrized nonlinear systems with analytic feature functions. The findings demonstrate that non-active i.i.d. noises are capable of efficiently learnin... | Rebuttal 1:
Rebuttal: Thank you very much for your helpful suggestions! We discuss your concerns and suggestions below. (For figures, please refer to rebuttal.pdf)
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`Weakness 1 & Limitation`:
**On actively designing experiments in certain scenarios**
`A:` In certain scenarios, active exploration can be prefer... | Summary: The authors study the problem of system identification from a trajectory generated by an unknown linearly parametrized nonlinear system whose nonlinearity is an analytic function. Specifically, they theoretically analyze to estimators: the least squares estimator, and the set membership estimator. Both estimat... | Rebuttal 1:
Rebuttal: Thank you very much for your constructive comments! We will address your concerns below:
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`Weakness:`
>While the theoretical arguments presented seem sound, I am not sure that the extension of system identification results to the case of linearly parametrized smooth nonlinear systems is suf... | Summary: The authors extend the work of Simchowitz et al. (2018) [linear] and Sattar et al. (2022) [bi-linear] to linear in the parameters but analytic nonlinear features showing that PE of inputs still results in PE of the states which for general nonlinear systems is not true. From this result LSE results like those ... | Rebuttal 1:
Rebuttal: Thank you very much for your constructive comments! We will address the comments below.
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`Weakness:`
>The biggest weakness of the paper is that very little time is spent on the analysis in the main text. yes their is a proof sketch for the main theorem, but it would be nice to get more intu... | Rebuttal 1:
Rebuttal: Thanks for your time and valuable comments! The attached is a pdf that contains new plots for addressing the questions from Reviewer wxwx and Reviewer QHWP.
Please feel free to ask us if you have any other questions. Looking forward to hearing your feedback!
Pdf: /pdf/f12e9be4644f6a4c3df89dd8a1c8... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FineCLIP: Self-distilled Region-based CLIP for Better Fine-grained Understanding | Accept (poster) | Summary: This paper integrates three existing techniques in vision-language pre-training into a single end-to-end fine-tuning framework, referred to as FineCLIP:
- global contrastive: This aligns the global representation of an image with the text embedding.
- reginal contrastive: This aligns the pooled region-level re... | Rebuttal 1:
Rebuttal: Q1: The performance of FineCLIP on both OV-COCO and OV-LVIS benchmarks is significantly lower than the original baseline of CLIPSelf. Why not directly increase the input resolution of FineCLIP instead of increasing the scale of the dataset?
A1: Thanks for your suggestion. We appreciate the oppor... | Summary: This paper attempts to overcome the problem of CLIP lacking fine-grained details when adapting to dense prediction tasks. It proposes a unified framework with three training losses: contrastive loss for global image-text pair, region alignment for region-region annotation, and self-distillation for image regio... | Rebuttal 1:
Rebuttal: Q1: With the same COCO validation set, training with CC2.5M shows worse performance on retrieval task but better top1 box classification accuracy. Any idea about this result?
A1: Good question. We believe this result can be explained by the differences between retrieval and box classification tas... | Summary: The paper introduces FineCLIP, a novel vision-language model designed to enhance fine-grained understanding in image-text tasks. It addresses limitations in existing models like CLIP, which struggle with dense prediction tasks due to a lack of fine-grained detail comprehension. The authors propose two main inn... | Rebuttal 1:
Rebuttal: Q1: As a common practice, the contribution of self-distillation scheme is not novel. What are significance and advantages of "real-time" capability of the self-distillation scheme? Why does using only $L_{SD}$ for supervision lead to model collapse?
A1: We fully understand your concerns. To bett... | Summary: To address CLIP's limitations in understanding fine-grained details, the authors propose FineCLIP, a method for training CLIP-based architectures that proposes two novel losses, a real-time self-distillation loss and a regional contrastive loss. The regional contrastive loss is designed to encourage learning o... | Rebuttal 1:
Rebuttal: Q1: The authors could provide more semantic analysis to better explain the performance of FineCLIP on downstream tasks.
A1: Following your suggestion, we add semantic distribution statistics in Table 1 of the attached PDF document.
The results show that the training data in CC2.5M related to the... | Rebuttal 1:
Rebuttal: We'd like to thank all the reviewers for the valuable comments and suggestions. We will respond to common questions in this general rebuttal.
Q1: The authors should provide more information of the training time cost and GPU memory usage to demonstrate the ease of FineCLIP.
A1: Good suggestion. W... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Metric Distortion Under Probabilistic Voting | Reject | Summary: Metric distortion is a framework to evaluate the "accuracy" of social choice rules, by considering a worst-case candidate and voter embedding in a metric space, and by assuming that reported votes are derived from distances in the metric space. So far, votes were assumed to be a deterministic function of the d... | Rebuttal 1:
Rebuttal: Thank you very much for your detailed and constructive feedback.
**Clarification on the proof of Lemma 3**
Indeed, as you note, just using the constraint $|d_i - b_i| \leq d_i + b_i $ will not be sufficient to obtain the required constraint in problem (7). However, we additionally have that $|d... | Summary: This paper extends the framework of metric distortion, measuring how well voting rules minimize the social cost in a given metric space, to probabilistic voting scenarios where the preferences of voters are drawn from a probability distribution defined by the relative distances between candidates and each vote... | Rebuttal 1:
Rebuttal: Thank you for your review. We have tried to address your concern regarding the relevance of our paper to the NeurIPS community in the overall response, we will mention that again here.
As Reviewer 2kBY observed, the increasing significance of social choice in machine learning, and the importance ... | Summary: The paper studies the problem of metric distortion in single-winner elections. The key assumption is that the voters' preferences are not exactly compatible with the metric space, but they rather agree with it with a certain probability. The authors propose several axioms that formalize the requirements for th... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We agree that the presentation could be improved, and we are happy to incorporate the suggestions in the final version.
**Regarding the motivation behind Axiom 2**
We acknowledge that Axiom 2 (Independence of Other Candidates) may not always hold in certain real-life... | Summary: This paper considers metric distortion in probabilistic models of voting. In the metric distortion framework the voters and alternatives are embedded in a metric space, and given the ranked preferences the goal is to find an alternative with low distortion. In this setting these rankings come from a probabilis... | Rebuttal 1:
Rebuttal: Thank you for your feedback on our paper. We acknowledge that there is scope for improvement in the presentation of the paper and rearrange the order in which some concepts are introduced. We also agree with the reviewer that our explanations of the probabilistic models could have been more detail... | Rebuttal 1:
Rebuttal: We thank the reviewers for their invaluable feedback. We address a common concern below and respond to specific questions separately to each reviewer.
**Regarding relevance to NeuRIPS**
As Reviewer 2kBY observed, the increasing significance of social choice in machine learning, and the importanc... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Self-Distilled Depth Refinement with Noisy Poisson Fusion | Accept (poster) | Summary: This paper introduces a Self-distilled Depth Refinement (SDDR) framework to enhance robustness against noise. This framework primarily includes depth edge representation and edge-based guidance. And they design an edge-guided gradient loss and an edge-based fusion loss. Furthermore, experiments on five benchm... | Rebuttal 1:
Rebuttal: Dear Reviewer 9uAR:
Thanks for your positive feedback and valuable questions. We address all your comments as follows.
# Weakness 1: Sensor and Modality
We specify the modality of depth models and datasets.
**(1) Models.** Similar to previous monocular depth models, SDDR takes a RGB image as inp... | Summary: The paper introduces a novel framework called Self-Distilled Depth Refinement (SDDR) to enhance depth refinement, which aims to infer high-resolution depth maps with fine-grained edges from low-resolution depth estimations. The authors propose modeling depth refinement as a noisy Poisson fusion problem, addres... | Rebuttal 1:
Rebuttal: Dear Reviewer Ca4W:
Thanks for your positive feedback and valuable questions. We address all your comments as follows.
# Weakness 1 and Limitation 1: Applications and Limitations
**(1) Applications.** SDDR produces accurate depth with meticulous edge and
consistent structure, suitable for variou... | Summary: This paper presents a novel framework, SDDR, for enhancing the resolution and detail of depth maps generated by estimation models. By conceptualizing depth refinement within the context of noisy Poisson fusion, the authors have developed a method that effectively tackles the prevalent issues of inefficiency an... | Rebuttal 1:
Rebuttal: Dear Reviewer DKoq:
Thanks for your valuable feedback. We address all your questions as follows.
# Weakness 1: Dependence on Initial Depth
**SDDR achieves strong robustness regarding the quality of initial depth, noticeably improving depth edges and details, even when faced with low-quality ini... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewer DKoq, Ca4W, and 9uAR:
We would like to express our sincere gratitude for your insightful comments and constructive suggestions on our paper. In the rebuttal, we have diligently incorporated comprehensive discussions and experiments to address all the raised queries, comments, and co... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Diffusion Models Meet Contextual Bandits with Large Action Spaces | Reject | Summary: The authors propose diffusion Thompson sampling, which uses a diffusion model to leverage reward under similar actions for more efficient exploration. The authors derive efficient posterior approximations under a diffusion model prior and prove a regret bound in linear instances. To efficiently compute and sam... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and valuable time. Below, we provide our response to your question regarding the heuristic for choosing $L$.
**Heuristic for Choosing $L$**
Your intuition is correct: a higher $L$ increases the regret bound, while a smaller $L$ may result in a prior that fail... | Summary: This work presents the use of Diffusion models as priors for Thompson sampling.
Namely, they propose to learn diffusion models (as replacement to other parametric priors) to accommodate more complex correlations between context, action and reward functions than with simple parametric form priors.
Given that ... | Rebuttal 1:
Rebuttal: Thank you very much for your positive feedback and time. We provide point-by-point responses to your comments.
**Offline Prior Pre-Training**
Current experiments do not include diffusion model pre-training. Since the true distribution of action parameters is defined by a diffusion model, we dire... | Summary: The work provides a great example of diffusion modeling on bandit action parameter for better exploration.
Strengths: The work provides a comprehensive description on how to employ diffusion modeling on bandit parameters for contextual bandit problems.
The discussion on linear and non-linear diffusion model... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive feedback and recognition of our work. Below, we provide our responses to your comments.
**Assumptions**
- **(A1)** is common in the literature and can be easily satisfied in practice by normalizing contexts. For example, in a recommender system, normalizing ... | Summary: The paper considers the problem of contextual bandits in large action spaces. In this problem, the reward of an arm is a function of the context and an unknown, arm specific parameter vector. To efficiently learn good policies in such large action spaces, the paper places a structured-prior distribution on th... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable feedback and time. We provide point-by-point responses to your comments.
__Offline Samples for Prior Pre-Training.__
We address this question in our global response above (please see points __(1)__, __(2)__ and __(3)__).
__Related Work.__
Thanks for prov... | Rebuttal 1:
Rebuttal: We are very grateful to the reviewers and AC for their valuable time. This global response includes additional experiments and discussions on the impact of pre-training on dTS performance (Reviewers 89g4 and YAin). We have included a PDF with figures related to these experiments.
The attached PD... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Rad-NeRF: Ray-decoupled Training of Neural Radiance Field | Accept (poster) | Summary: This paper claims that training with those rays with invisible 3D points (occlusions in complex scenes) that do not contain valid information about the point might interfere with the NeRF training.
Based on this intuition, this paper proposes Rad-NeRF to decouple the training process of NeRF in the ray dimen... | Rebuttal 1:
Rebuttal: **R4-Q1: My main concern is the novelty. Although this work aims for different tasks, it is quite like Switch-NeRF.**
Thanks for this question. In our opinion, the most fundamental difference between Switch-NeRF and our Rad-NeRF is that Rad-NeRF is a ray-based multi-NeRF framework (the first ray-... | Summary: This paper proposes an innovative approach to enhance NeRF performance. The key observation is that due to occlusion, some objects may appear in one ray but not in another. While NeRF uses transmittance to mitigate this issue, the paper argues that this may not be sufficient. To address this, the authors propo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and thoughtful comments. We address the detailed questions and comments below.
**R3-Q1: Clarification of Figure.1(c) and motivation**
As the reviewer said, NeRF often compensates for RGB appearance with poor geometry. Figure 1(c) might have the... | Summary: Traditional NeRF models face challenges in rendering complex scenes due to interference from occluded rays, which leads to inaccurate training data. To address this, the authors propose Rad-NeRF, a framework that decouples the training process in the ray dimension by using multiple sub-NeRFs, each trained with... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and thoughtful comments. We address the detailed questions and comments below.
**R2-Q1: Clarification of the network structure**
Within Rad-NeRF, we adopt a multi-resolution learnable feature grid shared among all sub-NeRFs. Given a 3D point co... | Summary: - The authors decouple the training process of NeRF in the ray dimension and propose a framework where they create an ensemble of sub-NeRFs and train a soft gate module to assign gating scores to these sub-NeRFs based on specific rays.
- The gating module is a 4-layer MLP followed by a softmax function. The ga... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and thoughtful comments. We address the detailed questions and comments below.
**R1-Q1: Performance of Rad-NeRF**
Overall, compared to the Instant-NGP baseline, our scheme shows improvements in the PSNR metric across various datasets: 1.02 on m... | Rebuttal 1:
Rebuttal: Dear All,
We appreciate all the reviewers' time and efforts invested in reviewing our paper. We are encouraged that the reviewers recognize the effectiveness and scalability(nJqP,tZHj,PUvY,5G52), flexibility and compatibility with different neural rendering approaches (nJqP, tZHj), insightful mot... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning | Accept (poster) | Summary: The paper introduces a algorithmic framework that fine-tunes VLM using RL to enhance their performance in multi-step, goal-directed decision-making tasks. The authors highlight the limitations of traditional visual instruction tuning, which relies on pre-collected datasets and may not effectively train VLMs f... | Rebuttal 1:
Rebuttal: Dear reviewer gYxm,
Thank you very much for your positive feedback and insightful suggestions on our paper! We are delighted to hear that you appreciated the presentation of our work and our innovative method!
---
### On the computational cost of LoRA
We’d like to note that the computational ... | Summary: This paper provides a framework that fine-tunes a large vision-language model (VLM) with reinforcement learning (RL) for decision-making tasks requiring vision and language understanding.
In the framework, the VLM takes as input a state s_t that contains a visual observation o_t and input prompt v_t^{in}. The... | Rebuttal 1:
Rebuttal: Dear reviewer QEzb,
Thank you very much for your high appreciation of our work, we are glad to hear that you appreciated the novelty, technicality, and performance of our paper!
---
### Regarding the explanation of the limited performance on ALFWorld
Besides your appreciation, we would like to... | Summary: - This paper studies the training of vision-language models for decision-making tasks via reinforcement learning.
- The authors train a 7B parameter Llava model and a baseline CNN model with proximal policy optimization (PPO) on the alfworld and `gym_cards` environments.
- Additionally, the authors study the i... | Rebuttal 1:
Rebuttal: Dear reviewer RYx1,
Thank you very much for your high appreciation of our work. We are delighted to hear that you found our results in CoT and SFT insightful.
---
### General response
In addition to your appreciation, we plan to incorporate the following results and discussions into the updated... | Summary: The paper proposes an algorithmic framework for fine-tuning large vision-language models (VLMs) using reinforcement learning (RL) for multi-step decision-making tasks. The framework enhances VLMs' reasoning and decision-making capabilities by incorporating chain-of-thought (CoT) reasoning. The empirical result... | Rebuttal 1:
Rebuttal: Dear reviewer MKrq,
Thank you very much for your valuable review and your questions! We will definitely integrate them into the updated paper to address your concerns.
---
### General response
We sincerely thank you for your appreciation of our work and your insightful suggestions to improve o... | Rebuttal 1:
Rebuttal: Dear reviewers,
We would like to express our sincere gratitude for your overall positive recommendations and insightful suggestions regarding our work. Based on the overall feedback, **we have conducted two additional experiments**, which we believe further enhance the quality of our work. Specif... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Efficient Graph Matching for Correlated Stochastic Block Models | Accept (poster) | Summary: In this paper, the authors tackle two related problems: graph matching and community recovery of correlated balanced SBM under the logarithmic average degree regime. The authors extend proofs from the ER model for similar tasks and overcome the difficulties in the proofs raised by working with the SBM model.
T... | Rebuttal 1:
Rebuttal: Thank you for your review and comments!
We concur with the reviewer that lengthy papers present challenges with the conference format and its restrictions. However, we argue that this is a widespread general issue, not specific to our paper -- indeed, there are numerous NeurIPS papers with lengt... | Summary: The text presents progress in solving learning conditions within correlated stochastic block models with two balanced communities. The main result is the creation of the first efficient algorithm for graph matching. This algorithm works well when the average degree is logarithmic in the number of vertices and ... | Rebuttal 1:
Rebuttal: Thank you for your review and comments!
Comment #1 (runtime analysis):
In the literature on random graph matching, the quest for polynomial-time algorithms has been a major driving force behind the recent surge of papers on the topic. These culminated in the recent breakthrough works [33] and [... | Summary: The paper extends graph matching from correlated Erdos Reyni graphs to correlated SBM graphs.
To do so, they apply the recent breakthrough work of Mao, Wu, Xu, and Yu (STOC 2023) for ER graphs, which are based on counting a special kind of graph called chandeliers. A prerequisite for this method is to be able... | Rebuttal 1:
Rebuttal: Thank you for your review and comments!
Comment #1:
We would like to kindly make some clarifications regarding these comments.
Firstly, the correlated ER model is actually a special case of the correlated SBM model (not the other way around). Specifically, when the in-community edge density $... | Summary: The author(s) consider graph matching and community recovery on correlated stochastic block models. In the stochastic block model (SBM) the algorithm's input is a graph G generated by first partitioning vertices into two equal size clusters and then adding every intra-cluster edge with probability p and every ... | Rebuttal 1:
Rebuttal: Thank you for your review and comments!
We agree with the reviewer that there are several technical challenges to overcome, and a significant part of our contributions is indeed showing how to overcome these technical challenges.
However, we also believe that there are compelling conceptual ch... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper under revision addresses the problem of graph matching and community recovery in correlated stochastic block models (SBM) with two balanced communities. In particular, it studies the regime where the vertices of the parent graph have a logarithmic average degree, considering the within-community edge... | Rebuttal 1:
Rebuttal: Thank you for your review! | null | null | null | null | null | null |
Geometric Analysis of Nonlinear Manifold Clustering | Accept (poster) | Summary: The authors present a novel approach for nonlinear manifold clustering that comes with both a solid theoretical background and provable guarantees as well as some experiments indicating practical applicability.
Strengths: The paper is well-written and in this reviewers opinion fits very well into Neurips. The... | Rebuttal 1:
Rebuttal: We are grateful for your positive evaluation of our work, especially for acknowledging that *“the paper … in this reviewers opinion fits very well into Neurips”*. Thank you for the constructive comments, which we address below.
**How to choose hyperparameters:**
There are three hyperparameters o... | Summary: This paper attempts to present a theoretical analysis for a slightly modified sparse manifold clustering method, showing that under some condition on the data distribution, the separation between different manifolds and the curvature of the manifold, the optimal solution is point-wise subspace preserving and t... | Rebuttal 1:
Rebuttal: Thank you for your time in reviewing our paper. We are happy to address your comments below.
**(W1, W2, W3, W6) Comparison with SMCE**
It was questioned how our method differs from SMCE, why such a difference is needed, and if the difference is significant.
- We agree that our formulation (1.1)... | Summary: The authors addresses the problem of clustering high-dimensional data that lie on multiple, low-dimensional, nonlinear manifolds. They propose a new method that clusters data belonging to a union of nonlinear manifolds, providing geometric conditions for a manifold-preserving representation.
A significant con... | Rebuttal 1:
Rebuttal: Thank you for your strong support in the acceptance. Your comments are to the point, and we provide a reply below hoping to alleviate your concerns.
**How to choose the hyperparameter $\lambda$ in practice:**
Both reviewers XZ5T and Mh3Q pointed out this question. Our answer has two parts:
- Gu... | null | null | Rebuttal 1:
Rebuttal: Thanks to all the reviewers for their time and input on the paper. We appreciate that reviewers found the paper *novel and well-written* (XZ5T, Mh3Q), believe we provided *clear geometric conditions* (yHKx, XZ5T), *illustrated competitive performance* (XZ5T), and that our work is *relevant for man... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
UniTS: A Unified Multi-Task Time Series Model | Accept (poster) | Summary: This paper presents UNITS, a multi-task time series model that uses task tokenization to express predictive and generative tasks within a single model. UNITS can process heterogeneous time series with diverse variables and lengths without modifying the network architecture. Experiments show that UNITS demonstr... | Rebuttal 1:
Rebuttal: # Response to Reviewer bSMF Part I (Part I of II)
Thank you for your helpful feedback\! We appreciate your acknowledgment of the quality and novelty of our method and the state-of-the-art performance achieved by UniTS. Below, we address each of your concerns, provide further details, and present n... | Summary: The authors propose a unified model trained over multiple datasets to solve multiple tasks such as forecasting, imputation, anomaly detection, and classification. In the paper, the authors demonstrate their model abilities through extensive empirical results comparing with a large variety of baseline methods a... | Rebuttal 1:
Rebuttal: # Response to Reviewer EsEH Part I (Part I of II)
Thank you for your valuable feedback\! We appreciate your recognition of our novel architecture and methods, as well as the state-of-the-art results achieved in our work. We have carefully addressed each of your questions, expanded on implementatio... | Summary: This paper proposes UNITS, a multi-task time series model that handles multiple predictive and generative tasks within a single model. UNITS uses the mask modeling pre-training framework. To handle multiple downstream tasks, two new sets of tokens are concatenated with data tokens: 1)prompt tokens indicating t... | Rebuttal 1:
Rebuttal: # Response to Reviewer YrSc Part I (Part I of III)
Thank you for your valuable feedback\! We appreciate your recognition of the importance of multi-task learning on time series and the comprehensive experimental results in our work. We have carefully addressed each of your concerns, clarified our... | Summary: This work aims to present a pre-trained foundation model for time series. They proposed a model called UNITS, that performs multi-task learning (both generative and discriminative) on time series datasets. Specifically, they embed a transformer with prompt tokens and task tokens to perform prompt tuning or few... | Rebuttal 1:
Rebuttal: # Response to Reviewer h6vS Part I (Part I of IV)
Thank you for your detailed comments and valuable feedback. We appreciate your recognition of experimental results in our work. We have carefully addressed each of your concerns and have added new experiments and analyses based on your suggestions... | Rebuttal 1:
Rebuttal: Thank you to all the reviewers for thoughtful and insightful feedback\! We appreciate Reviewers acknowledging our contributions. Reviewers emphasized the importance of the issue studied, noting that **“using one shared model to model data from various domains and deal with multiple downstream task... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Generating Highly Designable Proteins with Geometric Algebra Flow Matching | Accept (poster) | Summary: This paper tackles de-novo protein design, focusing on structure generation, where the goal is to generate novel protein backbones. The paper builds on top of FrameFlow, a well-established and popular method that uses flow matching, a residue frame-based protein backbone representation, and equivariant neural ... | Rebuttal 1:
Rebuttal: We thank the reviewer very much for their detailed and helpful review. Below we will discuss the comments line by line.
**1. Scalability to larger proteins and datasets**
As suggested by the reviewers, we trained GAFL on the PDB dataset of FrameDiff, which contains proteins of length up to 512 a... | Summary: This paper introduces a new architecture based on Geometric Algebra Transformer (GAT) for protein backbone design. By using adapting the protein design IPA architecture with GAT, they train a SE(3) flow matching method to generate protein backbone. The contributions of the papers are essentially on the archite... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time invested in reading the paper and for their informative and constructive feedback. Below we will discuss the comments line by line.
**1. Training on longer proteins**
During the rebuttal period, we trained GAFL on longer proteins (on the FrameDiff dataset of ... | Summary: This paper extends frame-based protein backbone generation with projective geometric algebra. It allows higher-order geometric tensors in frame modeling as seen in EGNNs. Based on FrameFlow, it demonstrates great designability with relatively small increases in computational consumption.
Strengths: - Although... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time invested in reading the paper and for their constructive and helpful review. Below we will discuss the comments line by line.
**1. SE(3) and permutation equivariance of GAFL**
As in the original IPA formulation, SE(3) equivariance of the GAFL architecture is ... | Summary: This work focuses on the protein backbone generation task and improves FrameFlow, a flow-based protein backbone generation framework, based on geometric algebra. More specifically, this work proposes to represent residue frames with the elements of projective geometric algebra, which allows for the usage of hi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive and helpful review. Below we will discuss the comments line by line.
**1. Novelty of the architecture**
We consider the proposed model architecture as novel from a theoretical point of view since, to the best of our knowledge, it comprises the first m... | Rebuttal 1:
Rebuttal: We thank all reviewers cordially for reviewing our paper and appreciate their helpful comments.
We are happy to read that the reviewers find our approach novel (ZJDu,zVgj) and effective (ZJDu,5KkW), and consider the topic as important (zVgj,yVxp) and challenging (5KkW) and the paper to be clearly ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models | Accept (poster) | Summary: This paper discovers that the effect of classifier-free guidance in image-generating diffusion models varies with noise levels. It proposes a very simple method of performing guidance only at intermediate noise levels, demonstrating that this method can improve sample quality, diversity, and sampling cost both... | Rebuttal 1:
Rebuttal: Thank you for the review. We will next address the explicit questions:
>“Does the optimal interval vary with the model's performance? The results in Table 1 imply this, as the optimal intervals for EDM2 and DiT differ, and the optimal guidance weight for DiT is higher. If the optimal interval var... | Summary: This paper investigates the behavior of classifier-free guidance and proposes an adjustment in its application during sampling. Instead of applying a constant weight for the guidance scale across all sampling steps, the authors suggest that the guidance should be deactivated at high and low noise scales and ap... | Rebuttal 1:
Rebuttal: Thank you for the review. We will next address concerns and the explicit questions:
>“The main weakness of the work is a lack of discussion of existing works that address the issues of high-guidance scales.”
Thank you for providing pointers to additional relevant previous work. We are glad to ci... | Summary: This paper explains applying guidance in a limited interval improves sample and distribution quality in diffusion models, as shown in this title.
Strengths: The author provided intuitive figures, Fig.1 and 2, helped to understand the core of this paper easily.
The author provided a lot of experiments and resu... | Rebuttal 1:
Rebuttal: Respectfully, this terse review appears to present a personal preference, but makes no factually supported arguments to rebut. CFG is a crucial but poorly understood component of diffusion image generators, and despite its apparent simplicity, our technique extracts its key benefits while signific... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Recurrent Reinforcement Learning with Memoroids | Accept (poster) | Summary: This paper introduces the concept of *memoroids*, formalizing a method for computing reset-able episodes with recurrent models that use an associative operation for the latent state update. The authors argue that memoroids can simplify the computation of the loss function and further forego the error in gradie... | Rebuttal 1:
Rebuttal: Thank you for taking the time to read our paper and provide feedback. We are happy to hear that we present an "intuitive and simple, yet powerful generalization", and that our research is an "interesting and worthwhile endeavor".
We think your reject rating is quite harsh, given your comments. H... | Summary: The authors identify a shared structure in the update rules of linear recurrent models, analogous to monoids. Leveraging this insight, they introduce a mathematical framework called memoroids, which unifies the recurrent update rules of these models. They also derive an inline resettable memoroid, which elimin... | Rebuttal 1:
Rebuttal: Thank you for spending time to read and critique our paper. We are happy to hear that you consider our contributions novel. Below, we will respond to your questions and concerns.
### Weaknesses
> ...the experiment demonstrating that segment-based batching leads to poor recurrent value estimators ... | Summary: This paper proposes a new interpretation on how to sample from a buffer of data to avoid the well known trade-offs of truncated BPTT. The final proposed method interprets a recurrent networks as a monoid, and re-uses ideas developed for linear recurrent networks. This interpretation is time-invariant and enab... | Rebuttal 1:
Rebuttal: Thank you for taking the time to read our paper and provide useful feedback. We are glad to hear that our work "addresses an important problem" and that our "overall approach is well founded, and doesn't have any technical flaws."
Let us address your concerns and questions below.
### Weaknesses... | Summary: The authors present a novel approach to recurrent reinforcement learning aimed at improving efficiency and performance. They introduce the concept of memory monoids, algebraic structures used to represent and manipulate the memory of RL agents with recurrent components. They also rewrite Simplified State Space... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper, we are happy to hear that it was a pleasure to read!
> The experiments are carefully chosen to showcase certain features of memoroids. That said, they are fairly simple tasks which do not provide any insight about how well memoroids would perform on more challen... | Rebuttal 1:
Rebuttal: We thank the AC and all the reviewers for taking time to read our paper and provide useful feedback. In general, the reviewers consider our contributions beneficial:
`KMv5` writes
> The paper is very well-written. It was a pleasure reading this work!
> Clear experiments that demonstrate the supe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Few-Shot Adversarial Prompt Learning on Vision-Language Models | Accept (poster) | Summary: This paper proposes a few-shot adversarial training methodology for vision-language models to ensure robustness in downstream tasks of pretrained vision-language models. Specifically, instead of using traditional adversarial training methods, it adapts the TRADES loss, a prominent adversarial training approach... | Rebuttal 1:
Rebuttal: ### Official Response to AnonReviewer Rdwb:
We are genuinely grateful for your detailed assessment and valuable insights. Your constructive feedback has significantly contributed to the refinement and advancement of our study.
**1. [Re Weakness 1: How we solve drawbacks of previous methods.]**
... | Summary: This paper introduces a novel few-shot adversarial prompt framework for enhancing the adversarial robustness of vision-language models. The authors propose a method that achieves state-of-the-art zero-shot adversarial robustness using only 1% of training data, addressing limitations of existing approaches such... | Rebuttal 1:
Rebuttal: ### Official Response to AnonReviewer M4S3:
We sincerely thank you for your comprehensive examination of our paper and value the thoughtful feedback you have offered. Your helpful suggestions have played a crucial role in improving the overall quality of our research.
**1. [Re Weakness 1: Genera... | Summary: Adversarial prompt learning on vision-language models has traditionally focused on aligning text with corresponding images to ensure coherence and contextual accuracy. This paper extends this approach by making the image features of natural and adversarial examples distinct while still aligning them with the r... | Rebuttal 1:
Rebuttal: ### Official Response to AnonReviewer Lycp:
We sincerely thank you for your careful reading of our paper and appreciate the valuable feedback in your comments. The insightful and constructive suggestions have enabled us to effectively improve our work.
**1. [Re Weakness 1: Depth of Analysis for ... | Summary: This paper addresses adversarial robustness for image classification with VLMs (e.g. CLIP model). To this end, the authors proposed the FAP framework to adapt VLM models by learning prompt tokens in a few-shot manner with adversarial examples. The loss function is proposed to promote accuracy on clean images, ... | Rebuttal 1:
Rebuttal: ### Official Response to AnonReviewer KV22:
We deeply appreciate your thorough review of our manuscript and are grateful for the insightful feedback you provided. Your constructive comments have been instrumental in enhancing the quality of our work.
**1. [Re Weakness 1: Concerns about the compr... | Rebuttal 1:
Rebuttal: ### General response
We appreciate the reviewers’ insightful comments and constructive feedback on our manuscript. We are pleased to receive positive feedback from most of the reviewers. Furthermore, we are delighted to learn that the reviewers found the idea of the proposed method to be novel an... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Hyperbolic Embeddings of Supervised Models | Accept (poster) | Summary: Summary
This paper proposes a novel approach to embed supervised models, specifically decision trees and their ensembles, in hyperbolic space using the Poincaré disk model. The paper contributes three main advancements: linking loss functions for class probability estimation to hyperbolic embeddings, providing... | Rebuttal 1:
Rebuttal: We thank the reviewer for a review whose strengths (1-3) perfectly capture our key contributions, in particular the fact that we embed *models*, not *data*.
We would like to highlight the common point to all three weaknesses mentioned (1-3): they all come down to putting substantial material in a... | Summary: This work proposes a hyperbolic implementation of decision tree models, providing a link providing a link between class probability estimation and hyperbolic distances. In addition the authors identify key objectives/criteria for enabling “clean” hyperbolic embeddings, which is achieved by maintaining monotoni... | Rebuttal 1:
Rebuttal: We really appreciate the comment that our method “[...] addresses many fundamental issues in hyperbolic learning presenting [...]”. This is a strong selling point for our method, which unfortunately comes with a challenging presentation problem. We hope our rebuttal helps in clarifying views and q... | Summary: This paper introduces a method to embed decision trees into hyperbolic space, linking class probability estimation with hyperbolic distances. It proposes extracting monotonic subtrees and using a "tempered" integral for better visualization. The approach aims to enhance model interpretability and analysis with... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing a uniform “good” rating on all three paper metrics, highlighting that our approach is novel, our method is mathematically grounded and visualization is intuitive.
## weaknesses
> The authors present their method primarily on small-scale UCI datasets [...] la... | Summary: This paper presents an approach to embed the models (not data) into hyperbolic spaces. Hyperbolic spaces are especially suited for embeddings of hierarchies, but recent works have so far focused on embeddings of data.
The paper presents a framework consisting of measures to estimate the confidence of model's p... | Rebuttal 1:
Rebuttal: We thank the reviewer for noting that our approach is “[...] *very original* [...]” – it is indeed the first of its kind and we believe the problem we address is of broad importance. We also appreciate the comments on the formal part of our approach.
## strengths
> In my opinion, the methods for... | Rebuttal 1:
Rebuttal: ## To all reviewers
[ALL-1] We would like to thank all reviewers for granting unanimous approval on our paper from the “soundness” (“good” or “excellent”) and “contribution” (all “good”) standpoints. We understand the presentation is the bottleneck of our paper, which we attribute to the fact tha... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Evidential Mixture Machines: Deciphering Multi-Label Correlations for Active Learning Sensitivity | Accept (poster) | Summary: The paper introduces an active learning approach based on Evidential Mixture Machines (EMM) that compress the large, sparse label space of multi-label problems into a more manageable weight coefficient space. This approach combines mixtures of Bernoulli with a Deep Evidentiary models, and it leverages multiple... | Rebuttal 1:
Rebuttal: **Q1: (i) Introducing a motivating real-world example, and (ii) adding a section with an intuitive running example that explains in layman terms the contribution of each of EMM's components, together with the synergy among them.**
Thank you for this great suggestion! For the motivation real-worl... | Summary: This manuscript investigates multi-label active learning, a critical issue in contemporary machine learning. To address this challenge, the authors introduce a novel evidential mixture machines (EMM) model, which provides an uncertainty-aware connection from input features to the predicted coefficients and com... | Rebuttal 1:
Rebuttal: **Q1: How do the authors determine the parameters in the student-t distribution?**
The parameters are a crucial component of the Bayesian nature of the evidential model. The parameters for the student-t distribution ($\pi,\gamma,\frac{\beta(1+\nu)}{\nu\alpha},2\alpha$) are all obtained from netw... | Summary: This paper focused on multi-label classification problems in active learning settings, where the label relationship, especially for rare labels, is hard to learn. The authors proposed an Evidential Mixture Machine (EMM) that combines the mixture of Bernoulli with a deep evidential model, which allows joint lea... | Rebuttal 1:
Rebuttal: Thank you for providing constructive comments/suggestions. Below, we provide the response to the questions and comments.
**Q1: Typos and confusion.**
Thank you for pointing out the typos. We will correct them in the revised paper. The EMM model does perform better on Corel 5k and BibTex in the ... | Summary: This paper introduces the Evidential Mixture Machines (EMM) model, which addresses the multi-label active learning tasks, particularly in rare-class scenarios. EMM uses a mixture of Bernoulli distributions to capture label correlations and uses evidential learning to quantify uncertainties for more informed ac... | Rebuttal 1:
Rebuttal: Thank you for providing constructive comments/suggestions. Below, we provide the response to the questions and comments.
**Q1: Is the model robust to outliers?**
Thank you for the suggestion for this interesting challenge. We agree that being robust to outliers is an important challenge in real-... | Rebuttal 1:
Rebuttal: **General Response**
We would like to thank all reviewers for spending time to review our paper and providing constructive comments/suggestions. Below, we summarize some of our major responses:
- *Our contributions and how EMM could be used*: As stated in lines 83-87, our contributions include t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models | Accept (poster) | Summary: This paper introduces an automatic red-teaming framework that mines in-the-wild user chat logs and discovers various unique clusters of novel jailbreak tactics, which results in more diverse and successful adversarial attacks compared to state-of-the-art jailbreaking methods. Based on this framework, this pape... | Rebuttal 1:
Rebuttal: We greatly appreciate reviewer `wDdc` for their recognition of our comprehensive study and thorough experiments and for their support in accepting the paper. We will address their questions in the following section and are happy to follow up during the discussion period for any further inquiries.
... | Summary: The paper introduces WILDTEAMING, an automatic red-teaming framework that mines user-chatbot interactions to discover novel jailbreak tactics against large language models (LLMs). It uncovers vulnerabilities of frontier LLMs, leading to more diverse and successful adversarial attacks. The authors also created ... | Rebuttal 1:
Rebuttal: We greatly appreciate reviewer `idQi`'s thorough, insightful questions & constructive suggestions. We're delighted by the recognition of our work's originality, quality, clarity, & significance. **In general response we answer 4 shared questions, and respond to other questions below.** Our respons... | Summary: This work proposes, for LLM safety, a new jailbreak attack method, WildTeaming, and a new safety alignment dataset, WildJailbreak. WildTeaming first manually identify seed jailbreak tactics from large-scale in-the-wild adversarial user query datasets and then compose jailbreak attack by rewriting a vanilla har... | Rebuttal 1:
Rebuttal: We greatly appreciate reviewer `ZrdC` for recognizing our work’s unique open-source contribution with “scale and diversity”, our “effort of identifying comprehensive tactics for jailbreak”, and our method being “interesting”. We address their insightful questions in the following section and are h... | Summary: This paper introduces WILDTEAMING, an automated red-teaming framework designed to enhance the safety of large language models (LLMs) by identifying and mitigating jailbreak tactics from user interactions. The framework consists of two main steps: mining in-the-wild user-chatbot interactions to discover novel j... | Rebuttal 1:
Rebuttal: We greatly appreciate reviewer `e44H` for recognizing our work’s unique contribution in identifying a “more diverse and realistic set of adversarial challenges than previous methods”, our significant open-source contribution, and our “thorough evaluations” and “detailed analyses”. **We respond to ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their positive reviews and constructive suggestions! We are enlightened to see all reviewers recognize our thorough experiments and evaluations. Additionally, we thank reviewers for recognizing our open-sourced effort (`e44H`, `ZrdC`, `idQi`), the novelty of our method (... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Task-oriented Time Series Imputation Evaluation via Generalized Representers | Accept (poster) | Summary: This paper studies the problem of evaluating time series imputation methods in terms of the performance on downstream tasks. It proposes a fine-grained metric and uses RKHS to efficiently estimate the metric. Experiments demonstrate that the proposed method achieves better estimation than the influence functio... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and we have addressed each of the concerns raised by the reviewer as outlined below.
>Weakness 1
Thank you for your comments and we will revise the relevant wording in the revised version to attribute more properly. In addition, we would like to cl... | Summary: The authors propose a strategy that evaluates the effectiveness of various imputation methods used to fill missing values at different timestamps (time series). The effectiveness of each imputation method is evaluated based on the downstream task gain. Subsequently, rather than filling missing values with a ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and we have addressed each of the concerns raised by the reviewer as outlined below.
>Weakness 1
We sincerely apologize for the misunderstanding we caused, and we will make detailed modifications in the revised version.
>Weakness 2 & Question 7 & ... | Summary: The paper proposes a task-oriented time series imputation evaluation approach that assesses the impact of different imputation strategies on downstream forecasting tasks, rather than just the accuracy of the imputed values. The authors introduce a similarity-based method to efficiently estimate the impact of i... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and we have addressed each of the concerns raised by the reviewer as outlined below.
>Weakness 1
We apologize for the misunderstanding caused by our statements. Our main focus here is on time series forecasting tasks. Compared with other time serie... | Summary: This paper presents a novel strategy to evaluate time series imputation methods based on their impact on downstream tasks, without requiring multiple model retrainings. The proposed method leverages a similarity calculation to estimate the effect of imputed values efficiently, balancing performance and computa... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and we have addressed each of the concerns raised by the reviewer as outlined below.
>Weakness 1
To our knowledge, we are the first to examine the impact of missing values as training labels on downstream forecasting tasks, so there is no absolutel... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their valuable time and detailed comments, and we appreciate that the reviewer recognized the strength of our paper like the **theoretical analysis**(Reviewer **aC85**, **wGbj**), **innovative perspective**(Reviewer **wGbj**, **56k9**, **xbnY**), and **prac... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper focuses on the imputation of missing values in time series. By noticing that different imputation methods might affect the downstream forecasting tasks, this paper proposes the imputation evaluation approach regarding the downstream tasks' performance.
Then, the authors also developed some methods ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and we will address each of the concerns raised by the reviewer as outlined below.
>Weakness 1
Firstly, we would like to clarify the importance and necessity of handling missing values even though there is just a small difference. The phenomenon of ... | null | null | null | null | null | null |
BLAST: Block-Level Adaptive Structured Matrices for Efficient Deep Neural Network Inference | Accept (poster) | Summary: This paper proposes to compress linear layers by using a block low-rank structure with shared basis -- also known as the BLR^2 matrix structure. The basis U, V, and diagonal weights s of the low-rank block are computed through a gradient-based optimization that minimizes the Frobenius norm between the dense an... | Rebuttal 1:
Rebuttal: ### Q1. The BLAST matrix in this paper is an existing matrix structure known as BLR^2. The BLR^2 paper should be cited, and all mention of BLAST should be replaced with BLR^2.
Thank you for suggesting an important related work\! We agree that BLR^2 \[Ashcraft, Buttari & Mary, 2021\] should be cit... | Summary: The authors propose a learnable compressed representation (BLAST) for weight matrices used in deep learning which enables lower complexity matrix multiplications which approximate the full, uncompressed operation. A BLAST matrix decomposes the original matrix into a grid of blocks of diagonal matrices with sh... | Rebuttal 1:
Rebuttal: ### Q1. SparseGPT deserves mention.
We agree that SparseGPT \[Frantar & Alistarh, 2023\] is an interesting related work that deserves mention. SparseGPT uses *unstructured pruning,* which makes it inefficient compared to other structured matrices (including BLAST) for GPU execution since the prun... | Summary: This paper attempts to find efficient structures in weight matrices of deep learning models. The basic idea is to learn a group of block-wise low-rank matrices via gradient descent. The proposed method replaces original dense weight matrices and hence needs retraining.
Strengths: * The paper introduces an ori... | Rebuttal 1:
Rebuttal: ### Q1. The significance of the proposed method remains unclear. Compared with prior methods such as Gaudi-GBLR \[14\], the accuracy results of this work do not show consistent superior performance nor with a convincing explanation.
Please refer to Q3 of “Global Comments.”
### Q2. The method doe... | null | null | Rebuttal 1:
Rebuttal: We appreciate all reviewers’ efforts and constructive suggestions. We are encouraged by the reviewers’ positive feedback, specifically in that “the proposed approach seems to be entirely novel” (Reviewer xrxa) and that our approach “shows an improvement in validation accuracy compared to existing ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Interpreting and Analysing CLIP's Zero-Shot Image Classification via Mutual Knowledge | Accept (poster) | Summary: The paper investigates Contrastive Language-Image Pretraining (CLIP) for zero-shot image classification by exploring the mutual knowledge between visual and textual modalities. The study examines which concepts are commonly learned by both CLIP encoders and how they influence the shared embedding space. Using ... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your time and effort in reviewing our paper, and for the positive feedback. We address your concerns below:
> The introduction or Figure 1 does not clearly explain the motivation of the paper, making it difficult for me to understand.
Our main objective is to interpr... | Summary: The work deals with explainable artificial intelligence (XAI) in a multimodal (text-image) context. The proposed approach consists of first identifying the most important patches of a collection of images, and associating them to a textual description through CLIP. Hence, relying on a textual description of bo... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your time. We will address all your concerns here and will include all of them in the revised manuscript.
> it is not clear what is "the zero-shot predicted class" nor how it is obtained
The process of how CLIP performs zero-shot prediction is already described in l... | Summary: The authors propose to interpret CLIP models for image classification from the lens of mutual knowledge between the image and text encoders of CLIP. Specifically, the authors use textual concepts as the common medium of the two modalities by mapping visual concepts to textual concepts. The authors then calcula... | Rebuttal 1:
Rebuttal: Dear Reviewer, We thank you for your time and effort and for the valuable feedback you provided. We will address each of your concerns below:
> How are the descriptors generated? What specific prompts and LLM are used?
In the supplementary manuscript (line 843), we mentioned that we directly u... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Crafting Interpretable Embeddings for Language Neuroscience by Asking LLMs Questions | Accept (poster) | Summary: This study proposes QA-embedding for natural language text for multiple downstream tasks. Early embedding methods like bag-of-words and BM-25 cannot capture nuanced semantic feature of a sentence, and recent works primarily utilize the hidden states of large language models (LLM) as text embeddings, which lack... | Rebuttal 1:
Rebuttal: Thank you for your time and thoughtful comments - they have helped us to improve the paper.
**W1. Re: implementation procedures** - thanks, we have added some notes on the task setup as well as an additional section A.3 describing the full fMRI data collection details and preprocessing.
**W2. Re... | Summary: The paper explores obtaining interpretable embeddings through LLM prompting. To address the opaque nature of text embeddings, the authors introduce question-answering embeddings (QA-Emb) by asking LLMs a set of yes/no questions. Specifically, these questions are generated by GPT-4 using predefined prompts. The... | Rebuttal 1:
Rebuttal: Thank you for your time and thoughtful comments - they have helped us to improve the paper.
**(1) Re: prompt sensitivity** – Indeed prompting LLMs for this application requires some manual choices, although this can be a good thing for helping to inject domain knowledge into the problem (differen... | Summary: The paper proposes a method of prompting LLMs with a list of yes or no questions to obtain binary embeddings for texts. The list of questions is generated by prompting the LLM with task-specific knowledge. The proposed method, QA-Emb, is evaluated primarily on predicting fMRI voxel responses to texts. QA-Emb o... | Rebuttal 1:
Rebuttal: Thank you for your time and thoughtful comments - they have helped us to improve the paper.
**1) Re: question generation** – We wholeheartedly agree question generation is important and have added new experiments extending the results in Table A2 to analyze the process of question generation. We ... | Summary: The authors introduce question-answering embeddings (QA-Emb), where each feature in the embedding corresponds to an answer to a yes/no question asked to an LLM (e.g., LLaMA-3 8B). QA-Emb significantly outperforms an established interpretable baseline in predicting fMRI voxel responses to language stimuli. The ... | Rebuttal 1:
Rebuttal: Thank you for your time and thoughtful comments - they have helped us to improve the paper.
(1) Re: Motivation – Yes indeed, the entire pipeline at inference-time can use only smaller models (e.g. RoBERTA). We see this is a major strength, as it drastically reduces the inference cost of applying ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning De-Biased Representations for Remote-Sensing Imagery | Accept (poster) | Summary: In remote sensing, many datasets have a long-tail problem. As a result, models trained on these datasets often perform much worse on the tail classes than on the head classes. The authors propose a fine-tuning strategy called debiased LoRA that addresses long-tail distribution problems. Their proposed approach... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive feedback on our paper's clarity, effectiveness, and novelty! Thank you for these encouraging comments. We are also grateful for your insightful suggestions regarding ablation studies and the generalization of our method. We have addressed each point in detail ... | Summary: The authors propose a framework called debLoRA for adapting remote sensing foundation models. This approach aims to learn a de-biased feature representation which improves classification/detection performance on rare classes. Performance is assessed on transfer to two different RS datasets.
Strengths: Remot... | Rebuttal 1:
Rebuttal: **Weakness 1 & Question 1: Lack of clarity**
We sincerely appreciate the comment! In the revision, we will perform careful proofreading, *e.g.*, 1) by simplifying complex clauses; 2) by reducing starting sentences with ambiguous "It"; and 3) by fixing the issues noted by reviewer: correct modifie... | Summary: This work highlights the long-tail problem in transferring existing foundation models to RS domains, and provide a interesting pipeline consisting of clustering, calibration, and training.
1. A comprehensive summary on the transferring from natural images or between RS domains are provided.
2. Representation... | Rebuttal 1:
Rebuttal: We greatly appreciate your thorough review and insightful comments! Your positive remarks on our paper's structure, historical summary, and method effectiveness are encouraging. We value your critical questions and have addressed them in detail below. Thank you for helping us improve our research.... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their valuable feedback and constructive comments. We are pleased that the reviewers have recognized our work as:
- **Well-written** (Reviewer r61t) and **very well written** (Reviewer meTz)
- Technically sound (Reviewer r61t and meTz)
- **Impressive results o... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MGF: Mixed Gaussian Flow for Diverse Trajectory Prediction | Accept (poster) | Summary: This paper proposes to solve the trajectory prediction task by normalizing flow based model with mixture Gaussian assumption. The trajectories are clustered to multiple Gaussian distributions in the pre-processing stage over training data. During inference, priors are sampled from the mixture Gaussian distribu... | Rebuttal 1:
Rebuttal: **Mixed Gaussian assumption:** Our motivation is the difficulty of transforming a single-modal and symmetric original distribution to a complex target distribution by normalizing flow. We seek a solution to relieve the difficulty. We choose the problem setting to better provide a fair quantitative... | Summary: Due to the asymmetric and multi-modal nature of future trajectories, the authors point out that the standard Gaussian prior with a neural network-based transformation is insufficient for probabilistic trajectory prediction. They propose Mixed Gaussian Flow (MGF), a method that uses the mixed Gaussian prior in ... | Rebuttal 1:
Rebuttal: **Theoretical support behind our claim:** Great suggestion! Our claim about the difficulty of training normalizing flow to transferring a naive and symmetric distribution, e.g., simple Gaussian, to a complex distribution, is mainly from our empirical observations. But there are some insights behin... | Summary: The authors proposed a new normalizing flow-based human trajectory prediction method called Mixed Gaussian Flow (MGF), which promotes diversity and controllability of prediction. The model uses a mixture of Gaussian model as the initial distribution to transform, rather than single-modal standard Gaussian dist... | Rebuttal 1:
Rebuttal: **Writing issues:** We appreciate the detailed suggestions about writing, we could consider them and improve the writing details. L332 should be ADE and FDE.
**Normalizing flow overfitting:** We observe that normalizing flow tends to overfit to the single mode value (ground truth annotation) whe... | null | null | Rebuttal 1:
Rebuttal: # General Response (GR)
We thank all the reviewers for their valuable suggestions and comments. We add new experiments in the separate pdf file to assist our responses to the reviewers' questions. We would also resolve the writing issues mentioned by reviewers in the paper revision.
Pdf: /pdf/b53e... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
S2HPruner: Soft-to-Hard Distillation Bridges the Discretization Gap in Pruning | Accept (poster) | Summary: The paper titled "S2HPruner: Soft-to-Hard Distillation Bridges the Discretization Gap in Pruning" introduces a novel framework designed to address the challenges associated with the discretization gap in neural network pruning techniques. The authors propose a method that bridges the gap between the representa... | Rebuttal 1:
Rebuttal: **Q1: Figure 2 is recommended to explicitly show soft and hard networks.**
*Answer:* Thanks for providing the suggestion. We will emphasize the concept of "soft" and "hard" in Figure 2 and try our best to make it more comprehensible. The revised version can be referred to in Figure t2.
*... | Summary: In this article, the author proposes using a 0-1 mask (hard network) and a differentiable mask (soft network) with an accuracy gap as a starting point for network distillation, where the distillation function selects Kullback Leibler divergence as the gap measure（S2HPruner）. This method was tested on datasets ... | Rebuttal 1:
Rebuttal: **Q1: Explain the meaning of "bidirectional" in detail.**
*Answer:* We use "bidirectional" to describe that the knowledge transfer in our method is bidirectional. The soft network transfers knowledge to the hard one, improving the performance of the hard network. Simultaneously, the hard network ... | Summary: Discretization in pruning poses a huge threat to network performance. To alleviate this issue, the paper proposes S2HPruner, a pruning method that leverages distillation. In details, the pruning process involves two networks that share the same architecture. The difference is that the teacher network has a wei... | Rebuttal 1:
Rebuttal: **Q1: The typos in Abstract and the inconsistency of gradient notations in the pseudocode and the equations.**
*Answer:* Thanks for the suggestion. We will fix the typos and align the notation of gradients in the pseudo code and the equations.
**Q2: In Table 4, the epochs should be indic... | null | null | Rebuttal 1:
Rebuttal: Thanks for the valuable feedback provided by all reviewers. We appreciate the reviewers MZh4 (R1), aPhZ (R2), and ECuu (R3) for approving our contributions: (1) innovative method (R1, R3), (2) well-developed experiments (R2), (3) good writing and easy to follow (R2, R3). Besides, the concerns are... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Novel Unified Architecture for Low-Shot Counting by Detection and Segmentation | Accept (poster) | Summary: This paper introduces GeCo, a novel unified architecture for low-shot counting that integrates object detection and segmentation. GeCo addresses the limitations of current state-of-the-art methods by generalizing object prototypes across diverse appearances and introducing a new counting loss that directly opt... | Rebuttal 1:
Rebuttal: >**Why was SAM backbone chosen over other backbones?**
We primarily wanted to use SAM as a box refiner, due to its accurate mask prediction, which can easily be converted to a bounding box. For computational efficiency and to keep the framework unified, we then decided to also use the SAM backbon... | Summary: Paper tackles the task of few-shot and zero-shot class agnostic counting, and presents Geco, a unified counting framework that can detect, segment and count objects. Geco uses SAM backbone for feature extraction, and implements Dense Query Encoder (DQE) and Dense Query Decoder (DQD) to detect prototypes. For f... | Rebuttal 1:
Rebuttal: >**Prototype construction is conceptually similar to related methods.**
While Eq. 1 is indeed conceptually similar to the existing methods, there are fundamental differences in the function of the output and subsequent steps (Eq. 2).
The existing methods perform detection by correlating exemplar... | Summary: This paper address the issue for low-shot and zero-shot object counting, with an object detection-based approach. The proposed method heavily uses SAM framework, to provide feature embeddings and refine detection boxes. Attention-based feature aggregation and SAM-HQ are used to get the final features for objec... | Rebuttal 1:
Rebuttal: >**GeCo uses SAM, trained on the SA-1B dataset, as its backbone, potentially benefiting by seeing diverse data.**
Empirical evidence in the Experimental results (Section 4.1) indicates that the key performance gain does not come from the amount of training data in the SAM backbone. For example: t... | null | null | Rebuttal 1:
Rebuttal: Firstly, we sincerely thank the reviewers for their constructive feedback and hope that responses to your questions clarify the strengths and innovative aspects of GeCo. We appreciate the recognition of novelty and state-of-the-art counting and detection performance. In the following, we summarize... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Phased Consistency Models | Accept (poster) | Summary: The paper introduces the Phased Consistency Model (PCM), which enhances stability and speed in high-resolution image and video generation by improving the design of consistency (trajectory) models.
The main improvement of PCM is a new Parameterization of consistency function and the phase trajectory. There i... | Rebuttal 1:
Rebuttal: Thank you for the in-depth review. We appreciate the chance to more comprehensively compare PCM with CTM.
Q1:
**CIFAR-10**
We additionally implement the core idea "phasing" technique into CIFAR (unconditional generation) and ImageNet (conditional generation). For CIFAR, we train our models wit... | Summary: The paper titled "Phased Consistency Model" (PCM) introduces a novel model designed to address the limitations of Latent Consistency Models (LCMs) in high-resolution, text-conditioned image generation. The authors identify three primary flaws in LCMs: inconsistency, controllability, and efficiency. PCM is prop... | Rebuttal 1:
Rebuttal: Thank you for your very positive review, and constructive suggestions.
Q1: In terms of human evaluation metrics, we have re-evaluated the generation results of our method alongside all the comparative baselines mentioned in the paper. This re-evaluation was conducted over varying steps: 1, 2, 4, ... | Summary: The paper investigates three issues of Consistency Models on Latent space, thereby making a proposal named Phased Consistency Model that handles these weaknesses, supported by theoretical proofs and derivations. To assess the efficacy of their proposed solution, the authors conduct extensive experiments on two... | Rebuttal 1:
Rebuttal: Thank you for your recognition in our work and for providing constructive feedback on our paper.
Q1: Pre-defined number of sub-trajectories seems to fix the number of inference steps.
1. Note that inherently, each sub-trajectory of PCM can be perceived as a normal CM. Therefore, within each sub-... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their valuable comments and suggestions. We are sincerely grateful to the reviewers for dedicating their time and effort to review our work. We are delighted to see reviewers commenting on our paper with "detailed proofs", "solid and comprehensive experimental... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CONTRAST: Continual Multi-source Adaptation to Dynamic Distributions | Accept (poster) | Summary: This paper introduces a novel continual multi-source adaptation method to tackle a new Test-Time Adaptation (TTA) task involving dynamic distributions.
The method integrates multiple source models to adapt continuously to the evolving test data distribution.
It efficiently computes the optimal combination weig... | Rebuttal 1:
Rebuttal: **W1**: To demonstrate the linear combination of source distributions, we devise the following experiment. We linearly combine or blend same images from the test set of the Snow and Fog domains of CIFAR100-C using two different sets of weights. We then use CONTRAST to predict on the test set and a... | Summary: This paper introduces a new task to consider continual mutli-source test-time adaptation to dynamamically evolving distributions. A framework is proposed consisting of two key step: (1) learning the combination weights and (2) identify the most correlated source model to update. In order to speed up the optimi... | Rebuttal 1:
Rebuttal: **W1**: (a) There are two variables, $\( j \)$ and $\( t \)$, where $\( j \)$ is the index of the source model and $\( t \)$ is the index of the test batch. The $\( t \)$-th test batch refers to the batch of data streamed at time step $\( t \)$. Thus, $\( \theta_j^t \)$ represents the distance of ... | Summary: The work introduces a new framework called CONTRAST, designed for dynamically combining multiple pre-trained source models during testing to adapt to changing target data distributions. For each test batch, CONTRAST learns the optimal combination weights of the source models, ensuring that the test error of th... | Rebuttal 1:
Rebuttal: **W1**: Since there are no prior works on dynamic multi-source adaptation in test time, we do not have a direct baseline for comparison. Therefore, we followed the protocol of the first multi-source Unsupervised Domain Adaptation (UDA) method ([18]), where we compared our approach with the best so... | Summary: The manuscript address continual learning in the context of adaptation to multiple data distributions. The method employs a model ensemble for unsupervised domain adaptation to the dynamically evolving distributions.
The weights denoting the contribution of each model are calculated through optimization.
The ... | Rebuttal 1:
Rebuttal: **W1**: Thank you for pointing these out. We will fix these references in the camera-ready version.
**Q1**: When multiple source models are highly correlated and have nearly equal weights, updating all the models is an option. However, while updating all models might be effective in the short ter... | Rebuttal 1:
Rebuttal: In this paper, we propose CONTRAST, a novel method for continual adaptation to dynamic streaming data using multiple source models, without requiring access to source data. CONTRAST combines these models to adapt to test data that arrive in small batches without access to the original source data.... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs | Accept (oral) | Summary: The paper introduces a method called DuQuant, a new quantization method specialized for LLMs. The paper notes that "massive outliers" cause previous quantization approaches to be less effective or powerful, and then proposes a new quantization method which ameliorates the effect of such massive outliers. Theor... | Rebuttal 1:
Rebuttal: Thank you sincerely for your thoughtful and positive feedback on our work. We are particularly grateful for your recognition of the various aspects of our research. Below, we have provided a detailed explanation for your remaining concern as follows. Please do not hesitate to let us know if you ha... | Summary: This work proposed a transformation (composition of orthogonal and permutation transformation) that makes LLMs more quantization-friendly (accounting for the presence of outlier features). The approach is validated on several modern LLMs from Llama-1,2,3 families.
Strengths: The introduced method makes sense ... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's constructive comments on our paper. We will respond to the reviewer's feedback with detailed explanations for each point.
**W1**: Highlight of Ours and detailed comparison with QuaRot.
> - We appreciate the reviewer's comments. We have dedicated **Appendi... | Summary: The paper presents a new post-quantization method (DuQuant) that targets low-precision (4-bit / 6-bit) weight and activation quantization. The authors show how the presence of massive outliers affects quantization when using existing methods (smoothing is not sufficient with SmoothQuant / OmniQuant training is... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for providing valuable feedback. We detail our response below point by point. Please kindly let us know whether you have any further concerns.
**W1**: More evaluations on generative tasks.
> - To better access the generative ability of quantized models, we evaluate... | Summary: The paper explores new approaches in LLM quantization. The work tries to address the performance degradation due to massive outliers in the weights. The work show competitive performance across different settings, up to 4-bit weight activation quantization. The work also provides solid experiments and visualiz... | Rebuttal 1:
Rebuttal: **W1**: Confusion Regarding the Discrepancies Between Table 1 and Table 2 Results.
> - We would like to provide individual clarifications for the results in Table 1 and Table 2 below and explain the reasons behind their discrepancies.
> - **Table 1** presents perplexity (PPL) results on the WikiT... | Rebuttal 1:
Rebuttal: ### **General Response for All Reviewers**
>**Summary**:
>
> We sincerely thank all reviewers for their valuable time and insightful feedback, which is very helpful in further improving the quality of our paper. We are grateful that the reviewers appreciate (1) "the technical contributions of du... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes a new LLM quantization method named DuQuant (for Dual transformations Quantization). This method is able to quantize the weights and activations of an LLM to 4 or 6 bits without losing significant precision.
The paper identifies the issue of Massive outliers in the activations of a LLM. The... | Rebuttal 1:
Rebuttal: Thanks for your time in dealing with our work. We will answer the question and discuss point by point as follows. We hope that our response satisfactorily addresses the issues you raised. Please feel free to let us know if you have any additional concerns or questions.
**W1**: The comparison with... | null | null | null | null | null | null |
Autonomous Agents for Collaborative Task under Information Asymmetry | Accept (poster) | Summary: This paper focuses on the cooperation of LLM-based agents under the information asymmetry condition, which is a practical problem in the real world. It provides a clear definition of this new scenario. It proposes the method of InfoNav and mixed memory to improve the capability of agents. It constructs a new b... | Rebuttal 1:
Rebuttal: Thank you very much for your outstanding insights and suggestions regarding agent memory! The reference you shared, "A Survey on the Memory Mechanism of Large Language Model Based Agents," has been particularly enlightening, and we will include it in our related works. **Due to rebuttal length lim... | Summary: This paper studies the asymmetry of information handled by agents that represent users, i.e., each agent can only access the information of its human user, not others. To address this issue, the authors proposed Informative Multi-Agent Systems (iAgents) and a benchmark called InformativeBench.
Strengths: 1. T... | Rebuttal 1:
Rebuttal: Thank you very much for your careful review. Below is a detailed point-by-point response addressing your main concerns.
**Q1**: InfoNav benefits the most through the recursive communication
**A1**: InfoNav and recursive communication are two parallel designs within iAgents, and there is **no sit... | Summary: The paper presents an innovative approach to addressing the challenge of information asymmetry in multi-agent systems (MAS), a barrier to effective collaboration in various tasks. The paper introduces iAgents (Informative Multi-Agent Systems), designed to navigate and mitigate information asymmetry by enhancin... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and feedback. Below is a detailed point-by-point response addressing your main concerns. **Due to rebuttal length limits, we only reply to questions and leave the discussion (like a theoretical discussion on iAgents) in official comments.**
**Q1**: a more detail... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
P$^2$C$^2$Net: PDE-Preserved Coarse Correction Network for efficient prediction of spatiotemporal dynamics | Accept (poster) | Summary: The authors present a P^2C^2Net framework for efficiently solving and simulating PDEs, specifically on coarse grids. To overcome the challenges of simulating PDEs on coarse grids, including the difficulty of estimating numeric derivatives and the inaccuracies in the right-hand side of PDEs on coarse grids, the... | Rebuttal 1:
Rebuttal: Thank you for achknowledging the data-efficiency and satisfactory performance on OOD tests of our work. We address your concerns as follows.
### Weakness
> **Q1(a). Concerns on the larger size of the Poisson solver and neural correction modules compared to baselines.**
The Poisson solver is ess... | Summary: The paper solves the problem of predicting complex spatiotemporal dynamics on coarse mesh grids with only a small set of data. It proposed a learnable symmetric Conv filter to estimate the spatial derivatives on coarse grids and incorporated RK4 for correcting coarse solution at low resolution.
Strengths: The... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the novelty and the effectiveness of our proposed method. We address your concerns as follows.
### **Weakness**
> **Q1. Clarifications of each block component, their connections and differences.**
The clarifications of each block component and the connections can be f... | Summary: The paper introduces the $P^2C^2Net$, which is designed to solve spatiotemporal partial differential equations (PDEs) using minimal training data. The architecture consists of two main components: a trainable PDE block and a neural network block. The trainable PDE block updates the coarse solution using a high... | Rebuttal 1:
Rebuttal: ### Weakness
> **Q1(a). Generation of ICs of GS model for training and testing data.**
First, to create ICs for the GS equation, we define a grid based on the spatiotemporal resolution and initialize the concentrations of chemicals A and B. Second, we set different random seeds to add random nois... | Summary: In this paper the authors propose a PDE preserved coarse correction network for efficient prediction of spatio-temporal dynamics. The aim is to develop a learnable coarse model that accelerates simulation and prediction of spatio-temporal dynamics based on down-sampled data. The method mainly consists of 4 blo... | Rebuttal 1:
Rebuttal: ### Weakness
> **W1. Different BC encoding and derivative calculations.**
Despite we showcase the eficacy of our model on datasets with Periodic BCs, it is applicable to handle other types of BCs (see **Table R2**, **Table R5** and **Fig. R1** in the *1-page PDF rebuttal file*). **Please see our ... | Rebuttal 1:
Rebuttal: ## General reply
We deeply appreciate the insightful and constructive comments from the reviewers, which are helpful in improving our paper. We are pleased that all the reviewers recognized the novelty and excellent generalizability of our work. In particular, we thank the reviewers for recognizi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Validating Climate Models with Spherical Convolutional Wasserstein Distance | Accept (spotlight) | Summary: The paper proposes a new distance measure based on Wasserstein distance for data on a sphere. The work applies the methodology to climate model data, with primary focus on ranking climate models based on their agreement with reanalysis data.
Strengths: The paper is well-written and easy to follow. It provides... | Rebuttal 1:
Rebuttal: Thank you for taking the time to provide thoughtful feedback, we appreciate your attention to detail.
First, in response to your “Contribution” score of 1 and comment:
>**While the specific methodology is novel and may be of interest beyond climate modelling, it is a minor extension to the existi... | Summary: The paper defines SCWD as a special case of their proposal for functional sliced WD which it uses to compare CMIP members against reanalysis data. Additionally, with this new distance, it analyses the effectiveness of CMIP phase 6 over phase 5
Strengths: - The paper presents its ideas succinctly
- Motivates t... | Rebuttal 1:
Rebuttal: Thank you for your feedback, we particularly appreciate your attention to our climate application!
In response to your comment:
>**Would have been interesting to see VAEs as a baseline as proposed by** (Mooers et al.)
We’ve added this paper to our literature review in the introduction. We belie... | Summary: The paper introduces a new method for validating climate models by comparing their outputs to reanalysis data. The proposed method, Spherical Convolutional Wasserstein Distance (SCWD), accounts for spatial variability and local differences in the distribution of climate variables. The authors apply SCWD to eva... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and suggestions. In response to your comments on including additional climate variables/temporal resolutions, i.e.
>**While the method is well-validated on historical climate data, additional experiments on different climate variables and temporal resolution... | Summary: Developing metrics for comparison between high dimensional, multivariate climate models is an important and open area of study . Vissio et al (2020) proposed the use of the Wasserstein distance to quantify the similarity between climate models. However this approach involves spatial averaging, and therefore si... | Rebuttal 1:
Rebuttal: Thank you for your feedback and suggestions! In response to your comment:
>**I am wondering whether the method could be demonstrated on a simpler toy problem where the ground truth is better established, and the complexity and high dimensionality of the system is retained, before application to a... | Rebuttal 1:
Rebuttal: Thank you all for taking your time to provide a thorough review of our work. One shared concern from a few of the reviewers was the generalizability of our method to other tasks within climate science and ML. First, we provide some further insight on our contributions by responding to a comment fr... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DMNet: Self-comparison Driven Model for Subject-independent Seizure Detection | Accept (poster) | Summary: The authors present an algorithms for subject-independent automatic seizure detection. the algorithm exploits the dynamic behavior of seizures by including a contextual region of analysis, and a channel reference, where contextual refer to the time window close to the window of analysis, and the channel refere... | Rebuttal 1:
Rebuttal: **w1: Discussion of the impact of different frequencies of data on the analysis and model performance.**
We thank the reviewer for this thoughtful question. The public dataset has the original sampling frequency of 5000Hz; then we downsample the data to 2500Hz. For the clinical dataset, the origi... | Summary: The paper presents a seizure detection approach for spotting seizure segments in long recordings by comparing the differences in spectral content between the target segment to be labeled and surrounding context segments and some prototypical context segments, obtained as centroids by clustering the channel, al... | Rebuttal 1:
Rebuttal: **w1: Clarify the data division and hyper-parameter selection.**
We apologize for overlooking the detailed for the data division and hyper-parameter selection. Detailed settings please refer to **GR2** in **Global Response.** We will update this part in the manuscript.
**w2: Discuss the intuitio... | Summary: This paper revolves around subject-independent seizure detection using intracranial electroencephalography (iEEG) signals. The primary challenge is the domain shift in iEEG signals across different subjects, which hinders the generalization of seizure detection models to new subjects. Existing models often fai... | Rebuttal 1:
Rebuttal: **w1: Additional cross-dataset experiments to verify the efficacy of DMNet across different scenarios.**
Thank you for the good suggestion. I conducted 3 cross-dataset experiments on Clinical, MAYO, and FNUSA. We select one dataset for training and validation set (with distinct subjects), and the... | Summary: The paper proposes DMNet, a Difference Matrix-based Neural Network for subject-independent seizure detection using intracranial electroencephalography (iEEG). The model addresses the domain shift in iEEG signals across different subjects by leveraging a self-comparison mechanism that aligns iEEG signals and en... | Rebuttal 1:
Rebuttal: **w1: Clarify why reverse the L_cl to form R_cl and concatenate it on the right**.
Thanks for pointing this out. The detail explanation please refer to **GR1** in **Global Response.**
**w2: Discuss the number of subjects in iEEG dataset & Additional EEG datasets containing a large number of sub... | Rebuttal 1:
Rebuttal: # Global Response
**GR1. Clarify why reverse the L_cl to form R_cl and concatenate it on the right.**
The additional L_cl (reversed segments of R_cl) enables some originally different seizure sequences to generate similar difference matrix after fully differencing operation, which further enhanc... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Tight Rates for Bandit Control Beyond Quadratics | Accept (poster) | Summary: This paper studys online control with adversarial pertubations, bandit feedback and adversarial strongly-convex smooth cost functions. This setting is more general than previous works and the authors successfully achieve $O(\sqrt{T})$ regret by leveraging occasional update and Newton-based update.
Strengths: ... | Rebuttal 1:
Rebuttal: Thank you for your insights and valuable feedback! We will address your concerns here.
**Technical contribution**: we consider the main contribution of this work as pushing the frontier of bandit online control, by achieving optimal regret without quadratic loss or stochastic noise assumptions. T... | Summary: This paper considers the problem of online non-stochastic control, focusing specifically on scenarios where the loss function is characterized by bandit feedback, strong convexity, and smoothness, and the noise is adversarial. Prior research has typically managed to achieve $O(\sqrt{T})$ regret under assumptio... | Rebuttal 1:
Rebuttal: Thank you for your insights and valuable feedback. We will address your concerns here.
**Incorrect citation of Sun et. al. (2023)**: Thank you for pointing this out! We will fix the typo accordingly.
**Discussions of previous work**: Thank you for bringing the relevant paper into our attention! ... | Summary: This paper studies the Linear Quadratic Control (LQC) problem with adversarial perturbations, bandit feedback models, and non-quadratic cost. The authors propose an algorithm that achieves $\mathcal{O}(\sqrt{T})$ optimal regret for bandit non-stochastic control with strongly-convex and smooth cost functions in... | Rebuttal 1:
Rebuttal: Thank you for your insights and valuable feedback. We will address your concerns here.
**Contribution towards previous work (Suggala et. al. (2024))**: The algorithm and guarantees presented by Suggala et al. (2024) are limited to quadratic functions due to their reliance on a gradient estimator ... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Enhancing Reasoning Capabilities of LLMs via Principled Synthetic Logic Corpus | Accept (poster) | Summary: This paper aims to improve LLM's logical reasoning ability by constructing synthetic data used in continual training. This work is largely built upon FLD, and proposes four other design principles for the synthetic dataset. Namely, reasoning with unknown facts, illogical reasoning, diverse reasoning rules, and... | Rebuttal 1:
Rebuttal: We appreciate your constructive feedback.
Below, we will address your questions to the best of our ability.
> the training process is more like a continual training
We greatly appreciate your observation.
Indeed, *pre*-training doesn't make sense much.
We will rename it to **C**ontinual **L**ogi... | Summary: This work proposes Additional Logic Pre-Training (ALPT) to enhance logical reasoning abilities using synthetic rule-based data. The paper first discusses the design principles for creating a logical corpus and subsequently builds PureLogicDiverse (PLD). By training on PLD with RecAdam, models demonstrate impro... | Rebuttal 1:
Rebuttal: We appreciate your constructive feedback.
Below, we will address your questions to the best of our ability.
> (...) restricting the vocabulary size appears to have minimal impact on the final performance and could also help decrease the dataset size, potentially improving data efficiency. Do you... | Summary: The paper discusses a novel approach to improve the logical reasoning capabilities of large language models (LLMs). The authors propose a method called Additional Logic Pre-Training (ALPT), which involves training LLMs on a synthetic corpus named PureLogicDiverse. This corpus is designed to include high-qualit... | Rebuttal 1:
Rebuttal: We appreciate your constructive feedback.
Below, we will address your questions to the best of our ability.
> I am curious to see the performance on other tasks like math or coding.
We conducted additional experiments on math and coding tasks.
Table F.9 (in the newly attached PDF on OpenReview) ... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable feedback!
We will update the paper to address the reviewers' suggestions as follows:
* **Additional experiments on math and coding suggested by reviewer aRM4 show that ALPT significantly enhances LLMs' capabilities in various tasks in these domains** (Tab... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FreeSplat: Generalizable 3D Gaussian Splatting Towards Free View Synthesis of Indoor Scenes | Accept (poster) | Summary: This paper introduces a generalizable 3DGS model which is capable of reconstructing geometrically consistent 3D scenes from long sequence input towards free-view synthesis. The key idea of this paper is introducing Low-cost Cross-View Aggregation, which makes it is possible to use more nearby views for feature... | Rebuttal 1:
Rebuttal: ## To Reviewer ZuRj (#R4):
1. **Computational Cost:** The full training time is around **2 days** for our 2-view and 3-view versions of our method and baselines, and **3 days** for our free-view version. Although our training time is similar to pixelSplat [1] and MVSplat [2], we consume **much f... | Summary: This paper proposes a FreeSplat, aiming at generalizable 3D gaussian splitting for long sequence inputs. Specifically, it uses an efficient CNN-based cost volume and eliminates redundant 3D gaussians observed across multiple views. Extensive experiments show that FreeSplat effectively reduces inference costs a... | Rebuttal 1:
Rebuttal: ## To Reviewer Hd37 (#R3):
1. **Evaluation Details:** For view range, as mentioned in Line \#431-432 in the appendix, the distance between nearby input views is fixed to 20 in ScanNet and 10 in Replica, thus the maximum gap increases linearly with the number of input views. Therefore, the long se... | Summary: 1. Low-cost Cross-View Aggregation: This efficient methodology constructs adaptive cost volumes between proximate views and aggregates features utilizing a multi-scale structure. This approach enables the processing of extended input sequences and the incorporation of more stringent geometric constraints.
2. P... | Rebuttal 1:
Rebuttal: ## To Reviewer emYN (#R2):
1. **Experiments on Re10k and ACID:** To further evaluate our model's generalization ability across diverse domains, we train our model on RE10K using 2-View setting and 5-View setting respectively. The results are shown in our ***rebuttal pdf Table 2, 3 and Figure 3***... | Summary: This paper proposes FreeSplat to reconstruct geometrically consistent 3D scenes from long sequence inputs. To this end, the paper presents Low-cost Cross-View Aggregation for feature matching and Pixel-wise Triplet Fusion for Gaussian triplets fusion. The outstanding results of long sequence 3DGS generalizatio... | Rebuttal 1:
Rebuttal: ## To Reviewer gTxT (#R1):
1. **Differences from existing MVS methods:** Compared to traditional MVS methods [1,2], the main difference of our backbone lies in the unsupervised scheme of depth estimation supervised purely by color images, while reaching comparable depth estimation accuracy. Due t... | Rebuttal 1:
Rebuttal: ## To all Reviewers:
We first thank all reviewers for your valuable time and inspiring comments. As summarized by our reviewers, our proposed method is "reasonable and novel" (\#R1) and "interesting" (\#R3), and our experimental results are "attractive and convincing" (\#R1), "exhibits enhanced p... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Non-asymptotic Global Convergence Analysis of BFGS with the Armijo-Wolfe Line Search | Accept (spotlight) | Summary: This paper establishes a unified non-asymptotic convergence analysis of the BFGS method with Armijo-Wolfe line search. It shows that BFGS exhibits two converging stages: (1) a linear global convergence rate, and the rate is independent of the condition number when Hessian is Lipschitz continuous. (2) a superl... | Rebuttal 1:
Rebuttal: **Response to weakness 1,2,3.** We will soften our claims and add “$B_0 = L I$” in the abstract. Our global superlinear convergence analysis is based on the results from the global linear convergence rates, so we need to present both the linear and superlinear convergence results. Moreover, the li... | Summary: This paper provides the non-asymptotic global linear convergence rate of $O((1-1/\kappa)^t)$ for BFGS method with inexact line search. It also shows the superlinear convergence rate of $O((1/t)^t)$ under the Hessian Lipschitz condition.
Strengths: See questions.
Weaknesses: N/A
Technical Quality: 4
Clarity... | Rebuttal 1:
Rebuttal: **Question 1.** *Unifying the global and local convergence rates into one framework of BFGS with line search is nice. We can also address this problem by a simple way, i.e., run (accelerated) gradient descent to enter the local region, then run standard BFGS to achieve the superlinear rate. Can yo... | Summary: The paper provides non-asymptotic global convergence for BFGW with Armijo-Wolfe (A-S) line search. It provides three main results:
(a) $O(1- 1/\kappa)^t$ rate globally
(b) with Lipschitz Hessian: $O(1-\alpha(1-\beta))^t$ rate (condition number independent) after iteration t is large enough
(c) with Lipsch... | Rebuttal 1:
Rebuttal: **Response to Weakness.** Thank you to the reviewer for raising this valid point. We would like to mention that our paper and reference [38] have similar goals, both aiming to establish the global convergence rate of BFGS under some line-search scheme. The primary difference is in the choice of li... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for providing all these valuable advice and constructive feedbacks. Here is the general response.
**Numerical Experiments**
We conducted some numerical experiments and we attached all our empirical results in Figure 1 of the uploaded file. We focus on the hard cubic object... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Explanations that reveal all through the definition of encoding | Accept (poster) | Summary: This paper claims that the main problem regarding the evaluation of explainability methods is encoding, which refers to the leakage of information in explanation (here defined as a boolean mask for selecting features) structure and not the value of the selected features. The paper tries to quantify the extent ... | Rebuttal 1:
Rebuttal: Thank you for the generous comments and detailed feedback. We fixed the writing issues in the paper. If our response below addresses your primary concerns, would you kindly consider raising your score?
**[rank explanations, compare with ROAR]**
We thank the reviewer for raising this point. Unli... | Summary: The paper presents a novel approach to evaluate feature attribution methods in machine learning by addressing the issue of encoding in explanations. The authors define encoding as when the explanation's identity provides additional information about the target beyond the selected input values. They categorize ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful feedback. If our response below addresses your primary concerns, would you kindly consider raising your score?
**[The proposed DET-X score may require complex implementation and computational resources, which might limit its adoption in practical scenarios.... | Summary: How to best evaluate explanations is an important open question in the field, and one specific challenge that has so far received less attention is how to detect when explanations encode prediction in the identity of the selected inputs. This paper proposes a formal definition of encoding, that they later use ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful feedback. If our response below addresses your primary concerns, would you kindly consider raising your score?
**[paper is very very dense and this makes it at times intelligible]**
We thank the reviewer for this feedback. We modified the draft to use the ... | null | null | Rebuttal 1:
Rebuttal: ## General response
We thank the reviewers for their feedback. We are glad that the reviewers found the following strengths in our paper
- The paper is interesting (Fr3U),
- Tackles a high priority problem in promising directions (PabQ),
- The definition of encoding is a novel and significant co... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Yo'LLaVA: Your Personalized Language and Vision Assistant | Accept (poster) | Summary: This paper studies personalization for lage multimodal models (LMMs). More specifically, how can a model understand that "my dog" refers to the dog of the user. The proposed model, Yo'LLaVA, learns latent tokens to encode personalized subjects using a handful of images of for each personalized concept.
Regar... | Rebuttal 1:
Rebuttal: > **While I acknowledge that personalized datasets don't exist, it would be nice to have datasets in other domains.**
Thanks for your suggestion. Our current dataset includes humans, pets, and objects for personalization. We anticipate that future research will introduce more datasets in other do... | Summary: This paper proposes a new task of adapting a LLaVA model on personal images on specific instances, e.g., a specific pet dog, and answer visual questions about the instance. The authors proposed a finetuning pipeline to learn identity tokens, and retrain the original LLaVA ability while being able accept the ne... | Rebuttal 1:
Rebuttal: > **From reading the paper, it is unclear to me if we need to train a separate model for each instance, or we can train a single model on N instances together using N*16 learned identity tokens. If it is the later, does the model has a number of objects limit that it can learn together?**
As stat... | Summary: The paper attempts to personalize LLM's by adding personal details like dog etc. The overall idea is to add the corresponding tokens in the LLM and fine-tune the last output layer for the newly added tokens in the embedding space. This results in the ability to do personalized question answering and recognitio... | Rebuttal 1:
Rebuttal: > **The overall paper relies on the fact that LLaVA-like architecture cannot do multi-image conversations (L253) and the only available model is GPT-V at present**... [omitted]
Yo’LLaVA can learn personalized subjects more efficiently using fewer tokens and more effectively encode visual attribut... | Summary: The paper introduces Yo'LLaVA, a novel approach to personalizing Large Multimodal Models (LMMs) to handle user-specific concepts and contexts. The proposed method embeds a personalized subject into a set of latent tokens given a handful of example images, enabling personalized textual and visual conversations.... | Rebuttal 1:
Rebuttal: > **While the paper provides promising results, the evaluation is somewhat limited to specific tasks. A broader evaluation across more diverse tasks and real-world scenarios would strengthen the claims.**
We agree that a broader evaluation across more diverse tasks would strengthen the paper.
Ho... | Rebuttal 1:
Rebuttal: We introduce the *novel task of personalizing LMMs* and present *Yo'LLaVA* -- a framework to embed personalized subjects (e.g., your pet) into a comprehensible prompt for LLaVA.
We are encouraged by positive feedback from reviewers on our paper!
- **Originality**: “novel” (#PZy9, #wZcX), “good” (... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning-Augmented Approximation Algorithms for Maximum Cut and Related Problems | Accept (poster) | Summary: For the Max Cut Problem, the authors first consider predictions that are independently correct with probability 1/2 + $\epsilon$ (noisy prediction model). They obtain an approximation guarantee of $0.878 + \Omega(\epsilon^4)$, improving upon best-known approximation guarantees. The idea of the algorithm is to ... | Rebuttal 1:
Comment: We thank the reviewer for the comments and the constructive encouragement. | Summary: Authors study algorithms constraint satisfaction problems, in particular, MAX-CUT
which are provided with predictions mildly correlated with the optimal solution.
In the case of MAX-CUT, we have a prediction +1 or -1 for each vertex of the graph
which suggests which side of the maximum cut should it belong to.... | Rebuttal 1:
Comment: We thank the reviewer for the comments and the constructive feedback.
- *Is the precision parameter of the predictions epsilon known to the algorithm?*
The parameter $\varepsilon$ does not need to be known: for example, we can run our algorithm for each $\varepsilon$ that’s a power of $\frac{1}{2... | Summary: **Problem Studied**
This paper studies the Max Cut and 2-CSP problems in a setting where there is some noisy prediction of the optimal solution. In particular, the paper considers the following three settings:
- Max Cut in the "noisy prediction model": Here, each vertex gives its true label with probability $... | Rebuttal 1:
Comment: Thanks for the comments and constructive encouragement.
- *For the noisy prediction model of Max Cut, a natural algorithm that comes to mind is to return the better of the GW cut and the predicted cut. Is there an example where this algorithm does not do better than $\alpha_{GW}$?*
Yes: consider ... | Summary: The authors discuss about a setup if the approximation ratio of the known approximation algorithms for offline NP-hard problems can be improved in the cases where we have access to noisy or partial predictions. They answer this investigation positively for MaxCut and Constraint Satisfaction (CSP) problems. The... | Rebuttal 1:
Comment: We thank the reviewer for the comments and constructive encouragement.
- *Could you please motivate more about the advantages of utilizing predictions in the offline setting with practical applications?*
Predictions in both the offline and online settings help overcome worst-case outcomes by prov... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Online Relational Inference for Evolving Multi-agent Interacting Systems | Accept (poster) | Summary: The paper introduces the Online Relational Inference (ORI) framework to identify hidden interaction graphs in evolving multi-agent systems using streaming data. The framework employs online backpropagation, updating the model with each new data point, thus adapting to dynamic environments in real-time. ORI fea... | Rebuttal 1:
Rebuttal: **W1. [Efficiency Analysis]** Thank you for the suggestion. We agree that more efficiency analysis on ORI will provide important information to readers. We compare the overall computational complexity, including the number of trainable parameters, FLOPs, and running time, in ORI with NRIr decoder ... | Summary: The paper introduces a novel framework called Online Relational Inference (ORI) designed to identify hidden interaction graphs in evolving multi-agent systems using streaming data. ORI employs online backpropagation and treats the adjacency matrix as a trainable parameter, optimized through an adaptive learnin... | Rebuttal 1:
Rebuttal: **W1. [Encoder-less Design for Supervised Learning?]** This paper focuses on a fully unsupervised setup for learning the relation graph. By ‘supervised learning’ in this comment, we assume the reviewer is referring to a scenario where the true relation graph is available for the training set. We a... | Summary: This paper focuses on online relational inference (ORI) for dynamical systems. It points out from the optimization perspective that in the existing encoder-decoder framework, the encoder responds slowly to streaming data when inferring the evolving interaction graphs. It proposes to learn the adjacency matrix ... | Rebuttal 1:
Rebuttal: **W1. [Addition and Deletion of Node]** Thank you for pointing it out. We agree that the current ORI is studied when the number of nodes is constant. We will clarify this assumption in the revised draft, and add this as a limitation of the current study.
We expect that ORI can be extended to scen... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers fSnF, mc8m, arYB for their positive feedback: carefully designed experiments and analysis beneficial for later research (fSnF), the first model-agnostic online relational inference framework for multi-agent systems (mc8m), well-written with sufficient experimental ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Reciprocal Learning | Accept (poster) | Summary: This paper introduced a unifying framework that generalizes a range of ML algorithms that consist of data selection in a *reciprocal learning* fashion. The paper then presents requirements that guarantee convergence of these algorithms. It shows when and how fast these algorithms converge to an approximately o... | Rebuttal 1:
Rebuttal: We wish to thank Reviewer Br7x for the thorough and helpful review of our work. We are glad the reviewer acknowledges the “significant contribution to the ML theory literature” of our paper.
Below, we answer both the reviewer’s questions. We start, however, by addressing the only weakness mentio... | Summary: This paper models the general process of learning where the data and the parameters are learned iteratively under a new framework of reciprocal learning. Moreover, it provides convergence results given regularity.
Strengths: The paper provides a general view of the learning tasks and is novel to my knowledge.... | Rebuttal 1:
Rebuttal: We wish to thank Reviewer y1vN for the thorough and helpful review of our work.
We are glad the reviewer acknowledges both the soundness and the contribution of our paper as “good”. We completely agree that the presentation (“fair”) could be improved, as we did in response to the reviews, see bel... | Summary: The paper presents a new, unifying framework called reciprocal learning for studying learning scenarios in the batch setting --- which, in contrast to one-shot ERM, may go through an entire sequence of ERMs where each previously fitted parameter gives rise to new data that the next ERM procedure will be traine... | Rebuttal 1:
Rebuttal: We thank Reviewer T1mz for the thorough and helpful review. We are glad about the generally positive feedback and address the reviewer’s two concerns with the initial state of the paper below.
$~$
**"presentation and writing"**
We really owe a great deal of thanks to the reviewer for these conc... | Summary: This paper presents reciprocal learning, a framework that enables proving convergence for various classes of Machine Learning algorithms including classes of self-training methods, bandit algorithms and active learning methods.
Strengths: - General framework that presents convergence guarantees that shows the... | Rebuttal 1:
Rebuttal: We thank reviewer MkM4 for the thorough and helpful review! We address all your remarks point by point:
**“algorithmic stability”**
Our results address the question of whether (and at what rate) a wide range of machine learning algorithms stabilize (converge). The referenced papers deal with the... | Rebuttal 1:
Rebuttal: $~$
**Authors’ summary of reviews:** *The paper is found to have sound and rigorously stated results for the unifying, interesting framework of "reciprocal learning" with relevant and novel implications for self-training, bandits, and active learning. Presentation of those implications could be i... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Latent Functional Maps: a spectral framework for representation alignment | Accept (poster) | Summary: The paper demonstrates the possibility to use the functional map tool on the embedding space of neural networks. The idea is that the embedding space of a neural network is usually lay on a low dimensional manifold, and networks that are trained for the same tasks even with different architecture result in sim... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and suggestions. We are gonna address their questions and concerns in the following, and remain available for any additional questions.
**Know the correspondence**
We fully agree with the reviewer that when the correspondence is known there is no need to ... | Summary: This paper proposes using the functional maps paradigm for comparing and aligning the latent spaces of different neural architectures, possibly trained with different setups: initialization, different datasets, noise, etc.
The main contribution of this paper is to view the latent space as a Riemannian manifo... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and suggestions. We are gonna address their questions and concerns in the following, and remain available for any additional questions.
**Comparison with [29]**
The method in [29] is one of many recent works [13,18, 20,22, 25, 27] that focus on the emerge... | Summary: The paper presents a new way to compare neural representations: Latent Functional Maps (LFM). The later one is achieved by 1) building symmetric knn graphs 2) calculating Laplace eigenfunctions 3) calculating optimal mapping between them.
Applications include: (i) compare different spaces in an interpretable w... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and suggestions. We are gonna address their questions and concerns in the following and remain available for any additional questions.
**Theoretical contribution**
We would like to respectfully point out that the goal of the paper is to make aware of the r... | Summary: The paper tackles the problem of modeling relationships between latent spaces learnt by different models. It proposes using functional maps that have been used in 3D vision and graph matching for the purpose. It does so by approximating the latent space structure using a knn graph constructed using anchor poin... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and suggestions. We are gonna address their questions and concerns in the following and remain available for every additional question.
**Writing quality**
We have significantly improved the manuscript's clarity, making it self-contained and clear. In pa... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their feedback and suggestions. In the following we addressed some general comments raised by the reviewers and attached a pdf with the experiments performed during the rebuttal period. We remain available for any further question or clarification during the discussi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Dueling over Dessert, Mastering the Art of Repeated Cake Cutting | Accept (poster) | Summary: This paper studies the game of repeated cake cutting. The cake is modeled as the unit interval $[0,1]$, and in each round $t \in \\{1,\ldots,T\\}$, player $A$ chooses a point $a_t$ to cut the cake into two pieces $[0,a_t]$ and $(a_t, 1]$, and player $B$ chooses one of the two pieces, either after observing $a_... | Rebuttal 1:
Rebuttal: Thank you for your review.
For question 2: Equitability requires that each player gets the same value. As you pointed out, not all equitable allocations are equally good. When $V_A(Z_A) \geq 1/2$ and $V_B(Z_B) \geq 1/2$, the allocation is also proportional. Proportionality is a fairness notion t... | Summary: The paper deal with a repeated division problem where at each round a new cake (modeled as the interval $[0,1]$), identical to previous ones, arrives. Alice acts first and cuts the cake in two parts. Then, Bob chooses the piece he prefers, leaving the remainder for Alice. Alice (resp. Bob) valuation preference... | Rebuttal 1:
Rebuttal: Thank you for your review. The question you raise about more general cutting models is interesting and likely to be tractable for many of them.
For example, suppose Alice can cut at two points $a_{t,1}$ and $a_{t,2}$ each day $t$ (placing each of the resulting three pieces in one of two bins labe... | Summary: The paper considers a problem of sequential cake cutting. Each day for $T$ days, 2 players, Alice and Bob, must divide the cake. The cake is a unit interval $[0, 1]$ which they each value with some density function that in total adds up to $1$. They have the same preferences across days. The first player, Alic... | Rebuttal 1:
Rebuttal: Thank you for your review.
Regarding playing the game repeatedly, a high level scenario is where the salespeople of a roofing company are paid by commission for solar panel installation and maintenance services. Each day, they might divide areas of town among themselves for door-to-door sales. D... | Summary: This paper considers the problem of repeated cake cutting among two agents. In this problem the same cake appears at each round and Alice cuts the cake based on her utility function over the cake. Bob has to choose one of the two parts after seeing the cut.
The authors show that if Bob almost always chooses h... | Rebuttal 1:
Rebuttal: Thank you for your review. We will bring the preliminaries in the main body of the paper and shorten related work as necessary to make this possible.
If Alice and Bob take turns cutting and choosing, the feedback model is richer. Alice observes how Bob behaves (1) as a chooser, thus learning abou... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DiffPO: A causal diffusion model for learning distributions of potential outcomes | Accept (poster) | Summary: This paper proposes DiffPO, a causal diffusion model, to predict individual potential outcomes. The authors motivate why predicting potential outcomes can be a more complex problem than (conditional) average treatment effect (CATE) prediction in practical settings. They adapt a conditional denoising diffusion ... | Rebuttal 1:
Rebuttal: Thank you for your positive review and your helpful comments! We appreciate that you find our paper interesting, important, and comprehensive. We are very delighted to answer your questions and improve our paper as a result.
## Response to “Weaknesses”
* Thank you very much for your suggestions!... | Summary: This paper aims to develop a model which focuses on potential outcomes (POs) instead of CATE. It focuses on estimation of POs and proposed an estimation method which is an extension of the well know diffusion model with a different loss function.
Strengths: - This work proposes a diffusion model based estimat... | Rebuttal 1:
Rebuttal: Thank you for your review and your helpful comments!
## Weaknesses
**(W1) Relevance of potential outcomes**
- We would like to emphasize that potential outcomes are highly relevant in many decision-making settings such as medicine [1]. Example: predicting the survival probability under treatme... | Summary: This paper applies the technology of diffusion models to estimation of potential outcome and treatment effect. Owing to the capability of modelling distribution in diffusion model, this paper can not only give point estimation but also uncertainty. Specifically, it proposes to use variational inference to lear... | Rebuttal 1:
Rebuttal: Thank you for your review and your helpful comments! We appreciate that you find our proposed method important, sound, and effective. We further would like to emphasize that we perform CATE experiments to show that our method is flexible. Nevertheless, the primary objective of our method is to _le... | Summary: This paper describes DiffPO, a causal diffusion model for predicting distributions of potential outcomes, as well as related causal quantities such as CATE estimates. DiffPO accounts for the confounding between covariates and outcomes through a simple weighting procedure, under standard potential outcomes assu... | Rebuttal 1:
Rebuttal: Thank you for your positive review and your helpful comments! We appreciate that you find our paper novel, useful, and with strong experiment performance. We are very happy to answer your questions and improve our paper as a result.
## Response to “Weaknesses”
**(W1) Additional performance meri... | Rebuttal 1:
Rebuttal: Thank you very much for the constructive and positive evaluation of our paper and your helpful comments! We addressed all of them in the comments below and uploaded **additional results as a PDF file**.
Our **main improvements** are the following:
* **We provide a theoretical guarantee:**
We p... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Semantics and Spatiality of Emergent Communication | Accept (poster) | Summary: # Problem :
Emergent Communication (EC) protocols have been shown to be counterintuitive even when they enable agents wielding them to solve their related task.
# Contributions :
Rather than assuming that meaningful communication is taking place when a goal-oriented communication protocols enables the goal ... | Rebuttal 1:
Rebuttal: Thank you for this insightful and detailed review. We are glad you have found our theoretical contributions novel and valuable. The papers that you have mentioned, especially regarding compositionality and input representation, are indeed relevant to our core contributions, and we will add referen... | Summary: The authors consider a collaborative multi-agent (2 agent) setting with inter-agent communication, where the communication protocol is learned by the agents in order to maximize their common objective, which can be either a reconstruction or discrimination task. However, the authors note that this communicatio... | Rebuttal 1:
Rebuttal: Thank you for this insightful review. We are glad you have found the novel theoretical tools useful. Following your feedback and others, we have prepared some major modifications to be added in the next revision.
You have mentioned as a weakness the empirical results with regard to spatial meanin... | Summary: This paper explores the properties of communication protocols that emerge when artificial agents are trained to perform collaborative tasks through a communication channel. The authors identify a key prerequisite for meaningful communication, termed "semantic consistency," which demands that messages with simi... | Rebuttal 1:
Rebuttal: Thank you for this insightful review. We are glad you have found the theoretical part innovative and well structured. Following your feedback and others, we have prepared some major modifications to be added in the next revision. We would now like to address your comments one by one.
**Comment 1... | Summary: This paper investigates and analyzes the emergent communication protocols developed by agents during collaborative tasks that necessitate message transmission to solve given problems. The authors contend that traditional performance measures, including task performance and properties like compositionality and ... | Rebuttal 1:
Rebuttal: Thank you for this insightful review. We are glad you have found the paper to be rigorous and well written. Following your feedback and others, we have prepared some major modifications to be added in the next revision.
**Question 1**
The explained variance relates to the average proximity of in... | Rebuttal 1:
Rebuttal: Dear reviewers,
We greatly appreciate the time and effort you have dedicated to evaluating our paper.
We have submitted individual responses to your reviews. To this message we have attached a pdf with new results on the Shapes dataset, along with visual illustrations of the trained agents' perf... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
RGMDT: Return-Gap-Minimizing Decision Tree Extraction in Non-Euclidean Metric Space | Accept (poster) | Summary: This work provides an upper bound on the return gap between the DRL policy and its extracted DT policy. Based on this, it proposes the RGMDT algorithm with a simple design that can be extended to multi-agent settings using an iteratively-grow-DT procedure. The RGMDT algorithm outperforms other DT-based algorit... | Rebuttal 1:
Rebuttal: # Interpretability of RGMDT
Note that RGMDT is **the first work for multi-agent DT with performance guarantees**. Since the agent's decisions jointly affect state transition and reward, converting each agent's decision separately into decision trees may not work and accurately reflect the intertwi... | Summary: The authors proposes a method called Return-Gap-Minimization Decision Tree (RGMDT) to extracting interpretable decision tree policies from learned parametric RL policies. The authors first propose a method to quantify the return gap between an oracle RL policy and its extracted decision tree policy, which prov... | Rebuttal 1:
Rebuttal: # Query on Simple DT Baselines
We include the requested **Imitations DT** baseline using **CART**, directly trained on the **RL policy's actions and observations** (lines 265-271) as described, without resampling. The observations and actions are **features** and **labels** respectively. The **res... | Summary: This paper considers extracting decision tree (DT) based policies from DRL policies for the purpose of interpretability. The authors present an upper bound on the return gap of the oracle policy and the DT policy, which helps formulate the DT extraction problem into a non-euclidean clustering problem. The auth... | Rebuttal 1:
Rebuttal: # Interpretability Presentation
We will **move DT visualization to the main body** to better illustrate RGMDT's interpretability. To illustrate non-euclidean clustering labels interpretation, we run RGMDT on a 2-agent grid-world maze for easy visualization and **add four additional figures** (**Fi... | null | null | Rebuttal 1:
Rebuttal: # Three more experiments are added in PDF
# Response to Reviewer p7eT's Query on the Interpretable RL Related Work
We have had a section discussing interpretable RL in the related work section which includes all the three mentioned references (**[6],[12],[14]**), but we deleted it due to the page ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Variational Continual Test-Time Adaptation | Reject | Summary: The authors propose a variational continual adaptation method. Where a sequence of test-time domain adaptation problems are shown to a model. More specifically a labeled dataset is given as an initial dataset to learn from, and then afterwards a sequence of unlabelled datasets with domain shifts are presented ... | Rebuttal 1:
Rebuttal: ### Weakness of Noverty
**1. CIFAR10 dataset is not enough**
**Response**: Many CTTA methods focus on classification problems because validating classification on foundational and well-recognized datasets can reduce potential confounding factors and more accurately assess the effectiveness of th... | Summary: This paper introduces VCoTTA, a novel variational Bayesian approach to address the Continual Test-Time Adaptation (CTTA) task, which focuses on effective domain adaptation during continuous domain shifts at test time. The authors' main contributions include a method to measure uncertainties in CTTA, addressing... | Rebuttal 1:
Rebuttal: ### Weakness 1: Further analysis on efficiency
**Response**:
Our approach incorporates a Variational Warm-Up (VWU) strategy during pretraining and utilizes VCoTTA for test-time adaptation. We conduct additional cost analyses under various settings, including different batch sizes and model sizes.... | Summary: The paper proposes a method to continually adapt a pre-trained classifier to an unlabeled stream of test data. They address the problem of continual test-time adaptation through the lens of Bayesian deep learning. Their method consists of three main components: (1) a variational warm-up strategy to turn any so... | Rebuttal 1:
Rebuttal: ### Weakness 1: Why superior to SOTA
**Response**: Our method outperforms the SOTA approaches because it leverages the BNN ability to estimate model uncertainty, which reduces error accumulation from continual unknown domains during the testing phase. We find that the unreliable priors may affect... | Summary: The paper presents a variational Bayesian approach to handle uncertainties in continual test-time adaptation (CTTA). The source pretrained model is made Bayesian by variational warm and a mean-teacher update strategy is used at test time. To avoid drift due to uncertainty of priors using only unlabeled data at... | Rebuttal 1:
Rebuttal: ### Weakness 1: Bayesian approach for CTTA and why superior to SOTA
**Response**:
(1) *Bayesian approach for CTTA*
Bayesian networks have already been applied in the field of TTA task. For example, [1] develops a continuous-time Bayesian neural networks to process non-stationary streaming dat... | Rebuttal 1:
Rebuttal: Dear Reviewers:
We thank the reviewers for their careful examination of our paper and for providing a wealth of valuable suggestions. We also appreciate the reviewers' recognition of our work in terms of originality, relevance, quality, clarity, and significance. **The attached PDF contains the m... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning Cooperative Trajectory Representations for Motion Forecasting | Accept (poster) | Summary: This paper introduces V2X-Graph, a novel framework using trajectories of agents (include ego and others) and vector map as inputs for cooperative motion forecasting that fuses trajectory features in an interpretable, end-to-end manner. The authors evaluate V2X-Graph using V2X-Seq, a vehicle-to-infrastructure (... | Rebuttal 1:
Rebuttal: Dear Reviewer w4tS: \
Thanks for your valuable feedback on our work.
We have carefully considered your suggestions and would like to respond to each of your main comments regarding our weaknesses and questions.
**W1.**
**The structure and information of the input data for V2X-Graph primarily fol... | Summary: This paper presents the V2X-Graph method for cooperative motion forecasting. In a cooperative autonomous driving setting, an autonomous driving vehicle receives sensor data from surrounding vehicles and infrastructure-side devices.
Existing cooperative autonomous driving approaches focus on perception complet... | Rebuttal 1:
Rebuttal: Dear Reviewer vBSp: \
Thank you for your valuable feedback on our work.
We have carefully considered your suggestions and would like to respond to each of your main comments regarding our weaknesses and questions.
**W1.**
Instead of single-frame perception completion method, the proposed traject... | Summary: This paper tackles the cooperative motion forecasting problem for vehicles. This paper introduces additional information other than the ego view agent from other view to expand the perception field of the prediction. The authors propose a graph network to extract information for multimodal trajectory predictio... | Rebuttal 1:
Rebuttal: Dear Reviewer vdKw: \
Thanks for providing valuable feedback on our work.
We will address each of the limitations you have pointed out in your comments.
**W1.**
Yes, **we focus more on the representative scenario unit at the current stage, which involves two vehicles and one roadside device. It ... | Summary: The paper introduces a novel graph-based framework called V2X-Graph for learning cooperative trajectory representations in motion forecasting for autonomous vehicles. V2X-Graph aims to enhance the motion prediction capabilities of autonomous vehicles by leveraging cooperative information from vehicles and traf... | Rebuttal 1:
Rebuttal: Dear Reviewer zJGd: \
Thanks for your thorough review and valuable suggestions on our work.
We have carefully considered your suggestions and would like to respond to each of your main comments regarding our weaknesses and questions.
**W1.**
Graph neural networks (GNNs) are common practice for th... | Rebuttal 1:
Rebuttal: ## General Rebuttal for Commen Concerns
We will respond to the common concerns raised by the reviewers here.
**1. Our V2X-Graph is a pioneering work exploring trajectory-based feature fusion for cooperative motion forecasting.**
Most of the existing works in cooperative autonomous driving commun... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Once Read is Enough: Domain-specific Pretraining-free Language Models with Cluster-guided Sparse Experts for Long-tail Domain Knowledge | Accept (poster) | Summary: This paper first shows that infrequent sentences tend to have gradients dissimilar to the gradient of the majority data points. This paper thus proposes a variant of mixture-of-expert (MoE) module, Cluster-guided Sparse Expert (CSE), where inputs are routed to the k-mean clusters they are mostly similar to. T... | Rebuttal 1:
Rebuttal: We appreciate your thorough review of our paper. Our key contribution addresses the challenge of learning long-tail domain data during the pretraining stage, substantially reducing the need for an expensive, labor-intensive second domain-specific pretraining stage in practical applications.
> Q1 .... | Summary: The authors propose to add a simple and efficient Cluster-guided Sparse Expert (CSE) layer to Language Models to improve their capability on long-tailed knowledge. The authors demonstrate that pretraining LMs using CSE leads to better performance on domain specific tasks than vanilla pretraining and suggest th... | Rebuttal 1:
Rebuttal: > Q1 Typo in line 189: Should be "Cluster" and not "Clsuter"; Typo in Table 1, 4th row: Should be "legal" and not "lgeal".
**A1** Thank you for your comments. In our revised version, the error on line 189 will be corrected to _Cluster_, and the typo in Table 1, fourth row, will be updated to _leg... | Summary: This paper proposes a novel approach called Cluster-guided Sparse Experts (CSE) to improve language models' ability to learn long-tail domain knowledge during pretraining, potentially eliminating the need for domain-specific finetuning. This study introduced CSE layers that cluster semantically similar long-ta... | Rebuttal 1:
Rebuttal: We appreciate the time and expertise you have invested in reviewing our submission. Below, we outline our responses to the specific points raised in your reviews.
> Q1 Typo: line 189/300 should be Cluster rather than Clsuter.
**A1** Thank you for your comments. It has been corrected to be _clust... | Summary: The paper presents an innovative approach to address the challenge of finetuning language models (LMs) for domain-specific tasks. The authors argue that the traditional pretraining-finetuning paradigm is suboptimal due to the high cost and time consumption of finetuning. To tackle this, the authors propose the... | Rebuttal 1:
Rebuttal: We appreciate the time and expertise you have invested in reviewing our submission. Below, we outline our responses to the specific points raised in your reviews.
> Q1 The improvement in the GPT style model is subtle compared to the baseline MoE method, which may undermine the advantage of the pr... | Rebuttal 1:
Rebuttal: # Global Rebuttal
Dear AC and Reviewers,
We sincerely appreciate the time and expertise you devoted to reviewing our submission.
Given that experiments involving larger scales or a greater number of tasks is a shared concern among the reviewers, we have detailed the outcomes of such experiments... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper focuses on the problem of modeling long-tail domain knowledge in language models, which can be missed during the general-purpose pretraining. While most approaches capture long-tail domains as a second domain-specific pretraining step, the paper proposes a cluster-guided method that encourages the mo... | Rebuttal 1:
Rebuttal: We appreciate the time and expertise you have invested in reviewing our submission. Below, we outline our responses to the specific points raised in your reviews.
> Q1 The baseline results in Tables 1 and 2 are surprising as, for example, BERT/med (further trained on medical) would be expected to... | null | null | null | null | null | null |
Polynomial-Time Computation of Exact $\Phi$-Equilibria in Polyhedral Games | Accept (spotlight) | Summary: The authors this generalize the Ellipsoid Against Hope and develop a simple algorithmic framework for efficiently computing saddle-points in bilinear zero-sum games, even when one of the dimensions is exponentially large.
Strengths: The main strength of this work is the significance of the problem considered.... | Rebuttal 1:
Rebuttal: Thank you for the positive review and for helping us improve the presentation of our paper. We will incorporate your suggestions in the final version. We respond to your questions below:
* Page 1: Here, with "multi-player games" we refer to games with any number $n$ of players.
* Page 2: Uncoupl... | Summary: This paper gives an algorithm for computing exact $\Phi$-equilibria in polyhedral games. The algorithm follows the general framework of the "Ellipsoid Against Hope" method for computing exact correlated equilibria in succinctly represented normal form games. The paper generalizes this method to computing linea... | Rebuttal 1:
Rebuttal: Thank you for your review and observations on our paper. We respond to the weaknesses and questions you raised.
* > [Weakness] straightforward modifications of the ellipsoid against hope method
>
Our framework balances generality with simplicity, as it greatly simplifies the algorithm of [1] for ... | Summary: This paper studies the problem of computing phi-equilibria in a general class of games called polyhedral games. Phi-equilibria are a class of game-theoretic equilibria where each player has low regret with respect to some class Phi of linear transformation functions (e.g. this captures various notions of corre... | Rebuttal 1:
Rebuttal: Thank you for the positive review. You are right that in our generalization of the EAH, it is critical that the time complexity depends on the dimensionality of the action space and not on the number of pure strategies (which might be exponentially many in the dimensionality of the action space, a... | Summary: The paper, titled "Polynomial-Time Computation of Exact $\Phi$-Equilibria in Polyhedral Games," proposes a novel algorithmic framework to compute saddle-points in bilinear zero-sum games, particularly when one dimension is exponentially large. This framework extends the Ellipsoid Against Hope algorithm and int... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and your comments on the paper. We respond below to the raised weaknesses and questions.
* > [Weakness] Complexity and Practicality
>
Indeed, the degree of the polynomial in our algorithm's time complexity is extremely high, rendering it impractical. However, it... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ECMamba: Consolidating Selective State Space Model with Retinex Guidance for Efficient Multiple Exposure Correction | Accept (poster) | Summary: The manuscript proposes a novel model that deeply embeds Retinex theory into the Mamba model. The proposed model consists of two modules: Retinex estimator and exposure correction. Comparative experiments on five datasets demonstrate the superiority of the proposed method, and subsequent ablation experiments d... | Rebuttal 1:
Rebuttal: Thank you for your comprehensive and insightful review. We answer your questions in turn below:
> **Q1:** In page 1, line 35, the authors stated that 'Retinex theory has not been deeply integrated into…', which could cause some negative effects? Please provide some explanations.
Multi-exposure c... | Summary: The authors propose a novel Mamba architecture for the exposure correction task based on Retinex theory. They design a separate Retinex estimator and two exposure correction modules to restore reflectance and illumination. In these modules, the authors improved efficiency in terms of time and resources by intr... | Rebuttal 1:
Rebuttal: Thank you for your review and comments. We provide our point-to-point response below:
> **Q1:** The design motivation of the Retinex estimator
The exposure correction task involves handling various exposure levels while also addressing complex issues such as color distortion and detail loss. Com... | Summary: This paper introduces a new pipeline called ECMamba for multiple exposure correction. Based on the analysis of Retinex theory, the authors develop a dual-branch framework, and each pathway is designed to restore the reflectance image and the illumination map, respectively. Besides, considering the powerful and... | Rebuttal 1:
Rebuttal: We sincerely thank you for the valuable comments on our paper. We will explain your concerns point by point.
> **Q1:** What constraint is adopted to train the proposed two-branch network? Is it possible that $ \mathbf R_{out} $ and $ \mathbf I_{out} $ are quite different from their corresponding ... | Summary: This paper introduces ECMamba, a novel framework that integrates Retinex theory and the Mamba framework to address the complex issue of exposure correction. ECMamba adapts the Retinex theory to suit the needs of exposure correction and develops a Retinex estimator to assess both reflectance and illumination ma... | Rebuttal 1:
Rebuttal: Thank you for providing valuable feedback for our paper. We address your concerns in turn below. We hope our response can well address all your concerns.
> **Q1:** The rationale for employing Retinex theory and the Mamba framework in multi-exposure correction is not adequately articulated. The au... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers (**R1** 5Zdz, **R2** fpJd, **R3** EgGV, and **R4** zooA) for their detailed reviews and constructive comments. The reviewers agree that:
**Novel or interesting approach:**
- **R1:** "This study **expands the theory's application**, adapting it to scenarios..."
- ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Stochastic Optimal Control and Estimation with Multiplicative and Internal Noise | Accept (poster) | Summary: This paper targets the stochastic optimal control problem with signal-dependent and internal noise by Todorov (2005). The authors question an assumption made in the original paper and propose an approximate solution to solving the problem without this assumption. The authors empirically show that in the case o... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We will respond to each comment point by point.
- unbiasedness and first question
We first note our new result, not present in the paper: unbiasedness does not hold even in the absence of internal noise (see Fig 1 of the uploaded pdf). Therefore, for th... | Summary: The authors found that the algorithm developed by Todorov (TOD) for optimal feedback control problems, in the presence of internal and sensory noise, assumes that the state estimation is unbiased at all times. However, they show that this assumption does not hold in the presence of internal noise and, even whe... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We will respond to each comment point by point.
- unbiasedness
We first note our new result, not present in the paper: unbiasedness does not hold even in the absence of internal noise (see Fig 1 of the uploaded pdf). Therefore, for the unbiasedness to b... | Summary: The paper extends a popular stochastic optimal control framework for explaining the algorithmic function of sensorimotor circuits in the brain to the case where there is multiplicative noise in feedback and motor output, which is a more realistic assumption for noise in the brain. Prior work assumed the estima... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We will respond to each comment point by point.
- Switching linear dynamics
Thanks a lot for the interesting suggestion. We discuss here how to extend our approach to switching linear dynamics.
One of the underlying assumptions in this work and in [7],... | Summary: The work introduces a new algorithm that adapts to the noise typical in human sensorimotor systems. It includes different types of noise like those from control movements to improve how accurately we can predict motor behaviors. This joint optimization of control and estimation significantly outperforms tradit... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We will respond to each comment point by point.
- Theoretical guarantees
Our numerical algorithm, being a GD, converges to, at least, a local minima. In [7] convergence to at least local minima is also found.
Convergence of our algorithm is numerically s... | Rebuttal 1:
Rebuttal: - switching linear dynamics
We discuss here how to extend our approach to switching linear dynamics.
One of the underlying assumptions in this work and in [7], as outlined in Section 2, is that the agent has complete knowledge of the updating rules of the latent dynamical system. By using the sa... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards Robust Multimodal Sentiment Analysis with Incomplete Data | Accept (poster) | Summary: The paper proposes a Language-dominated Noise-resistant Learning Network (LNLN) to enhance the robustness of Multimodal Sentiment Analysis (MSA) under conditions of incomplete data. The model leverages the language modality, considered dominant due to its rich sentiment cues, to correct and reconstruct missing... | Rebuttal 1:
Rebuttal: # Response to Reviewer LeNg
## **Response to W1**
LNLN is well-suited for applications where multimodal data is often incomplete, which is a common challenge in many real-world s*cenarios*. For example, in platforms like Twitter, Instagram, and TikTok, users often express sentiments through a co... | Summary: The paper addresses data incompleteness in Multimodal Sentiment Analysis (MSA) by presenting the Language-dominated Noise-resistant Learning Network (LNLN). By considering language as the dominant modality, LNLN introduces a Dominant Modality Correction (DMC) module and Dominant Modality-Based Multimodal Learn... | Rebuttal 1:
Rebuttal: # Response to Reviewer xa9F
## **Response to Limitations**
**Regarding the Use of Transformer:**
**First,** our primary focus is on the innovation of the algorithm, not on the innovation of the Transformer. We have utilized Transformer layers in a similar manner to how CNNs and LSTMs are commonly... | Summary: This paper presents LNLN, which aims to address the challenge of data incompleteness in real-world scenarios caused by sensor failures or automatic speech recognition issues. The core idea is that even if other modalities are missing, the system can still work if the information from the dominant modality is c... | Rebuttal 1:
Rebuttal: # Response to Reviewer ugFZ
## **Response to W1**
We believe that our contributions are significant and address the gap in current MSA research. The robustness of MSA models in real-world, noisy environments is an important area of study. Most previous methods [1, 2, 3, 4, 5, 6, 7, 11] are evalu... | null | null | Rebuttal 1:
Rebuttal: # General Response
Dear Reviewers, ACs and SACs,
We would like to express our sincere gratitude for your thoughtful questions and valuable feedback. We greatly appreciate the time and effort you have invested in reviewing our paper. **We are eager to engage in further discussions with you to add... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DDGS-CT: Direction-Disentangled Gaussian Splatting for Realistic Volume Rendering | Accept (poster) | Summary: 1. The proposed direction-disentangled 3DGS (DDGS) method decomposes the radiosity contribution into isotropic and direction-dependent components, able to approximate complex anisotropic interactions without complex runtime simulations. specifically, it modeling isotropic and anisotropic contributions via dist... | Rebuttal 1:
Rebuttal: ### Beer-Lambert law:
We would like to kindly correct the reviewer. The exponential transmittance model $T(t)$ used in NeRF/GS, to describe the light attenuation as it travels through a medium from point $r(t_0)$ to $r(t)$, is **also based on Beer-Lambert law** [17, r1-4]:
$T(t)=\exp(-\int_{t_0}... | Summary: Digitally reconstructed radiographs (DRRs) are simulated 2D X-ray images generated from 3D CT volumes, widely used in preoperative settings but limited in intraoperative applications due to computational bottlenecks. In this paper, the author proposes an approach that combines realistic physics-inspired X-ray ... | Rebuttal 1:
Rebuttal: ### SOTA status of X-Gaussian and DiffDRR/DiffPose:
We respectfully disagree with the reviewer w.r.t. where the SOTA stands. Since we started writing this paper, DiffPose [12] (an extension of DiffDRR [11] by the same authors) has been presented at **CVPR 2024 (_oral_)**, and X-Gaussian [7] has be... | Summary: This paper proposes DDGS, a Gaussian splatting (GS) based method for rendering realistic 2D X-ray images from 3D CT volumes. Taking advantage of GS, the proposed method operates in real-time. Moreover, the DDGS model employs isotropic Gaussians and anisotropic, direction-dependent Gaussians to model complex X-... | Rebuttal 1:
Rebuttal: ### Effectiveness of the radiodensity-aware dual sampling:
We believe that the accuracy increase compared to the SOTA [7] (i.e., performing uniform sampling) brought by this single contribution (novel radiodensity-based sampling) is still significant.
We should further highlight that our novel CT... | Summary: This manuscript presents a novel method called Direction-Disentangled Gaussian Splatting (DDGS-CT), tailored for balancing realistic X-ray simulation and efficient DRR generation using 3D Gaussian Splatting (3DGS). It addresses the challenges posed by intricate physics computation, which often harms the applic... | Rebuttal 1:
Rebuttal: ### Isotropic vs. anisotropic sets — motivation, initialization, and rendering:
We hope that the following points will clarify some misunderstandings:
- We define 2 different functions for the absorption contribution of the 2 Gaussian sets: one function is isotropic to approximate average radio-ab... | Rebuttal 1:
Rebuttal: We are very grateful to the reviewers `Zg8U`, `YFhr`, `ZGY5`, and `g5Vp` for the constructive feedback, as well as the recognition of our paper's strengths, such as its novelty with regard to existing analytical DRR renderers, its clarity, and its conclusive evaluation on downstream tasks.
In thi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering | Accept (poster) | Summary: Recently, Large Language Models (LLMs) based agents have revealed huge potential in automating tasks. In this paper,
Strengths: This work has presented a powerful AI Agent designed for software engineering, which configured agents with multiple functions like file viewer, file editor and so on. Such a system ... | Rebuttal 1:
Rebuttal: Thanks for your thorough review of the paper and suggestions - the feedback has been helpful to clarify several details.
**W1: Why these tools?**:
> We discuss the design of SWE-agent’s interface in _Section 3_ and our motivation for this design based on principles laid out in _Section 2_.
The... | Summary: This paper introduces SWE-agent, a system that enables language models to autonomously perform software engineering tasks by interacting with computers through a specially designed agent-computer interface (ACI). The authors argue that LM agents represent a new category of end users with unique requirements, n... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments, all of which are very helpful for improving our work! We greatly appreciate your conclusion that ACI is a novel and important concept.
**Q1, W1: Generalizability, Applying ACI to Other Domains**
> Building a good ACI for a specific domain is tough, as we s... | Summary: The paper introduces SWE-agent, a system designed to enable language model (LM) agents to autonomously perform software engineering tasks through a custom agent-computer interface (ACI). The study posits that LM agents can benefit from interfaces tailored to their specific needs, similar to how human software ... | Rebuttal 1:
Rebuttal: Thank you so much for your interest in our research. We greatly appreciate your feedback and insights.
We’re especially happy that you see the novelty of the concept of ACI and the potential to impact future work in LM agents.
We’ve tried to address your particular concerns below:
**What’s the o... | Summary: The paper presents SWE-agent, a system designed to enhance language model (LM) agents' performance in software engineering tasks through a specialized agent-computer interface (ACI). The ACI allows LMs to efficiently navigate, edit, and execute code within repositories, significantly improving performance over... | Rebuttal 1:
Rebuttal: Thank you so much for your time and consideration. You’ve brought up some excellent points in your feedback that we try to address below.
Regarding your questions / weaknesses:
**W1, Q1: How does the ACI manage context?**
> The main mechanism by which SWE-agent manages to keep memory short, is ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Compositional Atlas for Algebraic Circuits | Accept (poster) | Summary: This paper unifies two lines of work:
1) a compositional approach to tractability of queries over logical [12], probabilistic [35] and causal circuits [36] and
2) a (commutative) semiring-based perspective over different computational tasks ([12] among many others).
The benefit is the characterization of cert... | Rebuttal 1:
Rebuttal: > The high-level contributions are not very clear from the text (in particular in the first sections). [35] clearly listed their contributions at the end of their introduction, in contrast, I found lines 39-42 more vague in this sense, giving the (wrong) idea that the compositional approach is a n... | Summary: The paper mainly focuses on the problem of deriving tractability conditions for compositional operations over circuits as to solve a number of queries. By fixing the language of circuits over semirings, the paper generalizes known results and introduce novel ones regarding the tractability of queries solved vi... | Rebuttal 1:
Rebuttal: > I think that some results requiring particular semirings and homomorphisms require so many properties that make me wonder their actual contribution. Tractable mapping (Theorem 4) requires the following combination of properties to be both true...
Please see response to Q1/Q2 below.
> Theorem 3... | Summary: The paper presents sufficient conditions under which certain problems (e.g. 2AMC are tractable when performed on circuits.
Strengths: The paper is generally well-written and technically sound. It also makes a solid effort in trying to unify tractability conditions in the context of algebraic circuits.
Weakne... | Rebuttal 1:
Rebuttal: > My main problem with the paper is that it only discusses sufficient conditions (also acknowledged by the authors). This is contrast to the probabilistic circuit atlas [1].
Although we only discuss sufficient conditions, for Table 1, the necessity of these conditions in general follows from the ... | Summary: They investigate algebraic circuits on semi-rings (with sums and products).
They give criterions that allow efficient combinations of circuits and aggregation of the variables of one circuit (e.g. the sum over all inputs of the circuit).
They give algorithms and hardness results for Algebraic Model Counting.
... | Rebuttal 1:
Rebuttal: >the conditions under which the algorithms are efficiently applicable seem rather restrictive
We agree that the sufficient conditions for tractability of many compositional queries can be strong. However, part of our contribution is that our framework is able to derive weaker tractability conditi... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their time and effort spent reviewing our paper, and for their helpful feedback and comments. Please find individual responses to each reviewer below.
We attach a PDF here addressing corner cases in Algorithm 2 mentioned by Reviewer oGJN.
Pdf: /pdf/94... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification | Accept (poster) | Summary: The study proposes a framework utilizing optimal transport theory to derive mechanism-specific and mechanism-agnostic guarantees for subsampling mechanisms in differential privacy. It focuses on leveraging conditional optimal transport to establish tight bounds on privacy parameters such as α and β, crucial fo... | Rebuttal 1:
Rebuttal: Thank you for your review!
### Concerning appendix length
Please let us explain how we ended up with the current page count. Our work proposes a framework for conducting subsamling analysis.
We claim that this framework lets us prove any known subsampling guarantee (as well as novel guarantees, s... | Summary: The paper proposes a principled approach to analyzing group-privacy amplification through sub-sampling by generalizing the coupling arguments of Balle et al., 2018. This generalization extends the analysis from $1$-neighboring datasets to $K$-neighboring datasets. The core idea is to define a coupling between ... | Rebuttal 1:
Rebuttal: Thank you for your review!
Please excuse our brevity, there is a character limit and you posed a lot of interesting questions. We cannot upload a revision during rebuttals, but will include your suggestions as soon as its possible.
### Specific vs agnostic for group size 1
For group size 1, and ... | Summary: The authors propose a general framework for deriving mechanism-specific differential privacy guarantees for amplification by subsampling. The current methods are generally only tight in a mechanism-agnostic sense, but may possibly be significantly more private. The authors propose a new framework using conditi... | Rebuttal 1:
Rebuttal: Thank you for you are review!
We are glad to hear that you find the studied problem important and the proposed framework broadly applicable.
### Application to other practical areas
We agree that, while group privacy is highly important in practice, future work should focus on applying our fra... | Summary: The authors propose a framework for privacy accounting of amplification by subsampling. An existing principle for this problem is to consider couplings between the output distribution of the mechanism on neighboring datasets and apply joint convexity of the privacy measure at hand. The primary contribution is ... | Rebuttal 1:
Rebuttal: Thank you for your review and your suggested editorial changes!
Please let us first respond to your higher-level comments, before discussing the smaller editorial changes.
Note that we cannot update the manuscript during the rebuttal period, but will include all your suggestions in a revision as s... | Rebuttal 1:
Rebuttal: We are very grateful for the helpful reviews we received.
While we have already individually responded to each of the reviewers' insightful comments, we would like to use this global rebuttal comment to
1. Provide an overview of the figures in the attached pdf file
2. For the area chair's conven... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This work proposed a novel analysis of mechanism-specific amplification via subsampling. The authors decompose the subsampled mechanism into two parts: batch subsampling + mechanism. Then the analysis decompose the probability density into sums of pdf of every batch. The authors then provide upper bound to the... | Rebuttal 1:
Rebuttal: Thank you for the review!
We are excited to hear that you find our submission worthy of acceptance.
### Computational cost of computing Theorem 3.4+Proposition 3.5
Before discussing the main question of your first bullet point,
we would like to briefly clarify the following:
The optimal transport... | null | null | null | null | null | null |
LiteVAE: Lightweight and Efficient Variational Autoencoders for Latent Diffusion Models | Accept (poster) | Summary: This paper provides a LiteVAE structure to replace the original one, aiming to reduce the computations when training on large-scale datasets, which can boost the performance of latent diffusion models with more potential augmentations during training.
Strengths: 1. The new encoder could reduce more parameters... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's helpful comments and the positive reaction to our work. Please find our responses to the individual comments below.
### **Information about the decoder**
As mentioned in line 149, we use the same decoder architecture as SD-VAE, and the encoder and decoder networks are... | Summary: This paper presents LiteVAE. LiteVAE is an efficient and lightweight modification to latent diffusion models (LDMs) that incorporate 2D wavelet transform into the encoding structure. It then uses a feature aggregating model (UNet-based architecture) to fuse multiscale wavelet coefficients into a unified latent... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing constructive comments and for recognizing our paper as high-quality with numerous strengths and significant contribution. Please find our answers to the comments below.
### **Throughput of other LiteVAE models**
We thank the reviewer for pointing out this quest... | Summary: This paper introduces LiteVAE, a novel approach that combines multi-scale VAE and discrete wavelet transform to reduce computational cost and enhance reconstruction capabilities. Both components are well-grounded and supported by experimental results. Additionally, the paper provides a detailed pipeline and ab... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's helpful suggestions, as well as the positive assessment of the influence and quality of our work. Below, we provide detailed responses to the reviewer’s comments.
### **Question about multiscale VAE and wavelets**
We would like to note that the multiscale str... | Summary: The authors propose LiteVAE, a novel architecture for the VAE decoding step of latent diffusion. They show that LiteVAE can achieve comparative perfomance to SD-VAE, the default latent diffusion decoder, while using fewer parameters. The efficiency gain comes from using a more lightweight network, and a wavele... | Rebuttal 1:
Rebuttal: We wish to thank the reviewer for the helpful comments and for finding our work novel with detailed evaluations, good presentation, and significant contribution. Please find our answers to the comments below.
### **Impact of the work**
The VAE component in latent diffusion models is responsible f... | Rebuttal 1:
Rebuttal: We thank all reviewers for recognizing our paper as well-structured and easy to read, and for highlighting its interesting ideas and detailed evaluations.
We would like to clarify that the primary goal of LiteVAE is to study the efficiency and reconstruction capabilities of the autoencoder, as w... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SeeA*: Efficient Exploration-Enhanced A* Search by Selective Sampling | Accept (oral) | Summary: This paper proposes a novel search strategy, SeeA*. SeeA* employs a selective sampling process to screen a dynamic candidate subset based on an additional strategy. Under certain assumptions, SeeA* theoretically has better efficiency than A* search when SeeA* uses uniform select strategy and the heuristic valu... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We will revise the paper accordingly and define symbols clearly. The key algorithms will be moved to the main paper. We hope that our response addresses your concerns.
Q1: Why Figure 1 lists (n) with ($10^4$).
A1: Setting the number of nodes to $10^4$ in the... | Summary: The paper introduces a method for prioritizing nodes for expansion during heuristic search that builds on A* search. However, instead of selecting the node with the lowest cost in OPEN, it samples a subset of OPEN and selects the node with the lowest cost from that subset. The sampling procedure is done using ... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable feedback. We hope that our response addresses any concerns you may have.
Q1:Is the clustering strategy susceptible to collapsing to a single cluster?
A1: This scenario may indeed occur, and in such situations, running multiple iterations with varied initial... | Summary: This work introduces a refined version of the A* search algorithm that integrates selective sampling to improve exploration & efficiency. The developed algorithm balances exploration and exploitation when heuristic guides are off the mark with the help of three sampling strategies. Also it outperforms traditio... | Rebuttal 1:
Rebuttal: We thank the reviewer for constructive comments and suggestions. We will revise our paper carefully. Hope our explanation below can address your concerns.
Q1: Adding a maximum iteration limit to guarantee termination.
A1: Adding a limit to guarantee termination is necessary, and is adopted in ou... | null | null | Rebuttal 1:
Rebuttal: A1: The assumption in Corollary 4.2 that the prediction error for $f^*$ is uniformly distributed is quite strong. To further illustrate the applicability of the algorithm, we also prove that Corollary 4.2 is established if the noise follows a Gaussian distribution. Denoting Gaussian distribution a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Improving the Training of Rectified Flows | Accept (poster) | Summary: By retraining with Rectified flows, they straighten the ODE, allowing sampling for small number of steps. They propose using rectified flow as replacement to complex distillation methods such as consistency models. They reflow only once to straighten the path which makes it as efficient as distillation. They p... | Rebuttal 1:
Rebuttal: Thank you for the review. On ImageNet 64x64, the additional memory overhead of using LPIPS is less than 5%. We expect this would be even smaller on more large-scale settings since the generative model’s size would be relatively huge compared to the feature extractor such as AlexNet or VGG.
---
R... | Summary: The paper targets efficient training of a class of flow models called Rectified Flow (RF) trained using flow matching objective. The paper has two broad contributions: (1) justification of 2-RF (‘reflow’-ed once) being close to optimal, (2) and using those findings to improve training of 2-RF.
Authors argued ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's valuable comments.
## Why does the training loss of rectified flows (and FM with independent coupling) have a nonzero lower bound?
When two interpolation trajectories cross, given the intersection, there are two possible directions to go. As the neural net we use is d... | Summary: This paper mainly improves the training of rectified flows empirically, making it comparable to the distillation method in terms of performance with fewer steps.
Strengths: 1. This article provides a comprehensive analysis of a single return, and there is a clear motivation for improvement.
2. The authors ma... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's valuable comments.
## The main weakness of this paper is that all the improvements are incremental and empirical.
We respectfully disagree with the reviewer's comment. Our proposed techniques improve the FID of 2-rectified flow **from 12.21 to 3.38 on CIFAR-10, 12.39... | Summary: The method introduces an one-stage training of rectified flows, mitigating the costly process of multi-iteration training of the former model. Particularly, the authors propose a U-shaped timestep distribution for sampling and modified LPIPS-Huber loss. The method demonstrates superior FID scores for 1-NFE set... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's valuable comments.
## Have the authors tested with more than two-NFE like 100 to directly compare with 2-rectified flow? Does model still get consistent improvement?
We tested up to 16 NFEs and observed consistent improvement (see Figure 4). Compared to 2-rectified f... | Rebuttal 1:
Rebuttal: # General response
We thank the reviewers for their valuable comments. Here, we provide an additional background to help clarify some of the points raised in the reviews. We also have fixed some typos and clarified notations. The supplementary PDF file is attached.
**Question: Clarify notation**... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents | Accept (poster) | Summary: This paper proposes a decision making framework called AudoGuide. It leverages domain knowledge from offline experiences to generate context-aware guidelines. This framework improves LLM agents in downstream decision-making tasks.
Strengths: 1.Design of context aware guidelines is ingenious.
2. The proposed f... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the positive evaluation of our paper and providing constructive comments. We have addressed individual comments below and conducted additional evaluations. We will also carefully incorporate your feedback into an updated version of our paper.
> “... As the context-a... | Summary: The paper introduces AUTOGUIDE, a novel framework designed to enhance LLM agents' performance in unfamiliar domains like web navigation by automatically generating context-aware guidelines from offline experiences. These guidelines are expressed in concise natural language and follow a conditional structure, c... | Rebuttal 1:
Rebuttal: Thank you for your constructive review and helpful questions. We have addressed each comment individually and conducted additional experiments based on your insightful feedback. We will also carefully incorporate your feedback into an updated version of our main paper and appendix.
> “The author ... | Summary: This paper introduces AUTOGUIDE, a framework for enhancing large language model agents' performance in sequential decision-making tasks by automatically generating context-aware guidelines from offline experiences. The method consists of two main components: a context identification module and a guideline extr... | Rebuttal 1:
Rebuttal: We greatly appreciate your positive evaluations of our paper and insightful feedback. Below, we respond to your valuable comment. We will also carefully incorporate your feedback into an updated version of our main paper and appendix.
> “For each different task, the guidelines have to be construc... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Simple yet Scalable Granger Causal Structural Learning Approach for Topological Event Sequences | Accept (poster) | Summary: This paper presents S2GCSL (Simple yet Scalable Granger Causal Structural Learning), a novel method for learning Granger causal graphs from topological event sequences in telecommunication networks. The authors present a simple and scalable method that uses a linear kernel to model activation interactions betw... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your detailed and thoughtful review of our paper. We greatly appreciate your positive evaluation and the time you have taken to provide us with constructive feedback. Below are our responses to your comments:
Weaknesses:
1.Real-Time Granger Causal Discovery:
You have... | Summary: This paper presents S2GCSL, a novel method designed to efficiently identify the root causes of alarms in telecommunication networks by learning Granger causal graphs from topological event sequences. This method uses a linear kernel and gradient descent optimization, while incorporating expert knowledge as con... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your thorough and insightful review of our paper. We appreciate your positive evaluation and the time you have invested in providing such detailed feedback. Below are our responses to your comments:
Weaknesses:
1.Hyperparameter Tuning:
Thank you for raising this impo... | Summary: The paper presents S2GCSL, a scalable and efficient method for Granger causal structural learning from topological event sequences, specifically designed for telecommunication network fault diagnosis. The approach uses a linear kernel to model interactions and employs gradient descent for optimization, incorpo... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your thorough and insightful review of our paper. We appreciate your positive evaluation and constructive feedback. Here are our responses to your comments:
Weaknesses:
1.Assumption of Poisson Processes:
Thank you for highlighting this point. It is indeed a valuable ... | Summary: This paper presents S2GCSL, a novel approach for Granger causal structural learning from topological event sequences in telecommunication networks. The methodology leverages a linear kernel to model interactions among event types and employs gradient descent for efficient optimization of the likelihood functio... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your thorough and insightful review of our paper. We appreciate your positive evaluation of our work and your constructive feedback. We address your comments and questions as follows:
Weaknesses:
1. Applicability to Other Network Topologies:
We appreciate the reviewer... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Quality-Improved and Property-Preserved Polarimetric Imaging via Complementarily Fusing | Accept (poster) | Summary: This paper proposes a method to fuse a pair of short-exposure (noisy) and long-exposure (blurry) captures to produce clean and clear polarized snapshots. The proposed method consists of three phases to reconstruct the irradiance, texture, and polarization.
Strengths: The paper is well-written, the proposed me... | Rebuttal 1:
Rebuttal: ## Reviewer WQ4j
* Issues about the synthetic dataset.
* The information can be found in Line215-223. Our synthetic dataset is generated from the PLIE dataset [32] (a dataset about polarized image low-light enhancement), which provides short-exposure ($T_{short}$) polarized snapshots that suff... | Summary: This paper proposes a polarimetric imaging framework that can produce clean and clear polarized snapshots by complementarily fusing a degraded pair of noisy and blurry ones. It adopts a neural network-based three-phase fusing scheme with specially designed modules tailored to each phase, which can not only imp... | Rebuttal 1:
Rebuttal: ## Reviewer SSTW
* Why not choose the LLCP dataset as the source data?
* This is because the quality of PLIE dataset [32] used in our paper could be better than the LLCP dataset [25]. For example, overexposed regions often appear in the reference images of the LLCP dataset [25]. Training a netw... | Summary: This paper proposes the first method for polarimetric image enhancement by fusing noisy and blurry pairs. While a short exposure polarimetric image produces sharp but noisy DoP and AoP, a long exposure makes them smooth but blurred. To effectively exploit the complementary advantages of these two images and sa... | Rebuttal 1:
Rebuttal: ## Reviewer 4C9V
* Issues about the PSNR and SSIM values of the DoP and AoP.
* PSNR is highly sensitive to small changes in pixel values, whereas SSIM considers structural information and spatial relationships within the image. Our method leverages the clean information from the blurry input, e... | null | null | Rebuttal 1:
Rebuttal: ## Common issues
We sincerely thank all reviewers for their valuable comments and suggestions. We feel encouraged that the novelty and performance of our method are acknowledged by the reviewers:
* Propose a novel fusing scheme to effectively use complementary polarimetric information of noisy ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
In-Trajectory Inverse Reinforcement Learning: Learn Incrementally Before an Ongoing Trajectory Terminates | Accept (poster) | Summary: In this paper, a novel framework for performing Inverse Reinforcement Learning (IRL) from an ongoing trajectory, i.e., to learn a reward function that induces an optimal policy that best explains the expert's demonstrations sequentially, without waiting to having observed the expert's trajectory entirely. Auth... | Rebuttal 1:
Rebuttal: Thanks for your constructive reviews. We believe that our discussion will lead to a better paper. Before addressing your comments, we would like to clarify the goal of IRL and what role the reward unidentifiability issue plays. IRL aims to learn a reward function that can explain the expert demons... | Summary: The paper "Learn Incrementally from An Ongoing Trajectory: A Provable In-Trajectory Inverse Reinforcement Learning Framework" proposes an innovative approach to inverse reinforcement learning. The authors introduce an online learning algorithm to address the IRL problem with incomplete expert demonstrations. A... | Rebuttal 1:
Rebuttal: Thanks for your insightful reviews. We believe that our discussion will lead a better paper. We address your comments below:
**Weakness 1**: In the experiments, I don’t understand why different algorithms have different initial points? When t=0, are you using a same random reward function or the ... | Summary: The authors consider a new problem setting, called in-trajectory IRL, where a reward function and a corresponding policy need to be learned from an ongoing trajectory. The authors propose a novel reward update mechanism specially designed for this scenario and incorporate a meta-regularization strategy to embe... | Rebuttal 1:
Rebuttal: Thanks for your constructive review. We believe that our discussion will lead to a stronger paper. We address your comments below:
**Weakness 1**: Assumption 1 assumes that the parameterized reward is smooth, which can be a strong assumption, especially for neural networks with non-smooth activat... | null | null | Rebuttal 1:
Rebuttal: **How can we extract a meaningful reward even though we have not observed the expert’s policy for
the entire horizon?**
The reason is that our reward update approximates the entire expert horizon and learns from the approximate entire expert trajectory (detailed in lines 188-219). We include a fi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Compare | Accept (spotlight) | Summary: The paper introduces an LMM-based no-reference IQA framework that can generate qualitative comparisons between images and translate those discrete comparisons into continuous quality scores. During training, it generates comparative instructions by comparing image pairs within the same IQA dataset, allowing fl... | Rebuttal 1:
Rebuttal: Thanks for recognizing the merits and strengths of our paper. Point-to-point responses are given as follows.
**Q1. The generalization capabilities.**
**A1:** To assess the generalization capability of Compare2Score to unseen distortions and datasets, we conduct the cross-distortion experiments w... | Summary: The paper presents a framework that trains an LMM as a visual quality comparator using relative image comparisons, and converts the discrete comparison outputs to continuous quality scores via a soft comparison method. It generates paired image comparisons from existing IQA datasets to train the LMM, and uses ... | Rebuttal 1:
Rebuttal: Thanks for recognizing the merits and strengths of our paper. Point-to-point responses to specific comments are given as follows.
**Q1. Including the IDEFICS2 in Tables 3 and 4**
**A1:** We have included the performance of IDEFICS2, the latest version of the IDEFICS family. As shown in Tables 1 ... | Summary: This paper introduces Compare2Score, a novel NR-IQA model that harnesses the robust capabilities of LMM to interpret and integrate complex textual and visual inputs. The model is trained using a relative quality comparison strategy. Additionally, the authors propose a soft comparison approach that transforms d... | Rebuttal 1:
Rebuttal: Thanks for recognizing the merits and strengths of our paper. Point-to-point responses to specific comments are given as follows.
**Q1. How is the standard deviation determined when constructing image pairs? How do variations in standard deviation affect the pairing process and subsequent quality... | Summary: This work presents a method named Compare2Score, which is capable of producing qualitatively comparative responses and effectively translating these discrete comparative levels into a continuous quality score. The method utilizes the predefined anchor images to calculates the likelihood and get the quality sco... | Rebuttal 1:
Rebuttal: Thanks for recognizing the merits of our work and for your insightful suggestions. Point-to-point responses to specific comments are given as follows.
**Q1. It is still somewhat unclear how to utilize the anchor images to align the difference scales among datasets. Are these anchor images from t... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Bounds for the smallest eigenvalue of the NTK for arbitrary spherical data of arbitrary dimension | Accept (poster) | Summary: This paper gives a lower bound of the smallest eigenvalue of the NTK matrix for shallow and deep connected ReLU networks through the application of hemisphere transform.
Strengths: The main significance of this paper is dropping the requirement of the input data dimension from [Nguyen2021] on the same topic, ... | Rebuttal 1:
Rebuttal: We are grateful for the positive feedback. In regard to your question,
> How tight is the lower bound shown in Theorem 1 where the quantity is defined in terms of the $\delta$-seperatedness of the data, besides the case where data distributed uniformly on the sphere mentioned in line 152-153,
t... | Summary: This theory paper fits within a general framework in which one tries to get information on training of deep learning models using the formalism of the so-called Neural Tangent Kernel.
Specifically, the topic is smallest eigenvalue control for the NTK kernel, and the authors study the minimum eigenvalue under ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their overall positive feedback and are confident we can address each of the concerns raised. In light of our responses below we hope the reviewer will consider increasing their score.
> The main weakness is that requirement on the data distribution to be delta-separated ... | Summary: This work provides bounds on the smallest eigenvalue of the Neural Tangent Kernel corresponding to fully connected ReLU networks trained on data supported on spheres. The novelty is that usual assumptions coupling the input data dimension to the sample size are able to be weakened. Similarly, assumptions on th... | null | Summary: This paper investigates the neural network optimization and memorization in terms of the bounds on the smallest eigenvalue of NTK, without requiring distributional assumptions on the data. The theoretical results are technically sound and contribute to the understanding of neural network convergence behavior.... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and thoughtful comments. We are confident that we can address your concerns and hope in light of our responses the reviewer might consider increasing their score.
> The current results are constrained to scenarios where the activation function is ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper derives new bounds on the smallest eigenvalue in NTK kernel matrices crucially used in the analyses of neural network training and generalization. Hereby it uses new analytical techniques. One main point improving over most previous bounds is that they are widely distribution independent. The only (s... | Rebuttal 1:
Rebuttal: We thank the reviewer for their overall positive feedback on our work. We are confident that we are able to address your concerns and hope in light of our responses below that you might consider raising your score. First in regards to the highlighted weaknesses we offer the following comments.
> l... | null | null | null | null | null | null |
MaNo: Exploiting Matrix Norm for Unsupervised Accuracy Estimation Under Distribution Shifts | Accept (poster) | Summary: The paper addresses the challenge of estimating the test accuracy of pre-trained neural networks on out-of-distribution (OOD) samples without access to ground-truth labels. Current logit-based methods often suffer from overconfidence, leading to prediction bias. The authors propose a new method called MANO, wh... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments as well as the insightful suggestions. We will address the reviewer's concerns below. Please let us know if any issues remain, we would be happy to continue this discussion to address them.
**1. The universality of the Low-Density Separation (LDS) a... | Summary: This paper presents MANO, a straightforward and efficient training-free approach for estimating test accuracy in an unsupervised manner, leveraging the Matrix Norm of neural network predictions on test data. The method is inspired by the low-density separation assumption, which posits that optimal decision b... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments and very valuable suggestions to improve the quality of our paper. We address the reviewer's concerns below. Please let us know if any issues remain.
**1. How to use $\texttt{MaNo}$ in practice?**
This work demonstrates the strong correlation betw... | Summary: This paper proposes the OOD accuracy estimation method, named MANO, by leveraging the positive correlations between features to decision boundary distance and generalization performance. Along with Softtrunc for preventing error accumulation in overconfidence scenarios, the proposed method outperforms existing... | Rebuttal 1:
Rebuttal: We thank the reviewer for their precious comments which help us further improve the paper. We hope our answers below could precisely address the reviewer's concerns. Please let us know if any issues remain.
**1. Hyperparameter tuning and OOD labels.**
We thank the reviewer for this comment. In o... | Summary: The paper presents MANO, a method for unsupervised accuracy estimation under distribution shifts. The method addresses the challenge of estimating model performance on out-of-distribution (OOD) samples without access to ground-truth labels. Firstly, the authors investigate the correlation between logits and te... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive support and constructive comments on this work! Please find our responses below.
**1.1. Relation to entropy-based methods such as ATC.**
We thank the reviewer for this comment. The proposed MaNo and the ATC indeed share similarities as they both belong to t... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and valuable suggestions. We are deeply grateful to them for acknowledging the **novelty and quality** of our study (Reviewer 6N1W, A5Zk, Uw9o) while noting its **effectiveness and superiority on large-scale experiments** (Reviewers 6N1W, 5Gdr, A5Zk, Uw9o). We... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Long-tailed Object Detection Pretraining: Dynamic Rebalancing Contrastive Learning with Dual Reconstruction | Accept (poster) | Summary: This paper proposes Dynamic Rebalancing Contrastive Learning with Dual Reconstruction (DRCL) to tackle long-tailed object detection challenges. DRCL integrates dynamic rebalancing to address instance-level imbalance and a dual reconstruction strategy to enhance feature representation for tail categories. Exper... | Rebuttal 1:
Rebuttal: ## Thank you for the positive comments. Below please find our point-to-point responses.
*Comment_1: One weakness of the paper is that some section titles could benefit from further refinement. For instance, the titles of sections 3.1.1, 3.1.2, and 4.3 may not fully capture the content they encomp... | Summary: The paper proposes DRCL, an object detection pretraining methodology for datasets with long-tailed object class distributions. Their proposed framework consists of three losses: 1) image-level constrastive instance discrimination, 2) object-level contrastive instance discrimination, and 3) a reconstruction los... | Rebuttal 1:
Rebuttal: ## Thanks for the comments.
Comment_1: The proposed method is not self-supervised.
Response_1: The self-supervised components mentioned in our paper refer specifically to the Holistic-Object Contrastive Learning and Dual Reconstruction training modes. In the final version, we will eliminate any ... | Summary: This paper tackles the underperformance of object detection on long-tailed datasets using a novel pretraining methodology called Dynamic Rebalancing Contrastive Learning with Dual Reconstruction (DRCL). DRCL addresses biases in classifier weight norms and feature representation by integrating holistic and obje... | Rebuttal 1:
Rebuttal: ## Thank you for the positive comments. Below please find our point-to-point responses.
*Comment_1: The paper lacks qualitative analysis, which could provide a more in-depth understanding of the model's performance and behavior.*
Response_1: We have already included analyses in our paper with Fi... | Summary: The authors proposed a pre-training method for long-tail object detection. Specifically, the authors integrated holistic and object-level contrast within a contrastive learning framework, used a dynamic rebalancing technique to transition from image-level resampling to instance-level resampling, and implemente... | Rebuttal 1:
Rebuttal: ## Thank you for the comments. Below please find our responses to some specific comments.
*Comment_1: The author should give more discussion about the existing works and the proposed method.*
Response_1: Our proposed method addresses a significant gap in long-tailed object detection by introduc... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Interventional Causal Discovery in a Mixture of DAGs | Accept (poster) | Summary: This paper deals with using interventions to learn the causal links in a mixture-of-DAGs model.
They find the minimum number of intentions that are required to learn "true edges" where a true edge from a node X to a node Y indicates that at least in one mixture component, X is a parent of Y. They also present ... | Rebuttal 1:
Rebuttal: We thank the reviewer for thoughtful evaluations and feedback. We hope the following explanations can clarify the reviewer’s concerns.
**Running example:** Thank you for the suggestion. Figure 2 in Appendix D illustrates the mixture of DAGs and construction of $\cal{I}$-DAG. Due to space limitati... | Summary: The paper studies the setting where data is generated from a mixture of DAGs and one wishes to recover the "true edges" (edges that exist in at least one of the underlying DAGs). Similar to the usual causal discovery setting, observational data alone is insufficient and interventions are required. The paper ch... | Rebuttal 1:
Rebuttal: We are grateful for the exceptionally detailed and thoughtful review. We address the raised questions as follows.
## General questions
**Motivation for recovering the true edges**: The observation is correct that, without further assumptions, we cannot identify which true edges belong to which... | Summary: This work studies an important problem in causal discovery for its relevance in the real world -- identifying the causal relationship when the underlying data-generating process comes from a mixture of different DAGs. They give the necessary and sufficient size of intervention set to identify the union of all ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments and assessment of our paper. We address the raised questions as follows.
**Experiments**: In the additional experiments in the global response, we demonstrate that our algorithm is scalable to higher dimensions – up to $n=30$ nodes and $K=10$ com... | Summary: In the case of a single DAG, conditional independence tests specify the skeleton (under faithfulness) and interventions are limited to orienting edges. In the case of data coming from a mixture of DAGs, it is possible for two variables to not be adjacent in any of the components but still be conditionally depe... | Rebuttal 1:
Rebuttal: We thank the reviewer for a thorough review and insightful comments. We address the questions as follows.
**Modified distributions of an intervened variable:** We considered an intervention model in which an intervened node $i$ has distribution $q_i(X_i)$ for all component DAGs. The reason is tha... | Rebuttal 1:
Rebuttal: We thank all reviewers for their thorough evaluation and thoughtful questions. To demonstrate the scalability of our algorithm, we performed additional experiments under the same settings described in the paper.
**Increasing the number of nodes**: The submitted paper presents experiment results ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Exact Gradients for Stochastic Spiking Neural Networks Driven by Rough Signals | Accept (poster) | Summary: Spiking neural networks face the problem of non-differentiability of loss function due to the Heaviside activation function, which works as the spiking function. To back-propagate the loss through the network, Heaviside is replaced with surrogate functions as a workaround.
The paper establishes the theoretic... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read the paper and provide constructive feedback. We hope that the points below in combination with the author rebuttal answers all the questions that the reviewer may still have.
**W**: The paper demonstrates it implementation through a toy parameter ... | Summary: This work develops a mathematical framwork to compute the gradients of stochastic spiking neural networks, or more generally Event SDEs. The proposed framework is an alternative to existing surrogate gradient frameworks and an extension of prior adjoint based work (e.g. EventProp) in the presence of stochastic... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read the paper and provide constructive feedback. We hope that the points below in combination with the author rebuttal answers all the questions that the reviewer may still have.
**W**: Use of jargon and mathematical assumptions which are sometimes no... | Summary: The paper introduces a mathematical framework using rough path theory to model stochastic spiking neural networks as stochastic differential equations with event discontinuities, driven by càdlàg rough paths. This framework accommodates potential jumps in both solution trajectories and driving noise. Furthe... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read the paper and provide constructive feedback. We hope that the points below in combination with the author rebuttal answers all the questions that the reviewer may still have.
**Q**: How does the performance of the proposed gradient-based training ... | Summary: This paper introduces a general mathematical framework to model stochastic spiking neural networks (SSNN) as stochastic differential equations with event discontinuities, and identifies sufficient conditions ensuring the existence of gradients. With a newly defined loss function, SSNNs can be trained as genera... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read the paper and provide constructive feedback. We hope that the points below in combination with the author rebuttal answers all the questions that the reviewer may still have.
**W**: There are several assumptions in the analysis. It would be better... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for taking the time to go through the paper and providing valuable feedback. We agree with many of the points that have been raised and believe that most, if not all, can be accommodated in a camera-ready version. Apart from minor points and clarifications,... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
START: A Generalized State Space Model with Saliency-Driven Token-Aware Transformation | Accept (poster) | Summary: This paper considers the domain generalization problem and analyze generalization risk across different domains based on the State Space Model (SSM). At the beginning, the advance of SSM over ViT and CNN is introduced. Specifically, the generalization risk bound is formulated by the token-level maximum mean di... | Rebuttal 1:
Rebuttal: Thanks for your valuable reviews. Due to the character limit of $6000$ here, the tables are displayed in the ''glocal_tables.pdf''.
### **Q1: The number of source domains.**
Thanks for your constructive feedback.
Following previous works [9,13,66], we consider $N \geq 1$ source domains to theore... | Summary: This paper targets adapting Mamba for Domain Generalization. The authors find that find the input-dependent matrices in SSMs could accumulate and amplify domain-specific features, so they hinders model generalization. To address this paper, the authors selectively perturb and suppress domain-specific features ... | Rebuttal 1:
Rebuttal: ### **Q1: Comparision with CNN-based or ViT-based DG methods on VMamba.**
Thanks for your feedback.
Indeed, the previous DG method could be transferred to VMamba. However, these CNN-based or ViT-based DG methods ignore the accumulation of domain gaps in the state space modeling process of Mamba, ... | Summary: Advancements in state space models (SSMs), particularly a model called Mamba, have demonstrated efficient performance in supervised learning, offering linear complexity in training and rapid computation during inference, similar to RNNs. This paper explores the potential of the Mamba model for DG and identifie... | Rebuttal 1:
Rebuttal: ### **Q1: Computational costs including inference time.**
Thanks for your valuable advice. We have provided a comparison of inference times. The batch size for evaluating inference time is set to $64$, and the inference time is averaged over $100$ experiments. Since STARR-M and START-X are only ac... | Summary: This paper studies the role of MAMBA architectures on Domain Generalization benchmarks and adapts the architecture to achieve robust generalization.
The motivation to use MAMBA-style architectures is their linear complexity. The authors theoretically analyze conventional MAMBA for DG and make an important fin... | Rebuttal 1:
Rebuttal: ### **Q1: Motivation of our method.**
Thanks for your feedback. Some pioneering work have demonstrated the effectivenes of Mamba architecture on various supervised visual tasks. However, few works have studied the generalization ability of Mamba under distribution shift, especially the problem tha... | Rebuttal 1:
Rebuttal: We would like to thank the ACs and reviewers for their constructive comments on our paper. We are encouraged by the positive feedback, including remarks such as "the aspect is important", "this paper contains novelty and is technically sound", "the theoretical analysis is a significant contributio... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper aims to enhance the generalization ability of state space models (SSMs), i.e. Mamba. This paper first provides a theoretical investigation on the generalization ability of the Mamba model under domain shifts and finds that input-dependent matrices within SSMs could accumulate and amplify domain-spec... | Rebuttal 1:
Rebuttal: Thanks for your valuable reviews. Due to the character limit of $6000$ here, the tables are displayed in ''glocal_tables.pdf''.
### **Q1: Revisions of Proposition 1 and 2.**
Thanks. We have modified Propositions 1 and 2 to be more clear and detailed:
**Proposion 1 (Accumulation of Domain Discr... | null | null | null | null | null | null |
Directional Smoothness and Gradient Methods: Convergence and Adaptivity | Accept (poster) | Summary: The work propose different directional smoothness function and use them to establish sub-optimality bounds that are adaptive to the optimization trajectory. This approach can be explicitly used in quadratics or with an exponential search technique for convex objectives to use adaptive stepsizes that enjoy bett... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your time and review. We were pleased to see that you thought
our "tighter, adaptive guarantees are appealing due to the observed success
beyond the theoretical convergence bounds" and that "the technique naturally
yields results for several existing methods."
We un... | Summary: The paper proposes a new type of non-uniform smoothness, which the authors label _directional smoothness_. Directional smoothness replaces $L$ in the typical $L$-smoothness inequality by a function $M(x,y)$, which describes the smoothness along the line between $x$ and $y$. The authors proof multiple basic pro... | Rebuttal 1:
Rebuttal: 1. **Many of the results until Section 4.2 are direct consequences of the
(directional) descent inequality.**
The reviewer has listed this as one of the weakness of our paper, but we would
argue that this is a strength. It is this clear link between the definition of
directional smoothness, the d... | Summary: This paper develops refined sub-optimality bounds for gradient descent in the convex setting. The authors consider directional smoothness, a local and path dependent smoothness condition, instead of assuming globally bounded smoothness constants in classical analyses. They discussed several interesting example... | Rebuttal 1:
Rebuttal: **1. I wonder if there are any interesting applications for which their rates
can be more explicit and show clear improvement over classical ones.**
Yes, and we provide such an example in Section 4.1 for quadratics. In this
example, instead of the convergence rate relying on the largest eigenval... | null | null | Rebuttal 1:
Rebuttal: **1. Extensions to the non-convex setting.**
Deriving meaningful results for non-convex functions is challenging.
For example, we can immediately use the directional descent lemma to obtain,
$$\\begin{aligned} \\eta_k (1 - \\frac{\\eta_k M_k}{2}) \\| \\nabla f(x_k) \\|^2_2 & \\leq f(x_k) - f(x_{... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Bayesian Adaptive Calibration and Optimal Design | Accept (poster) | Summary: This paper addresses the problem of calibrating simulation models. Simulation models depend on inputs set by the user, referred to as designs, and parameters representing unknown physical quantities, called calibration parameters. The task is to find calibration parameters such that simulations match real obse... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their positive feedback and for letting us know about the clarity issues. Table 3 consists of the results discussed in Sec. 6.4, which unfortunately missed a direct reference to the table. We will make sure to address these and the other issues in the revisi... | Summary: The paper proposes a more data-efficient algorithm inspired by Bayesian adaptive experimental design. This algorithm runs maximally informative simulations in a batch-sequential process, estimating posterior distribution parameters and optimal designs by maximizing a variational lower bound of the expected inf... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the detailed comments and insightful feedback. We provide a global response clarifying the contrast with the state-of-the-art and details on an additional baseline, but further elaborate on specific details relevant to the reviewer's comments below.
**Novel... | Summary: This paper addresses the challenge of calibrating expensive-to-evaluate computer models using Bayesian adaptive experimental design. The novelty of the proposed method (BACON) lies in using the expected information gain (EIG), which is a principled information theoretic criterion for active learning, to perfor... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their positive and insightful feedback. We will revise the text addressing the issues raised. In addition to our global response in the main author rebuttal, our response to the reviewer's specific questions follows below.
### Weaknesses
Regarding the issue... | Summary: This paper considers the problem of calibration of computer models as an active learning problem. Given the objective being maximizing the expected information gain (EIG) about calibration parameters, and based on the assumption of linear dependency between simulator outcome and true observation, this work pro... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their comments and feedback. Indeed, the dimensionality of the problem consists of the sum of the dimensionalities of the design space and the calibration parameters space. The purpose of this paper, however, was to propose a general method for Bayesian cali... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their constructive feedback and the time and effort applied in reviewing our manuscript. We provide individual responses to each review, but we also address some of the main common points here. In addition, we have *new results* with additional baseline... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fetch and Forge: Efficient Dataset Condensation for Object Detection | Accept (poster) | Summary: 1) The paper introduces a dataset compression technique and applies it for the first time in object detection tasks.
2) For large-scale object detection tasks, the condensation of datasets can not only shorten model training time but also save a lot of computational resources.
3) The method proposed in the pa... | Rebuttal 1:
Rebuttal: ### Q1 [Algorithm needs to be optimized]
In this work, we analyze why the traditional bidirectional framework is challenging for detection tasks (lines 35-40). We propose the fetch and forge two-phase framework, which avoids the computational cost of second-order derivatives and achieves critical ... | Summary: The paper attempts to generalize dataset condensation to object detection. It proposes Fetch and Forge. Fetch: training a normal object detector on the original dataset as usual. Forge: the synthetic images are sampled from the original data and optimized by the detection loss. Experiments on VOC and COCO show... | Rebuttal 1:
Rebuttal: ### Q1 [Notation issue: see IPE in Eqn. 6 and Step 5 of Alg.1.]
We note the inconsistency in the notation for I_PE between Equation 6 and Step 5 of Algorithm 1. We will correct this inconsistency in the revised version of the paper. Thank you for bringing this to our attention.
### Q2 [Why is row ... | Summary: The paper looks at the task of dataset condensation for object detection. While there have been many works looking at dataset condensation for classification this is the first work to look at it for detection which is more challenging as each image can contain multiple objects of different categories. This is ... | Rebuttal 1:
Rebuttal: ### Q1 [Background suppression and its impact on object detection.]
We acknowledge the importance of background in detection tasks but prioritize updating the foreground in image synthesis while retaining the background for better results. "Suppress background" means limiting updates to background... | Summary: This manuscript introduces, as far as I’m aware, the first method to do object detection dataset condensation. The method accomplishes this by first training a detection model on the original dataset called the Fetch stage, then uses this trained model to synthesize a condensed dataset through model inversion ... | Rebuttal 1:
Rebuttal: ### Q1 [Analyze the class distribution of small/medium/large objects in the K-Center and Herding.]
Based on our analysis, we compare the distribution of small(area<32x32), medium, and large(area>96x96) objects sampled by the k-center and herding methods with the original dataset, presenting the re... | Rebuttal 1:
Rebuttal: We are grateful to all reviewers for acknowledging our work and providing valuable comments and suggestions.
Common strengths noted:
1.Recognition of our motivation and contributions as pioneers in studying detection dataset condensation.
2.Demonstrated effectiveness of our method on two widely-us... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Consensus over matrix-weighted networks with time-delays | Reject | Summary: The paper studies the conditions that lead to consensus in matrix-weighted consensus networks when constant time delays are present. The analysis considers both leaderless and leader-follower settings. The paper considers single integrators with uniform time delays, heterogeneous time delays, and double integr... | Rebuttal 1:
Rebuttal: **Q1: The relevance of the submitted paper with Neurips**
- Although the paper does not directly concerns with any trending topics in AI/ML such as generative AI, LLMs, NLP,... the theoretical results are still relevant with Neurips. The problem formulation and analytical techniques are originate... | Summary: Paper under review analyzes the consensus of agents over a network. The agents have arbitrary but identical state space dimension, so not just scalar dynamics. The communication between agents is delayed and can be heterogeneous. Lyapunov–Razumikhin functionals with an LMI (that grows with the size of the netw... | Rebuttal 1:
Rebuttal: **Rebuttal on the reviewer's questions**
- Olfati-Saber and Murray (2004) considered a single-layer consensus network and their proof is based on a Nyquist criterion (frequency method). The submitted work considers a matrix-weighted consensus network, which is a generalization of the single-layer ... | Summary: The paper investigates consensus conditions for matrix-weighted consensus networks, both leaderless and leader-follower, in the presence of constant time-delays. It explores delayed consensus algorithms for networks of single- and double-integrators using relative positions. The study derives conditions for ne... | Rebuttal 1:
Rebuttal: **About the mutual interests of Neurips and the submitted paper:** Although the paper does not directly concerns with any trending topics in AI/ML such as generative AI, LLMs, NLP,... the theoretical results are still relevant with Neurips. The problem formulation and analytical techniques are ori... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
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