title string | paper_decision string | review_1 string | rebuttals_1 string | review_2 string | rebuttals_2 string | review_3 string | rebuttals_3 string | review_4 string | rebuttals_4 string | global_rebuttals string | dataset_source string | conference_year int64 | review_5 string | rebuttals_5 string | review_6 string | rebuttals_6 string | review_7 string | rebuttals_7 string | review_8 string | rebuttals_8 string |
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$\textit{Trans-LoRA}$: towards data-free Transferable Parameter Efficient Finetuning | Accept (poster) | Summary: This paper proposes a nearly data-free method for transferring pre-tuned PEFT components (e.g., LoRA) between different models. To address issues of data inaccessibility, the authors propose to generate synthetic data from the target base model. To ensure that this synthetic data is in-distribution, they intro... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for valuable feedback and comments on our paper. We appreciate the opportunity to address your concerns and clarify any misunderstandings. Below, we provide detailed responses to each of your comments.
>The application scope of the proposed method appears limit... | Summary: This paper proproses Trans-LoRA, a method that utilize synthetic data to transfer abilities learned using LoRA across different downstream tasks. Trans-LoRA first uses the source model for synthetic data generation. The generated synthetic data is used to train discriminator LoRA for filtering synthetic data f... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for valuable feedback and comments on our paper. We appreciate the opportunity to address your concerns and clarify any misunderstandings. Below, we provide detailed responses to each of your comments.
> I understand that the current trend is to apply PEFT on d... | Summary: The paper presents Trans-LoRA, a novel approach for transferring Low-Rank Adapter (LoRA) parameters across different base models without requiring access to the original task data. Trans-LoRA utilizes synthetic data generation and a discriminator model to filter this synthetic data, ensuring that the transferr... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for valuable feedback and comments on our paper. We appreciate the opportunity to address your concerns and clarify any misunderstandings. Below, we provide detailed responses to each of your comments.
>While the paper demonstrates the effectiveness of Trans-Lo... | null | null | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for reviewing our paper and providing valuable and constructive feedback.
We are grateful that the reviewers have highlighted our work as:
- well motivated (7oKi)
- innovative and novel in approach (qZWK)
- well-written and clear to understand (qZWK, K48... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FedGMKD: An Efficient Prototype Federated Learning Framework through Knowledge Distillation and Discrepancy-Aware Aggregation | Accept (poster) | Summary: FedGMKD addresses data heterogeneity using a dual-enhancement approach through Cluster Knowledge Fusion (CKF) and Differential Aggregation Technique (DAT). This method effectively enhances both local and global model performance without relying on public datasets or complex server-side models.
Strengths: The ... | Rebuttal 1:
Rebuttal: # 1. Broader Applicability and Merit of FedGMKD
Thank you for your insightful feedback regarding the scope of our experiments and the potential broader applicability of FedGMKD. We appreciate the opportunity to clarify and expand upon our work by exploring its performance on datasets beyond the c... | Summary: The authors introduce a novel federated learning algorithm aimed at addressing the challenge of data heterogeneity among distributed clients. The key innovation of this work is the integration of Cluster Knowledge Fusion (CKF) and the Differential Aggregation Technique (DAT). CKF employs Gaussian Mixture Model... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback. We appreciate the opportunity to address the concern regarding the exploration of a wider range of hyperparameters to demonstrate FedGMKD's robustness& sensitivity and discuss the justification of using GMM in FedGMKD.
# 1. Hyperparameter Exploration in FedG... | Summary: This paper introduces FedGMKD to tackle data heterogeneity by CKF and DAT. Specifically, to get a prototype of each class at each client, CKF is proposed by GMM and aggregates prototypes of the same class from different clients via discrepancy-aware weight.
Strengths: This paper analyzes convergence and conve... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We appreciate the opportunity to discuss the weakness and questions.
# 1. Motivation and Insights
## a. Motivation and Insight of FedGMKD
FedGMKD tackles key challenges in personalized federated learning, particularly the non-IID client data that can lead to suboptim... | Summary: The authors proposed FedGMKD, a federated learning framework designed to handle data heterogeneity across distributed clients. FedGMKD introduces Cluster Knowledge Fusion (CKF), which uses Gaussian Mixture Model (GMM) clustering to generate prototype features and soft predictions, facilitating knowledge distil... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback. We appreciate the opportunity to discuss FedGMKD with more complex neural network architectures, its adaptability, and its real-world impacts and applications.
# 1. FedGMKD with More Complex Neural Network Architectures
To address your suggestion, we conduc... | Rebuttal 1:
Rebuttal: We sincerely appreciate all the reviewers for their constructive and valuable feedback. We are pleased that our work has been recognized for its innovative approach to addressing data heterogeneity in federated learning through the integration of Cluster Knowledge Fusion (CKF) and Differential Agg... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data | Accept (poster) | Summary: This paper introduces a robust two-stage framework leveraging observational and instrumental variable data to predict conditional average treatment effects (CATEs), addressing biases from unobserved confounders and low compliance.
Strengths: - This paper studies leveraging observational data and encouragement... | Rebuttal 1:
Rebuttal: **Strengths**
Thank you for your positive feedback. We appreciate your recognition of our work on leveraging observational and encouragement data with low compliance to predict CATEs, and for acknowledging the utility of our two-stage framework.
**Re: CATE Identification**
You are absolutely co... | Summary: The paper presents a novel method for estimating Conditional Average Treatment Effects (CATEs) by integrating weak instrumental variables (IV) and observational data. This method addresses the challenges of unobserved confounding and low compliance often encountered in causal inference studies. The proposed fr... | Rebuttal 1:
Rebuttal: **Strengths**
Thank you for your thoughtful review. We appreciate your positive feedback on our method for estimating CATEs by integrating weak instrumental variables and observational data, and your acknowledgment of its novelty and effectiveness.
**Re: Questions**
* Assumption 2 states that ... | Summary: The authors propose an approach for conditional average treatment effect estimation using instrumental variables, extending existing work in IVA to settings with weak instruments (i.e., low treatment compliance in some population subgroups). In particular, the approach leverages a two-stage estimation setup: f... | Rebuttal 1:
Rebuttal: **Strengths**
Thank you for your encouraging feedback. We are pleased that you recognize the novelty of our approach for estimating conditional average treatment effects using IVs, particularly in scenarios with weak instruments. Your positive feedback on the clarity of our exposition, the intui... | Summary: The paper tackles the problem of estimating CATE when unobserved confounding is present in an observation study but an IV experiments is accessible, though the instrument could be weak. The paper proposes a two stage framework to first learn a biased CATE from observational data and makes a bias correction usi... | Rebuttal 1:
Rebuttal: **Strengths**
Thank you for your insightful feedback. We appreciate your recognition of our novel approach to combining an observational dataset with an IV study with weak instruments. We are glad that our efforts to ensure the paper is both rigorous and accessible are apparent.
**Re: More Exper... | Rebuttal 1:
Rebuttal: We thank all our reviewers for their thoughtful comments and constructive feedback. We are encouraged by the consensus on the novelty and effectiveness of our method, as well as its theoretical and empirical contributions. We have addressed additional questions and concerns in individual responses... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Splatter a Video: Video Gaussian Representation for Versatile Processing | Accept (poster) | Summary: The work introduces a video Gaussian representation that leverages 2D priors, such as depth and optical flow, to regularize 3D Gaussians in for various 2D video tasks. This representation can be used for several downstream video processing and editing applications, including dense tracking, enhancing temporal ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's valuable feedback and address the concerns raised.
**Training Speed**: While our method's training time relative to video duration is lengthy, it demonstrates significantly faster training speed compared to other methods, as shown in Table R1. We also report superior ... | Summary: The goal in this work is to represent a video with a set of 3D Gaussian primitives, rendered with the 3DGS pipeline. The approach is optimisation-based and outputs video-specific Gaussians following a pre-defined trajectory model — hybrid of a polynomial and a Fourier series. The optimisation involves the use ... | Rebuttal 1:
Rebuttal: Thank you for your valuable advice! We’d like to emphasize that using 3D representations to model and process casually captured in the wild videos, different from dynamic NeRF/GS, is a much ill-posed problem. Here, we design a system that integrates representations and regularization: 1) using ... | Summary: This paper introduces a novel explicit 3D representation for video processing using video Gaussians. This method embeds videos into 3D Gaussians to model video appearance and motion in a 3D canonical space. By leveraging 2D priors such as optical flow and depth estimation to regularize the learning of video Ga... | Rebuttal 1:
Rebuttal: Thank you so much for your support in our work! We are delighted to answer the concerns raised.
**Compare with 4DGS based methods:** Thank you for your suggestions. We have incorporated a comparison with 4DGS [1] on Tap-Vid DAVIS benchmarks for reconstruction and tracking tasks respectively. The ... | Summary: This paper presents a "Video Gaussian Representation" (VGR) to explicitly model the dynamic 3D scene contained in a monocular video. The VGR employs 3D Gaussian splatting as the backend, associating each Gaussian with time-dependent motion attributes. 2D priors, such as depth, optical flow, and optionally segm... | Rebuttal 1:
Rebuttal: We thank the reviewer for your valuable time and the acknowledgment of our writing and illustrations. We are here to respond to the listed concerns.
1. **Novelty**:
We would like to emphasize that our work is the first for dynamic GS without the camera dependency. Our novelty doesn’t lie in a... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable comments. We are glad and appreciate that the Reviews for the comments of “well structured, easy to follow” (**rMXd, 8X9e**), “novel and well motivated” (**8X9e, rPzr**), and recognize that the versatility video processing ability is “well demonstrat... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Quantifying and Optimizing Global Faithfulness in Persona-driven Role-playing | Accept (poster) | Summary: This paper introduces the Active-Passive-Constraint (APC) score to evaluate and optimize the faithfulness of AI-driven persona interactions in role-playing applications. The authors propose a novel method by quantifying interactions using a fine-grained, constraint-based scoring system, significantly advancing... | Rebuttal 1:
Rebuttal: We are very pleased to receive your positive recommendation for our work. Your suggestions and questions are insightful and valuable to further improve the quality of our paper's content and writing. We will address your concerns with the following clarification and experiments.
## **Character Co... | Summary: The paper introduces an evaluation method for persona-driven role-playing (PRP) using the Active-Passive-Constraint (APC) scoring system. This system measures the faithfulness of AI responses to predefined persona statements by calculating APC scores and applying Direct Preference Optimization (DPO) to improve... | Rebuttal 1:
Rebuttal: We are grateful for your positive attitude towards the quality and contribution of our work. We also find your suggestions are insightful and beneficial to polish our work. We will include the following experiments and clarification to further solidify our conclusions and address your concerns.
#... | Summary: The paper presents a novel approach to evaluating and optimizing the faithfulness of persona-driven role-playing (PRP) in AI characters. It addresses the limitations of existing coarse-grained faithfulness criteria. The authors introduce the Active-Passive-Constraint (APC) score, which discriminates persona st... | Rebuttal 1:
Rebuttal: We are with great pleasure to see your strong recommendation of our work, thank you! We make the following further improvements and discussions to our paper corresponding to your insightful questions and suggestions,
## **Student model selection**
We select DeBERTa as the student model because t... | Summary: The paper proposes a new evaluation metric, delta APC score, which uses constraint satisfaction inspiration to tackle the evaluation of faithfulness to persona descriptions. Then, evaluations are conducted on the experience upload, RAG, and long-context memory approaches. To do this, 3 personas are created wit... | Rebuttal 1:
Rebuttal: We appreciate your positive attitude towards our work and the effort to provide valuable suggestions to further polish our work. To address your concerns, we will add the following extra experiments and further clarification to the final version of our paper.
## **More demographic-level represent... | Rebuttal 1:
Rebuttal: We are sincerely thankful for all reviewers' positive feedback and insightful suggestions to improve the quality of our work. We are glad to address your concerns with further clarification and more experiment results for support. Here we include the responses to weaknesses and questions mentioned... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Revisiting motion information for RGB-Event tracking with MOT philosophy | Accept (poster) | Summary: This work proposes a novel RGB-E tracking framework, CSAM. CSAM first predicts candidates and then tracks both targets and distractors using MOT philosophy. Comprehensive experiments are conducted on three RGB-E tracking benchmarks, showing that CSAM achieves state-of-the-art performance.
Strengths: 1. The mo... | Rebuttal 1:
Rebuttal: We want to express our gratitude for your support and valuable insights. It's reassuring to know that you recognize the originality and efficacy of our research. We would greatly appreciate your continued support in championing our work.
**W1 and Q6. More experimental results under varying illumi... | Summary: A novel RGB-E tracking framework with MOT philosophy has been proposed in order to keep track of both targets and distractors to robustly track a single object. It includes a Candidate Encoding Module, a Spatial-Temporal Transformer Encoder and a Dual-branch Transformer Decoder. Within these modules, the autho... | Rebuttal 1:
Rebuttal: We extend our heartfelt gratitude for your perceptive insights and your recognition of the unique and meaningful contributions made by our research. Your support is highly valued, and we would be honored if you could serve as an advocate for our work.
**Q1. The ablation study about N/M/T/$\tau_{t... | Summary: • This paper proposes a novel RGB-E tracking framework, i.e., CSAM, which first predicts the candidates by using an appearance model and then keeps track of both targets and distractors with an MOT philosophy. The model show significantly improved state-of-the-art results.
Strengths: 1. The paper is well-writ... | Rebuttal 1:
Rebuttal: We greatly appreciate your constructive and insightful comments. Thanks for your recognition of our work.
**Q1. Putting some qualitative analysis in the main text.**
Due to the limited page space, our previous manuscript only provides the qualitative analysis in the supplementary materials. We ... | Summary: This paper focuses on RGB-Event tracking. The authors propose to leverage MOT philosophy to distinguish the distractors to enhance the robustness of the tracker. Following a tracking-by-detection framework, the authors first generate a series of candidates and then match them with historical known priors with ... | Rebuttal 1:
Rebuttal: We greatly appreciate your constructive and insightful comments. Thanks for your recognition of our work.
**Q1. The motivation of the paper is unclear. Lack of analysis.**
**Motivation:**
As shown in Fig. 1 (a) in the manuscript, the co-occurrence of distractor objects similar in appearance to ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the comprehensive reviews provided by all the reviewers. The valuable feedback has significantly contributed to improving the quality of our manuscript. We extend our gratitude to Reviewer KN3J, Reviewer SevM, and Reviewer fErD for recognizing the novelty of our work. Their... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Soft Superpixel Neighborhood Attention | Accept (poster) | Summary: The paper proposes soft superpixel neighborhood attention, with the motivation that attention is more efficient when it is local, and superpixels are better for local attention as patches, and furthermore, soft superpixels are more robust to errors than hard superpixels.
Strengths: The idea of soft superpixel... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments. We are encouraged to learn that you find our proposed method makes sense and that our illustrations are good and appropriate.
We are disappointed to learn you find image denoising not a significant enough task for publication.
We are also disappointed to ... | Summary: The paper proposes a new attention mechanism based on Soft Superpixel Attention. The main idea is to use superpixel segmentations directly in the attention module. The paper proposes rigorous proof that the proposed mechanism is the optimal denoiser. Results show an improved denoising performance.
Strengths: ... | Rebuttal 1:
Rebuttal: Thank you for your comments. We are grateful for your positive feedback.
**Q1.** In principle, SNA can directly replace (or augment, depending on your perspective) current NA modules for applications beyond image denoising. The practical restriction is our prototype (and slower) implementation of... | Summary: This paper proposes a soft superpixel neighborhood attention (SNA). It proves that SNA is the optimum denoiser under Gaussian noise. Experiments show that SNA outperforms other local attention modules for the image denoising task.
Strengths: - This is an interesting theoretical study, backed up with experimen... | Rebuttal 1:
Rebuttal: Thank you for your comments. We are encouraged by your positive feedback. We acknowledge our current prototype implementation of SNA is slower than NA. Your comment is similar to reviewer XzEJ’s comment about computational complexity, so we address this weakness in the “Global Rebuttal.”
**Q1.** ... | Summary: This paper proposes an attention module in which the dot product weights are modified with
superpixel probabilities, named Superpixel Neighborhood Attention (SNA). By doing so, the
optimization process is arguably made easier by letting attention avoid learning spurious
interactions, which prior work into the ... | Rebuttal 1:
Rebuttal: Thank you for your comments. We are encouraged by your positive feedback. Thank you for pointing out the incorrect citation; we will update the reference in our paper. We note that we do not compare with self-attention because the computational complexity of a global search makes self-attention im... | Rebuttal 1:
Rebuttal: **Summary.**
We thank the reviewers for their thoughtful feedback. We are encouraged by the positive comments.
Two reviewers comment favorably on the novelty of the paper. One reviewer states the approach is “unique” [XzEJ] and another reviewer states the idea is “elegant and novel” [nsbe]. Of ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Long-Range Feedback Spiking Network Captures Dynamic and Static Representations of the Visual Cortex under Movie Stimuli | Accept (poster) | Summary: Authors propose a deep spiking network with feedforward and feedback connectivity trained on natural movies and on static images, and compare the similarity of representations in their artificial network to the similarity of representations evaluated in the mouse visual cortex. Using such measure, they find a ... | Rebuttal 1:
Rebuttal: We thank the reviewer for being supportive of our work and for the constructive comments. We will try our best to address the comments. Below are our detailed responses.
**1. About line 318.**
We apologize for not refering to the corresponding table. The conclusion comes from the results on the ... | Summary: The authors in this work proposes a long-range feedback spiking network (LoRaFB-SNet) whose architecture is similar to neuronal and synaptic behavior in the cortical regions of the brain. Furthermore they propose a Time-Series Representational Similarity Analysis framework to measure the similarity between mod... | Rebuttal 1:
Rebuttal: We thank the reviewer for the clear and thoughtful comments. We will do our best to address the reviewer's concerns and answer the questions in the following.
**1. The novelty of our model architecture.**
The work of Xiao, Mingqing focuses on deriving the equilibrium state of a spiking neural ne... | Summary: To better understand visual processing in the brain, this paper presents a spiking neural network with top-down connections. It follows the trend over the past few years of building deep neural network models to approximate brain architecture and match brain and behavioral data. Simply put, the goal is to have... | Rebuttal 1:
Rebuttal: We thank the reviewer for the notable and perceptive comments. We will do our best to address the comments and provide detailed responses point by point, below.
**1. Importance of Spiking Mechanisms.**
In our work, we design a deep spiking network based on rate coding and pretrain it on large-sc... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their valuable time and their thoughtful and constructive comments. We do our best to answer the questions raised by reviewers in each individual rebuttal.
Since some reviewers are concerned about the importance of the spiking mechanism of our model, we clarif... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Non-parametric Direct Learning Approach to Heterogeneous Treatment Effect Estimation under Unmeasured Confounding | Accept (poster) | Summary: In this paper, the authors proposed a general framework for estimating CATE with a possible unmeasured confounder using instrumental variables. They construct estimators that exhibit efficiency and robustness against various scenarios of model misspecification. The efficacy of the proposed framework is demonst... | Rebuttal 1:
Rebuttal: We appreciate your feedback and the time you have taken to review our paper. However, we are unsure about the question being raised in this comment. Our goal is to estimate the conditional average treatment effect of $A$ on $Y$ given $X$. We are unsure what you meant by heterogeneous treatment eff... | Summary: The author mainly introduces a method of Directly Learning using Instrumental Variables (IV-DL) to estimate the conditional average treatment effect (CATE) $\Delta(x)$ and optimal Individualized Treatment Regime (ITR) $\hat{d(x)}$ in the presence of unobserved confounding. They propose two efficient and robust... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and constructive feedback on our paper. We have carefully considered each comment and will make revisions to address the concerns raised. Below, we provide detailed responses to the reviewer's comments, along with descriptions of the changes that will be ma... | Summary: The authors study the problem of estimating the conditional average treatment effect (CATE) under the assumption of unmeasured confounding. The authors focus on the specific scenario where some observed variable acts as instrument w.r.t. unmeasured confounder but might be confounded by some other observed conf... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and constructive feedback on our paper. We have carefully considered each comment and will make revisions to address the concerns raised. Below, we provide detailed responses to the reviewer's comments, along with descriptions of the changes that will be ma... | Summary: This paper introduces a new type of CATE estimator using instrumental variables. The proposed method employs the direct learning approach.
Strengths: 1. The paper is self-contained and comprehensible.
2. Besides developing the CATE estimator, the paper also proposes an estimator for finding the optimal treatm... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and constructive feedback on our paper. We have carefully considered each comment and will make revisions to address the concerns raised. Below, we provide detailed responses to the reviewer's comments, along with descriptions of the changes that will be ma... | Rebuttal 1:
Rebuttal: We would like to extend our sincere gratitude for the thorough and constructive feedback on our paper. We have carefully considered all comments and will make revisions to address the concerns raised. Below, we provide a summarized response to the main comments, along with descriptions of the chan... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Self-Labeling the Job Shop Scheduling Problem | Accept (poster) | Summary: This paper is empirical in nature and studies generative models for the job shop scheduling problem (JSP). JSP is well-studied in the scheduling community, both theoretically and empirically, in part because of its many applications. In JSP, a DAG is given of the precedence ordering of a set of operations (eq... | Rebuttal 1:
Rebuttal: Please find our responses to the main concerns and questions below:
- **A1 (CP observation):** We agree: metaheuristics and CP require more time to provide quality solutions, especially on large instances. **This is the goal of Appendix D, showing that CP scales worse on very large instances.** No... | Summary: This paper proposes a job-shop scheduling method based on self-labeling strategy and pointer network. The structure of the paper is clear. The method is evaluated on public benchmarks Taillard and Demirkol’s.
Strengths: Overall speaking, the self labeling strategy is an interesting approach because it only re... | Rebuttal 1:
Rebuttal: Please find our responses to the main concerns and questions below:
- **A1 (large $\beta$ and utilization):** Fig. 2 of our paper proves that training the SPN with $\beta = 32$ significantly outperforms CL, the best neural constructive. **Increasing $\beta$ enhances performance, but high $\beta$ v... | Summary: The paper proposes learning a constructive neural heuristic for the Job Shop Scheduling problem (JSP). The proposed policy network is an auto-regressive attention-based encoder-decoder model. A JSP instance is represented by a (commonly used) disjunctive graph with additional hand-crafted features. The paper p... | Rebuttal 1:
Rebuttal: Please find our responses to the main concerns and questions below:
- **A1 (greedy solution concern):** We kindly disagree. If the model is highly confident in a decision, random sampling tends to align with a greedy argmax strategy. Whereas, similar to top-k and nucleus sampling (with small $k$ a... | Summary: This paper introduces an effective method for learning to solve the Job Shop Scheduling Problem (JSP). The contribution is twofold: a pointer network architecture (encoder-decoder) to effectively represent the problem and an efficient learning paradigm based on self-supervised learning termed “self-labeling” i... | Rebuttal 1:
Rebuttal: Please find our responses to the main concerns and questions below:
- **A1 (TSP pilot):** As noted by the reviewer, there are recent follow-up works adopting self-labeling and proving our method is applicable to other problems. We further back this claim up by including in Fig. 2 of the attached P... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their valuable comments.
We are glad the **contribution** and the **significance** of our work have been recognized by all the reviewers.
Note that after the initial submission and based on your feedback, we have made the following minor modifications/... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition | Reject | Summary: The authors present two potential sources of error which can arise when composing sub-sampled DP mechanisms. On one hand, they discuss cases in which the composition of worst-case datasets does not yield the expected result, on the other hand, they disambiguate guarantees for mechanisms with Poisson sampling v... | Rebuttal 1:
Rebuttal: We appreciate the reviewer agreeing that the findings are interesting.
We respectfully disagree that the findings may be too niche or specialized, or too "narrow [in] scope" for highlighting at a conference like NeurIPS.
As just one example, the concurrent work ``How Private are DP-SGD Implement... | Summary: This paper examines the discrepancies between privacy accounting methods and their implementations, highlighting several cases where these mismatches lead to incorrect results. Specifically, it compares the noise requirements for achieving privacy guarantees under Poisson sampling versus sampling without repla... | Rebuttal 1:
Rebuttal: > No viable technical solutions are provided for the identified issues, which might be a difficult research problem.
As the reviewer suggests, this is indeed a very difficult research direction, which we have invested substantial time and effort into.
The main intention of our work is to draw th... | Summary: The two main contributions of the paper is as follows:
the privacy guarantee of composition of subsampled mechanism may not be defined by worst-case dataset(s) for the underlying mechanism
Poisson subsampling and sampling without replacement may not have similar privacy guarantee.
Strengths: The paper studie... | Rebuttal 1:
Rebuttal: > There are some typos
If the reviewer could point out specific typos that they noticed that would be highly appreciated.
> ...the result for the gap is shown empirically. I have to state that I have not seen the Appendix so if the authors have a provable guarantee for this gap in the Appendix, ... | Summary: This paper studies the notion sampling with replacement for differential privacy. Most of the literature on machine learning with differential privacy benefits from privacy amplification by poisson sampling in the privacy analysis. However, when implementing the mechanisms, engineers ofter use the sub-sampling... | Rebuttal 1:
Rebuttal: There are no issues with the correctness of the results questioned by the reviewer. We discuss the technical details of Proposition 9 below.
> You introduce the notion of dominating pairs of distributions, but then you talk about dominating datasets. You need to clarify the relation. Specifically... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Flow Snapshot Neurons in Action: Deep Neural Networks Generalize to Biological Motion Perception | Accept (poster) | Summary: This paper uses AI models to investigate how humans can accurately predict actions based solely on minimal action patterns without prior training. The authors propose a new motion recognition model called Motion Perceiver (MP). This new model first extracts patch-level semantic features from images using a DIN... | Rebuttal 1:
Rebuttal: **[eynA.1 - Unfair comparison with baselines and VideoMAE]** All the baselines are action recognition models pre-trained on Kinetics 400. In contrast, the DINO architecture in our MP is pre-trained on ImageNet. All the models are then fine-tuned on RGB videos in the training set of our BMP dataset... | Summary: The paper introduces a novel, neuroscience-inspired approach to performing biological motion perception from videos. The videos are part of a dataset that the authors also introduce, depicting 10 different actions. The videos range from fully RGB frames to point-light displays that only cover the joints. While... | Rebuttal 1:
Rebuttal: **[WFYf.1 - Fair comparison and learning curves]** All the baselines are action recognition models pre-trained on Kinetics 400. In contrast, the DINO architecture in our MP is pre-trained on ImageNet. All the models are then fine-tuned on RGB videos in the training set of our BMP dataset. We would... | Summary: The paper introduces the Motion Perceiver (MP), a novel AI model designed to improve the generalization of action recognition in biological motion perception (BMP) tasks. It leverages patch-level optical flows and introduces flow snapshot neurons that learn and store prototypical motion patterns and motion-inv... | Rebuttal 1:
Rebuttal: **[buWx.1-More comparisons to prior works]** Thanks. We will remove [71] from this sentence in the final version. Moreover, we added two more new baselines [a] and [c]. In [a], E2-S-X3D is a two-stream architecture processing optical flow and spatial information from RGB frames separately. In [c],... | Summary: This paper proposes a new biologically inspired architecture for action recognition. The motion perceiver computes a patch-level optical flow from DINO features which is then processed in a two-stream architecture with one pathway using slot-attention to recognize different motion patterns and the other one in... | Rebuttal 1:
Rebuttal: **[nybp.1 - missing AFD and two-stream models]** Thanks! We will cite and discuss this paper in the final version. Complementary to the AFD dataset in Illic et al., we introduce the BMP dataset containing stimuli on point-light displays, also commonly studied in psychology and neuroscience.
In a... | Rebuttal 1:
Rebuttal: We appreciate all the reviewers' feedback. Results are provided in the tables here, and we encourage reviewers to refer to the PDF file containing additional figures. To differentiate these new figures and tables in the rebuttal from those in the main text, we have prefixed them with "R" in the re... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Consistency-Aware Spot-Guided Transformer for Versatile and Hierarchical Point Cloud Registration | Accept (poster) | Summary: This work proposed a coarse-to-fine matching approach for point cloud registration, where a consistency-aware spot-guided transformer is proposed to ensure the coarse matches to be geometric consistent and around the overlap regions. This method is compared with SOTA PCR methods on three benchmarks and the exp... | Rebuttal 1:
Rebuttal: Thank you for your constructive review and valuable suggestions. Below, we offer detailed responses to each of your comments and questions. If there are any points where our answers don't fully address your concerns, please let us know, and we will respond as quickly as possible.
- **Weakness 1:*... | Summary: This paper introduces a consistency-aware, spot-guided Transformer, adapted from 2D image matching techniques, to minimize interference from irrelevant areas. It incorporates a consistency-aware self-attention module that enhances matching capabilities by using edge length constraints to filter correspondences... | Rebuttal 1:
Rebuttal: Thank you for your constructive review and valuable suggestions. Below, we offer detailed responses to each of your comments and questions. If there are any points where our answers don't fully address your concerns, please let us know, and we will respond as quickly as possible.
- **Weakness 1-3... | Summary: This manuscript mainly focuses on the learning based feature matching of point cloud registration. The authors propose a consistency-aware spot-guided transformer and a lightweight fine matching module. Experiments on both indoor and outdoor benchmarks prove the effectiveness of the designs. However, this arti... | Rebuttal 1:
Rebuttal: Thank you for your constructive review and valuable suggestions. Below, we offer detailed responses to each of your comments and questions. If there are any points where our answers don't fully address your concerns, please let us know, and we will respond as quickly as possible.
- **Weakness 1.3... | Summary: This paper focuses on feature matching for point cloud registration. To this end, it aims to improve the effectiveness of the coarse-to-fine matching mechanism by designing a consistency-aware spot-guided Transformer (CAST). More specifically, the proposed method incorporates a spot-guided cross-attention modu... | Rebuttal 1:
Rebuttal: Thank you for your constructive review and valuable suggestions. Below, we offer detailed responses to each of your comments and questions. If there are any points where our answers don't fully address your concerns, please let us know, and we will respond as quickly as possible.
- **Weakness 1: ... | Rebuttal 1:
Rebuttal: Dear reviewers,
Thank you for dedicating time to review our paper. We thank the reviewers **L6BY,LKCi,UeYt** for your appreciations of our idea using consistency in feature aggregation, and thank the reviewer **UeYt** for pointing out that we make learning-based registration more efficient and sc... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fast Encoder-Based 3D from Casual Videos via Point Track Processing | Accept (poster) | Summary: The paper presents TRACKSTO4D, a fast, encoder-based method for reconstructing 3D structures and camera positions from casual videos with dynamic content. It processes 2D point tracks using a single feed-forward pass, leveraging inherent symmetries and low-rank approximations. TRACKSTO4D is trained unsupervise... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's positive assessment of our method, particularly the recognition of our consideration of symmetry. We're pleased that the reviewer finds our approach solid and our evaluation results comprehensive. We'll now address the specific points and questions raised in the review... | Summary: The paper introduces a method for fast 3D reconstruction of dynamic structures (or 4D reconstruction) from monocular video. The model is a transformer architecture that takes a set of 2D point tracks as input, and lifts them to 3D. It is learned with re-projection losses on 2d tracks without 3D ground-truth. T... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's recognition of the efficiency and the good performance of our approach.
**Q**: The permutation invariance of point sets has been discussed in early literature… and it might be better to connect…
**A**: We appreciate the reviewer's suggestion. While our architectur... | Summary: This paper proposes a feed-forward network that takes a set of 2D TAP curves as input, outputs the 3D curves as well as extracts the camera rigid SE(3) poses. This paper exploits the permutation and time shift equivariance when designing the encoding network. To train the model, the main loss is the re-projec... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's positive feedback on our paper's style and its significance to the community. Below we answer specific questions and suggestions.
**Q**: Maybe 3D-track or exploiting depth models may lead to better performance instead of learning everything from scratch.
**A**: We ... | Summary: This paper presents TracksTo4D, a feed-forward approach for estimating 3D structure and camera poses from 2D point tracks. Authors propose a novel architecture that directly processes 2D point tracks, takes into account symmetries present in the data, and assumes movement patterns can be represented by a low-r... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's positive feedback on our paper's relevance, novelty, and clarity. We will now address your specific questions and suggestions raised by the reviewer.
**Q**: Limited Evaluation, MiDaS is quite old. Compare to video depth estimation methods.
**A**: Thank you for yo... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their thoughtful feedback and constructive suggestions. We were happy to see that all reviewers gave us positive ratings and appreciated the positive comments on our method's efficiency, performance, and generalization ability highlighted by multiple reviewers.... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Advancing Cross-domain Discriminability in Continual Learning of Vision-Language Models | Accept (poster) | Summary: The paper points out that current VLM-based incremental learning tasks face the issue of text being limited to the corresponding task. It aims to propose a method that can achieve better incremental classification performance on a broader range of texts.
Specifically, the paper proposes Regression-based Anal... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback, which we believe will improve our final manuscript. Please see our responses to the helpful points raised in your review below:
***
>***W1** Compared to existing methods, one dataset is missing. Why was CIFAR-100 removed from the forgetting benchmark? Is it b... | Summary: This paper proposes a Regression-based Analytic Incremental Learning (RAIL). It utilizes a recursive ridge regression-based adapter to learn from a sequence of domains in a non-forgetting manner and decouple the cross-domain correlations by projecting features to a higher-dimensional space. Additionally, the p... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's positive feedback on our work, and further thank the reviewer for finding our work novel in adopting traditional machine learning techniques for continual learning and appealing in its approach to absolute memorization based on analytic techniques. Below, we address th... | Summary: Continual Learning (CL) with Vision-Language Models (VLMs) has a challenge that the model must not forget both previously learned knowledge and VLM pre-trained knowledge. Existing methods realize this by using large-scale reference data or domain identity hints, which is not practical. This paper proposed RAIL... | Rebuttal 1:
Rebuttal: Thank you for detailed feedback and valuable suggestions.
***
>***W1** Concerns of paper understanding.*
**A1** Thank you for bringing up your confusions. Based on your suggestion, we decide to make following revisions in the final version.
- We will update the introduction with clearer explanati... | Summary: This paper proposes a novel setting called Cross-domain Task-Agnostic Incremental Learning (X-TAIL), in which the model is required to incrementally learn from multiple domains and test images from both seen and unseen domains without any domain identity. Additionally, the authors introduce two Regression-base... | Rebuttal 1:
Rebuttal: Thank you for your overall supportive review of our work. Moreover, we are glad that you found this paper is well-written with good soundness and contribution to the NeurIPS community. Please see our responses addressing your specific concerns below:
***
>***W1** There is little difference in perf... | Rebuttal 1:
Rebuttal: We appreciate for all reviewers' insightful and valuable comments. We thank Reviewer ThWm and Reviewer rqEC, who agree that our work is **well-written** and **easy to follow**. We are pleased that Reviewer Tz45 finds **our setting more realistic**, recognizing our efforts to address the requiremen... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Revealing Distribution Discrepancy by Sampling Transfer in Unlabeled Data | Accept (poster) | Summary: The paper proposes a novel concept called Importance Divergence (I-Div) to address the lack of labeling of test samples and to measure the difference between training and testing distributions. Through the computation of importance samples, density ratios, and likelihood ratios, I-Div is able to assess the app... | Rebuttal 1:
Rebuttal: We appreciate your valuable feedback and suggestions on the limitations and potential improvements of our method. Here are our responses.
**W1. General Feasibility**
The estimation of the density ratio and likelihood ratio does not limit the applicability of our method. We transform the issue of... | Summary: This paper investigates the applicability of hypotheses derived from training datasets to distinctly different test datasets through a series of experiments using various datasets, including CIFAR10, SVHN, PACS, and Office-Home. T
Strengths: New Algorithm Introduction: The paper introduces the I-Div algorithm... | Rebuttal 1:
Rebuttal: We appreciate your valuable feedback and constructive criticisms. Here are our responses to your identified questions.
**Q1&2. Baselines and Backbones**
The paper has already considered post-2021 algorithms, such as NNBD (ICML’21) [R1] and R-Div (NeurIPS’23) [R2] shown in Tables 1, 2, 4 and 5. ... | Summary: This paper presents a novel approach called Importance Divergence (I-Div) to address the challenge of measuring the discrepancy between training and test distributions when test sample class labels are unavailable. I-Div transfers sampling patterns from the test distribution to the training distribution by est... | Rebuttal 1:
Rebuttal: We appreciate your detailed feedback and thoughtful questions. Here are our responses to your raised concerns.
**W1. Likelihood Ratio**
It is impossible to completely solve this problem because the class labels of the test samples are unknown. However, we theoretically decompose this problem to ... | Summary: This paper proposes a discrepancy to measure the difference between two distributions within a common scenario: the labeled training set distribution and the unlabeled test set distribution. This discrepancy arises from the expected risk difference between these two distributions, considering a model pre-train... | Rebuttal 1:
Rebuttal: Thank you for constructive suggestions. We appreciate the opportunity to address your concerns.
**W1. Impact of Pre-trained Classifier**
As discussed in Line 24, our goal is to measure the discrepancy between training and test datasets for a given pre-trained classifier, thereby assessing its a... | Rebuttal 1:
Rebuttal: We thank all reviewers for constructive comments and are encouraged by the overall positive feedback from the review. Specifically, the reviewers found that our work addresses an important and practical problem (Reviewers yuQt, 1gtU, fpGs, f37S), introduces a novel and intuitive approach (Reviewer... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Decoupled Kullback-Leibler Divergence Loss | Accept (poster) | Summary: The paper proposed the Decoupled Kullback-Leibler Divergence Loss (DKL), mathematically equivalent to the KL divergence. To solve the asymmetries issue of KL/DKL, the authors introduce the Improved KL divergence, which consists of class-wise global information and cross-entropy loss for soft labels. The propos... | Rebuttal 1:
Rebuttal: Thanks for your suggestions and valuable comments. Here we provide our responses to address your concerns.
*Q1: Ablation with TRADES and MART.*
Thanks for your suggestion. TRADES is our baseline. We have already listed the results in Table 1 of the main paper. To highlight the significant improv... | Summary: The paper investigates the Kullback-Leibler (KL) Divergence loss and demonstrates its equivalence to the Decoupled Kullback-Leibler (DKL) Divergence loss in a limited setting, which consists of a weighted Mean Square Error (wMSE) and a Cross-Entropy loss with soft labels. By addressing the asymmetric optimizat... | Rebuttal 1:
Rebuttal: Thanks for your suggestions and valuable comments. Here we provide our responses to address your concerns.
*Q1: It should be explicitly clarified that Theorem 1 and the subsequent analysis are solely based on the assumption that the probability distribution is a categorical distribution.*
Thanks... | Summary: In this paper, the authors analyzed the optimization gradient of the commonly used KL Divergence loss metric in adversarial training and distillation. KL loss can be reformulated as a Decoupled KL (DKL) Divergence loss term through antiderivative operations. The gradients of both terms are equivalent in the ca... | Rebuttal 1:
Rebuttal: Thanks for your suggestions and valuable comments. Here we provide our responses to address your concerns.
*Q1: Theoretical evaluation is not convincing enough. There are no further justifications for why the specific anti-derivative formulation expressed in Theorem 1 is chosen as the main decoup... | Summary: This paper demonstrates that KL divergence can be decoupled into a weighted mean square error (wMSE) loss term and a cross-entropy term with soft labels. Based on this decoupling, the authors propose an improved version of the KL loss. In the context of knowledge distillation, the proposed method addresses iss... | Rebuttal 1:
Rebuttal: Thanks for your suggestions and valuable comments. Here we provide our responses to address your concerns.
*Q1: We achieve significant improvements over the baseline and state-of-the-art adversarial robustness on RobustBench*
*For adversarial robustness*
**First**, with the same experimental se... | Rebuttal 1:
Rebuttal: We thank all the reviewers' efforts and valuable comments on our paper. Here we respond to the most concerned questions from reviewers.
*Q1. New applications of our IKL loss in semi-supervised learning, knowledge distillation on imbalanced data, semantic segmentation knowledge distillation.*
Tha... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper presents an in-depth analysis of the Kullback-Leibler (KL) Divergence loss, a critical component in training deep learning models. The authors mathematically demonstrate that the KL loss is equivalent to a Decoupled Kullback-Leibler (DKL) loss, which is composed of a weighted Mean Square Error (wMSE)... | Rebuttal 1:
Rebuttal: Thanks for your suggestions and valuable comments. Here we provide our responses to address your concerns.
*Q1: Besides the adversarial training and the knowledge distillation on balanced data, how does the IKL loss perform on other tasks, like semi-supervised learning, knowledge distillation on ... | null | null | null | null | null | null |
Secret Collusion among AI Agents: Multi-Agent Deception via Steganography | Accept (poster) | Summary: Evaluates models' ability to collude via steganography, evading human oversight. Various monitoring and capability-limitation mitigation strategies are proposed. The paper concludes that while current models have limited steganographic capabilities, continuous monitoring of future models is needed.
Strengths:... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their insightful feedback and are happy to see they find our paper interesting.
Please find our responses below.
## Questions
**(1) R**: *"Did you consider model-internals-based probes/activation-monitoring techniques to detect collusion/use of steganography?"*
Many th... | Summary: In this paper, authors propose a way to determine whether LLMs are secretly collaborating to hide, prevent, or misguide users that ask for a specific input. The authors show that, through steganographic techniques, it is possible to embed secret instructions retrieved only by hitting particular queries, while ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and efforts. **Please also refer to the global rebuttal concerning our concern's about the Reviewer's misunderstandings of our paper's focus as evidenced by their summary.**
## Strengths
**(1) R**: *"Interesting point of view. Steganography can be used to inst... | Summary: The paper formalizes the problem of secret collusion in systems of generative AI agents, leveraging steganographic techniques. The authors propose a model evaluation framework to systematically test capabilities required for various forms of secret collusion. The paper provides extensive empirical results acro... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their effort in evaluating our work and for their valuable feedback. We are happy to address their concerns and add corresponding improvements to our paper.
## Weaknesses
**(1) R**: *Real world case studies and deployment scenarios*
We agree that further evaluation of ... | Summary: With the rapid development of AI, the safety of large language models (LLMs) is becoming a significant topic, such as the privacy and security issues between communicating generative AI agents. In this manuscript, noticing the potential risk emerging from groups of current generative AI agents, the author(s) f... | Rebuttal 1:
Rebuttal: We thank the Reviewer(R) for their effort in evaluating our work and valuable feedback. We address their concerns below.
## Weaknesses
### Weakness 1
**R**: *"The mitigation strategies were discussed but might not provide enough detail on how these strategies could be implemented in practice."*... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their time, effort, and insightful comments on our paper. Taking all feedback into account, we respond to each review individually below. Please also refer to the attached PDF for additional details. Note that all rebuttals share a common bibliography at the en... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Can Graph Learning Improve Planning in LLM-based Agents? | Accept (poster) | Summary: This work proposes to investigate the integration of LLMs and GNNs for task planning. Specifically, LLMs are employed for the request decomposition stage while GNNs are employed for the task retrieval stage. Experiments demonstrate gains on various datasets across various LLMs.
Strengths: + the intersection o... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for your insightful and constructive feedback.
---
> Weakness 1: Methodology figure
Thank you for your suggestion. The methodology figure is provided in Figure 6 of original paper and better illustrated in **Figure 2 of the PDF in the global response**. We will ... | Summary: The paper proposes to use GNNs alongside LLMs for decision making problems represented in natural language and task graph inputs. Experimental results show that using GNNs provide improvements over just using LLMs.
Strengths: The results highlight that the approach makes sense and work, with improvements acro... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the insightful and constructive feedback.
---
> Weakness 1, 3, 4 and Question 1: Definition of task planning and user request; Task planning and PDDL; Related work concerning GNNs for PDDL
This paper investigates task planning for language agents, a new and ... | Summary: The paper proposes a GNN+transformers method to address some theoretical and practical issues in graph planning such as hallucinations. The task consists of matching a prompt that expresses a number of sub-tasks to a larger graph of tasks (the task pool) an agent (e.g., an LLM) can solve and invoke the right p... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for your insightful comments and recognition of this work, especially for the motivation to adopt GNNs in this new application.
---
> Weakness 1: Figure 2 is never referenced
We apologize for the confusion regarding Figure 2. The experimental settings are the sa... | Summary: The paper formulates task planning as a graph decision-making problem. Through theoretical analysis, the paper shows the biases of attention and auto-regressive loss impede LLMs’ ability to effectively navigate decision-making on graphs. To mitigate this, the paper proposes to integrate GNNs with LLMs. Experim... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for your insightful comments and recognition of this work, especially for acknowledging our theoretical contributions and superior performance.
---
> Combining LLM with GNN for graph-related tasks has been studied in many previous works and is not very novel.
Th... | Rebuttal 1:
Rebuttal: # Global Rebuttal
We sincerely thank all the reviewers for your constructive feedback and recognition of this work, especially for acknowledging **the theoretical contributions** (Reviewer s7yo), **the motivations to adopt GNNs** (Reviewer s7yo and ZJrg), and **the superior performance** (Reviewe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ChatQA: Surpassing GPT-4 on Conversational QA and RAG | Accept (poster) | Summary: This paper proposes a family of fine-tuned models (ChatQA) that surpass GPT-4 on conversational QA and RAG. It introduces a two-stage instruction fine-tuning method to enhance the model’s capability of using the retrieved context for generation. In addition, it shows that fine-tuning a single-turn retriever (D... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and feedback. We will address your questions below.
> “1. Training an open foundation model with curated instruction data is not new. The paper could be improved if it demonstrates why the selected mixture of training data is effective, and training on the... | Summary: This paper explore RAG in conversational QA senarios. It proposes a two-stage instruction tuning method for conversational QA in a RAG manner, accompanied by a comprehensive benchmark. The training of LM involves two stages: (i) SFT on dialogue and QA datasets and (ii) context-enhanced instruction tuning which... | Rebuttal 1:
Rebuttal: Thank you for your detailed comments and feedback. We will address your concerns and questions below.
> “There is a lack of technical novelty…it would be helpful if the authors could further elaborate on the innovative aspects of their methodology to highlight its novelty”
- The innovative aspe... | Summary: This paper introduces ChatQA, a suite of models that outperform GPT-4 on retrieval-augmented generation (RAG) and conversational question-answering (QA) tasks. The key contributions of the paper include:
- A two-stage instruction tuning method that improves RAG performance.
- A dense retriever optimized for co... | Rebuttal 1:
Rebuttal: Thank you so much for your detailed comments and feedback. We will address your questions below.
---
> “The study primarily focuses on Llama2 and Llama3 of sizes 7B/8B, 70B as base models. Including a wider range of base models could provide insights into the generalizability of the proposed meth... | Summary: The paper introduces ChatQA, a suite of models designed to excel in retrieval-augmented generation (RAG) and conversational question answering (QA). The authors propose a two-stage instruction tuning methodology to bolster generative capabilities and a dense retriever optimized for conversational QA to improve... | Rebuttal 1:
Rebuttal: Many thanks for your detailed comments and feedback. We will address your questions below.
> “The paper primarily focuses on evaluating the "unanswerable" scenario using a small set of samples. A more extensive evaluation involving diverse "unanswerable" scenarios would enhance the robustness of ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
OnlineTAS: An Online Baseline for Temporal Action Segmentation | Accept (poster) | Summary: This paper proposes an online baseline for temporal action segmentation. The method is built upon causal TCN and integrates a GRU, attention-based feature aggregation, and a memory bank. A heuristic online post-processing method is also proposed.
Strengths: 1. I like the statement claiming the contribution as... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding our contribution claims to the meaningful online TAS task modest and accurate. We address the reviewer's comments below.
**Weaknesses**
----
**W1. Inference speed**
**A**: Runtime evaluation, conducted on our Intel Xeon Gold 6442Y (2.6 GHz) and a single Nvidia... | Summary: This paper introduces OnlineTAS, the first fully-supervised online framework for temporal action segmentation (TAS). The main contributions include:
1. A context-aware feature augmentation (CFA) module that incorporates an adaptive memory to enhance frame representations with temporal context.
2. An adaptive m... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the novelty of our CFA module for the online TAS problem and the effectiveness of the post-processing for mitigating the over-segmentation issue.
**Weakness**
----
**W1&4. Runtime analysis and computational requirements**
**A**: We thank the reviewer for t... | Summary: This paper presents the first online framework for temporal action segmentation. At the core of the framework is an adaptive memory designed to accommodate dynamic changes in context over time, alongside a feature augmentation module that enhances the frames with the memory. A post-processing approach is propo... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the task importance and our effective presentation of the work.
**Weaknesses**
----
**W1&2. Novelty on memory bank, semi-online inference, self- and cross-attention**
Thanks for your comments. Indeed, attention and memory banks are common architectural... | null | null | Rebuttal 1:
Rebuttal: We thank all reviwers for their constructive comments and address each of their concerns in separate rebuttals. The attched global rebuttal file contains
1) 1 figure (Fig. RA) in response to Reviewer xPL1's W5 regarding the segment-accuracy tradeoff in post-processing.
2) 1 table (Table RA) in... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LSH-MoE: Communication-efficient MoE Training via Locality-Sensitive Hashing | Accept (poster) | Summary: The authors focus on the communication overhead in large-scale MoE training, specifically under the expert parallel plus the data parallel regime. They reduce the communication workload by transmitting only the clustering centroids, which are calculated on the fly using LSH functions. To reduce the compression... | Rebuttal 1:
Rebuttal: # W1
Thank you for your suggestion to deepen our analysis of communication overhead.
To address the review's comment, we have conducted a detailed deduction in the global response to analyze the communication overhead. In particular, the ratio of communication time to computation time can be form... | Summary: The paper introduces LSH-MoE, a communication-efficient training framework for Mixture-of-Experts (MoE) models using Locality-Sensitive Hashing (LSH). The authors identify the inefficiencies in existing MoE training methods, particularly the high communication costs due to all-to-all communications among GPUs.... | Rebuttal 1:
Rebuttal: We would like to address the reviewer's concerns about the speedup over larger MoE models with both experiments and analysis.
**Experiments**
We conducted experiments on the GPT-MoE 52B model, and the results are summarized in Fig. E of the one-page PDF file attached with our global response.
T... | Summary: This paper presents a method to speed up MoE large model training with locality-sensitive hashing. It conducts experiments on both language models and vision models for both pre-training and fine-tuning tasks, and achieves 1.28-2.2× of speedup.
Strengths: 1. This paper introduces an efficient LSH-based compre... | Rebuttal 1:
Rebuttal: # W1
To address the reviewer's comment, in the attached PDF file of our global response, we have included a figure (Fig A) to depict the schematic of MoE Training with Locality-Sensitive Hashing (LSH-MoE).
This figure highlights the key components of LSH-MoE, including the *LSH-Based Clustering* ... | null | null | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers for their thorough evaluations and valuable feedback.
In the attached PDF, we have provided a detailed response to each comment from all reviewers. To aid in understanding our responses, we have included the figures recommended by the reviewers.We hope that t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Model Collapse Demystified: The Case of Regression | Accept (poster) | Summary: This paper analyses a ridge regression model of the recently described phenomenon of model collapse in various settings (unregularised linear regression, large dimensional RMT limit regression), acquiring analytical expressions for scaling limits in the case of heavy tails under capacity and source conditions,... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and the dedicated time and effort you invested. We genuinely appreciate your recognition of the key strengths in our work.
1. > To begin with, the model considered here is extremely simplistic and places itself much closer to the distillation literature, rathe... | Summary: This paper provides a theoretical analysis of the "model collapse" phenomenon that can occur when machine learning models are trained on synthetic data generated by other AI models. The key contributions include:
1. A mathematical framework for studying model collapse in high-dimensional regression settings.
... | Rebuttal 1:
Rebuttal: We appreciate your review and positive evaluation of our work, as well as your acknowledgment of its strengths.
1. > The analysis primarily focuses on regression tasks, which may not fully capture the complexities of more advanced machine learning models like large language models or image genera... | Summary: Authors analyze the problem of ´model collapse´, i.e., what happens when a generative model is retrained on data generated by itself. The authors study a slightly different and simplified setup: linear regression, where the target is iteratively generated by previous estimates of the coefficients. Based on ran... | Rebuttal 1:
Rebuttal: Thank you for your feedback, especially on the presentation of our material, which is challenging, given the amount of technical work required for our ((necessarily lengthy) proofs, and the difficulty of partitioning this content petween the main body and the appendix. Our aim for the main body of... | Summary: This paper studies the model collapse problem in the context of ridge regression. The authors train a sequence of linear models which are trained on Gaussian data and characterize the test error under various settings. Their results are intuitive and theoretically support the ongoing research area of model col... | Rebuttal 1:
Rebuttal: We thank you for your thoughtful comments, and are delighted you appreciate the technical heavy lift that was required to prove our results and paint a picture of the regimes of "model collapse" in the setting of ridge regression. Thank you for pointing out that we should highlight what other tech... | Rebuttal 1:
Rebuttal: We appreciate the time and effort the reviewers have dedicated to reviewing our paper. We are delighted to see that overall reception has been positive, with all reviewers acknowledging our theoretical contributions, particularly in analytically characterizing model collapse across a wide range of... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps | Accept (poster) | Summary: Due to problems with momentum and a non-stationary target, the authors propose resetting the value of ‘t’ in Adam, which is used to determine bias correction on the momentum terms, when updating the target. This approach is validated on PPO and DQN in Atari and Craftax.
Strengths: - Performance benefits over ... | Rebuttal 1:
Rebuttal: Thank you for your review.
Regarding your comment on new insight, we ask where Adam's $t$ parameter is studied in either of the papers your shared, or any prior work? Furthermore, is there any suggestion that resetting this parameter can theoretically bound the update size, or have the empirical ... | Summary: One of the main challenges of reinforcement learning is its inherent nonstationary nature. Such non-stationarity can cause learning difficulties. The tools currently available for deep reinforcement learning are largely borrowed from deep learning, such as the Adam optimizer, which this paper focuses on. The a... | Rebuttal 1:
Rebuttal: Thank you for your review of our paper. We're glad you find our method to be "novel and simple" and capable of "wide adoption in the future". We also appreciate your comments about the clarity of our writing and theoretical justification, in addition to our evaluation being "extensive". We respond... | Summary: This paper introduces a simple approach to address the issue of large updates commonly encountered with Adam optimizers in deep learning applications. The authors focus on a specific scenario prevalent in deep reinforcement learning: the updating of target networks. Instead of the conventional approach of rese... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We were pleased with your extensive praise of our paper, finding the writing "generally clear, particularly benefitting from a detailed explanation of the Adam optimizer's mechanics", the "simplicity of the method a significant advantage", the exp... | Summary: This paper studies the effect of non-stationarity on the Adam optimizer. It shows that the standard use of the Adam optimizer can lead to large updates, which can cause sub-optimal performance. To address this issue, Adam-Rel is introduced, which resets Adam's timestep parameter to zero when the target network... | Rebuttal 1:
Rebuttal: Thank you for your review of our work. We are pleased that you found the paper "generally well-written", studying an "important problem" and the proposed solution "simple and easy to implement".
We direct you to the common response for a summary of the issues raised by reviewers and our response ... | Rebuttal 1:
Rebuttal: We summarise the strengths and weaknesses from all reviews below.
## Strengths
**Writing** Reviewer **JkaJ** praises the paper as "generally well-written", whilst Reviewers **uFqs** and **37TD** praise the paper's clarity, respectively commenting that the paper "benefits from a detailed explanat... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Multistable Shape from Shading Emerges from Patch Diffusion | Accept (spotlight) | Summary: This paper deals with the problem of normal map estimation from images. The authors trained a conditional diffusion model to sample a normal map output given an image input by processing 16x16 patches. The paper suggests a multiscale approach for resampling consistently at multiple scales. Due to the model’s n... | Rebuttal 1:
Rebuttal: **Weaknesses**:
> Limited applicability. Most of the tested images are either synthetic or captured in a controlled setup. The compared baseline methods are not limited to textureless and shadowless Lambertian shading.
Yes, it is absolutely true that the comparison methods are not limited to Lam... | Summary: The authors argue that monocular shape reconstruction models ought to produce distributions of outputs rather than point estimates or tight distributions, to adequately cope with known ambiguities, and draw additional motivation from multistable perception in humans. By training a small patch-based diffusion m... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and the detailed suggestions!
**Weaknesses & Limitations**:
Please see the global response.
**Questions**:
**Technical**
> L41: I wonder if training on common objects free of illusions, similar to what humans are most used to, is necessary for t... | Summary: This paper presents a patch-wise diffusion based Shape-from-shading strategy to recover multiple shapes satisfying concave/convex ambiguity from the images. It models shape inference as a generative process conditioned by light intensities. The generative process is governed by small, non-overlapping patches ... | Rebuttal 1:
Rebuttal: **Weaknesses**:
> The reconstruction (figure 8) obtained by the proposed method is quite accurate, quite similar to Wonder3D. However, since the method yields multiple solutions, how is this particular solution chosen and what is the range of variations in the different solutions obtained.
The s... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers for their thoughtful comments. We are encouraged that all reviewers find our research problem interesting and our lightweight patch diffusion model novel and effective. Reviewers (3BEx) and (E5e1) also find our presentation to be well-written and reviewer (xHhf) finds the ab... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Guiding a Diffusion Model with a Bad Version of Itself | Accept (oral) | Summary: The paper introduces a novel conditioning method as an alternative to Classifier-Free Guidance (CFG), which allows for better control over image quality generation without compromising data diversity. This approach involves guiding the diffusion model generation with a lower-quality model (either less trained ... | Rebuttal 1:
Rebuttal: Thank you for the review. Regarding the concerns and questions:
> The results on images do not clearly demonstrate the distribution coverage shown in the toy example. It appears that the low-quality model provides low-frequency guidance during generation, while the high-quality model focuses more... | Summary: This paper presents a novel perspective on classifier-free guidance (CFG). It improves the generation quality by directing the generative model towards high-probability regions. The authors identify that this improvement stems from the quality difference between the conditional and unconditional components in ... | Rebuttal 1:
Rebuttal: Thank you for the review. Regarding the questions:
> Q1. I am still not quite clear about the necessity of the similar degradation of $p_1(x|c;\sigma)$ and $p_0(x|c;\sigma)$ empirically. (I am convinced by your synthetic experiment). Even if $p_1(x|c;\sigma)$ and $p_0(x|c;\sigma)$ suffer from dif... | Summary: The paper proposes autoguidance, a new method that simulates the behavior of classifier-free guidance by using a worse version of the model itself instead of an unconditional module. The authors demonstrate that inconsistencies between the predictions from the conditional and unconditional parts of CFG are res... | Rebuttal 1:
Rebuttal: Thank you for the review. Regarding the concerns and questions:
> More visual examples are needed to show how the diversity of generations changes as the guidance scale increases. In the final version, please include a batch of examples with a fixed condition and compare the sampling with CFG and... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Optimistic Verifiable Training by Controlling Hardware Nondeterminism | Accept (poster) | Summary: The authors present a novel approach to achieving identical results in model training across different GPU types and introduce a verifiable training scheme. To achieve this, they:
- proposed a technique for two parties training the same model on different GPU types to achieve identical results by sharing round... | Rebuttal 1:
Rebuttal: Thank you for your review! We address concerns regarding pytorch version and fairness comparisons below.
**PyTorch Version**
We have re-run our experiments with PyTorch 2.3.1, and can confirm our method achieves perfect training replications both within the new version, and between versions. Thi... | Summary: Machine learning is increasingly compute power consuming, and as a result, clients may delegate the training to external parties with high computation power. A challenge is how to verify the external parties is training the model as promised.
This work examines a way of auditing by having a trusted third part... | Rebuttal 1:
Rebuttal: Thank you for your review! We would like to provide further clarification on the motivation of our work, which arose from real problems faced by industry partners, as the need for robust verification schemes is increasing rapidly (e.g. Together AI, which offers fine-tuning services for open source... | Summary: This paper studies the problem of verifying the correctness of the training process of model. In particular, the user who lacks sufficient resources pays a service provider with sufficient resources to train models. Then a trusted third-party auditor will check whether the training process is legit. The propos... | Rebuttal 1:
Rebuttal: Thank you for your review! Our work is the first to show how to apply Teutsch & Reitwießner (2019)’s method, which was developed specifically for blockchain verification, to a machine learning setting. As such, our method needs to address GPU nondeterminism challenges that Teutsch & Reitwießner (2... | null | null | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their valuable feedback to improve our work. Reviewers found our work addresses an important problem (RB9A), our technical contribution interesting (DQT4), and that our proposed method is both robust and practical in the context of large-scale foundation mo... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Active Learning with LLMs for Partially Observed and Cost-Aware Scenarios | Accept (poster) | Summary: This paper explores the problem of active learning where we can choose not just which data point to annotate, but also which features to obtain, when given an unlabeled pool of data points for which only some features are known. The paper proposes using generative models to sample the missing features and appl... | Rebuttal 1:
Rebuttal: *We are grateful to the reviewer for their insightful feedback and constructive comments that have improved the paper.*
---
## [P1] Inclusion of label cost in Equation 1
Thank you for your concerns. We appreciate the opportunity to clarify the inclusion of label cost in Equation 1.
In Equation ... | Summary: This paper introduces a novel problem setting in Active Learning (AL) called Partially Observable Cost-Aware Active-Learning (POCA). POCA deals with situations where acquiring both features and labels comes with a cost and where datasets are partially observed, meaning some features might be missing for certai... | Rebuttal 1:
Rebuttal: *Thank you for your feedback. We have addressed your individual points in this response and in the revised manuscript*
---
## [P1.1] GSM for metric estimation and training
---
Thank you for your suggestion; we acknowledge that this important detail should be clear.
**Update.** In response, befor... | Summary: This paper addresses the challenge of efficiently gathering features and labels in partially observed settings to enhance model generalization while considering data acquisition costs. It introduces POCA and its Bayesian instantiation, leveraging Generative Surrogate Models (GSMs) to impute missing features a... | Rebuttal 1:
Rebuttal: *We are grateful to the reviewer for their insightful feedback that has improved the paper.*
---
## [P1] Emphasizing novelty
Thank you for highlighting this concern. Allow us to further clarify our novelty and theoretical contributions for partially observable data acquisition.
**Theoretical ana... | Summary: This paper introduces a novel active learning framework for optimizing data acquisition in scenarios with partially observed features and high costs. The proposed method, POCA utilizes GSM, specifically LLMs, to impute missing features and improve active learning metrics. By integrating Bayesian methods to max... | Rebuttal 1:
Rebuttal: *We are grateful to the reviewer for their insightful feedback that has improved the paper.*
---
## [P1] Computational costs of MC sampling
Thank you for highlighting this concern. We agree that it is important to discuss $\mu$POCA's computational costs. We approach this discussion by **(1)** pr... | Rebuttal 1:
Rebuttal: ---
*We are grateful to the reviewers for their insightful feedback.*
We are happy to hear that reviewers found our work as a "novel task, optimizing the acquisition of data (features and labels) in scenarios where information is partially observed and the cost of acquiring additional data" (**t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DeTikZify: Synthesizing Graphics Programs for Scientific Figures and Sketches with TikZ | Accept (spotlight) | Summary: The paper proposes a technique for generating TikZ graphics based on handwritten sketches. It open-sources the datasets used for this task, including a large TikZ dataset, a small sketch-to-TikZ dataset, and a large dataset of scientific figures with accompanying texts.
The model uses a vision-language model ... | Rebuttal 1:
Rebuttal: We thank the reviewer and are delighted to see that they generally like our work.
> I think it would strengthen the paper to provide more examples of the model inference [...]. More generally, ablation study likely can also be strengthened by highlighting the amount of contribution brought in my ... | Summary: This paper introduces DeTikZify, a multimodal model that automatically generates TikZ code for scientific figures and sketches. The authors create and leverage three novel datasets: DaTikZv2 (a large dataset of TikZ graphics), SketchFig (paired human-drawn sketches and scientific figures), and SciCap++ (a meta... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thorough review and are pleased that our work is generally well received. We now address the remaining questions and perceived weaknesses.
> The main issue that I have with this work is the potential data leakage. [...] For the public models, it is said “we only inclu... | Summary: This paper proposes DeTikZify, a multimodal language model that generates scientific graphics in TikZ from hand-drawn illustrations. The authors provide three datasets: DaTikZ v2 (TikZ source code and figures), SketchFig (TikZ and hand-drawn sketch pairs), and SciCap++ (figures and captions). They also propose... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's time and are pleased to know they generally liked our work.
> A dataset named SciCap++ already exists and is not cited in this paper.
We acknowledge the potential for confusion regarding similar dataset names, despite a slight variation in the suffix (our dataset is ... | Summary: To create high-quality scientific figures the work trains a multimodal language model, DETixZirv, a new multimodal language model from sketches and existing figures. Trained on ATIXZ.2 (over 360k human-created TikZ graphics), SKETCHFIG (hand-drawn sketches paired with scientific figures), and SciCAr++ (diverse... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's time and their feedback on our work. We noted a few misunderstandings in the reviewer's summary, as well as the listed strengths and weaknesses, which we would like to clarify.
First, while we generally agree with the summary, for accuracy and to prevent any confusion... | Rebuttal 1:
Rebuttal: We would like to extend our gratitude to all the reviewers for their feedback and we are delighted that our work was generally well-received. In response to the comments from reviewers uo3w and tgj9, we have uploaded a PDF containing additional examples along with this general response. For more d... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Robust Sleep Staging over Incomplete Multimodal Physiological Signals via Contrastive Imagination | Accept (poster) | Summary: This manuscript proposes an end-to-end framework that incorporates many procedures into an automatic sleep staging system that is specifically designed in the context of missing data. This includes a module for missing modality imputation, a module for distribution alignment, and a module for feature extracti... | Rebuttal 1:
Rebuttal: **Weaknesses:**
R1: Thank you for your valuable comments. Similar to mainstream sleep staging methods [1][2], the EEG, EOG, and EMG signals we use are all single-channel. Taking EEG as an example, multi-channel EEG requires expensive equipment such as PSG with strict environmental requirements fo... | Summary: This paper presents CIMSleepNet, a framework designed to address the challenges of automated sleep staging when faced with incomplete multimodal physiological data. This framework is particularly relevant in real-world applications where sensor malfunctions or detachment often results in incomplete data, there... | Rebuttal 1:
Rebuttal: **Weaknesses:**
R1: Thank you for your valuable suggestions! We have further analyze the computational efficiency and resource requirements of CIMSleepNet. Please refer to the Author Rebuttal for details.
**Questions:**
R1: As schematized in Table 2 of the original manuscript, we have compared ... | Summary: This paper proposes a robust multimodal sleep staging framework named Contrastive Imagination Modality Sleep Network (CIMSleepNet) to address the issues of missing modalities and temporal context modeling in automated sleep staging (ASS). CIMSleepNet combines a Modal Awareness Imagination Module (MAIM) for imp... | Rebuttal 1:
Rebuttal: **Weaknesses**:
R1: Thank you for your valuable comment. In fact, our CIMSleepNet supports incomplete multimodal data in both the training and testing phases. Our proposed MAIM can recover the missing modality data from other available modality data of the same sample in incomplete multimodality ... | Summary: The paper introduces a framework designed for sleep staging that addresses challenges associated with missing modalities in multimodal physiological signal datasets. The proposed model incorporates a modal awareness module and a semantic & modal calibration contrastive learning mechanism to handle missing data... | Rebuttal 1:
Rebuttal: **Weaknesses**:
R1: Please see **Author Rebuttal** for details.
R2: In response to this issue, we would like to clarify and add the following: Firstly, we conduct a comprehensive validation of the CIMSleepNet's effectiveness under random partial missing and complete missing conditions across fiv... | Rebuttal 1:
Rebuttal: Thank you for your decision and constructive comments on our manuscript. We have tried our best to modify some of the content and uploaded a PDF consisting of figures and tables. In the subsequent rebuttal per review, we will refer to it as "PDF". The blue font part in the PDF is the newly added c... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Challenges of Generating Structurally Diverse Graphs | Accept (poster) | Summary: This work considers the problem of generating graph topologies that are structurally diverse. The core argument for considering this task is that training and evaluating models for graph learning requires good "coverage" of the space of possible graphs to draw meaningful conclusions. The paper discusses variou... | Rebuttal 1:
Rebuttal: Thank you for your review! We address the questions and concerns below.
> W1. In my opinion, the main weakness is that the work stops short of the main motivating use case of why generating diverse graphs is important. Namely, the work does not carry out an assessment of whether the generated di... | Summary: The authors investigate the problem of generating graphs that are structurally diverse. Specifically, the graphs should be as different from each other as possible in terms of their properties.
Towards this, the authors first propose a new way to measure graph diversity based on the idea of energy(combined wit... | Rebuttal 1:
Rebuttal: Thank you for your review! We address the questions and concerns below.
> 1. It is not clear to me whether there is a data distribution which is learned.
One of the main differences between our setup and previous works on graph generation is that we do not have any data distribution that we want... | Summary: This paper focuses on the problem of generating structurally diverse graphs, an important problem for evaluating graph algorithms. In short, if you are trying to generate a set of graphs to evaluate an algorithm's performance (runtime or otherwise) one desires a set of graphs that "span the space" of possible... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and positive feedback! Let us address the weaknesses and questions.
> the problem is still unsolved
We agree with this and we are not expecting this problem to be completely solved in the near future. The problem is challenging, starting from the difficul... | Summary: This paper goes through the diversity of graphs and proposes the relevant generation process for diverse graphs. Various generation methods, such as genetic algorithms and greedy algorithms (based on diverse random graph generators), are studied and theoretical results are provided to guarantee the lower bound... | Rebuttal 1:
Rebuttal: Thank you for your review and positive assessment of our work! We address the weaknesses below.
> W1. The novelty of this paper should be better stressed, especially for the framework of measuring the diversity via energy.
To the best of our knowledge, the problem of generating structurally dive... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fast yet Safe: Early-Exiting with Risk Control | Accept (poster) | Summary: This paper investigate how to adapt frameworks of risk control to early-exit neural networks (EENN). The new framework is designed to exit without severely degrading performance as well as to guarantee the reliability of the prediction during early exiting. The applied risk control technique offers a distribu... | Rebuttal 1:
Rebuttal: Dear reviewer q2CX, we thank you for your time and helpful comments, and address your two key concerns in the following.
> The main weakness of this paper is under the strong assumption that all exiting thresholds are set to the same values. I admit that relaxing this assumption by adopting RC t... | Summary: This paper introduces a risk control framework for early-exit neural networks to balance the trade-off between inference efficiency and model performance. Compared to the conventional methods that manually set the confidence thresholds for early exiting, this work proposes a method to determine exit points fro... | Rebuttal 1:
Rebuttal: Dear reviewer LhAs, we thank you for your time and comments, and address your raised point in the following.
> A small suggestion is to demonstrate the accuracy-FLOPs curves as in the compared methods (MSDNet, L2W-DEN, and Dyn-Perceiver), because the current x-axis, "risk level", might be insuffi... | Summary: This paper proposes a method for improving the efficiency of early-exit neural networks (EENNs) while maintaining performance. EENNs allow for predictions to be made at intermediate layers, resulting in faster inference. However, the challenge is determining when it is safe to exit without sacrificing perf... | Rebuttal 1:
Rebuttal: Dear reviewer c7rs, we thank you for your time and comments, and address your two raised concerns in the following.
> The empirical validation of the approach is limited to a range of vision and language tasks. It would be beneficial to see the application of the method to other domains as well.
... | Summary: This manuscript revisits the confidence-based thershold tuning for early-exit networks following a risk-control formulation. The proposed approach aims to provide an enhanced mechanism to identify confident predictions early-on during the computation, and exit early to improve inference efficiency with minimal... | Rebuttal 1:
Rebuttal: Dear reviewer qAvq, we thank you for your time and engaged questions.
> The reliance of the proposed approach to marginal monotonicity between performance and depth can be a limiting [...] (see Elhoushi et al, LayerSkip, 2024 - Fig.2).
This is a great point and we fully agree that for a particul... | Rebuttal 1:
Rebuttal: We thank all reviewers for their efforts, time and comments, which are greatly appreciated.
We are glad that you found the work **tackles an interesting and important problem** (qAvq: “very interesting and timely problem”; c7rs: “addresses an important problem”; LhAs: “interesting and important ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SuperVLAD: Compact and Robust Image Descriptors for Visual Place Recognition | Accept (poster) | Summary: The paper addresses the problem of learning a global image descriptor suitable for visual place recognition.
The work builds on top of NetVLAD, i.e. performs a soft assignment of local descriptors to learnable clusters, with the main difference that the model directly aggregates the local descriptors rather th... | Rebuttal 1:
Rebuttal: Thanks for your positive recommendation and constructive comments, as well as the recognition of our compact descriptor (especially for 1-cluster VLAD). We will move the limitations discussion to the main paper as you suggested. The following are Responses to the Questions (denoted as **Q**) and W... | Summary: The authors proposed a new method called SuperVLAD for visual place recognition. The authors aims to fix the shortage of the previously mature NetVLAD, which is NetVLAD is not robust against domain gap and have to use high dimensional features.
SuperVLAD reduces the number of "clusters" used to smaller value ... | Rebuttal 1:
Rebuttal: Thanks for your positive recommendation and insightful comments. The following are Responses to the Question (denoted as **Q**) and Weaknesses (denoted as **W**).
**Q. Inconsistencies in datasets between Table 2 and Table 3:**
Sorry to confuse you. We compare our SuperVLAD with other methods on... | Summary: In this paper, the authors focus on reducing the dimension of NetVLAD method for the task of visual place recognition. More specifically, the proposed method named SuperVLAD combines previous techniques including the powerful DIVOv2 as backbone, free from clusters, and ghost clusters. Experiments demonstrate t... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and suggestions. We hope the following clarifications will be able to address your concerns.
**W1. About the number of clusters:**
Sorry to confuse you.
- First, since we did not specifically emphasize that as the capabilities of the backbone model improve (e... | Summary: The paper introduces SuperVLAD for addressing Visual Place Recognition (VPR) task. The proposed SuperVLAD descriptor is an improvement to NetVLAD descriptor, by reducing the dimensionality, by using fewer clusters and
Strengths: * Simple but effective method, reducing the number of parameters in VLAD (by redu... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments and valuable suggestions. The following are Responses to the Questions (denoted as **Q**) and Weaknesses (denoted as **W**).
**Q1. Results of NetVLAD in Table 2:**
Both of our results and the results in the SALAD paper are correct. We use the original version... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their valuable time and constructive comments on our work. We reply to the concerns of each reviewer individually and will incorporate the suggestions in the revised paper. We also attach a PDF containing figures, which we refer to when answering specific q... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This work introduces SuperVLAD, a novel image descriptor for Visual Place Recognition. It eliminates the need for cluster centers, addressing NetVLAD's performance degradation due to data bias while being more lightweight. SuperVLAD generates compact descriptors with fewer clusters. Additionally, a 1-cluster V... | Rebuttal 1:
Rebuttal: Thanks for your positive recommendation and encouraging words. The following are Responses to the Questions.
**Q1. Integrate SuperVLAD with visual localization and SLAM systems:**
Since SuperVLAD is a general visual place recognition (VPR) method, it can be used for visual localization and SLAM ... | null | null | null | null | null | null |
The Fairness-Quality Tradeoff in Clustering | Accept (poster) | Summary: The paper studies fair clustering from a novel and quite fresh perspective. So far in the literature, fairness in clustering has been mostly defined as an additional constraint the solution should satisfy. Given that definition, algorithms are trying to optimize the clustering objective (usually some metric ob... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and positive feedback!
**Individual fairness definitions:** To first answer the question: we think our framework is best suited for notions of group fairness, where there is some additional information about the nodes aside from their features used in the c... | Summary: The authors consider an important problem in the realm of fair clustering in this work-- is it possible to output the entire Pareto fairness-utility frontier when undertaking fair clustering? This can allow practitioners to answer questions of the form-- "how much utility can be sacrificed to improve fairness ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the questions and positive feedback!
**Definitions of fairness:** The notions of fairness we discuss cover a range of fairness definitions used in a significant part of the literature, focused on group fairness notions. We would be glad to include a discussion on additi... | Summary: This paper considers the Pareto-front between quality and fairness in clustering problems, which captures the trade-off between these two objectives. A clustering is on the Pareto front if its clustering cost and fairness objectives are strictly worse than the pair of other clustering.
They first consider th... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and question!
**Question about FCBC:** we’re not sure why a lower bound on the clustering cost would be desirable, since the clustering cost objective is a minimization objective (hence, lower cost is better). To modify FCBC with a lower bound on the cost g... | Summary: The paper is concerned with finding the Pareto front in fair clustering. Specifically, the clustering cost along with a fairness objective are considered simultaneously. Algorithms that can approximate the Pareto front are shown. The run-time is exponential however for the special fairness notion of sum of imb... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful questions and comments!
**Theoretical results:** We appreciate the reviewer’s suggestions and we also think that an excellent direction would be some negative/hardness results that can decisively address what approximations can and cannot be obtained in p... | Rebuttal 1:
Rebuttal: We appreciate all reviewers’ suggestions and positive comments. In particular, reviewers found our proposed problem **novel** and **interesting**, and with **solid theoretical contributions**.
In this general response, we’d like to comment on a few main threads opened by reviewers:
**Runtime... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
InfLLM: Training-Free Long-Context Extrapolation for LLMs with an Efficient Context Memory | Accept (poster) | Summary: This paper proposes a key/value retrieval-based method for long-context processing in Transformers. To address the distraction issue in long-context processing, the authors introduce an efficient method that retrieves blocks of relevant keys and values for attention computation. Through extensive evaluations o... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough review of our paper. Here are some clarifications regarding the points raised:
### Q1: Novelty
Please refer to Q1, Q2, Q3 in the Global Response. We implement an LLM with token-level memories, and it takes 2 hours to process a sequence with 128K tokens. The co... | Summary: This paper introduces InfLLM, which is a training-free memory-based method for long context extension. The key mechanism is to incorporate the sliding window attention with an efficient context memory. Each token only attends to local and relevant contexts from the memory. The paper conducts extensive experime... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough review of our paper. Here are some clarifications regarding the points raised:
### Q1: Evaluation on RULER
Please refer to Q5 in the Global Response.
### Q2: Comparison to LLMs with 128K Context Window
Please refer to Q4 in the Global Response. We compare InfL... | Summary: This paper focuses on the issue of extending context windows in LLMs by proposing a training-free block memory retrieval module. Specifically, aside from retaining initial tokens and local window tokens, other tokens are recalled using KNN at the block level. For efficient inference acceleration, LRU and chunk... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough review of our paper. Here are some clarifications regarding the points raised:
### Q1: Comparison to Existing Memory-based Methods
Please refer to Q1, Q2, and Q3 in the global response. Regarding the articles you mentioned, Unlimiformer employs token-level me... | Summary: This paper proposes breaking the context into blocks, and using a heuristic to compute representative tokens for the block. Then the attention score is first computed over these representative tokens, and the full attention is computed only over blocks corresponding to top-scoring representative tokens. The au... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough review of our paper. Here are some clarifications regarding the points raised:
### Q1: Comparison to Existing Memory-based Methods
Please refer to Q1, Q2, and Q3 in the global response. We are the first to construct a block-level training-free memory module f... | Rebuttal 1:
Rebuttal: ### Q1: Training-Free Context Extrapolation is Crucial
- Existing works for LLMs mainly focus on extending the model's context window through continued pre-training. However, these approaches face the following challenges:
a) Long-sequence training requires substantial computational resources a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Denoising Diffusion Path: Attribution Noise Reduction with An Auxiliary Diffusion Model | Accept (poster) | Summary: This paper proposes an approach to interpreting deep neural networks, specifically they focus on a path-based approach where they utilize the denoising ability of the diffusion model to construct a path to be integrated to compute the attribution of the target image.
Strengths: 1. I am not an expert in the fi... | Rebuttal 1:
Rebuttal: Thank you for appreciating the novelty of the proposed method and better quantitative results compared to existing methods. We would like to address your concerns as follows:
**Weakness 1: Presentation in Section 3**
Thank you for pointing out these issues. We will provide a more detailed descri... | Summary: This paper proposes a Denoising Diffusion Path (DDPath) to address the challenge in path-based attribution methods, where intermediate steps often deviate from the training data distribution. By leveraging the denoising power of classifier-guided diffusion models, DDPath constructs a piece-wise linear path to ... | Rebuttal 1:
Rebuttal: Thank you for appreciating the interesting idea of the proposed DDPath and comprehensive experiments. We would like to address your concerns as follows:
**Weakness 1: About details of the Algorithm 1**
First, the input timestep is $t \in [0, \ldots, T-1]$, and $t$ also denotes the steps along th... | Summary: This paper proposes Denoising Diffusion Path (DDPath), a method to reduce noise in path-based attribution for deep neural networks. DDPath uses diffusion models to create a path with decreasing noise, resulting in clearer attributions. It can be integrated with existing methods like Integrated Gradients and ma... | Rebuttal 1:
Rebuttal: Thank you for appreciating the smart problem framing, clear method explanation, and thorough testing. We would like to address your concerns as follows:
**W 1: Computational cost**
We acknowledge that the current DDPath might be computationally inefficient. However, we argue that the consistent ... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers,
We thank all the reviewers for their thorough summaries and valuable feedback. All the reviewers appreciated the **Novelty** and interesting idea of this work. The reviewers appreciate that our DDPath, combining diffusion models and DNN’s attribution, is Smart, Interesting, and No... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
One Token to Seg Them All: Language Instructed Reasoning Segmentation in Videos | Accept (poster) | Summary: This work introduce a Sparse Dense Sampling strategy and a One-Token-Seg-All approach to enhance the temporal ability of LISA. For the Sparse Dense Sampling strategy, the work preserves dense tokens of some frames and extract sparse tokens of interleaved frames. For the One-Token-Seg-All approach, the work app... | Rebuttal 1:
Rebuttal: We thank the reviewer for reviewing our paper.
**Q1**: This work utilizes two vision encoders, which will influence the speed of the model.
Response:
- We thank the reviewer for pointing out the valuable problem. In fact, the two vision encoders in the model are both necessary as they have their... | Summary: This work proposes VideoLISA, a video-based multimodal large language model that addresses the challenges of language-instructed reasoning segmentation in videos, leveraging various strategies to enhance temporal understanding and consistent object tracking, and showing promising generalization capabilities. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for reviewing our paper.
**Q1**: It is not clear whether the model can segment multiple objects
Response:
- Firstly, we would like to emphasize that single object segmentation is the standard and most popular setting in the general field of **language-guided object segment... | Summary: This paper introduces VideoLISA, a multimodal LLM for reasoning segmentation in videos. A Sparse Dense Sampling strategy is proposed to balance the temporal context and spatial detail for video modeling. Extensive results on various benchmarks demonstrate the effectiveness of the proposed method.
Strengths: T... | Rebuttal 1:
Rebuttal: We thank the reviewer for reviewing our paper.
**Q1**: The two proposed modules seem relatively simple.
Response:
- We would like to first emphasize that we do not pursue complexity in the model design. Instead, we aim to design a framework that is effective and suited to solve the problem. The ... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and efforts in reviewing our paper. We respond to the reviewers' questions in their own thread separately and place the mentioned tables in the PDF.
Pdf: /pdf/2825f92c6fb960cfc5b5b5d1d8dc259ef0f2e5cf.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Hybrid Reinforcement Learning Breaks Sample Size Barriers In Linear MDPs | Accept (poster) | Summary: This paper studies hybrid RL in linear MDPs, aiming to address the problem of whether hybrid RL can improve upon the existing lower bounds established in purely offline and purely online settings, without relying on the single-policy concentrability assumption. By combining offline dataset with online interact... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful comments. We are glad that you find our findings interesting, and further thank the reviewer for their kind words that our motivation is compelling, our rationale is clear, and that our work has potential downstream impact. We address the reviewer’s question... | Summary: This paper studies the hybrid reinforcement learning problem in the linear MDP setting. It provides two algorithms (one focused on improving the offline error and the other on improving the online error) with theoretical analysis on their sample complexity. Though the algorithms are not optimal in terms of sam... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and questions. We are glad that the reviewer confirms the soundness of our results and that our result is state-of-the-art for the hybrid linear MDP setting. We answer your questions below.
**Response to Weaknesses and Questions**
**Q1: Aside from the su... | Summary: In this work, the authors develop sample and computationally efficient hybrid RL algorithms that are provably better than online-only and offline-only algorithms for linear MDPs. Without relying on the single-policy concentrability assumption, the authors take both online-to-offline and offline-to-online appro... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing helpful comments to our paper! We also thank the reviewer for believing that our contribution is non-trivial, and for confirming the soundness of the proofs. We have revised our paper based on your suggestions, including new numerical experiments. If you think o... | Summary: The paper presents studies Hybrid Reinforcement Learning for linear MDPs, where Hybrid RL addresses the limitations of purely offline and online methods by combining offline data and online exploration. The paper introduces two specific algorithms: Reward-Agnostic Pessimistic PAC Exploration-initialized Learni... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing helpful comments to our paper! We are glad that you believe our paper is well-organized with a thorough literature review and that our methods are a solid improvement upon previous algorithms in the literature. We have revised our paper based on the suggestions ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their comments and suggestions on our paper. We also thank the reviewers for their kind comments that our findings were interesting and our results were sound and nontrivial.
**Technical Contributions**
For the benefit of everyone, we provide a summary of our technica... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Vivid-ZOO: Multi-View Video Generation with Diffusion Model | Accept (poster) | Summary: The paper introduces "Vivid-ZOO," an innovative diffusion model designed for Text-to-Multi-view-Video (T2MVid) generation. This is a novel approach that addresses the challenges of generating high-quality multi-view videos from textual descriptions. Specifically, the authors propose a new method that leverages... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the positive comments and constructive suggestions. We are encouraged that the reviewer agrees that "an innovative diffusion model", "novel approach", "a clever solution to address domain gaps", "contribute to the wider research community”, and "offering an effici... | Summary: This paper proposes a Text-to-Multi-view-Video generation algorithm capable of generating multi-view video content based on text descriptions. The method decouples the multi-view spatial and temporal dimensions, utilizing pretrained models like MVDream for multi-view generation and animatediff for video genera... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the positive comments and constructive suggestions. We are encouraged that the reviewer agrees that our work "achieved impressive visual results", "performance metrics showing improvements", "reduces the number of parameters and training time needed", and "mitigate d... | Summary: This paper focuses on generating multi-view consistent videos given a text prompt, specifically from 4 orthogonal views. It fine-tunes MVDream with a temporal layer adopted from AnimateDiff. To mitigate the domain gap between the two layers, some connected layers called 3D-2D and 2D-3D alignment layers are int... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the constructive comments. We are encouraged that the reviewer agrees that our work is "the first paper to study", "easy to follow", and "A multiview video dataset ... is collected".
**S1: This is the first paper to study the problem of text-conditioned m... | Summary: The paper presents a novel diffusion-based pipeline designed to generate multi-view videos from text prompts. The core challenge addressed by the paper is the generation of dynamic 3D object videos from multiple viewpoints, a task not extensively explored with existing diffusion models. The authors factor the ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the positive comments and constructive suggestions. We are encouraged that the reviewer agrees that "The task is extremely challenging and important", "tackles a relatively unexplored area of diffusion models,", "a novel diffusion-based pipeline", "a new dataset", "... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive comments and valuable suggestions. We are encouraged by the reviewers' positive feedback on novelty, writing, methodology, and experiment, such as
- novelty: "a novel approach", (VRVx), "a novel diffusion-based pipeline" (MoTs), "an innovative diffu... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Identifying Equivalent Training Dynamics | Accept (spotlight) | Summary: This paper proposes a method for identifying equivalent training dynamics of deep neural networks from the perspective of dynamical systems theory, in particular the spectral analysis of Koopman operators. The authors propose to use the notion of topological conjugacy, i.e. two dynamical systems are considered... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and detailed comments. We are encouraged that they found our work well written and our framework novel! Below, we respond to the specific questions the reviewer had.
**"It would be impactful if the proposed method identifies conjugacy of seemingly non-equivale... | Summary: To compare two training dynamics, the paper proposes to compare Koopman operators for them, especially their eigenvalues, based on the previous result showing that consensus in the Koopman eigenvalues implies the equivalence of two dynamics up to some homeomorphism. Their framework is applied to compare variou... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and helpful comments. We are encouraged that they found our work well written and our framework applicable. Below, we respond to the specific questions the reviewer had.
**"There remains a concern about the computational efficiency of computing Koopman eigenva... | Summary: This paper applies Koopman mode decomposition (KMD) to examine whether the training dynamics of different model architectures/optimizers are equivalent or not. In particular the authors examine 4 cases: 1) the same objective learned by online mirror descent vs online gradient descent (which are equivalent), 2)... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and detailed comments. We are encouraged that they found our work well written and our framework novel. Below, we respond to the specific questions the reviewer had.
**"The main issue is what would constitute a "low" vs. "high" Wasserstein distance between the... | Summary: The authors utilize topological conjugacy and Koopman operator theory to create a framework for identifying between conjugate and non-conjugate training dynamics in DNNs. To validate their approach, they first show that their framework can accurately identify the known equivalence between online mirror descent... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and detailed comments. We are encouraged that they found our work enjoyable to read and insightful! Below, we respond to the specific questions the reviewer had.
**"How can we investigate topological conjugacy if the training dynamics are more complex, such as... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and thoughtful comments. A response to each individual reviewer’s comments is provided in the thread of the associated review. We believe that addressing these questions has greatly improved the quality of our work. Here, we highlight three new results that we... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Bridging Multicalibration and Out-of-distribution Generalization Beyond Covariate Shift | Accept (poster) | Summary: The authors propose utilizing multicalibration algorithms to achieve distributional robustness w.r.t. both concept and covariate shift. They do this by allowing subgroups to not only be a function of the features X (as is standard in multigroup fairness), but also a function of the label Y. There are numerous ... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and efforts to improve our paper! We appreciate your recognition of our contribution's originality and significance. We believe we have included relevant details in our submission that address many of your questions. We will incorporate your questions and feedbac... | Summary: The authors explore an extension of multicalibration which includes joint grouping functions; groups that depend on both x and y. They show that multicalibration confers robustness to distribution shift problems. The authors then develop an optimization framework that post-processes a model to multicalibrate i... | Rebuttal 1:
Rebuttal: Thank you for your support\! We really appreciate it that the reviewer identifies a compelling reason for applying multi-calibration to algorithmic robustness, and highly evaluates the simplicity and efficiency of our approach, the feasibility of our assumption, as well as the organization of the ... | Summary: The paper explores multicalibration in the context of concept shifts, theoretically demonstrating the equivalence of multicalibration and invariance while providing a structural analysis of multicalibration. It introduces a novel algorithm that simplifies model selection and improves performance on real-world ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s acknowledgement of our paper as both a solid theoretical work and a practical tool. Since the reviewer raises questions regarding the scope of experiments and the feasibility of assumptions, we are ready to offer more details for the reviewer’s evaluation.
### W1: Sco... | Summary: This work studies an extension of multicalibration to include grouping functions of both covariates and labels. They show that just as multicalibration with respect to covariate density functions guarantees robustness to covariate shift, that extended multicalibration can imply robustness to concept shift. The... | Rebuttal 1:
Rebuttal: We really appreciate the reviewer for pointing out the significance of our result for being the first to consider a label-dependent grouping function in multi-calibration, as well as the soundness and organization of our paper\! The reviewer has raised questions over the learnability of the extend... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning Discrete Concepts in Latent Hierarchical Models | Accept (poster) | Summary: This paper studies the framework that identifies the discrete hierarchical latent variables for learning concepts from observed data examples. The proposed theory can be used to interpret the generating process of latent diffusion probabilistic models from the perspective of constructing object concepts.
Stre... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and dedicating your valuable time to our work! We address your concerns as follows.
>W1: “From Sec. 2, given the description that the continuous latent variables c seem to control a lower level of features of data, while in Figure 1. a, it seems to be indepen... | Summary: This work introduces a novel identifiability analysis for a hierarchical latent models where latent variables are discrete and observations are continuous. The novelty of the result resides in the fact that previous results consider mainly continuous latent variables or make stronger assumptions on the form of... | Rebuttal 1:
Rebuttal: Thank you for recognizing our theoretical contribution as “interesting, important, and novel” and we highly appreciate your detailed, constructive suggestions on the writing.
In light of your suggestion, we have put a lot of effort into revising the paper to explain all the involved definitions cl... | Summary: This paper introduces a theoretical framework for learning discrete concepts from high-dimensional data using latent hierarchical causal models. The key contributions are:
1) Formalizing concept learning as identifying discrete latent variables and their hierarchical causal structure from continuous observed ... | Rebuttal 1:
Rebuttal: Thank you for your encouraging words and thoughtful comments! We address your concerns as follows.
>W1: “The identification conditions (Condition 3.3 and 3.7) may be too restrictive… for complex high-dimensional data.”
Thank you for your comments. Maybe counterintuitively, the high dimensionalit... | Summary: This paper presents a theoretical framework for learning discrete concepts from high-dimensional data using latent hierarchical models.
The authors propose formalizing concepts as discrete latent causal variables within a hierarchical causal model, and discuss under which condition the identification of thes... | Rebuttal 1:
Rebuttal: Thank you for your careful assessment and thoughtful comments on our work! We address your concerns point to point as follows.
>M1. "Practicality of Recovering Hierarchical Graphs".
Thank you for the great question! In light of your suggestion, we’ve included in our revision the following experi... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their efforts and helpful comments regarding our paper. We are encouraged that all reviewers appreciate our theoretical contribution to the identifiability of latent discrete hierarchical graphs and our novel formalization of concept learning as a latent causal... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Convergence of Adafactor under Non-Convex Smooth Stochastic Optimization | Reject | Summary: The paper examines the convergence properties of Adafactor, an adaptive learning rate optimizer designed for deep learning tasks, particularly in memory-constrained environments. The study focuses on Adafactor’s performance in non-convex optimization scenarios and provides theoretical convergence proofs under ... | Rebuttal 1:
Rebuttal: We thanks a lot for the reviewer's feedback and valuable suggestions. Below are our responses to the major concern.
**Response to Weakness: we will add some deeper discussion per your suggestions (refer to the global rebuttal for details)**
- We kindly refer the reviewer to see the **global rebut... | Summary: This paper studies the convergence of Adafactor for non-convex smooth objectives. The paper looks at both full batch and stochastic cases and analyze the convergence rate. Experiments are provide to validate some of the findings about the hyperparameters.
The main contributions of this paper are: (1) converg... | Rebuttal 1:
Rebuttal: Thanks a lot for you time on our paper and feedback.
**To summarize, Reviewer ExZW has the following two major concerns (Please correct me if I was wrong).**
- **Concern 1**. Lack of novelty for convergence analysis of Adafactor comparing with other adaptive methods such as Adam and AMSGrad.
-... | Summary: This paper studies the convergence of a memory-efficient, adaptive algorithm, Adafactor, under non-convex smooth settings. First, the authors show that in the full-batch setting (with appropriate hyperparameters), Adafactor converges to a stationary point at an $\tilde{O}(1/\sqrt{T})$ rate. For the stochastic ... | Rebuttal 1:
Rebuttal: We thanks a lot for the reviewer's effort invested on our paper.
**Response to Weakness: We will revise accordingly per your suggestions on the presentation issue.**
- We present a proof sketch for Theorem 6.1 (the stochastic case) as follows. The proof sketches for other main results will also... | null | null | Rebuttal 1:
Rebuttal: In this general rebuttal, we have
- **clarification on major contribution and proof novelty**
- **formulation comparisons between Adafactor and Adam, and extra experiments in the attached PDF**.
-----
**The contribution of our paper could be enough to warrant acceptance:**
- Introduced by [26]... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Complexity of Identification in Linear Structural Causal Models | Accept (poster) | Summary: The paper examines the computational complexity of Generic and Numerical Identifiability problems. It provides some reduction from these problems to R complete classes. Based on these results, the paper proposes an algorithm for Generic Identifiability, which improves the state-of-the-art double exponential ti... | Rebuttal 1:
Rebuttal: Answer to questions:
1. At the moment, we consider the main contribution of the paper on the theoretical side. We are currently working on implementations.
2. We would like to emphasize that since the formulation of the parameter identification problem in linear SEMs in the 1960s, this problem... | Summary: The submission studies the problems of generic parameter identification and numerical identification, both of which represent prominent tasks in causal analysis. The results include a PSPACE (i.e., single-exponential) algorithm for generic parameter identification, as well as ForallR-hardness and a single-expo... | Rebuttal 1:
Rebuttal: - Recursive (or, in graphical language, acyclic) models are fairly standard and most commonly used in causality. They are the basic structural causal models used in causal inference (e.g. in the famous Pearl's do-calculus, Markov-equivalence of causal models, etc.) and in causal structure learning... | Summary: This paper looks into the parameter identification problem in linear structural causal models using only observational data. Under the Gaussian linear SEMs, the paper provides a polynomial-space algorithm that runs in exponential time. The paper also provides the hardness of the problem.
Strengths: In my know... | Rebuttal 1:
Rebuttal: Regarding weaknesses:
- We agree that learning the graph structure of the causal model and/or the topological ordering of variables is a difficult and very challenging task. However, we would like to emphasize that learning causal structures and/or the topological orderings was not the subject of... | Summary: This paper is a theoretical examination of the problem of parameter identifiability in structural equation models for mixed graphs (which has applications in causal modeling), significantly improving upon known upper bounds and establishing a hardness result for the problem's complexity.
Strengths: **Original... | Rebuttal 1:
Rebuttal: Answers to main questions:
1. The Gaussian recursive mixed graph models imply causal assumptions. The value (distribution) of each node is only affected by its parents and the bidirected edges. That yields Reichenbach's assumption, that if two nodes are not independent, there is either a direct c... | Rebuttal 1:
Rebuttal: - We thank the reviewers for their helpful comments. We make some remarks here addressing questions that were raised by more than one reviewer. Specific questions of the referees are discussed in the individual answers.
- We would like to stress that the identification in structural causal models... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper tries to attach an important theoretic problem: the complexity of identification in linear SEMs. The author proposed an algorithm for generic identification with exponential running time while previous the best algorithm is in double exponential running time.
Strengths: 1. The paper is well organize... | Rebuttal 1:
Rebuttal: We agree it is not guaranteed that theoretically faster algorithms will be better in practice. We are working on an implementation of our algorithms. However, experience shows that drastic improvements on the theoretical running time typically come with similar improvements on the practical side. ... | null | null | null | null | null | null |
Efficient LLM Jailbreak via Adaptive Dense-to-sparse Constrained Optimization | Accept (poster) | Summary: This paper is trying to address an important problem on discrete prompt optimization and proposes a token-level jailbreaking attack that relaxes the discrete optimization into continuous optimization. The main idea is to gradually increase the sparsity of the continuous vector. Experimental shows that the prop... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful feedback! We address the reviewer’s concerns as follows.
>The comparison against baselines is not fair.
Our method can be run on a single GPU and the reported time is the average run time of attacking a single example using a single GPU machine.
The code `srun -p gpu... | Summary: This paper focuses on improving the efficiency of white-box token-level jailbreaking attacks. Current approaches, such as GCG, employ a computationally intensive method that uses cross-entropy loss between a target string and the LLM’s response to greedily search for adversarial tokens one by one in the discre... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful feedback! We address the reviewer’s concerns as follows.
> ablation studies to evaluate the effectiveness of the proposed Adaptive Sparsity
We compare our method (adaptive sparsity) with baselines that use a constant sparsity of 1, 2 and 3. The following table shows t... | Summary: This paper analyses the problem of optimizing prompts to perform jailbreaks and yield harmful outputs. Optimizing over tokens is challenging because many efficient optimizers function in continuous space, however, any found adversarial example in this way will need to be cast back into discrete values to form ... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful feedback! We address the reviewer’s concerns as follows.
> It could have been useful to evaluate the attack in a black box fashion on closed source models such as ChatGPT and Claude2.
We consider a transferable attack of our method that jointly optimizes the same suffi... | Summary: This paper proposes a new jailbreaking attack against LLMs. This approach transforms the discrete input space into a continuous space and optimizes the adversarial tokens in the continuous space. Compared to existing methods, the proposed attack is more efficient. The authors use AdvBench and Harmbench to demo... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful feedback! We address the reviewer’s concerns as follows.
> conduct an ablation study that does not use adaptive sparsity
We compare our method (adaptive sparsity) with baselines that use a constant sparsity of 1, 2 and 3. The following table shows the ablation study. ... | Rebuttal 1:
Rebuttal: We first would like to thank all reviewer and AC's efforts and time reviewing this paper and suggestions for making it better.
According to the reviewers' feedback, we add two major experiments:
1. An ablation study about the adaptive sparsity.
| mode | adaptive sparsity (ours... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction | Accept (poster) | Summary: This works tackles the known problem of model heterogenity in FL, in which each client is allowed to use a different model according to its computational capabilities, while contributing to learn a global model. The proposed approach (FIARSE) samples submodels out of the global model by estimating the importan... | Rebuttal 1:
Title: Rebuttal by Authors
Comment: **Response to [W1].** Thanks for your question. While we acknowledge the limitations of unstructured pruning in the *Conclusion*, we also highlight significant industry progress in Appendix A, supported by notable evidence [1,2]. For instance, Nvidia GPUs support unstruct... | Summary: The authors address the low device capacities in federated learning. The authors notice that existing submodel extraction methods lack awareness of parameter importance when reducing the model, which may limit the model's performance. They propose an importance-aware dynamic submodel extraction method without ... | Rebuttal 1:
Rebuttal: **Response to [W1].** Thanks for your question. We train ResNet-18 using CIFAR-10, which is partitioned into 100 clients using a Dirichlet distribution with a parameter of 0.3. In each communication round, we sample 10 clients, and these clients locally train the model for four epochs with a batch... | Summary: The authors proposed FIARSE, a model-heterogeneous federated learning framework that addresses the limitations of existing static and dynamic sub-model extraction methods. They introduced an importance aware sub-model extraction technique to extract heterogeneous sub-model from a shared global model. This appr... | Rebuttal 1:
Rebuttal: **Response to [W1/Q1].** Thanks for your suggestion. We train ResNet-18 for 800 rounds using CIFAR-10, which is partitioned into 100 clients in a pathological scenario, where each client holds two classes. All these clients are equally distributed into four computation capacities {1/64, 1/16, 1/4,... | Summary: This paper tackles the problem of model heterogeneity in federated learning, where clients have different computational capabilities. The authors propose FIARSE, a method that extracts submodels of varying sizes from a global model based on the importance of parameters. The key idea is using a threshold-contro... | Rebuttal 1:
Rebuttal: **Response to [W1].** Thanks for your question. Given an arbitrary neural network, it is impossible to state the optimal architectures for different sizes so that all these submodels can achieve their best performance after federated learning. Therefore, we should **explore different submodel comb... | Rebuttal 1:
Rebuttal: **Figure 1:** We conduct the experiments to demonstrate the number of parameters that have been explored by different model sizes at $t$-th round and the model differences between two models of $(t-50)$-th and $t$-th communication rounds, where $t \in \{50, 100, \dots\}$. In specific, we train Res... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards training digitally-tied analog blocks via hybrid gradient computation | Accept (spotlight) | Summary: State-of-the-art (SOTA) analog hardware accelerators consist of both analog and digital components supporting major and auxiliary operations. Moreover, they typically suffer from device imperfections on their analog parts. In this paper, the authors propose feedforward-tied energy-based models (ff-EBMs) for di... | Rebuttal 1:
Rebuttal: We thank Reviewer pevw for their detailed review and we are happy that they've appreciated our work.
i) *The paper does NOT consider a detailed analog computing device model*.
We fully agree with Reviewer pevw that this is a current limitation of our work, as explicitly acknowledged inside the “... | Summary: This paper discusses a novel approach to improving the power efficiency of AI training by integrating analog and digital hardware. The authors introduce a hybrid model called Feedforward-tied Energy-based Models (ff-EBMs) that combines feedforward neural networks with energy-based models (EBMs). This model aim... | Rebuttal 1:
Rebuttal: We thank Reviewer kDhg for their detailed review and we are happy that they've appreciated our work.
i) *Energy efficiency claims, detailed comparisons with existing systems to substantiate these claims*. Please see our global rebuttal.
ii) *Why only ImageNet32?*
ImageNet32 was used for two rea... | Summary: This paper proposes a new building model block with analog forward circuits and energy-based blocks built on a digital-analog hybrid setup. A novel algorithm is further proposed to train the new block model. Experiments show the SOTA accuracy in the EP literature.
Strengths: * This paper achieves SOTA accura... | Rebuttal 1:
Rebuttal: We thank Reviewer 9JF8 for their comments and we are happy that they've appreciated our work.
i) *Why must we combine an analog forward circuit with a digital one for EP-based training?*
We first want to clarify that per our modelling choice, analog and digital parts are respectively modeled as ... | Summary: This paper presents Feedforward-tied Energy-based Models (ff-EBMs), a hybrid model that integrates feedforward and energy-based components, accounting for both digital and analog circuits. A novel algorithm is proposed to compute gradients end-to-end in ff-EBMs by backpropagating and "eq-propagating" through f... | Rebuttal 1:
Rebuttal: We thank Reviewer k7Rs for their comments and we are pleased they've appreciated our work.
i) *Accuracy performance on the ImageNet dataset*
As acknowledged inside the “Limitations and future work” paragraph (L.302-305), ff-EBM training by EP remains to be proved at scale on deeper models and m... | Rebuttal 1:
Rebuttal: We thank reviewers for their time and highly valuable comments. In light of these, we propose several clarifications which we hope render the value of our work more clear to readers and can quell some of the concerns expressed.
**I-Proposed additions to our paper**
- A discussion about the **rel... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: Analog in-memory computing is gaining traction as an energy-efficient platform for deep learning. However, fully analog-based accelerators are challenging to construct, necessitating a training solution for digital-analog hybrid accelerators. This paper introduces Feedforward-tied Energy-based Models (ff-EBMs)... | Rebuttal 1:
Rebuttal: We thank Reviewer Jt5z for their honest feedback. We consider these clarifications important to highlight our contribution for readers.
i) *Difference between this research and previous studies that have conducted training on analog in-memory systems using EP or backpropagation?*
We detail two r... | null | null | null | null | null | null |
Entropy testing and its application to testing Bayesian networks | Accept (poster) | Summary: In this paper, the authors first find the complexity upper and lower bound for the entropy identity testing problem: given sample access to a distribution $p$ and a fully described distribution $q$, the tester needs do distinguish between two hypotheses $p=q$ and $|H(p)-H)(q)|\geq \epsilon$. Based on this, the... | Rebuttal 1:
Rebuttal: Thank you for your time and review (and for taking a close look at the
proofs).
We would like to address the questions raised in the review below:
1. As is standard in distribution testing, we work in the Poissonized sampling
model. $\operatorname{Poi} (m)$ denotes a random variable distributed ... | Summary: The authors consider the entropy identity testing problem for two discrete distributions $p$ and $q$, which is a hypothesis test between $p=q$ and $|H(p) - H(q)| \geq \epsilon$. They propose an algorithm for this problem that is near-optimal in terms of the sample complexity. The main ideas of the algorithm ar... | Rebuttal 1:
Rebuttal: Thank you for your time and review.
1. Indeed, the formulation introduced is a bit atypical, but we emphasize that
even more standard formulations where the alternative is in terms of distances
(such as total variation distance or KL divergence, say) might still have a
similar objection, namely t... | Summary: * This work focuses on the problem of Entropy identity testing, i.e., whether for two distributions $p, q$ if $p = q$ or $|H(p) - H(q)| > \epsilon $ given samples from unknown $p$ and complete description of $q$ over a domain of size $k$.
* The authors show that the sample complexity of entropy identity test... | Rebuttal 1:
Rebuttal: We thank the reviewer for their encouraging comments and feedback.
We did not try to optimize the constants. This is primarily a theoretical
contribution which can serve as a proof of concept: any practical
implementation would be significantly optimized, and there is nothing a priori
inherent to... | Summary: This paper studies the problem of testing a null hypothesis against alternatives that are far in Shannon entropy. They provide information-theoretic minimax lower bounds that they also achieve up to polylogarithmic factors. They then apply these results to Bayes net testing.
Strengths: * Clear and well-writte... | Rebuttal 1:
Rebuttal: We thank the reviewer for their encouraging comments.
We understand the confusion, and will rewrite the statement of the two lemmas in the revision to make it clear that these are minimax lower bounds. For Lemma 2.7, we will add a paragraph to provide some
intuition, pointing out that it is based... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their detailed and thoughtful
comments (especially for taking a close look at the
proofs). We respond to each reviewer's comments individually below; and here focus on a common point raised across the reviews, that of the motivation or practical relevan... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
VQ-Map: Bird's-Eye-View Map Layout Estimation in Tokenized Discrete Space via Vector Quantization | Accept (poster) | Summary: To address challenges facing generating BEV maps posed by occlusion, unfavorable imaging conditions, and low resolution, this paper introduces a generative model to facilitate BEV estimation. A codebook embedding is used to encoder prior knowledge for the high-level BEV semantics in the tokenized discrete spac... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback!
**About clearly explaining the parameters of the ablation method and the table**: We apologize for not providing more details for the ablation methods in Tab. 4. The parameters that vary across the different ablation methods mainly include the layer and dim... | Summary: The authors propose to use a generative model similar to the Vector Quantized-Variational AutoEncoder (VQ-VAE) to obtain prior knowledge for high-level Bird’s Eye View (BEV) semantics in a tokenized discrete space. By leveraging BEV tokens and a codebook embedding that encapsulates the semantics for different ... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback! We are happy that you quite like our tokenization idea for the BEV map layout estimation task. We provide responses to the specific points below:
**About the state-of-the-art comparison with MapPrior[17] and DDP[19]**: Both of the two references of MapPrior... | Summary: This paper proposes to use a generative model to encode BEV semantic maps into tokenized sparse BEV representations with codebooks.
Specifically, it consists a two stage training scheme. First, train a BEV generation VQ-VAE, then use the BEV Tokens as a ground truth to train the second stage network where it m... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback! We are happy that you find our idea of using a pre-trained codebook interesting in the BEV map generation domain. We provide responses to the specific points below:
**About the IoU metric for measuring the performance of BEV map**: The common practice of us... | Summary: The paper proposes a novel approach, VQ-Map, for BEV map layout estimation, addressing the challenges of occlusion and low-resolution images in perspective views. By leveraging a VQ-VAE-like generative model, the authors introduce BEV tokens to bridge the gap between sparse image features and dense BEV represe... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback! We are happy that you recognized that our paper clearly identifies the challenges in BEV map layout estimation and provides a well-defined solution with a novel approach VQ-Map proposed. We provide responses to the specific points below:
**About the idea of... | Rebuttal 1:
Rebuttal: Thank you for the valuable reviews pointing out that our *novel* (`1hbx`) approach and the idea of a pre-trained codebook are particularly *interesting* (`rd8m`), and the visualization adds depth to the concept (`1JdE`). The tokenization approach we proposed appears to be *more robust against noi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Causal Contrastive Learning for Counterfactual Regression Over Time | Accept (poster) | Summary: the paper proposes a method based on CPC to estimate the causal effect of treatments over a period of time
Strengths: - Interesting paper
- well written , although some things could have been cleaner
- good experimentation and ablation study
- interesting use of cpc
Weaknesses: - Not clear from an intuitio... | Rebuttal 1:
Rebuttal: Thank you very much for your review!
## Weaknesses
1. Please see the global rebuttal.
2. We have reported the complexity of our model and that of baselines for experiments on both synthetic and semi-synthetic data. For instance, in Table 2 of the core paper, we show the training time of our mod... | Summary: This paper leverages Contrastive Predictive Coding (CPC) with RNN for counterfactual regression over time to provide a compelling alternative to transformer-based approaches (which are challenging to interpret). By leveraging CPC to capture long-term dependencies and InfoMax for "reconstructable" representati... | Rebuttal 1:
Rebuttal: Thank you very much for your review !
## Weaknesses
1. We have included the performance evolution on random trajectories for the pharmacokinetic-pharmacodynamic model of tumor growth in Figure 3 of the core paper. This figure illustrates the model's performance across multiple levels of confound... | Summary: The paper introduces a novel algorithm for long-term counterfactual forecasting over time through representation learning of historical information and balanced representation learning that predicts the outcome given balanced treatments. The representation learning process combines Contrastive Predictive Codin... | Rebuttal 1:
Rebuttal: Thank you very much for your feedback!
## Weaknesses
1. and 3. Please see the global rebuttal.
2. Using multiple synthetic datasets with random settings to obtain error bars is a valuable suggestion, but we face constraints: limited budget for computational resources, increased energy consumpti... | Summary: This paper presents causal CPC, a framework for predicting counterfactual responses under time-varying treatments. The proposed method consists of two components: an encoder that leverages contrastive predictive coding and infomax principle to learn a representation of the history H, and a decoder that leverag... | Rebuttal 1:
Rebuttal: Thank you very much for your review!
## Weaknesses
**On the importance of the INfoNCE loss**: We have provided extended results pointing out the discrepancy in errors for all forecasting horizons in Table 8, specifically for the semi-synthetic MIMIC-III data. We reported only the average error ov... | Rebuttal 1:
Rebuttal: ## Global Rebuttal
We would like to first thank the reviewers for their very constructive feedback and valuable comments.
1. We can use real data, like the MIMIC III dataset, instead of its semi-synthetic version. However, evaluating counterfactual trajectories is not possible due to the absence... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes a new method for counterfactual outcome prediction over time, with the goal of avoiding highly complicated models and expensive computation while achieving state-of-the-art prediction accuracy for time series with long-range dependencies. To this end, the presented method employs RRNs for l... | Rebuttal 1:
Rebuttal: Thank you very much for your review!
## Weaknesses
We conducted a similar experiment as suggested by the reviewer mdqp, where we reduced the sequence length seen during training but maintained a large forecasting horizon ($\tau$). It is important to note that the middle and right diagrams in Fig... | Summary: The paper presents a novel approach to counterfactual regression over time, particularly focusing on long-term predictions. It introduces a method that leverages RNNs combined with CPC and InfoMax. This approach aims to capture long-term dependencies in data and improve computational efficiency without relying... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable feedback!
## Weaknesses
### Complexity:
It is true that our model requires fewer parameters compared to SOTA models but has a relatively higher training time. However, several points mitigate this concern. The reported training time for CPC includes both e... | null | null | null | null |
VisMin: Visual Minimal-Change Understanding | Accept (poster) | Summary: The paper introduces VisMin, a new benchmark for assessing fine-grained understanding in VLMs. VisMin evaluates the ability of models to distinguish between minimally different images given a caption, testing recognition of changes in objects, attributes, counts, and spatial relationships. The benchmark is cre... | Rebuttal 1:
Rebuttal: **“which contradicts the authors' claim of improved text-image alignment in MLLMs”**
We do not claim that we improve general image-text alignment *for MLLMs*. We claim that we improve *CLIP’s* general image-text alignment and substantiate that claim by showing improvements on the standard COCO im... | Summary: This paper studies the fine-grained understanding of objects, attributes, and relationships between objects for VLMs and MLLMs. Specifically, it focuses on the capability of VLMs and MLLMs to distinguish between two very similar images given a caption. Firstly, by leveraging an image generation diffusion model... | Rebuttal 1:
Rebuttal: **Some notations are confusing**
We apologize for this. The captions C_0 and C_1 are minimal-change pairs as described in Fig 1 of the review version. The C in L247 is exactly the same as T in L294. We will clarify these and make the notations consistent in the camera-ready version.
**Details ab... | Summary: This paper introduces a new benchmark, VisMin, which mainly challenges models to detect semantic differences between visually similar but semantically different images. It uses an automated data curation pipeline and human verification to create dataset. The authors benchmark the dataset with current VLMs and ... | Rebuttal 1:
Rebuttal: **Some basic metrics like accuracy are also needed.**
The T (Text), I (Image), and G (Group) metrics we use *indeed measure accuracy*, i.e., the proportion of examples for which the model produces the correct output [1]. The correctness criteria is different for each of T, I, G, as stated in L244... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful feedback! We’re encouraged that they found the **motivation** of the paper to be **clear** (R1, R2), the **proposed idea** of using minimal change pairs to improve fine-grained understanding **promising** (R3), the **data curation pipeline** to be **well... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Multilinear Mixture of Experts: Scalable Expert Specialization through Factorization | Accept (poster) | Summary: In the paper, the author introduces $\mu$MoE layers, which factorize large weight tensors to facilitate implicit computation, thereby accelerating the computation process. By increasing the number of experts, the model enhances its specialization in vision tasks. Further, the author conducted experiments with ... | Rebuttal 1:
Rebuttal: We thank Reviewer 1sqG for their positive assessment of the paper; for praising the “innovative” quantitative metrics, the “comprehensive evaluation”, and “competitive performance with added benefits”. We address the stated weaknesses below:
## [W1] Limited model/dataset scope
The reviewer is c... | Summary: The paper proposes the use of (factorized) Multilinear Mixture of Experts as an alternative to Sparse MoEs, which are prone to training issues due to the sparse top-k activation. Several factorization options are described and compared, which result in models of better accuracy in vision tasks, when matching t... | Rebuttal 1:
Rebuttal: We thank Reviewer YTQ3 for engaging thoroughly with the paper. However, we respectfully argue below that the reviewer overlooks our stated goals and claims in the paper, and the benefits to scalable expert specialization the methodology brings that are otherwise infeasible:
In particular, muMoE ... | Summary: This paper proposes the Multilinear Mixture of Experts (µMoE) layer for scalable expert specialization by performing an implicit computation on prohibitively large weight tensors entirely in factorized form. Both qualitative and quantitative results show that scaling µMoE layers when fine-tuning foundation mod... | Rebuttal 1:
Rebuttal: We thank **Reviewer-XreH** for praising the well-motivated problem studied in the paper and the clear presentation of the factorization methodology.
## More results on language models
Whilst we do not have the computational resources to pre-train new additional large language models during the ... | Summary: The paper proposes a new way of computation in a mixture of expert (MoE) layer. Unlike SparseMoE, instead of using the top-K operator and then doing full computation only for that expert, this paper do not do topK, but does a linear combination of all experts, but each expert is now a low rank matrix, which sa... | Rebuttal 1:
Rebuttal: We thank **Reviewer-qC51** for their detailed review; for praising the ease with which the methodology can be implemented and the benefits of downstream applications in bias mitigation. We address the stated weaknesses below and draw attention to the existing ablation studies requested. Additional... | Rebuttal 1:
Rebuttal: We are grateful to each of the 5 reviewers for their thorough comments; for their positive assessments of both the paper and the novelty of the layer in facilitating parameter-efficient, scalable expert specialization:
- **Reviewer Su68** praises the “thorough” mathematical formulation, “technic... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper presents an interesting and novel approach with the µMoE layer, offering a method for scalable expert specialization. Extensive experiments in multi-dimensional have been conducted to show the effectiveness of the proposed method. However, the marginal improvements in results, coupled with several ot... | Rebuttal 1:
Rebuttal: We thank **Reviewer-Su68** for their detailed assessment of the paper: for praising the “novel[ty]” of the layer and the “technically sound” methodology. Furthermore, we are glad the reviewer appreciates the “extensive qualitative and quantitative evidence to support the claims” of the paper.
All... | null | null | null | null | null | null |
GarmentLab: A Unified Simulation and Benchmark for Garment Manipulation | Accept (poster) | Summary: The authors present GarmentLab, a content-rich benchmark and realistic simulation designed for deformable object and garment manipulation. Multiple tasks such as interactions between garments, deformable objects, rigid bodies, fluids, and human body are explored. A first real-world deformable benchmark along w... | Rebuttal 1:
Rebuttal: Thank you for your feedback! We have addressed your comments below.
## Novelty of Our Paper
We are grateful to your review on our **contribution**: "the substantial contribution to the community".
As for the **novelty** of our paper,
as our paper proposes the environment and benchmark for ro... | Summary: This paper proposes a content-rich benchmark and realistic simulation for deformation object and garment manipulation. The GarmentLab consists of the GarmentLab Engine, Assets, and Benchmark, supporting various research topics.
Strengths: * The GarmentLab provides a realistic and rich environment for research... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments! We have addressed them below, and hope to hear back from you
if you have further questions!
## Code Release
These mentioned components with the guidelines for installation and usage have been already made publicly available. Please refer to the **Global Respon... | Summary: This paper proposes GarmentLab, a content-rich benchmark and realistic simulation specifically designed for deformable objects and garment operations. It encompasses a diverse range of garment types, robotic systems, and manipulators.
Strengths: 1. The GarmentLab Environment offers a realistic and comprehensi... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments! We have addressed them below, and hope to hear back from you
if you have further questions!
# Efficiency of the Benchmark
To the best of our knowledge, GarmentLab is the first to support all of ray tracing, robot control simulation, and diverse deformable objec... | Summary: This paper introduces GarmentLab, a new set of garment manipulation environments, assets, annotations and tasks based on IssacSim. The differences compared to existing benchmarks is the support of a broader range of tasks (such as tasks involving interaction with other objects such as hanger, fluid, and human ... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. We have addressed them below, and hope to hear back from you
if you have further questions!
# Setup of Real-World Evaluation
Thanks for the appreciation of our proposed real-world assets,
and pointing out that the real-world evaluation setup is not detailed en... | Rebuttal 1:
Rebuttal: We extend our gratitude to reviewers for their careful reading, meticulous feedback and valuable insights!
We are glad that reviewers unanimously agree that our work:
(1) proposes a comprehensive and realistic environment for garment manipulation (wEM9: "very comprehensive, cover most of the ne... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CulturePark: Boosting Cross-cultural Understanding in Large Language Models | Accept (poster) | Summary: The author proposed a multi-LLM-agent framework, “CulturePark,” for cultural data collection. The proposed framework simulates cross-cultural human communications by LLM role-playing in different cultures, such that high-quality cross-cultural dialogues that align with human beliefs, norms, and customs can be ... | Rebuttal 1:
Rebuttal: **W1: ome of the comparisons may seem unfair, for example:**
- Comparing SeaLLM (which is LLaMA-2-based) with GPT-3.5-based models in Table 2.
- We agree that it is necessary to fine-tune gpt models on the training data of SeaLLM. However, a sad story is that their training data is not publici... | Summary: The paper introduces CulturePark, a LLM-powered multi-agent communication framework designed for cultural data collection. Addressing the pervasive issue of cultural bias in LLMs, CulturePark simulates cross-cultural human communication using LLM-based agents representing different cultures. This method genera... | Rebuttal 1:
Rebuttal: W1: The culture defined in the paper is too coarse-grained. [...].
We strongly agree that language is not equal to, but only a part of culture. But using language to study culture is possible due to the following aspects:
- Existing literature on culture understanding shows that culture boundarie... | Summary: This paper describes CulturePark, a simulation framework for LLM agents to converse about cultural norms, inspired by social theories. The authors set up simulated conversations between an "main contact" which is an English speaking LLM-based agent, and a "cultural delegate" which is an LLM-based agent that ro... | Rebuttal 1:
Rebuttal: **W1: There are missing details on: (1) how the authors use the extracted opinions on target culture to filter out examples (L176) (2) how are the LLMs finetuned (e.g., are they finetuned only on the "culture delegate" agent utterances?) (L187)**
Thanks for the reminder! We will append those deta... | Summary: This paper presents CulturePark, an LLM-powered multi-agent framework for cultural data collection through multi-agent communication. CulturePark can generate high-quality and diverse cross-cultural dialogue, which can be used to fine-tune cultural specific LLMs.
Strengths: The paper is a strong and well-writ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the positive support of our work. If you have any new questions, please do not hesitate to let us know. | Rebuttal 1:
Rebuttal: Dear Reviewers and AC,
We want to thank all reviewers for pointing out our strengths, including:
- problem significance: "The paper studies culture understanding which is an important problem.", "The paper focuses on an interesting problem of cultural alignment by using role-playing with LLMs."
-... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Discovering Creative Behaviors through DUPLEX: Diverse Universal Features for Policy Exploration | Accept (poster) | Summary: This paper proposes an algorithm, DUPLEX, that learns diverse yet high-performance policies for context Markov decision process. DUPLEX extends the previous SOTA DOMiNO by introducing context in the MDP and extending the diversity objective as context conditioned. DUPLEX also introduces three tricks to stabili... | Rebuttal 1:
Rebuttal: We appreciate your feedback, it has been very useful to strengthen our work.
**(Ln 42-43) It seems to have a..**
Humans adapt OOD contexts continuously. For example, if a person injures their leg, they can immediately balance on one leg and even jump forward without using the injured leg. We ai... | Summary: This is a work in the Reinforcement Learning domain, particularly relevant to policy exploration. They propose a method to better preserve the trade-off between the exploration diversity and near-optimality. This strategy utilizes a diversity objective with defined constraints such that it enforces the trained... | Rebuttal 1:
Rebuttal: We thank the reviewer for their interest in our work and the feedback provided. Below are the detailed responses:
**Relying on multiple hyper-parameters (introduced by equations 3-5, probably hard to scale across the domain).**
As indicated in lines 207-209 and 843-845, most of DUPLEX hyper-para... | Summary: This paper proposes to use successor feature (an embedding of state-action pair) to boost the behavior diversity of a population of policies.
Several tricks are proposed to compute the diversity intrinsic reward. These include using the running average of the extrinsic reward to scale the intrinsic reward and... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and appreciate the feedback provided.
**There is a strong assumption of the method: the context vector is given by the environment.**
We agree that not all environments are designed to provide a context vector. However, we comply with related work in zero-sho... | Summary: The authors introduce DUPLEX, a method that defines a diversity objective with constraints and uses successor features to robustly estimate policies' expected behavior. DUPLEX allows agents to:
1. Learn a diverse set of near-optimal policies in complex, highly-dynamic environments.
2. Exhibit competitive and d... | Rebuttal 1:
Rebuttal: We appreciate the time and feedback you provided on our submission.
**The paper does not compare its diversity objective to other existing diversity objectives in the literature, such as DIAYN (Eysenbach et al., 2019) and MaxEnt RL (Haarnoja et al., 2018). This omission makes it difficult to ju... | Rebuttal 1:
Rebuttal: Thank you for the detailed feedback and insightful comments. We appreciate the time and effort you have invested in this review which has significantly strengthened our contribution.
We considered each point highlighted by the individual reviewers and made several revisions to address their concer... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: introduced a method to enhance diversity in RL policies by using successor features for robust behavior estimation. Experiments show DUPLEX outperforms state-of-the-art baselines, achieving diverse, competitive policies in GranTurismoTM 7 and multi-task MuJoCo environments, even in out-of-distribution contexts... | Rebuttal 1:
Rebuttal: We appreciate the feedback you provided on our submission.
**In 4.1.1, the authors …**
Please note that our results conform to the literature [2, 3, DOMiNO] when evaluating the Lagragian-constrained optimization in canonical MuJoCo environments and we found it to outperform the non-lagrangian ... | null | null | null | null | null | null |
Stability and Generalization of Asynchronous SGD: Sharper Bounds Beyond Lipschitz and Smoothness | Accept (poster) | Summary: This paper talks about the generalization error and the excess generalization error of Asynchronous SGD, mainly under convex, smooth or holder continuous conditions.
Strengths: The theorems are sound, and the results are new.
Weaknesses: In th2, the generalization error is written as the form of containing w... | Rebuttal 1:
Rebuttal: > **Question 1.** In th2, the generalization error is written as the form of containing $\mathbf{w}_1$ and $\mathbf{w}_K$. This is some what not reasonable. I want to see the generalization error which is depended on the already setting constants like $\eta, K, n$ and so on, in this way, theorem c... | Summary: For distributed machine learning tasks, Asynchronous Stochastic Gradient Descent (ASGD) is an indispensable optimization algorithm. Considering the existing results that fail to reveal the intrinsic impact of asynchronous training, this paper establishes sharper stability and generalization bounds for ASGD wit... | Rebuttal 1:
Rebuttal: > **Question 1.** The authors provide the approximately non-expansive recursive property for ASGD. Without some limitations to $r$ and $\tau$, the terms $2\eta_{k}\beta^{2}r^{2}\sum_{j=1}^{\tau_{k}}\eta_{k-j}$ in Lemma 3 and $\mathcal{O}\Big(\eta_{k}\sum_{j=1}^{\tau_{k}}\eta_{k-j}+\eta_{k}^{\frac{... | Summary: The paper provides generalization analysis of Asynchronous stochastic gradient descent (ASGD) for both smooth and non-smooth (Holder smooth) lesses. For generalization error, the Lipschitzness of losses assumption is removed, while for excess generalization error, the loss is also required to be Lipschitz. Th... | Rebuttal 1:
Rebuttal: > **Question 1.** Although the problem studied is slightly different, the proofs of the main results almost follow from Lei (2021). The novelty of the paper remains a question. Specifically, Lei (2021) studies the generalization analysis of standard SGD for both smooth and nonsmooth (Holder smoot... | Summary: The paper explores the generalizability of ASGD through the lens of on-average stability, revealing how asynchronous delay, model initialization, the number of training samples, and iterations impact the generalization performance under both Lipschitz smooth and Hölder continuity conditions. The authors also c... | Rebuttal 1:
Rebuttal: > **Question 1.** *Suggestion:* Including a table that summarizes all the theoretical results (including necessary assumptions), along with their comparisons with existing works, would help clarify the contributions of this paper.
**Response.** Yes, we completely agree with your suggestion, and w... | Rebuttal 1:
Rebuttal: **We sincerely appreciate the reviewers for their meticulous review and valuable feedback.** We are encouraged by their endorsements:
1. It's interesting to investigate the generalization performance of ASGD for distributed learning and analyze the impact of different factors. [Reviewer UzMC]
2. T... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Opponent Modeling with In-context Search | Accept (poster) | Summary: This paper introduces an approach to opponent modeling in multi-agent environments, aimming to address the challenges of generalization and performance instability when trained agents interacting with unknown opponents during the test time. The proposed method, Opponent Modeling with In-context Search (OMIS), ... | Rebuttal 1:
Rebuttal: # Response to Reviewer **YkYd**
Thank you very much for your recognition of our paper's problem setting, paper writing, experimental results, and the valuable feedback you provided. In response to your comments, we would like to make the following clarifications and feedback. We hope our explanat... | Summary: This paper proposes a novel approach to opponent modeling called OMIS, which combines ICL and decision-time search to improve performance and stability in three distinct game settings over baselines. It also shows ablations that validate the need for specific components such as mixing technique, search, episod... | Rebuttal 1:
Rebuttal: # Response to Reviewer **eKwk**
Thank you very much for your recognition of our paper's methodologies, theories, empirical results, paper writing, and the valuable feedback you provided. In response to your comments, we would like to make the following clarifications and feedback. We hope our exp... | Summary: This paper addresses the problem of opponent modeling and leverages in-context learning to tackle the challenges posed by opponents using non-stationary and unknown policies. Specifically, the proposed method, OMIS, employs PPO to train a best-response policy for each opponent policy in the training set. OMIS ... | Rebuttal 1:
Rebuttal: # Response to Reviewer **G1Ke**
Thank you very much for your recognition of our paper's writing, methodologies, literature reviews, and the valuable feedback you provided. In response to your comments, we would like to make the following clarifications and feedback. We hope our explanations and a... | Summary: The paper addresses the challenges of opponent modeling in multi-agent environments, particularly the difficulties in generalizing to unknown opponent policies. The authors propose a Opponent Modeling with In-context Search (OMIS), which combines in-context learning-based pretraining and decision-time search. ... | Rebuttal 1:
Rebuttal: # Response to Reviewer **xSNm**
Thank you very much for your valuable feedback. In response to your comments, we would like to make the following clarifications and feedback. We hope our explanations and analyses can eliminate concerns and make you find our work stronger.
> the proposed method i... | Rebuttal 1:
Rebuttal: # Global Response
We extend our heartfelt thanks to AC and all the reviewers for your diligent work in evaluating our manuscript. We are deeply grateful for the insightful feedback and recommendations from each of you. In this global rebuttal comment, we provide additional experimental results an... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Extending Video Masked Autoencoders to 128 frames | Accept (poster) | Summary: This paper studies the MAE pretraining of long videos. To address the challenges of hardware memory and compute limitations, this paper propose an effective strategy of decoder masking, subsampling tokens as reconstruction targets. This strategy leverage the MAGVIT-based tokenizer, prioritizes the most importa... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s valuable feedback on our work, and for noticing the effectiveness of our method in improving both pre-training efficiency and downstream accuracy. Below, we address some of the concerns and questions raised by the reviewer.
### Q1. Gradually increasing the number of ... | Summary: The paper proposes a novel MAGVIT-based adaptive tokenizer & masking module to extend VideoMAE to 128 frames. The tokenizer & masking module is individually trained and applied offline, making it possible for VideoMAE to reconstruct sparser (but important) and more semantically informative targets. The experim... | Rebuttal 1:
Rebuttal: We appreciate the reviewers comments on our work being well-written, its importance in the field of video understanding, orthogonality to existing works, and that the effectiveness of our approach is verified through ablation experiments. Below, we address the additional questions raised by the re... | Summary: This video understanding paper extends the video mae idea to a longer 128 frames. They use the MAGVIT tokenizer to achieve this and test the approach on Diving-48 and epic kitchens. Both achieved an improved score despite using a pretty standard network and pre-training
Strengths: The use of a MAGVIT encoder ... | Rebuttal 1:
Rebuttal: ### Differentiating EK-Noun SoTA with EK-Verb SoTA
We appreciate the reviewer’s feedback and agree that our EK-100 noun accuracy is below SoTA. However, we would like to humbly point out that SoTA methods for EK-Noun rely on large-scale pretraining, while ours does not. The EK-100 noun categories ... | Summary: This paper focuses on efficiently extending VideoMAE to much longer videos. It proposes an adaptive decoder masking strategy that utilizes a MAGVIT tokenizer to localize the importance of each token, which becomes targets that reduce the memory/computation of decoders.
The motivation aims to scale the input v... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review and for recognizing the novelty, effectiveness and impact of our proposed approach. While we designed our experiments to study the impact of long-video MAE pre-training as such has not been attempted before, we agree with the reviewer that the proposed... | Rebuttal 1:
Rebuttal: ## Summarizing feedback
We thank all the reviewers for their valuable time and feedback. We are encouraged by the positive feedback from all the reviewers who found our work
- Novel (Reviewer LVuv, Reviewer UhsZ), orthogonal to prior work (Reviewer UhsZ), and addresses an important issue in video ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
VeLoRA: Memory Efficient Training using Rank-1 Sub-Token Projections | Accept (poster) | Summary: This paper proposes a novel algorithm named VeLoRA, which achieves memory-efficient training by compressing the intermediate activations of large-scale language models into a fixed one-dimensional subspace. VeLoRA divides tokens into smaller sub-tokens and projects them during the forward pass, then reconstruc... | Rebuttal 1:
Rebuttal: We thank the reviewer for praising the method's $\textbf{simplicity}$, $\textbf{comprehensive derivation}$ and $\textbf{analysis}$. We are excited to see that the reviewer positively rates the contribution and presentation of the paper. We hope that our rebuttal addresses the reviewer's issues abo... | Summary: This paper introduces VeLoRA, a novel method for memory-efficient training and fine-tuning of large language models. The key idea is to compress intermediate activations during the forward pass by projecting sub-tokens onto a fixed 1-dimensional subspace, then coarsely reconstructing them during backpropagatio... | Rebuttal 1:
Rebuttal: We thank the reviewer for highlighting the $\textbf{extensive experiments, broad applicability, and scalability}$. We also appreciate your description of the approach as $\textbf{highly practical and easy to adopt}$.
#### **1. Limited theoretical analysis: While the paper provides some intuition ... | Summary: This paper proposes VeLoRA, an activation-compression method to reduce memory consumption. VeLoRA compresses the activations by multiplying them with a vector, and the activations are then decompressed before gradient back-propagation. VeLoRA has proven to be effective on both vision and language models.
Stre... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing some constructive questions and suggestions for a more thorough and complete set of comparisons. We also appreciate the reviewer highlighting our proposed method as $\textbf{very interesting}$ and praising its $\textbf{experimental results}$.
#### **1. Comparis... | Summary: In this paper, the authors propose to compress the activations by down-projecting the input tensors with a vector to save memory for large-scale training. The empirical results show that their proposed method, VeLoRa, achieves better performance compared with previous work.
Strengths:
1. The topic of efficie... | Rebuttal 1:
Rebuttal: We thank the reviewer for praising the $\textbf{effectiveness}$ of our method, calling it $\textbf{important}$, $\textbf{well-written}$, and $\textbf{easy to follow}$. We also thank the reviewer for suggesting us to discuss and compare with two related works, and asking us for a clarification with... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' positive feedback on our method's **effectiveness** (sd52, KYfS, UGFV), **simplicity** (bRtV, UGFV), **comprehensive derivation and analysis** (bRtV), **writing quality** (sd52), and **thorough evaluation with comparisons to existing works** (UGFV). We're pleased that ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fair Allocation in Dynamic Mechanism Design | Accept (poster) | Summary: The paper explores an auction mechanism where an auctioneer aims to maximize discounted overall revenue while adhering to fairness constraints that ensure a minimum average allocation to two distinct groups. The study begins with a simple T = 1 scenario to establish the foundational optimal mechanism constrain... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and comments. Please find our answers below.
> The paper would benefit from a more comprehensive discussion of related works to better situate its contributions within the existing body of knowledge.
We acknowledge that the related work section ... | Summary: This paper studies the incorporation of fairness constraints into revenue-optimal single-item auctions. Specifically, it focuses on a scenario with two groups bidders. Within each group, bidders' private valuations are sampled i.i.d. from a distribution, with the two groups having different valuation distribut... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and comments. Please find our answers below.
> Minor Comments
We thank the reviewer for pointing out the typo. We also agree with the suggested improvements to our figures and would incorporate them in the final version.
> Experiments seem too ... | Summary: This paper studies a fair allocation problem where an auctioneer sells an indivisible good to two groups of buyers every round for T rounds. The auctioneer’s objective is to maximize their discounted revenue with fairness constraints. The authors show that for the static case with T=1, the optimal mechanism su... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and comments. Please find our answers below.
> The discount factor is mentioned in the introduction, better to make sure it’s also defined in the Model section for notation reference.
We thank the reviewer for this suggestion and acknowledge tha... | Summary: The authors study the problem of dynamic mechanism design when in which for $T$ rounds an auctioneer sells an indivisible good to two groups of people. The goal is to design a mechanism which incentivizes the agents first to participate in the auction and second to bid truthfully and moreover maximizes the dis... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and comments. Please find our answers below.
> The parameter $\delta$ is never clearly defined and it was very confusing to see it in line 112 without any proper previous description.
We defined the discount factor $\delta$ earlier in the introd... | Rebuttal 1:
Rebuttal: **Global Responses:**
We thank the review team for their thoughtful and detailed comments. Here, we provide our general answers before addressing each reviewer's questions separately.
**General comment 1: Motivating examples based on real-world applications**
Auctions are used in various real-w... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Disentangling and mitigating the impact of task similarity for continual learning | Accept (poster) | Summary: This paper analyzes the impact of task similarity on Continual Learning (CL) within a linear teacher-student model that incorporates low-dimensional latent structures. The findings indicate that high input feature similarity combined with low readout similarity is detrimental to both knowledge transfer and ret... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments.
We have included an additional figure to clarify the concept of readout similarity in a classification setting and to demonstrate the applicability of our framework to such settings. The lower half of panel A illustrates two tasks with low readout similarit... | Summary: The paper systematically investigated how feature similarity and readout similarity affect knowledge transfer and retention, under different gating scenarios, showing weight regularization based on Fisher information metric improves retention without compromising transfer. These are done with both linear teach... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments.
__Weaknesses:__
In panel A of the attached additional figure, we clarified the definitions of feature similarity and readout similarity in a classification setting. The green point in the left panel represents a scenario where two tasks have low feature si... | Summary: The paper investigates the challenge of continual learning in artificial neural networks, particularly when learning tasks with partial similarity. Task similarity can both facilitate knowledge transfer and increase the risk of interference and catastrophic forgetting. The authors develop a linear teacher-stud... | Rebuttal 1:
Rebuttal: Thank you for your comments. Please find our replies to your comments on the weaknesses below.
1. In the attached figure, we demonstrated numerically that the feature and readout similarity influence the continual learning performance in a manner predicted by our theory even in a classification s... | null | null | Rebuttal 1:
Rebuttal: Thank you all for your valuable comments. Based on your feedback, we have added a figure that explains the feature and readout similarity in the context of a classification task (panel A) and shows their impact on knowledge transfer and retention (panels B and C). We found that when performance is... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MALT Powers Up Adversarial Attacks | Accept (poster) | Summary: AutoAttack is a highly successful image-based adversarial attack method that combines targeted and untargeted approaches to effectively target a wide range of models. For targeted attacks, AutoAttack selects 9 adversarial target classes based on the model's confidence levels. However, this restriction is impos... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive and thorough review.
"**While MALT's primary innovation is rooted in its refined class selection process, which sets it apart from AutoAttack, this refinement may somewhat temper the overall novelty of this manuscript.**"
We acknowledge that the main contr... | Summary: This paper introduces MALT, a heuristic technique for selecting target classes for adversarial perturbations. The intuition behind MALT is to order attack targets in the order of high row norm in the jacobian. They show that this can beat an AutoAttack baseline with much less compute for CIFAR and ImageNet cla... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough review.
"**W1: This paper seems to be behind its time.**"
We first emphasize that all our experiments are done against the top robust models according to Robustbench, which is the de facto standard benchmark in this field. All the models we tested on are f... | Summary: The paper presents a novel adversarial targeting method, Mesoscopic Almost Linearity Targeting(MALT), based on local almost linearity assumptions. The proposed attack wins over the current state of the art AutoAttack on the standard benchmark datasets CIFAR-100 and Imagenet and for different robust models. The... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough review.
"**The proposed MALT's performance advantage over AutoAttack is not obvious.**"
We acknowledge that the additional attacked images are not numerous, resulting in a marginal improvement in terms of successful attacks over Autoattack. However, this i... | Summary: Several evasion attacks seek untargeted adversarial examples by performing multiple runs with a targeted loss against different target classes. This process often leads to better results with respect to using untargeted losses, as the optimization process might be more stable. Until now, the choice of the cons... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough review.
"**Some aspects of the attack evaluation should be clarified or integrated with additional assessments. As the improvements provided by MALT with respect to AutoAttack are quite marginal, the experiments should clearly show that these improvements a... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
NaRCan: Natural Refined Canonical Image with Integration of Diffusion Prior for Video Editing | Accept (poster) | Summary: This paper proposes to solve the issue of temporal consistency in video editing. They propose a hybrid deformation field architecture, with a trainable homography matrix H(u, v, t) and residual deformation MLP, representing object variations throughout an entire scene. Combining it with the diffusion before th... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's feedback and the opportunity to clarify our work. We acknowledge the concerns raised and will address them point by point:
### Strengths:
We're glad the reviewer recognizes our method's temporal coherence and faster convergence.
### Weaknesses and Questions:
> **Q1.... | Summary: In this study, the authors propose a hybrid deformation field and diffusion prior update scheduling to generate high-quality canonical images that maintain temporal consistency in video editing.
Strengths: The use of homography, a transformation method from the existing image processing field, to generate acc... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thorough assessment and constructive feedback. We acknowledge the concerns raised and will address them point by point:
### Strengths:
We're glad the reviewer recognizes the value of incorporating homography into our method.
### Weaknesses and Questions:
> **Q1. Met... | Summary: This paper proposes a novel approach for video editing in the scope of canonical image-based video editing. They argue the canonical images used in prior methods are unnatural, which degrades the performance a lot. To solve this problem, they propose to use a LoRA finetuned diffusion model to refine the unnatu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough assessment and valuable feedback. We appreciate the positive comments on our motivation, performance improvements, and experimental analysis. We'll address the concerns and questions raised:
### Strengths:
We're glad the reviewer found our motivation reas... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers and Area Chairs,
We sincerely thank all reviewers for their thorough assessments and valuable feedback. We appreciate the positive comments on our work:
Strengths:
1. Reasonable motivation and significant performance improvements (Reviewer b5x9).
2. Clear experimental analysis (Re... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Cracking the Code of Juxtaposition: Can AI Models Understand the Humorous Contradictions | Accept (oral) | Summary: This paper introduces YESBUT, a new benchmark for evaluating large vision-language models' ability to understand humor and contradictions in comics with juxtaposed panels. The benchmark consists of two-panel comics with contradictory narratives, along with annotations for literal descriptions, contradiction ex... | Rebuttal 1:
Rebuttal: Thank you for your insightful suggestions! We appreciate your recognition of our problem formulation as novel and our experimental and evaluation methods as comprehensive. We are also encouraged that you think our work can provide insights for future research. We will revise our paper and incorpor... | Summary: The paper proposes a new evaluation benchmark to evaluate how much current VLMs understand the humor and uses the new benchmark to compare various VLMs and LLMs.
Strengths: Although some previous studies such as [7] and [10] have proposed the humor benchmark for VLM, like author mentioned in 112-114, the prop... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback! We are pleased to learn that you consider our benchmark novel, which includes two input images per sample to emphasize the relationship between the images. Additionally, we are grateful for your acknowledgment of our task settings and good analysis. Below, w... | Summary: This paper presents a benchmark YESBUT, which contains pairs of images exhibiting a sense of humor via juxtaposition. For each pair, the authors employed human-AI collaboration methods to annotate detailed tasks to assess why the humor can be understood, including literal description writing, contradiction gen... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable comments and suggestions! We are pleased to learn that you find our introduction of the YESBUT benchmark is helpful and beneficial to future research, and our rich experimental studies and in-depth analyses offer a comprehensive view. We address your questions... | Summary: This paper investigates the capability of large vision language models (VLMs) to understand humor in comics through narrative contradictions. The authors introduce the YESBUT benchmark, comprising tasks designed to evaluate AI’s ability to recognize and interpret contradictory narratives in comics. Experiments... | Rebuttal 1:
Rebuttal: Thank you for your valuable and constructive comments. We appreciate your recognition of our work's innovative benchmarks, human-AI collaborative annotation, and insightful analysis. Below, we address your concerns and questions individually:
### W1 & W5: Regarding Novelty and Suitability
Thank... | Rebuttal 1:
Rebuttal: ## Overall Response to All Reviewers
We thank all reviewers for their valuable comments and suggestions. We are pleased to know that the reviewers consider our benchmark **novel** (reviewer wVUt, yTj4) and **innovative** (reviewer 2fnt), with a **rigorous** human-AI collaborative annotation proce... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Easy Regional Contrastive Learning of Expressive Fashion Representations | Accept (poster) | Summary: This paper focused on adapting CLIP-based VLMs to the fashion domain. The motivation is that directly finetuning CLIP models on fashion data will lead to insufficient learning of entity-related details like logos and composition. To tackle this challenge, this work proposed a region-based contrastive loss by i... | Rebuttal 1:
Rebuttal: We sincerely thank you for your efforts in reviewing this paper and giving valuable suggestions!
We appreciate your recognition of the effectiveness and rationale of our work. While our method consistently outperforms existing works and exhibits simplicity and efficiency in fashion domain, it als... | Summary: This paper proposes a framework to train CLIP models more adaptable to fashion domain (shopping item images and structured descriptions, i.e, “tags”, such as brand, composition etc.). The new framework (“E2”) outperforms other common methods by a large margin on a wide range of tasks, by 1) adding fusion block... | Rebuttal 1:
Rebuttal: We sincerely thank you for your efforts in reviewing this paper and giving valuable suggestions!
## W1
Thanks for your helpful suggestion! One of the highlights of our model is being lightweight with minimal additional cost.
We compare the training and inference speed with the vanilla CLIP as f... | Summary: The paper discusses adaptation of CLIP for fashion domain w/ an emphasis on importance of learning fine-grained visual representations. The authors propose E^2 with selection tokens and region contrastive loss to enforce extra attention.
Strengths: Emphasis on importance of learning fine-grained visual repres... | Rebuttal 1:
Rebuttal: Thank you for your valuable suggestions and the recognition of the analysis and experiments in our study!
In our final version, we will make the paper more understandable for the general ML audience in NeurIPS. Specifically, we will carefully introduce the background of our problem, and how our ... | Summary: This work proposes a new approach to learn improved and more expressive visual representations for fashion-specific tasks. Prior works have proposed to learn fashion representation either by complex multi-task objectives or fine-tuning strong pretrained visual features such as CLIP. However, such methods fail ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your efforts in reviewing this paper and giving valuable suggestions!
## W1
Thanks for the suggestion! In our current version, we have focused more on discussing the motivation and the benefits of our approach, including analytical discussions, in the Introduction and ... | Rebuttal 1:
Rebuttal: ## Reference
[1] Wu et al., Fashion IQ: A new dataset towards retrieving images by natural language feedback, CVPR 2021
[2] Irvin et al., Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison. AAAI 2019
[3] Johnson et al., Mimic-cxr, a de-identified publicly a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MatrixNet: Learning over symmetry groups using learned group representations | Accept (poster) | Summary: In this paper, authors study the question of what feature representations to use for learning tasks with inputs coming from a symmetry group. They propose MatrixNet, a neural network architecture that learns matrix representations of group element inputs instead of using predefined representations.
The main c... | Rebuttal 1:
Rebuttal: ## Response to Reviewer puxG
> The motivation is not so clear to me. The applications of group theory are everywhere in the real-world. Thus, how to choose a good representation for the group which is associated with the learning task is more important. In this paper, authors formulate this proble... | Summary: The paper describes MatrixNet, a method to learn group representations such that they are optimized for a certain task of interest using a neural network. , The neural network takes in a group element in the form of a sequence of generators that compose to form the group element and forms an intermediate outpu... | Rebuttal 1:
Rebuttal: ## Response to Reviewer KZHf
> Parts about the braid group are very difficult to follow and probably need a lot more background for readers and attendees for this conference.
Thank you for this feedback. We will make sure to revise this section to add more clarity. Intuitively the braid group def... | Summary: This paper studies feature representations of a group element for supervised learning. It considers a regression task where an input is a group element g of a finite group and a target is some label. Firstly, g is decomposed into a sequence of generators (g_1, ..., g_n) such that g = g_1 \circ ... \circ g_n. N... | Rebuttal 1:
Rebuttal: ## Response to Reviewer JC7A
> Limited applicability to real tasks. The current approach is only applicable to finite groups.
Our method is **not** limited to finite groups. One of our experiments focuses on the infinite Artin braid group. You are correct that our approach is not formulated for c... | Summary: The authors use neural networks to learn group representations. A group element is represented by its generators formatted as a sequence of learned matrix representations. These generators are mapped to a single matrix representation of the group element via the Matrix Block which enforces group axioms. The re... | Rebuttal 1:
Rebuttal: ## Response to Reviewer jQuD
>It’s unclear why naive MatrixNet and MatrixNet-MC cannot extrapolate to longer word length.
While MatrixNet and MatrixNet-MC underperform compared to our other two variants, it is overstated to say they cannot extrapolate to longer word lengths. Despite their high M... | Rebuttal 1:
Rebuttal: # NeurIPS Rebuttal
We thank the reviewers for their feedback and insightful comments. We are glad they found our work well-written(__jQuD__, __JC7A__). It is particularly encouraging that many reviewers found our design of architectural constraints to learn group representations novel(__KZHf__), ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning Frequency-Adapted Vision Foundation Model for Domain Generalized Semantic Segmentation | Accept (poster) | Summary: This paper deals with adapting a computer vision foundational model to a new domain. They propose to use the frequency based on Haar wavelets to to decouple the style and content information and then to address separately content and style domain adaptation. Experiments show state of the art results.
Stre... | Rebuttal 1:
Rebuttal: **Q1**: The adaptation for low-frequency and high-frequency components are different. The paper could provide more intuition to explain the particular adaptations chosen for low and high frequency components.
**R**:
Thanks for your general positive comments on our work and your insightful comment... | Summary: The paper proposes a Frequency-Adapted (FADA) learning scheme, where Haar wavelet transformation is introduced to decouple the frozen VFM features into low- and high-frequency components. Experiments demonstrate the proposed method achieves a better generalization on unseen target domains.
Strengths: The prop... | Rebuttal 1:
Rebuttal: **Q1**: Writing \& logic. 1) the relationship among three DGSS category methods; 2) Why the style-invariant properties of VFM is important and urgent? 3) The introduction is not accompanied by an explanation of Fig. 1.
**R**: Thanks for your valuable feedback so that we could have the chance to i... | Summary: This paper proposes a Frequency Adaptive (FADA) learning approach. Its core idea is to process content and style information separately, by using frequency tokens. Specifically, the FADA comprises two branches for low-frequency and high-frequency components. The high-frequency components learn scene styles and... | Rebuttal 1:
Rebuttal: **Q1**: Is the assumption of "similar content yet varied style" practical? The joint distribution of content and style? Can we assume that they are independent across the dataset?
**R**: Thanks for your insightful comment so that we could have a chance to further clarify the basic assumption in d... | Summary: This paper introduces a novel FADA learning scheme to improve domain-generalized semantic segmentation. The proposed method leverages the Haar wavelet transformation to separate style and content information into high- and low-frequency components, respectively, allowing for better handling of domain variation... | Rebuttal 1:
Rebuttal: **Q1**: Compare recent LoRA methods [a,b].
[a] Gao, Z., et al. (2024). Parameter-Efficient Fine-Tuning with Discrete Fourier Transform. ICML.
[b] Yang, Y., et al. (2024). Efficient low-rank backpropagation for vision transformer adaptation. NeurIPS.
**R**: We've compared both methods, namely, F... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and constructive suggestions, and are glad that the reviewers unanimously give appreciation in a few points:
- Technique Contribution \& Innovation (**esrP**: low-rank adaptation in the frequency domain has novelty; **eWsS**: the whole solution is simple and ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper focuses on adapting the vision foundation model for domain-generalized segmentation via frequency-aware adaptation. Specifically, the intermediate features are decomposed into low-frequency (content) and high-frequency (style) components, which are then being processed with separate low- and high- f... | Rebuttal 1:
Rebuttal: **Q1**: 1) Intuition on adapting in all the transformer layers. 2) Ablate the layer location.
**R**: Thanks for the constructive comment.
1) The high-level idea of our intuition focuses on low-rank adaptation (LoRA) in the frequency space.
As existing LoRA methods [24,58] implements the adaptati... | Summary: This paper leverages the vision foundation model (VFM) for domain-generalized semantic segmentation (DGSS). It proposes to adapt the VFM to the downstream task in the frequency domain. The proposed method decouples the low-frequency and high-frequency components of the VFM features and separately applies low-r... | Rebuttal 1:
Rebuttal: **Q1**: Justify: 1) domain-specific style and domain-invariant content are demonstrated by high- and low-frequency components 2) how the proposed method stabilizes domain-invariant content while mitigating domain-specific style.
**R**: Thanks for your valuable comment. To clarify:
1) In visual d... | null | null | null | null |
A Recipe for Charge Density Prediction | Accept (poster) | Summary: The paper proposes a new method for calculating charge density using orbital based representations along with an equivariant machine learning architecture. The paper starts by introducing density functional theory (DFT) and how charge density plays a central role for DFT. The important role of charge density i... | Rebuttal 1:
Rebuttal: We thank reviewer nCiL for helpful feedback and comments. We address each of the reviewer’s concerns below.
> Provide more details on how charge density prediction is different from ML potentials and why that distinction matters. Concretely, what advantages and disadvantages does charge density p... | Summary: This paper proposes a new effective approach to estimating the charge density of molecular systems using machine learning models. Although this topic has been recently actively studied, the present approaches suffer from either a lack of accuracy or scalability. The proposed approach alleviated the problem by ... | Rebuttal 1:
Rebuttal: We thank reviewer SS9K for helpful feedback and comments. We address each of the reviewer’s concerns below.
> The paper only performed the experiments on QM9...
Thank you for your feedback. We follow your suggestions and extend our experiments to the MD dataset used in [1]. Due to limited time a... | Summary: Overall this is a nice technical contribution towards the goal of machine-learning based prediction of charge densities trained from DFT calculations. The main merits are that the authors combine the recent eSCN equivariant network, which reduces the computational complexity of equivariant message passing comp... | Rebuttal 1:
Rebuttal: We thank reviewer L6ft for helpful feedback and comments. We address each of the reviewer’s concerns below.
> I have no doubt this is a valuable contribution. But my concern is lack of original ideas (all ideas were existing or straightforward) and lack of high impact application.
Thank you for... | Summary: This application-oriented paper uses equivariant GNNs to predict charge density, represented orbital basis sets.
Strengths: - the idea of this paper is sound. Representing the charge density using atomic orbital basis set sounds more efficient than the voxel-based methods.
- the performance of this new method... | Rebuttal 1:
Rebuttal: We thank reviewer paLd for helpful feedback and comments. We address each of the reviewer’s concerns below.
> I feel like the architectures are a simple variant of popular backbones. But this shouldn't affect the novelty of this application paper.
We agree the model backbone architecture is a si... | Rebuttal 1:
Rebuttal: Dear Area Chairs and Reviewers,
Thank you for your time and consideration in reviewing our paper. Following the suggestions from the reviewers, here we summarize our responses and several improvements we aim to make in the next version.
- We report the performance metrics of alternative model ba... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Does Reasoning Emerge? Examining the Probabilities of Causation in Large Language Models | Accept (poster) | Summary: This paper investigates the reasoning capabilities of large language models (LLMs) by examining the concepts of necessity and sufficiency, which are key elements of logical reasoning. To assess the LLMs' reasoning abilities, the authors introduce a framework that computes the probability of necessity (PN) and ... | Rebuttal 1:
Rebuttal: Thank you for highlighting the strengths of our paper. We appreciate your recognition of our systematic method for evaluating LLM reasoning through the probabilities of necessity and sufficiency. Your positive feedback on our dual-angle approach comparing the predictive vs the reasoning abilities ... | Summary: To assess the reasoning abilities of large language models (LLMs) in complex tasks e.g., causation scenarios, this paper introduces a novel framework that utilizes probabilistic measures of necessity (PN) and sufficiency (PS). Through a series of mathematical examples, the study computes approximations of PN a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments and positive feedback. We appreciate your acknowledgment that our exposition is clear and that the necessity and sufficiency are crucial elements in reasoning. We’re also pleased that you found our experimental design and results easy to understand... | Summary: The paper introduces a systematic method for assessing the reasoning capabilities of large language models (LLMs) by focusing on the concepts of necessity and sufficiency in logical reasoning. It leverages a probabilistic interpretation of these concepts and uses a reasoning graph based on boolean conditions t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the appreciation that the work proposes a novel method for a critical issue and the diagrams help to communicate our ideas. We appreciate the feedback, that we have now incorporated in the manuscript.
**Comment of weaknesses:**
*1. Incorporating more content in the main... | Summary: This paper evaluates the reasoning capabilities of LLMs within the framework of Judea Pearl's hierarchy of causality, focusing particularly on the ability to perform counterfactual reasoning.
LLMs are perceived as (non-deterministic) abstract machines within the HEX framework. The study assesses these models ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and appreciate the comment that our methods offers an nuanced evaluation that extends beyond the use of accuracy to evaluate reasoning in language models metric.
**Comment of weaknesses:**
*1. Emergence vs pattern recognition:* As illustrated in Figure 1, ... | Rebuttal 1:
Rebuttal: We thank the four reviews for the positive feedback and the comments that have helped to improve our work. It is encouraging to see that the reviewers find our approach to offer "... a nuanced evaluation that extends beyond the accuracy metric." and that they agree with us that ''...The examinatio... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Don't Look Twice: Faster Video Transformers with Run-Length Tokenization | Accept (spotlight) | Summary: In this paper, the authors propose run-length tokenization, an efficient video patch merging strategy to speed up the video transformer
Strengths: Please refer to Questions
Weaknesses: Please refer to Questions
Technical Quality: 3
Clarity: 3
Questions for Authors: ### Strength
1. The paper is well-writte... | Rebuttal 1:
Rebuttal: We deeply thank you for your helpful review and greatly appreciate your feedback. We are glad you found that our method is “intuitive and works well”, and that the paper was “well written and easy to follow”. We address each of your concerns below.
__Concern 1a: Evaluation: Two simple baselines ... | Summary: Current video models usually need to process every patch or tubelet of every frame, no matter if the video is very dynamic or contains patches that almost never change (e.g. static backgrounds). This submission proposes to simply omit tubelets that do not change significantly between frames, and optionally add... | Rebuttal 1:
Rebuttal: Thank you for your exceptionally thorough, helpful and clear review of our paper. We are glad you found our work to be “very well motivated”, “novel” and found it to “work better than other methods like random masking”. We address each of your concerns individually below.
__Concern 1: Learned em... | Summary: The authors present a compression or optimization technique applicable to video transformers for both training and inference paradigms. Through empirical evaluation, the work showcases efficiency gains achieved for fine-tuning video transformer models and also showcases inference time efficiency without any tr... | Rebuttal 1:
Rebuttal: We deeply appreciate your kind comments towards our work, and thank you for your detailed review. We are glad you found our work to be exceptionally presented, and our work to be high quality. To answer your question, the inspiration for this work came from one of the authors’ habit of watching li... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful comments and are grateful for their feedback. We are glad they found our work to be __“commendably original, very well-motivated and intuitive”__ (RB8qa
, RN711, RtWGP), __“state-of-the-art, useful, significantly faster and better“__ (RB8qa, RN711,Rt... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Historical Test-time Prompt Tuning for Vision Foundation Models | Accept (poster) | Summary: This paper introduces a framework designed to mitigate the knowledge forgetting problem in test-time prompt tuning. The proposed method, HisTPT, employs three types of knowledge banks-local, hard-sample, and global-to memorize useful knowledge from previous test samples, thereby enhancing prompt optimization d... | Rebuttal 1:
Rebuttal: **[Response 1] Further explanation of how and why the proposed knowledge memory banks effectively address the knowledge forgetting issue:**
Thank you for pointing out this issue.
As discussed in Lines 37-45, HisTPT introduces three types of knowledge banks to memorize the previously learnt knowl... | Summary: This paper takes a deep investigation into the memory bank and proposes a framework with three different memory banks for storing local knowledge, hard-sample knowledge, and global knowledge. The experimental results show that the proposed method delivers superior performance on many tasks.
Strengths: 1. This... | Rebuttal 1:
Rebuttal: **[Response 1] Further analysis of the three knowledge banks:**
Thank you for your suggestion! We analyze the three knowledge banks by visualizing their stored features along the test-time adaptation process. Three points can be drawn as illustrated in **Figure 2** in the attached PDF:
1) The gl... | Summary: The paper makes a comprehensive investigation of the memory bank in test-time prompt tuning for CLIP. To address the forgetting issue, the author proposes HisTPT. HisTPT aims to address this by memorizing useful knowledge from learned test samples, using three types of memory banks: local, hard-sample, and glo... | Rebuttal 1:
Rebuttal: **[Response 1] Evaluation of HisTPT over the continuous test-time adaptation task:**
We would clarify that we evaluated HisTPT over continuously changing test domains in Table 6 of the main manuscript. As discussed in Lines 280-293, HisTPT can tackle challenging scenarios when the domain of test ... | Summary: This paper proposes Historical Test-time Prompt Tuning (HisTPT), aimed at addressing the performance degradation issue of test-time prompt tuning methods in scenarios where test samples continuously change. HisTPT establishes a local knowledge bank, hard-sample knowledge bank, and global knowledge bank, each s... | Rebuttal 1:
Rebuttal: **[Response 1] Robustness of HisTPT to different confidence metrics:**
Thank you for your suggestion! We conduct the suggested studies by adopting different confidence metrics, i.e., Softmax probability [a], MC dropout [b], and Mahalanobis distance [c], over Cityscapes semantic segmentation task ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for your insightful feedback and constructive comments. We are highly encouraged by the reviewers' acknowledgement that our proposed method has good scalability in various vision tasks [43p7,CdiG] and effectively explores memory in test-time prompt tuning [ejR5... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Invertible Consistency Distillation for Text-Guided Image Editing in Around 7 Steps | Accept (poster) | Summary: The paper proposes distilation for both of forward and reverse path of ODE sampling of diffusion models in order to enable faster editing.
Strengths: The overall framework is quite interesting, and applying the consistency distillation for forward and reverse process is novel.
Also the method of dynamic CFG i... | Rebuttal 1:
Rebuttal: Thanks for your careful reading and valuable feedback! We respond to your questions below.
1. *it feels that the editing method is limited on Prompt2Prompt (P2P). Is it still possible to apply the model on non-rigid editing such as MasaCTRL?*
Our model is not limited to P2P. Alternative editing ... | Summary: This paper extend the idea of consistency distillation to inversion for image editing. By training a separate consistency model where the consistency is enforced at noise space rather than latent space. Additional cycle consistency loss is employed for more accurate inversion.
Strengths: 1. The paper tackles ... | Rebuttal 1:
Rebuttal: Thanks for your careful reading and valuable feedback! We respond to your questions below.
1. *The description of the method is unclear... we need to have a coupling of the image and noise.*
An interesting aspect is that neither additional data nor teacher inversion is required. Compared to the ... | Summary: This work introduces invertible Consistency Distillation (iCD), which enhances text-to-image diffusion models by enabling effective encoding of real images into latent space. iCD achieves both high-quality image synthesis and accurate image inversion in just 3-4 inference steps.
Strengths: 1. The adaptation o... | Rebuttal 1:
Rebuttal: Thanks for your careful reading and valuable feedback! We respond to your questions below.
1. *Discussion of Failure Cases*.
We present some inversion failure cases in the attached file (Figure 1). Our method sometimes oversaturates images for high guidance scales and struggles to reconstruct co... | Summary: This paper targets a novel problem of enabling image inversion and editing for models distilled with consistency distillation.
The authors identify the challenges of applying consistency distillation, which is designed for the denoising process, to the diffusion process and propose multi-boundary consistency ... | Rebuttal 1:
Rebuttal: Thanks for your careful reading and valuable feedback! We respond to your questions below.
1. *almost every technique comes from another earlier work...contribution less impactful.*
Our techniques present a natural continuation of previous works without compromising novelty, in our opinion. Inve... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their constructive feedback, which will help us significantly improve our work. In our individual responses, we address the raised questions and concerns and we attach a PDF file with supporting qualitative results.
Pdf: /pdf/69af0195fb0af6f782361c7129f3858... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Using Noise to Infer Aspects of Simplicity Without Learning | Accept (poster) | Summary: This paper explores the relationship between data noise and model simplicity across different hypothesis spaces, focusing on decision trees and linear models. The authors demonstrate that noise functions as an implicit regularizer for various noise models and show that Rashomon sets built from noisy data typic... | Rebuttal 1:
Rebuttal: Thank you for the review. We address the questions point-by-point below.
**W1. Connection to the previous works.**
Theorems 1 and 2 do show that noise acts as an implicit regularizer (similar to Bishop, Training with noise is equivalent to Tikhonov regularization, 1995; Dhifallah and Lu, On the i... | Summary: This work sets out to the study the effect learning with noise entails from the perspective of model complexity. This paper invokes formalism from Rashomon sets and derives theoretical results that show an equivalence between noise (in the form of labels flipped) and regularization and in particular show that ... | Rebuttal 1:
Rebuttal: Thank you for the review. We address the questions point-by-point below.
**W1. Limitations of analysis**.
We've been studying decision trees for over a decade and we are not convinced that the trade-off is, thus far, well understood. From our experience, often for tabular datasets, there exists a... | Summary: The paper explores the role of dataset noise in the possibility of training simpler models. They show that more noisy settings are more likely to contain simpler models due to an increase in the regularization factor for learning algorithms that employ regularization. In the same setting, they also show that t... | Rebuttal 1:
Rebuttal: Thank you for the review. We appreciate your points and feedback.
**W1. Theorem 3 conclusion**.
Thank you for pointing this out.
We expect $F_{out}$ to mostly contain more complex models, since more complex models are a "closer" fit (overfit) to the data and therefore perform worse when noise i... | Summary: This work uses Rashomen sets ($R_{set_D} (\mathcal{F}, \theta) = \{ f \in \mathcal{F} : Obj_D(f) \leq Obj_D(f^*_D) + \theta\}$) to understand how the complexity of the optimal classifier simplifies with random label noise and additive gaussian noise. They show that label noise has explicit and implicit regulat... | Rebuttal 1:
Rebuttal: Thank you for the review. We address the weaknesses point-by-point below.
**W1: Related literature.** Thank you, we will definitely add more relevant literature on implicit regularization as we will gain an extra page of space if the paper gets accepted. We will cite suggested papers on noise's e... | Rebuttal 1:
Rebuttal: We thank all the reviewers for the reviews. Below, we provide proof that models that exit the clean Rashomon set are complex and an initial experiment on neural networks under random label noise. In the response file, we also include empirical analysis for the mixed label noise model.
**Models in... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CreDes: Causal Reasoning Enhancement and Dual-End Searching for Solving Long-Range Reasoning Problems using LLMs | Reject | Summary: This paper develops a structured and generalized reasoning framework, CreDes, for long-range reasoning in LLMs. In the framework, the Causal Relationship Enhancement (CRE) is used to guarantee the solid causal rightness between each step of reasoning and state transition, and the Dual-End Searching (DES) appro... | Rebuttal 1:
Rebuttal: Dear Reviewer BKzM,
We want to express our gratitude for the thorough review and the constructive feedback on our paper.
# Weaknesses:
1.Causality does not guarantee correctness; ATE constraints ensure causal consistency between OSR and the next state, while cross-entropy control path choose c... | Summary: This paper introduces CreDes, a framework to improve the long-range reasoning capabilities of LLMs, consisting of two main components: Causal Relationship Enhancement (CRE) and Dual-End Searching (DES). CRE is developed to reduce causal hallucinations in LLMs by strengthening the causal relationships between r... | Rebuttal 1:
Rebuttal: Dear Reviewer yRix,
We want to express our gratitude for the thorough review and the constructive feedback on our paper.
# Weaknesses:
## Major concerns:
1.(1)In terms of scalability, due to the faster reasoning, it is expected for tasks with high FPS rate requirements, such as unstructured aut... | Summary: The integration of Causal Relationship Enhancement (CRE) and Dual-End Searching (DES) mechanisms presents a novel solution to addressing causal hallucinations and large search spaces in long-range reasoning tasks. The CRE mechanism’s use of Structural Causal Modeling (SCM) and Average Treatment Effect (ATE) is... | Rebuttal 1:
Rebuttal: Dear Reviewer MmUh,
We want to express our gratitude for the thorough review and the constructive feedback on our paper.
# Weaknesses:
ATE (Average Treatment Effect) is a metric used in causal inference to measure the average impact of a treatment or intervention on an outcome across a populati... | Summary: This paper aims to improve LLMs in dealing with long-reason reasoning problems, especially the challenges of causal hallucination (inconsistency between one-step reasoning and corresponding state transition) and large search space. To tackle the first challenge, average causal effect of the one-step reasoning ... | Rebuttal 1:
Rebuttal: Dear Reviewer Y4qM,
We want to express our gratitude for the thorough review and the constructive feedback on our paper.
# Weaknesses:
1.(a):
The concept is that, for multiple independent repetitions of the experiment, causal hallucinations in the model output are infrequent. Using the halluc... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper introduces a new framework, CreDes, designed to enhance causal reasoning in large language models (LLMs) and solve complex, long-range reasoning problems. The framework integrates two main innovations: the Causal Relationship Enhancement (CRE) mechanism, which applies cause-effect interventions to m... | Rebuttal 1:
Rebuttal: Dear Reviewer nWAC,
We want to express our gratitude for the thorough review and the constructive feedback on our paper.
# Weaknesses:
## 1.Limited Model Sizes:
We acknowledge that our experiments were primarily conducted on 7B parameter models. The choice was made due to computational constrain... | null | null | null | null | null | null |
Elucidating the Design Space of Dataset Condensation | Accept (poster) | Summary: The paper explores scalable dataset condensation techniques, introducing Elucidate Dataset Condensation (EDC) which integrates multiple design strategies such as soft category-aware matching and learning rate adjustments. These methods achieve state-of-the-art accuracy across different datasets, demonstrating ... | Rebuttal 1:
Rebuttal: Thank you for your recognition and acknowledgement on the theoretical contributions of our work, and for sharing valuable suggestions. We hope we have addressed your concerns.
**Q1:** _The comparison experiments presented are not sufficiently comprehensive._
**A1:** Thank you for your valuable s... | Summary: This paper proposes a design framework to address the limitations of existing dataset condensation methods. Specifically, the authors have introduced some strategies, such as soft category-aware matching and learning rate scheduling. The authors have provided theoretical and empirical analysis of these strateg... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and detailed suggestions on our paper's layout. We will accommodate all your comments in our revision.
**Q1**: _The differences between “bi-level” and “uni-level”._
**A1**: The main difference between “bi-level” and ”uni-level“ is that “bi-level” requires updat... | Summary: This paper studies the combination of some techniques of data distillation (DD) in terms of data synthesis, soft label generation, and post-evaluation. The limitations of the existing methods, which are solved by these techniques, are provided. The extensive experiments verified the promising improvement of th... | Rebuttal 1:
Rebuttal: We thank the reviewer for your constructive comments and valuable suggestions, such as raising many unclear definition issues for us. Below, we make detailed clarifications to each question from this reviewer.
**Q1**: _The definition of generalized data synthesis is somewhat unclear._
**A1**: Th... | Summary: The authors address the limitations of previous methods, such as high computational costs and less optimal design spaces, by proposing a novel framework called Elucidate Dataset Condensation (EDC). EDC incorporates strategies like soft category-aware matching and a smoothing learning rate schedule, achieving s... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's recognition of our comprehensive analysis and SOTA performance, as well as the valuable suggestions for improvement. We hope our responses can address your concerns effectively.
**Q1**: _Scalability Concerns_.
| SRe$^2$L | CDA | RDED | Ours | Original Dataset |
|---... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful comments and suggestions. We are pleased that our work received positive evaluations, with comments such as "Comprehensive Analysis (gMw3)", "A theoretical analysis is conducted (r3SQ)", "Solid theoretical analysis (ZPCz), "conducts a thorough inves... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
VCR-GauS: View Consistent Depth-Normal Regularizer for Gaussian Surface Reconstruction | Accept (poster) | Summary: The paper presents a method for improving geometry reconstruction for 3D Gaussian Splatting. It adopts a relatively flattened 3D Gaussian and incorporates normal regularization from monocular priors. Specifically, the paper proposes supervising the geometry by minimizing the differences between the normals der... | Rebuttal 1:
Rebuttal: ## Response to Reviewer 5p3w (R\#4)
**Q1**: Normal priors cause oversmoothing, as shown in the DTU results. Additionally, it remains unclear how the normal priors work for more general cases.
**A**: We agree that the monocular priors cause oversmoothing on the DTU results in Fig. 10 of our pape... | Summary: This paper presents a confidence-aided Depth-Normal regularizer that directly couples the normal with other geometric parameters, thus enabling the optimization of all geometric parameters from monocular normal priors. The paper also introduces a densification and splitting strategy to regulate the size and di... | Rebuttal 1:
Rebuttal: ## Response to Reviewer n7KL (R\#3)
**Q1**: The conversion between depth and normal is from VNL.
**A**: We have cited VNL in our paper as \[55\], where we mentioned in L39 and L177 that our depth-normal formulation is inspired by them. Nonetheless, we are different from VNL as follows: the dept... | Summary: This paper proposes to reconstruct surface from 3D Gaussians with a view-consistent depth-normal regularization. By introducing normal prior (DSINE/GeoWizard) to regularize the distribution of 3DGS, this approach is able to render smooth and view-consistent depth, facilitating reconstruction. This paper also t... | Rebuttal 1:
Rebuttal: ## Response to Reviewer 6weG (R\#2)
**Q1**: Actually, using prior from monocular normal estimation has been introduced in several works, e.g. DN-Splatter; Would it help if the d-normal (calculated from simple GS depth / ray-GS intersection depth) is applied?
**A**: As mentioned in L31 and L113-... | Summary: The paper introduces a novel view-consistent Depth-Normal (D-Normal) regularizer and an uncertainty-aware normal regularizer followed by a new densification and splitting strategy to address the limitations of existing Gaussian Splatting methods in surface reconstruction tasks, such as the supervision of rende... | Rebuttal 1:
Rebuttal: ## Response to Reviewer kSsq (R\#1)
**Q1**: A more in-depth analysis of scenarios where the method may fail, particularly with highly inconsistent normal predictions, would provide a clearer understanding of the method's boundaries.
**A:** Our method is likely to fail for ***semi-transparent ob... | Rebuttal 1:
Rebuttal: ## To all reviewers:
We thank the reviewers for their constructive feedback. As summarized by our reviewers, this paper introduces “novel insights” (R\#1) with “clear contributions” (R\#3), and the proposed method is “simple and effective” (R\#1, 3). Through “comprehensive evaluation” (R\#4), our... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Streaming Long Video Understanding with Large Language Models | Accept (poster) | Summary: This paper proposes VideoStreaming to understand arbitrary-length videos by streamingly encoding video tokens and adaptively selecting a constant number of them. Through careful designs, including a small LLM streaming encoder, prefix task, modified attention, memory propagation, and Gumbel-Topk token selectio... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for recognizing our novelty, method and paper writing. Below we provide point-to-point responses to all the raised questions:
> **Q1: The necessity of VideoStreaming on short-term videos.**
**A1:** For short videos, sampling multiple frames and treating it as a mu... | Summary: This paper introduces VideoStreaming, a Vision-Language Large Model (VLLM) designed for comprehensive video understanding. It effectively manages the challenges of computational overhead associated with processing long videos by innovatively using a constant number of video tokens. VideoStreaming demonstrates ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for acknowledging our model's ability to handle long videos with high efficiency. Below we would like to provide point-to-point responses to all the raised questions:
> **Q1: Related work on processing key clips.**
**A1:** Thanks for pointing out this related work... | Summary: This paper presents an advanced vision-language model designed to handle the complexities of understanding arbitrary-length videos. The model addresses the computational challenges posed by long video sequences by implementing two core techniques: Memory-Propagated Streaming Encoding and Adaptive Memory Select... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for acknowledging the significance of the research problem, the interesting and sound technical design, as well as sufficient experiments. Below we provide point-to-point responses to all the raised questions:
> **Q1: The use of summarization tokens.**
**A1:** Our... | Summary: In this paper, a vision-language model, named VideoStreaming, is proposed.
It adopts the memory based architecture to understand long video. Specifically, it segments a long video into multiple short clips and encodes each clip sequentially. During this encoding process, it exploits the memory feature to prop... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for recognizing our motivation, technical algorithm, and writing. Below we would like to provide point-to-point responses to all the raised questions:
> **Q1: The comparison on IntentQA dataset.**
**A1:** We compare our model with recent advanced methods in the Ta... | Rebuttal 1:
Rebuttal: Dear reviewers,
We sincerely appreciate the constructive feedbacks provided by all the reviewers. The reviewers acknowledged some aspects of our work, including the motivation and novelty (Reviewer FUM4, sqPz), the significance of the research problem (Reviewer QjSL), the technical design (Review... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fairness in Social Influence Maximization via Optimal Transport | Accept (poster) | Summary: This paper proposed a new metric for fairness in social influence maximization, namely mutual fairness. It is based on optimal transport theory. A parameter \beta is designed to achieve a balance between fairness and efficiency. When \beta = 0, it ignores mutual fairness. When \beta = 1, it enforces mutual fai... | Rebuttal 1:
Rebuttal: # W1
Thanks for pointing this out. We made the proposed correction in the manuscript.
# W2
Strategy A and Strategy B have the same mutual fairness score since they are equally (un)fair. Namely, they have the same distance from the diagonal. On the other hand, transporting Strategy A onto Strate... | Summary: The paper addresses the challenge of ensuring fairness in social influence maximization, where the goal is to select seed nodes in a social network to spread information equitably among different communities. The authors identify the limitations of existing fairness metrics, which often fail to account for the... | Rebuttal 1:
Rebuttal: # W1(/Q1): Potential Scalability Issues
We thank the reviewer for the positive feedback and insightful comments. We first address both scalability aspects as follows:
**Definition of fairness metric with $m$-groups**:
We agree with the reviewer that the multi-group case requires more detail. In... | Summary: This paper studies the problem of Fair Social Influence Maximization (SIM). Specifically, it introduces a new notion of fairness for SIM. The current literature on Fair SIM studies defines fairness in terms of expected values, e.g., a solution is fair if the expected ratio of influenced nodes from each demogra... | Rebuttal 1:
Rebuttal: # W1: Lack of theoretical guarantees for the S3D algorithm
We thank the reviewer for the positive feedback. To first address the point regarding the lack of theoretical guarantees: The S3D algorithm is similar to non-convex optimization methods such as Simulated Annealing. Such algorithms do not ... | Summary: This paper studies the problem of fairness in IM (Influence Maximization). They present a novel notion of fairness, namely, mutual fairness, which considers outreach distribution in different groups. Compared with previous notions, the proposed one could ensure a higher probability of fairness among groups by ... | Rebuttal 1:
Rebuttal: # W1
We agree that the multi-group case requires more detail. We will include a dedicated appendix. With $m$ groups, the outreach distribution is a distribution $\gamma$ on $[0,1]^m$. The reference distribution is again the ``ideal'' distribution $\gamma^\ast=\delta_{(1,\ldots,1)}$ which encodes t... | Rebuttal 1:
Rebuttal: Dear Chairs,
Dear Reviewers,
We thank you for the thoughtful feedback on our manuscript.
In the original submission, all four Reviewers found our results of interest to the wide readership of NeurIPS.
In particular, all reviewers agree on the fact that the proposed fairness metric is innova... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation | Accept (poster) | Summary: The paper proposes the Equivariant Transformer Flow (ET-Flow) to generate high-quality molecule conformations. The authors use rotational alignment, stochastic sampling, and chirality correction to improve the flow matching framework for this task. Additionally, the paper modifies the TorchMD-NET equivariant t... | Rebuttal 1:
Rebuttal: > **Lack of experiments. The experiments on the GEOM dataset are not enough to show the empirical performance of the framework. Some further experiments on large-scale datasets with more data and larger system size are necessary. I suggest the authors do experiments on OC20/OC22(Open Catalyst 2020... | Summary: The paper proposes the Equivariant Transformer Flow (ET-Flow) which predicts low-energy molecular conformations given the molecular graphs. Unlike existing methods that rely on large transformer-based models for conformed fields or complex internal geometry calculations, ET-Flow leverages flow matching with eq... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We first want to thank the reviewer for taking the time to review our work and ask thought-provoking questions. We hope that we are able to address said questions and concerns below.
> **It can be challenging to attribute the performance improvements of ET-Flow to the flow matchin... | Summary: The paper describes an equivariant flow matching model for conformer generation. The stated contributions are accurate - the model performance is state-of-the-art and largely due to good engineering.
Strengths: The paper is well written and the evaluations follow other published work. Informative ablation s... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We first want to thank the reviewer for taking the time to review our work and ask thought-provoking questions. We hope that we are able to address said questions and concerns below.
> **Very little evaluation of out-of-distribution performance is considered (a small evaluation is... | null | null | Rebuttal 1:
Rebuttal: We perform these additional experiments to support our rebuttals.
### **Out-of-Distribution Evaluation on GEOM-QM9**
To improve upon out-of-distribution evaluation, we test our model trained on GEOM-DRUGS on GEOM-QM9 (significantly smaller molecules).
| Method | Recall Coverage (mean) | Recall C... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Is Multiple Object Tracking a Matter of Specialization? | Accept (poster) | Summary: The paper introduces PASTA, a framework designed to address the challenges of training end-to-end transformer-based trackers in heterogeneous scenarios, specifically negative interference and poor domain generalization. PASTA leverages Parameter-Efficient Fine-Tuning (PEFT) and Modular Deep Learning (MDL) to d... | Rebuttal 1:
Rebuttal: **W1 - Comparison with other methods in zero-shot**
We herein test ByteTrack and other tracking-by-detection methods in a zero-shot setting from MOT17 to PersonPath22. The results on PersonPath22 are as follows:
|Tracker|Setting|IDF1|MOTA|FP|FN|IDsw|
|-|-|-|-|-|-|-|
|ByteTrack|fine tuned on Pers... | Summary: This paper proposes a new framework called PASTA (Parameter-Efficient Scenario-specific Tracking Architecture), which aims to improve the generalization ability of multi-object tracking (MOT) in diverse scenarios. The main contributions of this paper include: 1) proposing the PASTA framework to achieve efficie... | Rebuttal 1:
Rebuttal: **W1/Q1 - On the selection of modules**
In our work, we use a Domain Expert to select attributes, a practice that is not far from reality. For example, in fixed-camera scenarios, the mounting perspective is known and whether it will be indoors or outdoors. Lighting can be easily measured with a s... | Summary: This paper aims to address the domain gap across different multi-object tracking datasets. The proposed method is inspired by LoRA and introduces parameter-efficient fine-tuning for state-of-art end-to-end trackers. Specifically, these trackers are first trained on a large-scale dataset MOTSynth. After that, b... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating the novelty and efficacy of our proposed approach. We will answer in the following the doubts of the reviewer:
**W1 - Evaluation on MOT20**
We decided not to evaluate our method on MOT20 since its variety is limited in terms of attributes, as it contains on... | Summary: The paper introduces a fine-tuning framework, denoted PASTA, for multiple-object tracking that is aimed at reducing the cost of tine-tuning large models while mitigating negative inference to improve zero-shot transfer and domain generalization.
During training, the authors independently fine-tune per-domain ... | Rebuttal 1:
Rebuttal: As pointed out by the reviewer, a more detailed discussion about the computational costs and the relationship with other domain adaptation methods would enhance the quality of our manuscript. Below, we answer these questions and will integrate this information into the final revision.
**W1/Q1 On ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their thorough evaluations and constructive feedback. These comments and suggestions have contributed to enhancing the overall quality of our work. Below, we summarize the strengths and weaknesses reported.
**Strengths:** We greatly appreciate the recognition of ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper presents a multiple object tracking framework that can generalize to new domains by training specialized modules for each scenario attributes.
These modules are trained using Parameter-Efficient Fine-tuning and modular deep learning techniques on a transformer-based tracker.
This tracker is build on... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable questions and for having appreciated the originality and efficacy of the proposed approach.
**W2 - Dataset statistics**
We report the requested details in the following tables, which provide statistics on the employed datasets divided by per-sequence and pe... | null | null | null | null | null | null |
Few-Shot Task Learning through Inverse Generative Modeling | Accept (poster) | Summary: The paper presents an approach to a new approach to few-shot learning. Specifically, the proposed method first pre-trains a conditional classifier-free guidance diffusion model on task concepts and their corresponding demonstrations. Few-shot learning is possible by inverting the diffusion model by optimizing ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review and positive feedback.
>For each new task, we need to find the concept vector that corresponds to the new task. This might introduce further complications at inference time compared to few-shot learning in LLM works, the language-conditioned and the... | Summary: The paper addresses the challenge of learning the intents of an agent, such as its goals or motion style, from a few examples. The proposed approach, Few-Shot Task Learning Through Inverse Generative Modeling (FTL-IGM), leverages invertible neural generative models to learn new task concepts. The method involv... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review and suggestions for clarifying the paper.
>The concept of "task concept learning" is not rigorously defined.
In the Formulation section we define:
- task concepts as latent representations in $\mathbb{R}^n$.
- task concept learning as $argmax_{\tilde{c}}{\... | Summary: The paper focuses on learning concepts that describe behaviors seen in state-based trajectories, such as mocap data or simplified autonomous driving simulation. The proposed approach uses a generative model to predict state trajectories based on concepts annotated with natural language. Next, new trajectories ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review and positive feedback.
>is there something specific about the diffusion model that makes it “invertible”?
No, we state in Limitations that *“our framework is general for any parameterized generative model”*. We choose to implement our framework wit... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their feedback and for acknowledging that learning agent intent from limited data is an important problem, and that our method is evaluated in a diverse set of environments, surpassing baseline performance and demonstrates generalizability, including compositionality. Mo... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Logical characterizations of recurrent graph neural networks with reals and floats | Accept (poster) | Summary: This paper examines the relationship between GNN models based on real numbers, which are predominantly studied in theory, and GNN models based on floating point numbers, which are commonly used in practice. Additionally, the paper advances the state of the art in the study of recurrent GNNs, where the computat... | Rebuttal 1:
Rebuttal: We first address the weaknesses.
We will improve the accessibility of the paper by lightening the preliminaries by moving non-essential parts to the appendix and adding examples (including a GMSC-program for reachability, to complement the corresponding GNN example) and illustrating remarks.
Abo... | Summary: The paper presents logical characterizations of recurrent GNNs in terms of logical formalisms, following the line of previous work by Barceló et al. It is shown that recurrent GNNs have the same expressive power as the infinitary extension of graded modal logic, when arbitrary precision is allowed for feature ... | Rebuttal 1:
Rebuttal: Concerning question 1, GMSC translates into partial fixed-point logic with choice (see, e.g., David Richerby, Logical Characterizations of PSpace (CSL 2004)): it is easy to see that GMSC has PSpace data complexity upper bound (one simply keeps in memory a subset of the graph domain for each schema... | Summary: This paper introduces a new logical characterization for recursive Graph Neural Networks (GNNs) following the aggregation-combine or message-passing paradigm. Among other topics, it primarily focuses on understanding the differences between GNNs in the typically considered theoretical setting—where an arithmet... | Rebuttal 1:
Rebuttal: Concerning the weakness mentioned, we will make the preliminaries somewhat lighter---possibly moving some non-essential parts to the appendix---and instead add more examples. For example, we will include a GMSC program for reachability in order to complement the corresponding GNN example. We will ... | Summary: The authors analyze the expressive power of recurrent graph neural networks (GNNs) through the lens of logic, focusing on uniform expressibility, which refers to functions expressible over all input graphs. Their findings are as follows:
- Expressive Equivalence: GNNs with floating-point numbers and the graded... | Rebuttal 1:
Rebuttal: Concerning **W1**: Grohe's setup is different from ours in both [11] and [12]; most notably, Grohe uses non-recurrent GNNs rather than recurrent ones. Also, the articles use reals and dyadic rationals. We will add further discussion on Grohe's work as well as on [6] and [23].
Concerning the first... | Rebuttal 1:
Rebuttal: We thank the reviewers for the reviews, all of which help clarify the paper and summarize it.
We point out the following fact concerning our responses to three of the reviews: It follows from the results in [21] (by essentially the same argument as the one justifying Proposition 7 of that article... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
BAN: Detecting Backdoors Activated by Adversarial Neuron Noise | Accept (poster) | Summary: The paper proposes a novel detection and defense method for backdoored models. The new method is motivated by the finding that existing state-of-the-art trigger-inversion methods, like BTI-DBF, rely on strong backdoor features, which might not always be present, such as for BadNet-type triggers. As a solution... | Rebuttal 1:
Rebuttal: Thank you for your valuable suggestions. We address your comments as follows.
**Q1.** The evaluation only focuses on CNN architectures.
**A1:** We provide ViT results on a 12-layer Swin transformer in the following table.
For BadNets and Blend, we train the backdoored network using Adam as the ... | Summary: This paper provides an in-depth analysis of the SOTA trigger inversion-based backdoor defenses and finds that they suffer from high computational overhead and rely on prominent backdoor features. The authors tackle the challenges based on the previous findings on adversarial noise incorporating activation info... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We address your comments as follows.
**Q1.** The motivations of high computational overhead and reliance on prominent backdoor features are not well-illustrated in the method section. Only the limitations of BTI-DBF are discussed.
**A1:** High computational ... | Summary: This paper addresses the problem of efficient backdoor defense using the backdoor inversion approach. The authors leverage the past work “TOWARDS RELIABLE AND EFFICIENT BACKDOOR TRIGGER INVERSION VIA DECOUPLING BENIGN FEATURES” (BTI-DBF) which recovers a mask in the feature space to locate prominent backdoor f... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of our work. We address your comments as
follows.
**Q1.** missing explanations of the performance. For example, why is featureRE in Table 3 performing better than BAN? What would be the impact of a lambda value other than 0.5? Why is the Blend attack always better ... | Summary: This paper focuses on the backdoor defense task. The proposed detection method includes neuron noise, which leverages the differing robustness between regular and backdoored neurons, and a feature decoupling process using a mask. Additionally, a backdoor defense method is proposed, which achieves improved effi... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We address your comments as follows.
**Q1.** I might have a misunderstanding, but ... be decoupled.
**A1:** Our defense aims to activate the backdoor by neuron noise such that the backdoor networks behave differently from benign networks. The feature mask ... | Rebuttal 1:
Rebuttal: Dear reviewers and ACs,
We thank you for evaluating and providing thorough feedback on our work. We are glad to see that most reviewers rated the paper positively, agreeing on the topic relevance and results provided in our work, such as "The authors address a significant topic", "The experiment... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors propose a novel technique for detecting backdoor attacks on neural networks by incorporating extra neuron activation information to reduce the overhead from prior backdoor feature inversion methods. The experimental results show a higher detection rate on the tested datasets when compared to the pr... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of our work. We address your comments as follows.
**Q1.** A figure of the proposed method outlined in Sections 3.1 and 3.3 could be a helpful tool to visualize the proposed method.
**A1:** We will add a figure that illustrates the design of our method.
**Q2.** T... | null | null | null | null | null | null |
Collaborative Video Diffusion: Consistent Multi-video Generation with Camera Control | Accept (poster) | Summary: This paper tackles the problem of consistent multi-video generation, i.e., generating multiple videos capturing the same scene from various camera trajectories. For this, it proposes a cross-view synchronization module (CVD) based on the epipolar geometry. Training-wise, it proposes a hybrid training strategy,... | Rebuttal 1:
Rebuttal: # Response of Reviewer v3P6’s Review
## Q1: Clarification of training Strategy.
The two training phases are applied to the same CVSM parameters in a single training pass. (The CVSM in Fig.3a and Fig.3b are the same modules.) Specifically, we blend the data from Webvid10M or RealEstate10K togethe... | Summary: This paper studies video generation with camera trajectories. The proposed method improves the consistency across multiple views via a cross-video synchronization module, which is equipped with the existing epipolar attention. Training of the proposed method consists of two phases: training to learn geometric ... | Rebuttal 1:
Rebuttal: # Response of Reviewer EujL’s Review
## Q1: Lack of ablation study.
We would like to emphasize that our cross-video augmentations are applied to the pairs of videos, which serve as the training input throughout our experiments; It is infeasible to apply such augmentations to monocular video gene... | Summary: The paper introduces a framework to generate consistent multi-view videos of the same scene. Existing models lack precise camera control and consistency across views. The proposed CVD framework uses a cross-video synchronization module with an epipolar attention mechanism to maintain frame consistency from dif... | Rebuttal 1:
Rebuttal: # Response of Reviewer MCcD’s Review
## Q1: Clarification of Eq.(4) (how the attention masks are generated).
Thanks for pointing this out. The attention masks are calculated as follows: For each pair of pixels coordinated at x_1 and x_2 in two frames, respectively, the attention mask from x_1 to... | Summary: This paper proposes a diffusion-based video generation method that generates multiple videos of the same scene simultaneously from camera trajectories and a text prompt. A cross-video synchronization module is proposed, where epipolar attention is introduced to improve the consistency across multiple videos. E... | Rebuttal 1:
Rebuttal: # Response of Reviewer WFsL’s Review
## Q1: How are the LoRA modules used in the inference step?
As our model inherits the ability from AnimateDiff to generalize on different LoRAs, during inference we select the LoRA that best satisfies the needs in different settings. For the quantitative comp... | Rebuttal 1:
Rebuttal: We appreciate the thorough review and constructive feedback provided on our work. We are happy to see that the reviewers recognize our work as a novel attempt at a new and interesting task (Reviewer **WFsL, MCcD, v3P6**), our model is interesting (Reviewer **WFsL**) and achieves strong performance... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes a novel framework that generates multi-view videos from text input. The model builds upon the CameraCtrl pipeline and includes a Cross-View Synchronization Module to enforce consistency guided by a fundamental matrix.
Strengths: - The performance of the proposed method is impressive, genera... | Rebuttal 1:
Rebuttal: # Response of Reviewer 3sRF’s Review
## Q1: The black regions might affect the performance of video generation.
During our training, we removed the L2 loss in the unseen pixels (black regions) of the cloned video for data integrity. We show examples of homography warped videos in our attached PD... | null | null | null | null | null | null |
Foundations of Multivariate Distributional Reinforcement Learning | Accept (poster) | Summary: This paper studies the theoretical foundations of multivariate distributional RL, particularly providing the convergence proof under the MMD distance. The paper first investigates the aspect of particle-based multivariate dynamic programming in Section 4 and then shifts the attention to categorical representat... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed assessment of our work.
We appreciate your comments about the scalability of our approach and the motivations for studying foundations of multivariate DRL; we have discussed these in detail in the general response.
**Q1**: The reviewer claims that our work ... | Summary: The authors propose a tractable and convergence-guaranteed method, called randomized dynamic programming, for multivariate distributional reinforcement learning (distRL). They also introduce practical algorithms, multivariate EWP-TD and signed-categorical-TD, provide an upper bound on MMD with respect to dimen... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough assessment and for their interest in our work. We hope the responses below address the queries raised in the review, please let us know if you have any further questions.
**Q1**: With regard to $\leq$ vs $\in$ in Theorem 3 -- this was a stylistic choice. W... | Summary: In this submission, the authors combine a multivariate reward with distributional learning. They rely on a Maximum Mean Discrepancy based projection operator to obtain the first efficient and provably convergent algorithm in this setting. The key in their work is the extension to the multivariate setting of th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful assessment of our work.
> Could the authors comment on why they did not observe in practice the convergence suggested by their theorem in the EWP-based technique?
We did in fact observe convergence of the EWP-based algorithms in practice -- can you please ... | Summary: This paper studied the multivariate distributional reinforcement learning (RL) problem, in which the goal is to learn probability distribution of accumulated multi-dimensional rewards of the RL system. First, dynamic programming for multivariate distributional dynamic programming is established, then a randomi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback on the paper. We address the queries on notation below, and will include clarifications in the revised draft.
* The RHS of equation (2) is simply the set of all empirical distributions on $m$ points. That is, the distributions obtained by picking $m$ point... | Rebuttal 1:
Rebuttal: # General Response
We thank all authors for their assessments.
Reviewers praised our convergence theory (SWh2, FNo5, wmwv), rigorous proofs and discussions (FNo5, wmwv, 9dnf), and illustrative experiments (FNo5, wmwv); while the simplicity of the numerical experiments and the motivation for form... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Strategic Linear Contextual Bandits | Accept (poster) | Summary: The authors studied the problem of strategic agents who can modify their context in order to game the system under the linear contextual bandit framework. In this setting, each arm is a self-interested agent who wants to maximizes number of times it gets pulled by the learner. Prior work that did not explicitl... | Rebuttal 1:
Rebuttal: Dear Reviewer 8u5z, thank you for taking the time to review our paper and your helpful comments. We respond to your questions and comments below.
> - The paper did not provide matching lower bound analysis on the strategic regret in the two settings: when $\theta^*$ is known in advance and when... | Summary: This paper studies a variant of the linear contextual bandit problem, where each arm is an agent and can strategically misreport its feature vector to the learner. The authors propose the Optimistic Grim Trigger Mechanism (OptGTM) that incentivizes the agents to report their feature vectors truthfully while si... | Rebuttal 1:
Rebuttal: Dear Reviewer QjK5, thank you for your time and your review. We respond to your comments below.
> 1. There is no experimental evaluation in the paper. While I understand that many theoretical papers do not include experiments, it would be beneficial to see some empirical results to validate the... | Summary: This paper studies the strategic linear contextual bandit problem, where the agents can strategically change (report) their covariate to the principal. The authors propose an Optimistic Grim Trigger Mechanism (OptGTM) to encourage agents be truthful and achieve sublinear regret.
Strengths: The authors design ... | Rebuttal 1:
Rebuttal: Dear Reviewer 7kzE, thank you for reviewing our paper. Your time is highly appreciated. We respond to your questions below.
> 1. The authors assumed that the inequality in Assumptions 1 and 2 holds. Could the authors change it to inequality after taking expectations over X? What if these assump... | Summary: The paper introduces a new strategic variant to the stochastic contextual bandit, where the contexts of the arms are not public but are made available to the arms only. The arms may choose to strategize by misreporting their context to sway the decisions of the learning algorithms. The model is analyzed in two... | Rebuttal 1:
Rebuttal: Dear Reviewer XxQv, thank you for taking the time to read and review our paper. We respond to your questions below.
> 1. Regarding model description, my biggest concern is the assumption that the arms respond to the learning algorithm $M$ in Nash Equilibrium. Specifically, the definition of NE f... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and for your helpful comments.
Reviewer 7kzE, Reviewer QjK5 and Reviewer 8u5z suggested to include experiments in the paper. Following your suggestions, we conducted simulations of strategic context manipulation, which we added to the paper (see ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation | Reject | Summary: The paper presents a simple but novel approach to enhance image diversity in dataset distillation. previous methods face challenges in balancing computational efficiency and diversity in synthetic images. The proposed EarlyLate training scheme addresses these issues by partitioning predefined IPC samples into ... | Rebuttal 1:
Rebuttal: Thank you for the valuable and constructive comments. We will incorporate all suggestions in our revision. Below, we provide further clarifications to the reviewer's questions.
>Q1: Comparison to previous methods.
Thanks for your comments. We are confident that no prior work has specifically foc... | Summary: This paper proposes an EarlyLate curriculum learner, which distills the easiest samples first and gradually add harder samples. Based on batch-to-global distillation algorithms, the proposed method consistently enhances the distillation performance.
Strengths: - The writing is clear.
- The proposed curriculum... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's detailed comments and constructive suggestions, and would like to address some key points from our submission that may have been overlooked, which might have led to confusion and concerns to the reviewer.
>W1: Initialization with real samples is common. Suggest adding... | Summary: Recent advancements in dataset distillation have led to two main approaches: batch-to-batch and batch-to-global matching. While the former excels in small datasets, the latter, though popular for large datasets, faces a diversity challenge due to independent optimization. Authers propose an EarlyLate training ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's valuable and constructive commetns. We will accommodate all of the suggestions in our revision. In the following, we make further clarifications to reviewer's concerns.
>Q1: The motivation for the research is unclear, lacking an explicit articulation of the unifying c... | Summary: This work studies batch-to-global dataset distillation, optimizing the synthetic dataset by matching the statistical information of the synthetic batches to that of the full real dataset. Previous batch-to-global methods lacked diversity because each batch had the same optimization objective, leading to redund... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments. We will accommodate all the suggestions in our revision. In the following, we make further clarifications to reviewer's concerns.
>Q1: Introduction describes previous methods in too much detail.
We appreciate the constructive suggestion. We w... | Rebuttal 1:
Rebuttal: We appreciate all reviewers for their positive comments, e.g., this paper attempts to propose a new solution to the issue of previous batch-to-global methods indeed faced the problem of synthetic datasets receiving the same supervision signal, leading to redundant information being learned, this i... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
TabPedia: Towards Comprehensive Visual Table Understanding with Concept Synergy | Accept (poster) | Summary: This paper introduces a novel large vision-language model, named TabPedia, aiming to perform comprehensive visual table understanding (VTU) in a unified framework. In order to tackle the dilemma of modal isolation and task exclusivity, it presents a concept synergy mechanism, in which diverse VTU tasks and mul... | Rebuttal 1:
Rebuttal: We appreciate the detailed comments and acknowledgment of our contributions. We provide the responses as follows.
* **Q1:** In object detection tasks, the outputs for different objects are unordered. However, large language models generally produce serialized outputs. Could you please explain in... | Summary: This paper proposes TabPedia, a novel large-scale vision-language model designed for comprehensive visual table understanding (VTU) within a unified framework.
It addresses the challenge of modal isolation and task exclusivity by proposing a concept synergy mechanism that treats diverse VTU tasks and multi-sou... | Rebuttal 1:
Rebuttal: We appreciate the detailed comments and acknowledgment of our contributions. We provide the responses as follows.
* **Q1:** Please add the references of all datasets in Tab.1.
**A1:** We will add the references of all datasets in Tab.1 in our revised manuscript following your suggestions.
*... | Summary: This paper introduces TabPedia, a novel large vision-language model designed to address the challenges in visual table understanding (VTU) tasks. TabPedia incorporates a concept synergy mechanism that treats various VTU tasks and multi-source visual embeddings as concepts within a unified framework, allowing f... | Rebuttal 1:
Rebuttal: We appreciate the detailed comments and acknowledgment of our contributions. We provide the responses as follows.
* **Q1:** The novelty of TabPedia seems to be minimal, as existing VLM models are almost similarly structured.
**A1:** We would like to re-emphasize the main novelty, which has... | null | null | Rebuttal 1:
Rebuttal: Thanks for the thorough reading and fruitful reviews, below we address the key concerns and suggestions case by case. For more clarity, we append the pdf containing Figures and Tables.
Pdf: /pdf/5e5ea6c79f837527d0f06c1ff524a70cdc594d9e.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Differentially Private Graph Diffusion with Applications in Personalized PageRanks | Accept (poster) | Summary: The authors study the important problem graph learning methods (graph diffusion) with differential privacy. There is limited work in this important problem space (one exception being some work on PPR with DP). The authors present a non-trivial use of the PABI framework which uses Contractive Noisy Iterations t... | Rebuttal 1:
Rebuttal: We extend our gratitude to Reviewer y6QA for appreciating our theoretical and practical contributions and supporting the acceptance of this paper. Here, we are to respond to the weaknesses proposed by Reviewer y6QA.
>The paper lacks theoretical lower bounds showing the method is tight. (W1)
The ... | Summary: The paper titled "Differentially Private Graph Diffusion with Applications in Personalized PageRanks" proposes a novel graph diffusion framework that ensures edge-level differential privacy (DP) by injecting Laplace noise into the diffusion process. This framework leverages Privacy Amplification by Iteration (... | Rebuttal 1:
Rebuttal: We greatly thank Reviewer dwyJ for appreciating the novelty of our method and the corresponding theoretical analysis, and acknowledging the practical importance of the problem we studied. Here, we are to respond to the questions and weaknesses proposed by Reviewer dwyJ.
>Complexity of Implementat... | Summary: The paper presents a new differentially private personalized PageRank (PPR) algorithm. The paper extends and theoretically analyzes the privacy amplification by iterations (PABI) technique applied to a graph diffusion setting. Rigorous theoretical results demonstrate the privacy guarantees achieved by the appro... | Rebuttal 1:
Rebuttal: We greatly thank Reviewer sGeR for appreciating our contributions to the algorithm and corresponding theoretical results. Here, we are to respond to the questions and weaknesses proposed by Reviewer sGeR.
>The paper is difficult to read; consider simplifying terms, and including pseudocode. (W1)
... | Summary: Graph diffusion iteratively propagates signals through the graph, that are subsequently used for real-world applications like personalized page rank. This paper develops edge-level Differentially Private (DP) guarantees for personalized PageRank using Laplace noise addition. Unlike traditional perturbation-bas... | Rebuttal 1:
Rebuttal: We are deeply grateful to Reviewer ApdU for the comprehensive feedback. Here, we will address these questions.
>Edge DP is weak; consider node DP or stronger privacy notions. (W1)
We sincerely appreciate your suggestion to explore node-level differential privacy as a potential extension of our w... | Rebuttal 1:
Rebuttal: We sincerely appreciate the valuable feedback and insightful comments from all our reviewers. All reviewers recognize the significance of the problem we are addressing. Our work has contributed novel theoretical insights coupled with comprehensive empirical evaluations, which were particularly app... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Offline Reinforcement Learning with OOD State Correction and OOD Action Suppression | Accept (poster) | Summary: The issue of OOD actions is a well-known concern in offline RL research. The issue of OOD states, however, is relatively unexplored. In this paper, the authors shed light on the significance of OOD state correction and propose a SCAS algorithm to guide the agent back into high-value ID states when encountering... | Rebuttal 1:
Rebuttal: We appreciate the time and effort you are dedicated to providing feedback on our paper and are grateful for the meaningful comments.
**Q1: The authors did not cite works from the DICE literature.**
Thanks for the suggestion. We will cite and add discussions on DICE literature as follows.
Altho... | Summary: This paper presents SCAS (OOD State Correction and Action Supression), a model-based regularization approach that effectively addresses the challenges of out-of-distribution (OOD) states and actions in offline reinforcement learning (RL) algorithms.
The method unfolds in two main stages: (1) training a transit... | Rebuttal 1:
Rebuttal: We appreciate the time and effort you are dedicated to providing feedback on our paper and are grateful for the meaningful comments. We have conducted extensive experiments to address your questions and concerns.
**Q1: Unclear Motivation for Value-Aware OOD State Correction.**
> Is it strategica... | Summary: This paper focuses on OOD state issue, an important but overlooked issue in offline RL. This paper proposes aligning the OOD state towards In-Distribution (ID) states with high value, named as value-aware OOD state correction. Additionally, the paper discovers that the overestimation of OOD actions can also be... | Rebuttal 1:
Rebuttal: We appreciate the time and effort you are dedicated to providing feedback on our paper and are grateful for the meaningful comments.
**Q1: There is no regularization on the policy's output actions at ID states. How the paper solves the traditional issue with OOD actions?**
We apologize for the ... | Summary: The paper proposes a regularization term in offline RL, which can simultaneously address OOD state correction and OOD action suppression without pretraining a state transition model $N(s'|s)$. It shows good performance in D4RL benchmarks.
Strengths: I found it an interesting topic to consider the OOD state is... | Rebuttal 1:
Rebuttal: We appreciate the time and effort you are dedicated to providing feedback on our paper and are grateful for the meaningful comments.
**Q1: About antmaze version.**
Special thanks for your careful review. Actually, all the antmaze results presented in the paper are obtained from the **antmaze-v2*... | Rebuttal 1:
Rebuttal: ### **Global Response**
We thank all the reviewers for taking the time to read our manuscript carefully and for providing constructive and insightful feedback. We are encouraged by the positive comments of the reviewers, such as:
- Important, interesting, and overlooked research topic (Reviewers... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Exploring Prosocial Irrationality for LLM Agents: A Social Cognition View | Reject | Summary: The paper investigates the potential for Large Language Model (LLM) agents to exhibit prosocial behavior through irrational decision-making, paralleling human cognitive biases. It introduces the CogMir framework, which leverages the hallucination properties of LLMs to simulate and assess social intelligence th... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Hope we can address your concerns:
### _Have you ever imagined a future where AI possesses cognitive abilities? CogMir, an open-ended framework using hallucinations to boost social intelligence via cognitive biases, serves as a seed for developing cognitive AI!_
### __... | Summary: The paper implements a framework for evaluating LLMs’ social cognitive biases. The social science experiments are automatically collected by LLMs and then verified by humans. The framework includes two communication mode for interaction between multiple humans and multiple LLMs. The experiments include seven L... | Rebuttal 1:
Rebuttal: We are inspired by your recognition of our work! Thank you for your detailed and thoughtful reviews. Below are our responses:
### __W1 Black-box testing is conducted to eliminate internal wrong beliefs__
To ensure that LLMs do not inherently hold incorrect beliefs, we utilized rigorous black-box... | Summary: The paper introduces **CogMir**, a novel framework designed to assess the social intelligence of LLM agents to mirror human cognitive biases. Through an evolutionary sociology perspective, the authors systematically evaluate the social intelligence of LLM agents, revealing their tendencies towards prosocial be... | Rebuttal 1:
Rebuttal: Thank you for your thorough and thoughtful review of our paper! We are encouraged by your recognition of our work. Below are our responses:
### __Q1 Case Study Samples for Detailed Behaviors__
Due to words limited here, detailed behavior case studies for every subset sample will be included in ... | Summary: This paper explores the potential of LLM agents to exhibit irrational social intelligence by mirroring human cognitive biases through their hallucination properties. The authors propose CogMir, a modular and dynamic multi-LLM agent framework that utilizes hallucination to assess and enhance social intelligence... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper. We are pleased to hear your positive assessment of our contributions and experimental results.
Regarding the weaknesses you mentioned, we fully acknowledge the limitations of the CogMir framework in focusing primarily on language-based interactions, and we have ... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for taking the time to review our paper and for providing feedback and helpful suggestions regarding our work.
We are encouraged and inspired by Reviewer 1Z3B, HBKd, and wRbg’s positive feedback and recognition of our research's contribution and beneficial social... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows | Accept (poster) | Summary: The proposed method falls in the wider category of models that integrate MCMC methods with normalizing flows. Specifically, the method expands on the neutra-MCMC model, where the key idea is to run MCMC in a simpler reference space rather than in the (more complicated) target one. The main difference is that p... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their positive comments, and for their detailed and constructive feedback. We provide a point by point response to their review below.
We hope that we have been able to address the reviewers' questions, and that they will consider increasing their score ba... | Summary: A method called Markovian flow matching (MFM) for training neural ODEs (continuous normalizing flows) by flow matching to sample from distributions given as unnormalized density functions is proposed. The method obtains samples on which to perform FM training by an MCMC procedure that alternates two kinds of k... | Rebuttal 1:
Rebuttal: Many thanks to the reviewer for their thorough engagement with our work and for their constructive feedback. We provide a detailed point-by-point response below.
We hope that we have been able to address the reviewers' questions, and that they will consider increasing their score based on our res... | Summary: The authors propose a novel method for incorporating flow matching with MCMC; entailing constructing a Markov kernel as a mixture of regular MCMC step and flow step (from data space (\pi_1) to prior (\pi_0, typically Gaussian) and back with some added noise. The flow guarantees a likelihood and can hence be us... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and constructive comments. We provide a detailed point by point response to their review below.
**Weaknesses**
- **I worry how long it takes the flow network...** The reviewer is correct that flow-informed MCMC samples are relatively expensive to... | Summary: This paper aims to use continuous normalizing flows (CNFs) to define the proposal distribution in a MCMC framework. While the use of flow models for MCMC proposals is not entirely new, this paper introduces an interesting training procedure that iteratively updates the learned CNF model while performing MCMC. ... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for their positive remarks and their constructive feedback. We provide a detailed point by point response to their review below.
**Weaknesses**
- **I feel the set of experiments is too standard...** Thanks for raising this comment. While we agree with the reviewer ... | Rebuttal 1:
Rebuttal: **Summary**
Many thanks to all of the reviewers for their positive feedback about the paper, as well as their detailed and constructive comments. We provided a detailed point-by-point response to each of the reviewers' specific comments in the individual responses below.
**Additional Results**
... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Zipper: Addressing Degeneracy in Algorithm-Agnostic Inference | Accept (spotlight) | Summary: This paper proposes a method for quantifying model-agnostic goodness of fit to allow better comparison between two models / model classes etc. In particular, this paper deals with the problem of degeneracy under the null hypothesis of equal goodness. Prior work has addressed this by splitting the test set into... | Rebuttal 1:
Rebuttal: We express our sincere gratitude for your dedicated time and thoughtful review of our paper. We would like to further elucidate the novelty and significance of our contribution as follows:
**Broad applicability and compatibility with flexible training techniques:** Our proposed Zipper device is d... | Summary: The authors propose a new test statistic they call Zipper for algorithm-agnostic inference on goodness-of-fit testing. While previous solutions suffer from a degeneracy issue (i.e., fails to converge to a non-degenerate distribution under the null hypothesis of equal goodness), the proposed test statistic does... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and constructive feedback on our paper. Your insights are invaluable for refining our manuscript.
**Weaknesses:**
1. _Self-contained discussions:_
- _On conditions:_ Condition (C1) pertains to the optimality of the prediction function $f$, eliminating first-or... | Summary: This work presents a test of Performance(f, test_data_1) - Performance(f_subset, test_data_2) where f is the best model in a class F and f_subset is the best model in a subset of F. They focus on how precisely to split your samples between test_data_1 and test_data_2 to avoid degeneracies that can arise when a... | Rebuttal 1:
Rebuttal: We extend our sincere gratitude for your thoughtful and detailed review of our paper. We greatly appreciate your positive evaluation and constructive feedback. We are committed to further refining our manuscript and eagerly welcome any additional comments or questions you may have. | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Incorporating Surrogate Gradient Norm to Improve Offline Optimization Techniques | Accept (poster) | Summary: The article presents a method to improve offline optimization techniques by integrating the concept of model sharpness into the training. A constraint is introduced that limits the model sharpness to not exceed a user-specified threshold.
Strengths: 1. The approach is model-agnostic, making it applicable acr... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for recognizing the strengths of our work with an acceptance rating.
**Q1. Hyper-parameter Tuning.**
We agree with the reviewer that hyperparameter tuning is important to achieve best performance. This is also true in the broader context of machine learning (M... | Summary: This paper proposes a novel model-agnostic approach to enhance offline optimization methods by incorporating surrogate gradient norms. The paper provides a thorough review of existing literature, a clear problem definition, and detailed descriptions of the proposed methods and their implementation. The experi... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for recognizing the strengths of our work with an acceptance rating.
**Q1. How surrogate sharpness offers advantages over loss sharpness (SAM).**
We will highlight below an important intuition on the key difference between a direct application of SAM and its n... | Summary: This paper introduce a sharpness-aware optimization to improve out-of-distribution generalization. While inspired by SAM, the major difference is that this work considers the sharpness of predictor outputs rather than loss landscape. With the proposed notion of sharpness, practical algorithm, IGNITE, is develo... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for recognizing the clarity and viability of our technical development. Your questions are addressed below.
**Q1. Why the new notion of surrogate sharpness is better than loss sharpness?**
To understand this, note that a surrogate that minimizes its loss sharp... | Summary: The paper studies the problem of offline optimization for material design problems. The paper proposes a model-agnostic method that changes the parameters of a model by constraining the sharpness of the model's predictions. Since the model generates smoother predictions, the error between the predictions and t... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for recognizing our contribution with an acceptance rating.
**Q1.**
**A. More effective optimization techniques.** We agree with the reviewer that an in-depth investigation of the specific structure of the objective could reveal a more effective optimization ... | Rebuttal 1:
Rebuttal: We would like to thank the AC for securing seven reviews with high quality.
We thank **Reviewers WGWE, tYoe, qZmG, qc6B, and kVLN** for their accepting scores.
We thank **Reviewers xjU2 and CQYH** for the detailed questions, which help us highlight better the key contribution of our work.
O... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper introduces a model-agnostic regularization method that reduces surrogate model sharpness to improve generalization in offline optimization. By incorporating a surrogate sharpness measure into the training loss, they provide theoretical proof and extensive experimental validation showing that this ap... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for recognizing our contribution with an acceptance rating.
**Q1. Introduction Improvement.**
We would like to thank the reviewer for the suggestion. We will clearly emphasize the difference between surrogate sharpness and loss sharpness in the Introduction se... | Summary: The paper proposed IGNITE, a promising method for solving out-of-distribution issue in offline training by introducing model sharpness into the training loss of the surrogate as a regularizer. The key innovation of IGNITE lies on incorperating sharpness-aware minimization into offline training and developing t... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for recognizing the strengths of our work with an acceptance rating.
**Q1. Improving the introduction of SAM.**
We appreciate the reviewer's insightful recommendation. We will cite the suggested works and discuss their impact on the development of SAM in our ... | Summary: This paper focuses on offline optimization, presenting a novel approach to enhance the surrogate landscape's sharpness, thereby improving generalization. The authors introduce a gradient norm as an approximation of surrogate sharpness through a first-order Taylor expansion, resulting in a Lagrangian formulatio... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed feedback. Your questions are addressed below.
**Q1. Why not bounding $\mathcal{L}_\mathcal{X}(\omega)$ directly?**
Bounding $\mathcal{L}\_\mathcal{X}(\omega)$ helps reduce the averaged prediction error in the OOD region but it does not guarantee such errors... | null | null |
Debiasing Synthetic Data Generated by Deep Generative Models | Accept (poster) | Summary: The authors propose a method for debiasing DGM-generated tabular data such that the population average of the EIC for the observed data distribution and its estimation becomes zero, resulting in elimination of the corresponding biasing term. This is done through augmenting the DGM output such that the populati... | Rebuttal 1:
Rebuttal: We would like to thank Reviewer ZgH6 for their careful reading of the manuscript and constructive review.
**It would be good to put the work in contrast with related works <...>.**
We noted that other reviewers raised similar questions and have therefore included an extended comparison with rela... | Summary: This paper addresses the significant biases introduced in synthetic data generated by deep generative models (DGMs) that compromise the inferential utility of such data. The authors propose a new debiasing strategy based on techniques adapted from debiased or targeted machine learning. Their approach aims to r... | Rebuttal 1:
Rebuttal: We would like to thank Reviewer 4cJP for their careful reading of the manuscript and constructive review.
**How well does the debiasing strategy perform in high-dimensional settings <...>?**
We elaborate on this in our global response as well, but agree that future work should additionally inves... | Summary: This paper tackle the problem of creating/imputing unbiased synthetic data, and analyse the potential bias brought by these imputation methods.
Strengths: The tackled problem is important and very timely, and linked to the general question of biais of generative models [1].
[1] Wyllie, Sierra, Ilia Shumailo... | Rebuttal 1:
Rebuttal: We would like to thank Reviewer LYaP for their careful reading of the manuscript and constructive review.
**I do not know if this comes from my lack of knowledge in the field, but I found the paper very hard to understand.**
We are aware that the proposed strategy is not trivial and combines dif... | Summary: The paper considers the problem of debiasing synthetic data which can have signficant issues when used for statistical analysis. In particular, they show examples of mean estimation of a variable and parameter estimate of a regression model and demonstrate the benefits of the post-processing step of their appr... | Rebuttal 1:
Rebuttal: We would like to thank Reviewer eYfd for their careful reading of the manuscript and constructive review.
**What would be needed to handle more interesting estimators for downstream applications? It is unclear how one can generalize these to settings where the estimators are much more complex/ubi... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their careful reading of the manuscript and the constructive reviews. We were pleased to read that the presentation of our work was positively received by **Reviewers eYfd, 4cJP** and **ZgH6**, though we agree with **Reviewer LYaP** that the methodology sec... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Cell ontology guided transcriptome foundation model | Accept (spotlight) | Summary: This paper introduces scCello, a Transcriptome Foundation Model (TFM) which incorporates prior information from cell ontology graphs denoting the relationships between different cell types in order to guide cell representations. The pretraining objective in the scCello framework encapsulates masked gene expres... | Rebuttal 1:
Rebuttal: Thanks for positive reviews and invaluable suggestions We respond to your questions as below:
> **W1: Although cell type ontology labels allow the use of cellular ontology structural priors to guide TFM pre-training, the need for labeled data may limit scCello's scalability in fully utili... | Summary: The authors introduced a transcriptome foundation model (TFM) named scCello to resolve the current problem of most of the TFMs they treat cells as independent samples and ignore the taxonomic relationships between cell types. By integrating cell ontology information as well as incorporating three key objectiv... | Rebuttal 1:
Rebuttal: Thanks for your positive review and insightful comments! We respond to your concerns as below:
> **W1: In zero-shot cell type clustering, do authors use the optimal setting or the default setting for the Louvain Algorithm for Seurat, Harmony and scVI?**
In zero-shot cell type clustering, ... | Summary: This paper presents scCello, a single-cell, Cell-ontology guided Transcriptome Foundation Model (TFM) that leverages cell-type relationships from cell ontology graphs to enhance cell representation learning. scCello incorporates three levels of objectives during pre-training: masked gene prediction, cell-type ... | Rebuttal 1:
Rebuttal: Thanks for your comments! We respond to your concerns as below:
> **W1: This paper lacks of theoretical justification for deeper understanding of objective’s impact on learning and model’s generalization capability**.
Our paper focuses on developing a new algorithm to improve TFM pre-trai... | Summary: This paper introduces a new transcriptome foundation model (scCello) to generate cellular representations from single-cell RNA-seq data. The key contribution is the integration of known cell type labels (previously annotated by CellxGene submitters) within two novel objectives. First, the authors introduce a c... | Rebuttal 1:
Rebuttal: Thanks for the positive review and constructive comments, which we carefully address below:
> **W1: Provide example for cell types to analyze how the ontology relational alignment loss benefits clustering performance**
To compare scCello with its ablation excluding the relational alignme... | Rebuttal 1:
Rebuttal: We would like to appreciate all reviewers for your constructive suggestions and valuable comments on our paper!
Here is a brief summary of important points from all reviewers:
- **Performance comparison with more non-TFM traditional methods specialized for downstreams (Review BhVy, bUQe, E5ju)**... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper introduces scCello, a new transcriptome foundation model (TFM), which learns cell representations over RNA gene expressions. Apart from using Masked language modeling (MLM), masking random gene expressions in cells, the work leverages structural knowledge from ontologies to improve the learned repre... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments! We respond to your concerns as below:
> **W1: GNNs could offer a more elegant way to fuse structural knowledge into transformers than PPR metrics and contrastive objectives from scCello. GNN-fusion methods should be included as baselines.**
We would like... | null | null | null | null | null | null |
Symmetry-Informed Governing Equation Discovery | Accept (poster) | Summary: The authors developed an approach that allows exploitation of symmetry in common equation discovery algorithms and test their approach on dynamic systems with and without symmetry.
For this they make use of Lie groups.
Strengths: - Despite Lie groups being a new topic for me, I was able to follow the well wri... | Rebuttal 1:
Rebuttal: Thank you for the valuable feedback. We address key comments below, starting with the major question about Tab. 2.
> *If there is no symmetry*, why does our approach still outperform baselines?
There *are* symmetries in the tasks in Tab. 2. If there were no symmetry, our approach would not outper... | Summary: This paper propose to leverage the symmetry to discover underlying dynamics (especially the ones described by autonomous ODEs) correctly from data. Specifically, the proposed method combines conventional symbolic regression approaches like SINDy with the symmetry-based constraints or regularizations. The used ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and the recognition of our paper's significance and clarity. We address key comments below.
> Novelty
Our method is *not* a combination of existing techniques.
First, we develop the pipeline for using different kinds of symmetries in equation disc... | Summary: The authors consider an estimation technique similar to the well known SINDy technique for recovering interpretable parameterizations of ordinary differential equations (ODEs). The authors primarily consider the context where there exists a Lie symmetry that constrains the solution space of ODEs to search over... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. We address key comments below.
> The method focuses on cases where symmetries are linear. The results are primarily applicable in the cases where the symmetry is linear.
**This is a misunderstanding.** Our regularization method in Section 4.2 appl... | Summary: The paper proposes to leverage symmetry to guide the equation discovery process, compress the equation search space, and improve the accuracy and simplicity of the learned equations. Depending on the types of symmetries, the paper develops a pipeline for incorporating symmetry constraints into various equation... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and the recognition of our paper’s presentation and evaluation. We address the key comments below.
> Related work: Time-reversal symmetric ODE Network (TRS-ODEN)
Thank you for bringing this to our attention. This work aims to learn the physical dyn... | Rebuttal 1:
Rebuttal: We thank the reviewers for their detailed and valuable feedback. We are encouraged that they find our work to be a clearly motivated idea (R2,3) towards the interesting problem of equation discovery (R1). We are also glad that they find our paper well-written and easy to follow (R1,4), our method ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper focuses on incorporating symmetries to equation discovery pipelines for ODEs.
If the governing equation has a known linear symmetry (its solutions are invariant with respect to a known linear action by a known Lie group), this paper derives a set of conditions the ODE needs to satisfy, and incorpo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. We address key comments below.
> On the connection and difference from LaLiGAN
**Our approach has a completely different goal from LaLiGAN.** Our method aims to discover equations using symmetry as an inductive bias. LaLiGAN aims to discover unkno... | null | null | null | null | null | null |
SARDet-100K: Towards Open-Source Benchmark and ToolKit for Large-Scale SAR Object Detection | Accept (spotlight) | Summary: The authors address the limitations of existing datasets and the inaccessibility of source codes by creating a new benchmark dataset, SARDet-100K, which is a large-scale, multi-class dataset. Additionally, the paper proposes a Multi-Stage with Filter Augmentation pretraining framework designed to overcome the ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's insightful comments and suggestions. We have carefully considered the points raised and provide the following clarifications and additions:
While it is true that merging multiple datasets can improve performance in **general object detection** tasks where datasets sha... | Summary: The authors establish a new benchmark SAR object detection dataset (SARDet-100K) and open-source SAR detection pretrain method (MSFA). This initiative significantly addresses the limitations posed by the scarcity of public SAR datasets and the inaccessibility of source codes, fostering further research and dev... | Rebuttal 1:
Rebuttal: Sorry for the lack of clarity in the manuscript. The reported performance metrics are based on the test set of the SARDet-100K dataset. The models are trained on the training set for a total of 12 epochs, and the checkpoint from the 12th epoch are used for testing.
We acknowledge the importance o... | Summary: This study presents the a large-scale dataset designed for SAR object detection, alongside a Multi-Stage with Filter Augmentation pretraining framework. The authors address the challenges associated with the limited availability of public SAR datasets and the lack of accessible source codes. The proposed metho... | Rebuttal 1:
Rebuttal: We appreciate your feedback regarding the introduction and related work on handcrafted feature descriptors. We have indeed provided more extensive related work on these descriptors in the Appendix section of our submission. In the revised version of the paper, we will include comprehensive visuali... | Summary: This work introduces SARDet-100K, a new large-scale, multi-category dataset for SAR object detection. It also proposes a novel Multi-Stage with Filter Augmentation pretraining framework to mitigate domain and model gaps encountered when transferring models pretrained on RGB datasets to SAR datasets. A new benc... | Rebuttal 1:
Rebuttal: In the context of our study, the term 'model gap' refers to the inconsistency introduced when only the backbone of the detector is pre-trained, while other components, such as the neck and heads, are initialized randomly. For downstream object detection tasks, the model comprises the entire detect... | Rebuttal 1:
Rebuttal: We add Figure R1, comprehensive visualizations of different handcrafted feature descriptors (including HOG, Canny, GRE, Haar and WST) to enhance clarity and accessibility.
We revise the main paper Table 5 to Table R1 (Table for comparison of the proposed MSFA with previous state-of-the-art method... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning Elastic Costs to Shape Monge Displacements | Accept (poster) | Summary: Efficiently mapping one distribution of points to another is crucial in numerous machine learning applications. Optimal transport (OT) theory has become a favored approach for solving these matching problems across various scientific disciplines. This work zeroes in on a significant OT subtask: solving the Mon... | Rebuttal 1:
Rebuttal: We are very grateful for your careful review and positive grade, and were happy to see that you mentioned several times the originality of our contribution.
> **The first strength of this paper is the first visualizations of Monge maps beyond the standard gradient-convex Brenier maps, as well as ... | Summary: This paper explores the optimal transport problem with an elastic regularizer, to help deal with an underparametrized model. Specifically, the regularizer acts in a specific subspace, defined by a matrix $A$, which is also learned. A convex optimization problem is proposed and a method used to find this optima... | Rebuttal 1:
Rebuttal: We are very thankful for your encouraging grades and for your great comments.
> **A convex optimization problem is proposed […]**
There is a mild confusion that we would like to clarify: While the task of computing the entropic map given a cost $h$ and samples $\mathbf{X}, \mathbf{Y}$ weighted a... | Summary: The paper studies the problem of computing Monge maps between two distributions. Such maps are usually found with respect to the $\ell_2^2$ cost function, however the focus of the paper is on elastic cost functions which have an additional penality term (denoted by $\tau$). A numerical method is formulated for... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and for formulating many interesting questions.
> **A numerical method is formulated for estimating the Monge maps w.r.t elastic costs.**
We believe there might be a minor confusion here. You are referring to the proximal gradient method in Prop. 2. Please n... | Summary: This paper studies the problem of optimal transport with elastic costs. They demonstrate the performance of the MBO estimator on subspace losses. They then introduce a loss for learning the elastic cost function and provide experimental results on the performance of this cost learning scheme.
Strengths: * Cle... | Rebuttal 1:
Rebuttal: We are very grateful to your detailed reviews and many thought-provoking questions.
> **i) does the true $\theta$ optimize the elastic costs loss when the data is drawn from, e.g., $T_g^h$**
We cannot prove the ground truth parameter is the global optimum of our loss, but we do observe empirical... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for the quality of their review, and for taking the time to formulate many insightful questions. It was a pleasure on our end to do our best during this rebuttal week to clarify some of these concerns. As a result, our rebuttal text is fairly long, and we thank... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning World Models for Unconstrained Goal Navigation | Accept (poster) | Summary: The authors aim to improve learning in environments with sparse rewards by leveraging world models; this is achieved by proposing a novel exploration algorithm that as a result allows to create a richer buffer of experiences that are more appropriate for learning an accurate world model.
Strengths: The paper ... | Rebuttal 1:
Rebuttal: We appreciate your insightful feedback and constructive comments!
**R1. In line 123 it is stated that the representation might be inaccurate when moving backwards; it would be useful to ellaborate on why moving backwards needs to be taken into account, and not only forward**
We will revise the p... | Summary: The paper introduces MUN, a novel goal-directed exploration algorithm for model-based reinforcement learning. MUN focuses on improving the quality of world models by learning to predict state transitions between any pair of states in the replay buffer. This is achieved by training the world model on a bidirec... | Rebuttal 1:
Rebuttal: We appreciate your insightful feedback and constructive comments!
**R1. Limited Baselines**
We appreciate the reviewer’s suggestion. Our world model training strategy is compatible with any modern model-based RL framework. In this paper, we integrate it with the Dreamer framework due to Dreamer’... | Summary: This paper introduces a novel goal-directed exploration algorithm called MUN to address the challenges of efficient exploration in long-horizon, sparse-reward environments within the context of goal-conditioned reinforcement learning (GCRL). The key insight is that improving the generalizability of learned wor... | Rebuttal 1:
Rebuttal: We appreciate your insightful feedback and constructive comments!
**R1. MUN does not seem to outperform the baselines significantly as seen in Figure 5 (Figure 4?).**
Figure 4 might give a misleading impression as we blend the baselines and ablations in the same image. In this figure, the tool w... | Summary: **After rebuttal the score was changed from reject to weak accept.**
The paper proposes to use the experiences stored in an RL replay buffer differently when training a world model. The change is to attempt to not only use the experience in a "forward" direction but also a "backward" and "across traces" manne... | Rebuttal 1:
Rebuttal: We appreciate your insightful feedback and constructive comments!
**R1. The subgoal discovery heuristic assumes that distant points in the state space correspond to crucial key states. Whether this heuristic is beneficial will depend on the design or embedding of the state space**
MUN does *not*... | Rebuttal 1:
Rebuttal: We greatly appreciate the valuable feedback and suggestions provided by the reviewers! We will begin by addressing the concerns raised by the majority of the reviewers in the global rebuttal. We will address the concerns of each reviewer in the individual review responses.
**1 Quantitive Measurem... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
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