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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Online Learning of Delayed Choices | Accept (poster) | Summary: The paper studies a task of learning MNL parameters with delayed feedback. The authors study both the cases where feedback with extremely high delay is ignored or taken into account as well. They prove that for both settings the optimal regret is $\tilde{\Theta}(\sqrt{NT})$ where $T$ is the horizon and $N$ is ... | Rebuttal 1:
Rebuttal: First of all, we would like to extend our sincere thanks for your detailed review and constructive feedback. We have carefully considered your comments and have addressed them as follows:
> W1) I think that the main results could have been better presented. In the current formulation, it seems li... | Summary: The authors consider the setting in which a business is required to select a set of options to a customer in order to maximize the generated revenue. This task is challenging as the options presented to a customer may interact with each other and alter the choice of the customer, and the feedback on the choice... | Rebuttal 1:
Rebuttal: First of all, we would like to extend our sincere thanks for your comments. We have taken due diligence to address the concerns raised and we believe your suggestions have greatly helped us in improving our manuscript.
> W1) The definition of the "feedback observed by the seller", $o_{i,s,t}$ is ... | Summary: The authors studied the problem of learning with delated feedback under the Multinominal Logit model. Prior work in bandits with delayed feedback does not accommodate settings where multiple items can be offered simultaneously. The authors instead proposed two algorithms: DEMBA for thresholded setting where th... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and for your positive evaluation of our work. We appreciate your recognition of the strengths of our paper and your constructive feedback.
> Does the problem setup change dramatically when the reward for each item in the assortment is not drawn i.i.d.? For examp... | Summary: This paper works under the MNL bandit settings where the environment feedback is delayed, motivated by real-world application scenarios like e-commerce platforms, while balancing exploitation and exploration. For the two proposed algorithms, the authors provide corresponding theoretical analysis, resulting in ... | Rebuttal 1:
Rebuttal: First of all, we would like to extend our sincere thanks for your detailed review and for the insightful comments.
> One question from my side is that for the current theoretical analysis, the regret bound mainly depends on the expectation of the delay, without modeling the skewness of the delay ... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewers’ thoughtful and comprehensive comments and feedback. As per the suggestions of the review team, we’ve performed an additional experiment and are sharing the results in this file.
Pdf: /pdf/200d37a3e05dd134af4d047a1907ad17e7ae5e04.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper considers on online learning problem in the setting of discrete choice models with delayed feedback. The paper assumes a multinomial logit model where a decision maker has some (unknown) valuation v_i for item i. When presented with a menu S of choices, the agent chooses a single item from S such tha... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive feedback on our paper. We appreciate your recognition of the strengths of our work and the detailed suggestions for improvement.
Following your suggestion, we will expand the related work section in our revised manuscript to include a more comprehens... | null | null | null | null | null | null |
DH-Fusion: Depth-Aware Hybrid Feature Fusion for Multimodal 3D Object Detection | Reject | Summary: This study reveals that modalities have varying impacts depending on depth, leading to the proposal of DH-Fusion. This method
dynamically adjusts feature weights using depth encoding, improving multi-modal 3D object detection. Results on nuScenes show DHFusion outperforms prior methods.
Strengths: 1. This pap... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude to you for the valuable comments and constructive feedback. Below, we address each each of question or comment in detail.
**Comment 1:** "Lake of Novelty: The Depth Encoder in DH-Fusion is similar to the 3D Position Encoders in PETR (PETR: Position e... | Summary: This paper proposed a LiDAR-camera modality feature fusion method based on depth encoding for robust 3D object detection. Based on the observation that the LiDAR and camera modality information should have dynamic relative importance depending on the distance of object to be detected, the paper proposed a Dept... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude to you for the valuable comments and constructive feedback. Below, we address each of question or comment in detail.
**Comment 1:** "How about the algorithm's performance on small object detection? small object could be normal-sized object at far dis... | Summary: This paper introduces a novel strategy for LiDAR-camera 3D object detection that emphasizes the importance of depth information in feature fusion processes. The authors argue that different modalities, such as LiDAR point clouds and RGB images, contribute variably at different depths, and this variation has be... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude to you for the valuable comments and constructive feedback. Below, we address each question in detail.
**Comment 1:** "I noticed this paper encodes depth information using cosine functions, but I haven't seen experiments validating the impact of cosi... | Summary: The paper introduces DH-Fusion, a novel Depth-Aware Hybrid Feature Fusion strategy for multimodal 3D object detection that leverages LiDAR and camera data. The key innovation lies in dynamically adjusting the weights of point cloud and RGB image features based on depth encoding at both global and local levels.... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude to you for the valuable comments and constructive feedback. Below, we address each of question or comment in detail.
**Comment 1:** "What is the computational complexity of the DH-Fusion model, and how does it compare with other state-of-the-art meth... | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable comments and constructive suggestions, and are glad they appreciate that "The paper proposes a novel feature fusion strategy that adaptively adjusts the weights of LiDAR point cloud and RGB image features based on depth" (Reviewer tCBR), "The idea of depth... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DINTR: Tracking via Diffusion-based Interpolation | Accept (poster) | Summary: This paper applies the diffusion mechanism to the image interpolation process, which can realize object tracking tasks. Five object representation object tracking tasks, such as bbox, point, and text, are applied using diffusion-based interpolation. The benchmark experiments show promising results.
Strengths:... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's feedback regarding our details and experimentation.
#### 1. Process Details
We encourage the reviewer find our discussion about the Implementation Details with Reviewer **oiCF** and Section F in our Appendices. The training and inference processes are outlined... | Summary: The paper introduces DINTR (Diffusion-based INterpolation Tracker), an object-tracking framework that uses diffusion models to perform tracking in the visual domain. It proposes a new "Tracking-by-Diffusion" paradigm that reformulates tracking based on visual iterative diffusion models. DINTR uses an interpola... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and effort in reviewing our paper. We hope to address your concerns by directing your attention to relevant sections where these details were already discussed in our original submission.
#### 1. Model Size and Speed
We have included speed metrics in the rebuttal... | Summary: The paper "DINTR: Tracking via Diffusion-based Interpolation" introduces a novel approach for object tracking using diffusion models. The proposed methodology, Diffusion-based INterpolation TrackeR (DINTR), leverages diffusion mechanics to model temporal correspondences and reconstruct actual frames in video s... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's feedback regarding our ablation study, practicality, and comparison.
#### 1. Ablation Study
We have significantly expanded our ablation studies, as detailed in the global response. This comprehensive analysis provides a deeper understanding of DINTR's behavior... | null | null | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers' insightful comments and suggestions. The feedback highlights our paper's strengths, including its novel generative approach, the method's impressive versatility, thorough explanation for reproducibility, and comprehensive evaluation. Reviewers **ddR4** and **... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DHA: Learning Decoupled-Head Attention from Transformer Checkpoints via Adaptive Heads Fusion | Accept (poster) | Summary: The paper proposes a novel method to convert the Transformer attention mechanism from multi-head attention (MHA) to the proposed decoupled-head attention (DHA). DHA employs fewer unique KV heads than MHA or GQA by representing the heads as a linear combination of unique heads within the group, where each group... | Rebuttal 1:
Rebuttal: # Response to Reviewer wgtQ
Thank you for your thorough review and positive comments on our work.
**Q1: Inter-layer grouping of heads or only intra-layer grouping?**
**R1:** **Only intra-layer grouping and fusion** is conducted in DHA. Figure 1 meant to illustrate the *decoupled*-heads where th... | Summary: This paper introduces a novel mechanism to optimize large language models (LLMs) by addressing the computational and memory costs associated with the Multi-Head Attention (MHA) mechanism. The authors propose Decoupled-Head Attention (DHA), which adaptively configures the sharing of key and value heads across l... | Rebuttal 1:
Rebuttal: # Response to Reviewer Txec
Thank you for your appreciation to the novelty of our work and the thoughtful reviews.
**Q1:The effectiveness of DHA on a wider range of model architecture**
**R1:** Thanks for your suggestions! DHA is primarily designed for models based on the Transformer Decoder a... | Summary: The paper introduces Decoupled-Head Attention (DHA), a new efficient attention mechanism for large language models. DHA adaptively configures group sharing for key and value heads across layers, transforming Multi-Head Attention checkpoints through a three-stage process. It achieves 97.6% of the original perfo... | Rebuttal 1:
Rebuttal: # Response to Reviewer FLzu
Thank you for your appreciation to the novelty of our work and the thoughtful reviews.
**Q1:DHA's Compatibility with GQA**
**R1:** Thank you for your suggestions! Here, we provide two feasible methods to convert GQA to DHA.
- **Easiest method in less than 1 minute*... | Summary: The paper introduces a novel attention mechanism, Decoupled-Head Attention (DHA), designed to enhance the efficiency of large language models (LLMs) with minimal performance loss. DHA adaptively configures key and value heads across layers by leveraging insights from attention redundancy, leading to significan... | Rebuttal 1:
Rebuttal: # Response to Reviewer aEta
Thank you for your appreciation to our work and insightful comments.
**Q1: Comparison with GQA and MHA Training Data Requirements in Large Dataset.**
**R1:** Under the same training budgets, DHA performs better than GQA.
- **Steady improvement of DHA over GQA in lim... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Parameter Disparities Dissection for Backdoor Defense in Heterogeneous Federated Learning | Accept (poster) | Summary: This paper focuses on backdoor defenses in the setting of heterogeneous federated learning. The authors reveal that benign and malicious clients present distinct parameter importance degree. Based on these observations, they propose a method to exclude malicious participants by evaluating parameter importance.... | Rebuttal 1:
Rebuttal: ## **Response to Reviewer AqVq**
Dear Reviewer AqVq:
We thank the reviewer for the appreciation and valuable comments. We are pleased you found our paper well-organized and our literature review comprehensive. Your recognition of our novel approach and its effectiveness in various scenarios is e... | Summary: This paper presents an innovative approach to mitigating backdoor attacks in federated learning systems. The authors introduce the Fisher Discrepancy Cluster and Rescale (FDCR) method, which leverages Fisher Information to assess parameter importance in local distributions. By reweighting client parameter upda... | Rebuttal 1:
Rebuttal: ## **Response to Reviewer 7qoM**
Dear Reviewer 7qoM:
Thank you for your thoughtful review. We appreciate your recognition of our innovative use of Fisher Information in our method, the dual approach of client selection and parameter aggregation, and its robustness across various scenarios. We a... | Summary: The paper addresses the issue of backdoor attacks in federated learning systems, where malicious clients introduce triggers in their local models to compromise the global model. They use Fisher Information to determine parameter importance, reweight client updates, and identify malicious clients. The method is... | Rebuttal 1:
Rebuttal: ## **Response to Reviewer oCkz**
Dear Reviewer oCkz:
Thank you for affirming our work and raising insightful questions. We are pleased you found our method novel and effective, leveraging Fisher Information to quantify parameter importance and reweight client updates. We appreciate your acknowle... | Summary: This paper studies backdoor defenses in federated learning. Existing backdoor defenses either assume homogeneous data, existence of validation data or client optimization conflicts. In order to circumvent these limitations, the authors proposed FDCR method. FDCR is based on the observation that parameter impor... | Rebuttal 1:
Rebuttal: ## **Response to Reviewer M9TV**
Dear Reviewer M9TV:
We sincerely appreciate your time and effort in reviewing our paper. Your positive feedback on the novelty of our approach, the clarity of our writing, and the comprehensiveness of our experiments is very encouraging. We are glad that our nove... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fully Explicit Dynamic Gaussian Splatting | Accept (poster) | Summary: The author proposed a Fully Explicit Dynamic Gaussian Splatting method to decrease the training time and memory cost w/o losing the high fidelity. The core of the proposed approach based on the decomposition of the static and dynamic Gaussians during training is to sample dynamic Gaussians at sparse timestamps... | Rebuttal 1:
Rebuttal: [W1] The main motivation of separating dynamic points is to allow FESGS to handle temporal changes with either color or displacement variations, so stationary objects but temporally changing colors are also trained as dynamic points.
We carry out an additional experiment for the case where point... | Summary: The paper proposes a new method in the field of novel view synthesis for video input. The authors propose a Gaussian Splatting-based algorithm that introduces a fully explicit representation at keyframes and models interpolation of gaussians (position, rotation, opacity) between the frames. Additionally, the p... | Rebuttal 1:
Rebuttal: [W1] Each answer is as follows. The revised version will reflect this description.
1. FEDGS follows the densification algorithm of the original 3DGS. This algorithm accumulates the gradient magnitudes of visible Gaussians (that is, the gradient in the x,y direction of the image space) for a camer... | Summary: This paper models dynamic scenes using 3DGS, unlike other methods that model dynamic scenes with both implicit and explicit representations. It proposes Fully Explicit Dynamic Gaussian Splatting (FEDGS), a method that models 4D scenes using a purely explicit approach. FEDGS employs a Cubic Hermite Interpolator... | Rebuttal 1:
Rebuttal: [W1] We thank you for your careful comment about the static points. In practice, we observe that there are temporal color and luminance changes even if their position does not change. In this work, our method is designed to handle all the temporal changes as dynamic points. This includes not only ... | Summary: The authors propose a fully explicit dynamic Gaussian splatting method, based on keyframe interpolation. The authors separate a dynamic scene into static Gaussians and dynamic Gaussians during training and apply interpolation techniques under temporal explicit representation, including a polynomial basis inter... | Rebuttal 1:
Rebuttal: [W1, Q1] Thank you for letting us know the relevant papers and we believe that additional comparison with them makes this paper solid. We have thoroughly reviewed these models and will include them in the revised version. You can find the updated results in Table A and we report available values.... | Rebuttal 1:
Rebuttal: First, we would like to thank all the reviewers for giving us the valuable opinions for our paper. All reviewers agree that FEDGS is comparable in terms of performance and efficiency to other models. Reviewers Tqxz and Vpue comment on the clarity of the paper. Reviewers Tqxz, Vpue, and MZ9L highli... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Decision-Focused Learning with Directional Gradients | Accept (poster) | Summary: The paper introduces a new family of surrogate losses to the DFL with linear costs, called perturbation gradient losses (PG loss). It provides theoretical analysis to bound the approximation errors and regret bounds and uses extensive experiments to demonstrate the advantages of the proposed method.
Strengths... | Rebuttal 1:
Rebuttal: ### **Danskin's Theorem**
There are many versions of Danskin’s Theorem that apply under different regularity conditions. The version we use is well-summarized [here](https://statisticaloddsandends.wordpress.com/2022/11/10/what-is-danskins-theorem/). The key is part 4: When there are multiple sol... | Summary: This paper considers a predict-then-optimize framework for solving contextual optimization problems, in particular for the case where the set of decisions is combinatorial or polyhedral, or when the loss is non-differentiable. They define a family of surrogate losses that connect the loss to the directional de... | Rebuttal 1:
Rebuttal: ### **Clarifying Minor Weaknesses**
We believe the reviewer meant Lemma 2.1, as there is no Theorem 2.1.
#### **Role of $h$**
The role of h is intuitively described on top of pg. 5 Line 154 (i.e. before Lemma 2.1). This is further elaborated (quantitatively) after Corollary 3.3 (pg. 6 Line 214... | Summary: This paper addresses the predict-then-optimize problem by proposing a new family of surrogate loss functions. The key motivation is derived from Danskin's Theorem, which connects the expected downstream decision loss with the directional derivative of a particular plug-in objective. This objective is then appr... | Rebuttal 1:
Rebuttal: ### **Re Weaknesses: Experimental Evaluation**
Thank you for pushing us in this direction. In the Global Response Document, we’ve added two additional experiments: i) a harder instance of a shortest-path problem and ii) a portfolio optimization problem with **real data** and a low signal-to-noise... | Summary: This paper proposes a family of "perturbation gradient" losses for Predict-than-Optimize (PtO) that, if optimized for, can lead to best-in-class performance, even under model misspecification. On the theoretical side, this paper provides risk bounds that build on past theoretical work in PtO + the literature o... | Rebuttal 1:
Rebuttal: ### **Overview**
We’d like to recall it is NP-Hard to optimize the (true) decision-loss over linear functions [6], essentially because it generalizes binary classification. Hence, any method (including ours) that aims to learn a best-in-class policy for all data generation mechanisms MUST also be ... | Rebuttal 1:
Rebuttal: Attached is our global response document, which includes the following:
i) An updated shortest path experiment that embeds two "good" paths that methods must identify and choose between based on the context. This experiment increases the difficulty and reward of finding the oracle policy compared... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Point-PRC: A Prompt Learning Based Regulation Framework for Generalizable Point Cloud Analysis | Accept (poster) | Summary: In this work, the authors propose a regularization method for the prompt learning of generalizable point cloud analysis, which can strengthen the performances of learned representations on downstream 3D task while keeping its generalizability. The regularization consists of three components: mutual agreement c... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments. Below we address your concerns one by one. Further questions are welcome and we are happy to respond.
**Q1: Major concern on novelty**
We understand your concern regarding the novelty. And we answer the question in the global response section, kindly referrin... | Summary: This paper investigates the 3D domain generalization (3DDG) ability of large 3D models using prompt learning. They utilize parameter-efficient prompt tuning to boost the performance of 3D point cloud recognition models. The paper observes that while prompt tuning improves downstream tasks, it often reduces the... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback. Below we address your concerns one by one. Follow-up questions are welcome if something remains unclear.
**Q1: More rigorous motivation of this study is needed**
Thanks for your comments. We need to clarify the following points.
As we stated in the introducti... | Summary: This paper investigates the 3D domain generalization (3DDG) capability of large 3D models based on prompt learning. The authors propose a comprehensive regulation framework that employs lightweight prompt learning to improve both task-specific performance and domain generalization ability. The framework consis... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback. We address your concerns point by point. Feel free to ask follow-up questions if something remains unclear.
**Q1: Training time comparison between baselines and our method.**
As requested, we have added a comparison of the training time between our method and t... | null | null | Rebuttal 1:
Rebuttal: First of all, we sincerely thank all reviewers and ACs for reviewing our paper and providing valuable comments. There is no doubt that these suggestions and feedback are very valuable for refining the paper. We are encouraged by the appraising comments from the reviewers: ''significant originality... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CODE: Contrasting Self-generated Description to Combat Hallucination in Large Multi-modal Models | Accept (poster) | Summary: This paper introduce the CODE decoding method to contrast origin image with VLM-generated image description to reveal the missed or hallucinated content in naive decoding process. The method contains two innovations: 1) a Bounded Divergence guided selector to provide dynamic combining weight. 2) an adaptive in... | Rebuttal 1:
Rebuttal: **W1. The dynamic information flow control does not have that much technical novelty, as it is quite close to what IBD does.**
**A1.** We thank the reviewer for the valuable feedback, and we would like to clarify the primary differences between our CODE and IBD [R1].
The dynamic information flow... | Summary: The paper proposed a contrast decoding method named CODE for large multi-modal models. CODE, as mentioned by its name uses self-generated description as contrasting references during the decoding phase of LMMs to mitigate the hallucination issues. CODE works by dynamically considering the variations between th... | Rebuttal 1:
Rebuttal: **W1. The method has to generate $\cdots$ convenient.**
**A1.** Even if we have discussed in Discussion section and computational analysis in Table. 4, we acknowledge that our method requires additional computational resources to obtain (self-generated) textual descriptions from models themselve... | Summary: Large Multi-modal Models (LMMs) have made significant strides in understanding visual context and generating coherent responses. However, they face challenges such as hallucinations, where responses are incorrect and unrelated to visual inputs. To tackle this issue, this paper proposes COuntering DEscription C... | Rebuttal 1:
Rebuttal: **W1. From Table 1, it can be seen that CODE does not bring much performance improvement compared to greedy decoding in practice.**
**A1.** We respectfully argue that the use of CODE shows consistent performance improvements across 6 different models with varying sizes. Especially, when consideri... | null | null | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for the constructive feedback, which we will incorporate into the potential revised version. We also appreciate to all reviewers (*Z6jD*, *FLJA*, *9CKq*) for acknowledging the novelty of our paper, which is the use of self-generated comprehensive descriptions t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SPARKLE: A Unified Single-Loop Primal-Dual Framework for Decentralized Bilevel Optimization | Accept (poster) | Summary: This paper proposes SPARKLE, a single-loop primal-dual framework for solving bilevel optimization in the decentralized setting. Specifically, multiple devices collaborate to solve bilevel optimization problems and exchange information via communications over a network. From a theoretical angle, the authors pro... | Rebuttal 1:
Rebuttal: We thank the reviewer for the invaluable comments. We have thoroughly addressed all questions. Should there be any additional concerns or inquiries, we are more than willing to provide further clarification.
Re Weakness:
**1. Novelty.**
We respectfully disagree that our work has limited contrib... | Summary: This paper introduces SPARKLE, a unified single-loop primal-dual framework for decentralized stochastic bilevel optimization. SPARKLE is highly versatile, which can incorporate various heterogeneity-correction techniques and allows for different strategies to solve upper- and lower-level problems. The authors ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the invaluable comments. We have thoroughly addressed all questions. Should there be any additional concerns or inquiries, we are more than willing to provide further clarification.
Re Weakness:
1. Thanks for this concern. In fact, the second line of Table 5 in the manu... | Summary: The paper introduces SPARKLE, a unified framework for decentralized stochastic bilevel optimization that addresses several limitations in existing approaches. SPARKLE incorporates various heterogeneity-correction techniques, including EXTRA, Exact Diffusion, and Gradient Tracking, and allows for different stra... | Rebuttal 1:
Rebuttal: We thank the reviewer for the invaluable comments. We have thoroughly addressed all questions. Should there be any additional concerns or inquiries, we are more than willing to provide further clarification.
Response to Weakness:
1.**Motivation for the necessity and benefits of mixing strategie... | Summary: This paper studies a primal-dual framework for decentralized bilevel optimization. It unifies several heterogeneous correction techniques (gradient tracking and EXTRA). It also provides a shared rate analysis that applies to all variants and avoids several assumptions like gradient boundedness. Several other i... | Rebuttal 1:
Rebuttal: We thank the reviewer for the invaluable comments. We have thoroughly addressed all questions. Should there be any additional concerns or inquiries, we are more than willing to provide further clarification.
1. **Contribution of our work**
We appreciate the reviewer’s insightful feedback. Howeve... | Rebuttal 1:
Rebuttal: We sincerely appreciate the detailed feedback provided by all reviewers. Here we present our response to the common concerns raised by multiple reviewers and results of newly added experiments.
**1.Novelty and contribution of our work.**
SPARKLE yields **brand new algorithms**, and achieves **s... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
To Learn or Not to Learn, That is the Question — A Feature-Task Dual Learning Model of Perceptual Learning | Accept (poster) | Summary: This paper aims to replicate a variety of results from the perceptual learning literature using a model with two different forms of learning. It shows results that capture how specificity occurs under certain training conditions and transfer occurs under others. The two forms of learning are different in terms... | Rebuttal 1:
Rebuttal: Thanks for the very detailed comments.
We streamline reviewers' main concerns and address them one-by-one.
**Q1:** On the biological plausibility of location-specific plasticity. Based on a number of experimental evidence, we believe that location-specific plasticity is possible in certain condi... | Summary: In this article, the authors propose a novel model that accounts for two different phenomena observed in human learning: i) specificity, a feature-based mechanism restricted to the very specific statistics of the environment condition, and ii) transfer, a task-based mechanism that allows to transfer knowledge ... | Rebuttal 1:
Rebuttal: Thanks for the careful and valuable comments.
We streamline reviewers' main concerns and address them one-by-one.
**Q1:** On the goal of feature-based learning. In this study, we argue that the goal of feature-based learning is to capture the statistical characteristics of external features. The... | Summary: 1. The paper proposes a dual-learning model to reconcile two seemingly contradictory phenomena in perceptual learning: specificity and transfer.
2. The model consists of two learning processes:
- Task-based learning: Fast, enables quick adaptation to new tasks using existing neural representations.
- Fea... | Rebuttal 1:
Rebuttal: Thanks for the valuable comments of the reviewer.
We streamline reviewers' main concerns and address them one-by-one.
**Q1:** On the removal of the feature-extraction module.
The feature extraction module is akin to a vision representation extractor that has been trained through extensive expe... | Summary: The paper puts forth a theoretical framework for perceptual learning, in which two separate learning processes contribute to learning a perceptual task (a fast, flexible task-based learning that relies on existing feature representations; and a slow, task-specific feature learning). Repeated learning sessions ... | Rebuttal 1:
Rebuttal: Thanks for your encouraging and valuable comments. We streamline reviewers' main concerns and address them one-by-one.
**Q1:** In the current study, we have used a feature extraction module that remains unchanged during the learning process to reflect the pre-processing of visual inputs in the br... | Rebuttal 1:
Rebuttal: We acknowledge the very careful and valuable comments of all reviewers. We realize that there are common concerns about the aim of this study and the models we used to demonstrate the framework. In the below, we briefly summarize the motivation and main results of this work to clarify these concer... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Speculative Monte-Carlo Tree Search | Accept (poster) | Summary: The paper proposes a new variant of parallelization in Monte Carlo Tree Search (MCTS) algorithm in the context of AlphaZero and the game of Go. The modification builds on the anytime nature of MCTS and consists in forking the search for subsequent moves (actions) before the search of a given move (action) is c... | Rebuttal 1:
Rebuttal: **Q**: The experimental evaluation is insufficient for a comprehensive evaluation of the proposed MCTS parallelization. It seems that the strength of the method highly depends on the base number of simulations. How to choose a proper number of them is not explained in the paper.
**A**: As discuss... | Summary: This paper proposes Speculative Monte Carlo Tree Search (MCTS), which predicts the next move of MCTS before completing the search on the current node. This concept is similar to the branch prediction algorithm in CPU pipelining. Speculative MCTS proceeds to the next node by predicting the branching direction o... | Rebuttal 1:
Rebuttal: **Q**: It is unclear whether accelerating the training of a Go program is beneficial to the community, given the existence of many strong Go programs and the infrequent need to train a new one from scratch. Although the method itself is great, the lack of application scenarios could diminish the o... | Summary: The paper considers Monte-Carlo Tree Search (MCTS) and aims to increase parallelism.
It leverages the fact that MCTS is an Anytime Algorithm, meaning early termination can still yield a feasible solution.
This property is used for prediction - the MCTS for the next move is started before the previous move's MC... | Rebuttal 1:
Rebuttal: **Q**: Fundamentally, is the speedup from speculative MCTS achieved through higher degrees of parallelism?
**A**: Yes, the speedup comes from both higher degrees of parallelism and the synergies of caching to utilize available compute resources.
**Q**: Is the real-world Go model training alread... | Summary: The paper introduces Speculative Monte-Carlo Tree Search, which speculates by reducing the number of simulations in MCTS. Experiments demonstrate a two-fold acceleration in training on the Go game.
Strengths: The paper is well-written and easy to understand, even for readers not familiar with the RL.
The pro... | Rebuttal 1:
Rebuttal: **Q**: Inter-game and intra-decision parallelism seem to offer better acceleration than inter-decision parallelism, and they do not require speculation. Can the authors provide experimental evidence to demonstrate the importance of inter-decision parallelism?
**A**: As discussed in general respon... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable feedback. Below, we answer the questions/concerns common among multiple reviewers.
## #1. Why is inter-decision parallelism necessary beyond inter-game and intra-decision parallelism?
Inter-game parallelism can only enhance training throughput, while int... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
GFlowNet Assisted Biological Sequence Editing | Accept (poster) | Summary: The author proposed GFNSeqEditor, a novel sequence editing and generation model built on GFlowNet, which provides different modifications for each sequence to enhance desired features. Several experiments have demonstrated the performance of the proposed algorithm.
Strengths: A new biological sequence editing... | Rebuttal 1:
Rebuttal: Thank you so much for your review and letting us know your valuable comments. Please find below our responses to your comments.
## Evolutionary-Based Methods
We would like to clarify that we have already included the evolutionary method in AdaLead (reference [30] in the paper) among our baselines... | Summary: This paper introduced a new algorithm for biological sequence editing, GFNSeqEditor. This algorithm is designed based on pre-trained Generative Flow Networks (GFNs), and improves target properties by identifying and editing sub-optimal sites of input sequences. Through theoretical analysis and experiments on t... | Rebuttal 1:
Rebuttal: Thank you very much for taking time to review our paper and let us know your valuable comments. Please find below our responses to your comments and questions.
## Safety Assumption
It is generally expected that fewer modifications in biological sequences are less likely to result in significant f... | Summary: They propose a new sequence editing method using GFlowNets as priors and suggest additional hyperparameters to tune suboptimal gaps, randomness, and penalization. They theoretically analyze how these new hyperparameters can effectively control the lower and upper bounds of the number of edits. The performance ... | Rebuttal 1:
Rebuttal: We would like to express our gratitude for taking the time to review our paper and letting us know your thoughtful comments. Please find below our responses to your comments.
## Conditional GFlowNets
Conditional GFlowNets can be used for sequence editing by training the flow function with a seque... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
GraphTrail: Translating GNN Predictions into Human-Interpretable Logical Rules | Accept (poster) | Summary: The authors introduce a new global explanation generation method for GNNs. Their method uses the fact that message passing GNNs break a graph into a set of computation trees. They use Shapley values to then compute the influence of each computation tree. This is then mapped to a boolean formula over concepts. ... | Rebuttal 1:
Rebuttal: **Q1/W1: I suggest the author’s add additional experiments that strengthen this method’s claims. Adding other global explanation methods and even instance-level explanations could demonstrate the strengths of this method.**
*Answer:* We did not compare with any other explainer since none of the e... | Summary: This paper proposes a novel method for providing global explanations for GNNs by constructing logical formulas that offer easy-to-understand interpretations for each class. The authors first propose using computation trees instead of subgraphs to construct explanations. They then suggest using Shapley values t... | Rebuttal 1:
Rebuttal: **W1: The main weakness of this paper is that it does not adequately describe the specific calculation methods for Shapley values and the symbolic regression methods used. Instead, it merely cites the sources of these methods without providing appropriate descriptions in the text or the appendix. ... | Summary: This paper introduces GraphTrail, an end-to-end global GNN explainer providing logic formulas over sub-graphs level concepts. These concepts are extracted at subgraph level by using the Shapley values and, then, the GNN predictions are mapped into logic via symbolic regression. Different experiments show that ... | Rebuttal 1:
Rebuttal: **W1(a). Comparison vs GLGExplainer**
**Ans:** The algorithmic foundations of GLG and GraphTrail are significantly different (please see Lines 47-63). These include:
1. **Non-reliance on local explainers:** GLG assumes local explanations as an input and then operates over these to identify the ... | Summary: The authors propose a novel method to provide instance-level GNN explanations that uncover the combinatorial reasoning learned by a GNN from the training data. They do so by mining discriminative subgraph-level concepts using Shapley values and mapping them to human-interpretable boolean formulas over these co... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments on our work. Please find below our responses to the suggestions and concerns raised.
**Q1/W1: Did the authors consider trying their method on some datasets from a different domain / not describing collections of molecules?**
*Answer:* We note that ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their insightful and constructive feedback. Below, we provide a comprehensive point-by-point response to their comments. Additionally, **we have attached a PDF document** containing various new empirical analyses as suggested by the reviewers. Key revisions inc... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Agent-to-Sim: Learning Interactive Behavior from Casual Videos | Reject | Summary: The paper presents ATS (Agent-To-Sim), a framework to enable agent behavior modeling from multiple casual video captures in indoor scenarios captured during long spans of time. The proposed pipeline consists in (1) 4D reconstruction of the scene geometry and observer and agent motion, and (2) controllable agen... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback! Below please find our responses to your questions and comments.
**Q1 The paper focuses on environment-aware motion of agents in the presence of a (human) observer. Even if out of scope for this paper, it would be interesting to discuss more complex agent-env... | Summary: This paper discusses using an iPhone's RGBD camera to collect several hours of videos within a room over a time span of one month. Through these multi-view videos, a 4D reconstruction of the room is generated. A collection of rigid bodies is used to simulate agents (such as cats, dogs, etc.) in the room. Utili... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback! We added a table of notations to improve the clarity, and we will expand the explanation of individual symbols in the paper.
| Notation | Description |
|-----------... | Summary: This paper presents Agent-to-Sim, an approach to learn a 3D agent in a 3D environment from casual videos of the same agent captured over a long horizon. ATS first conducts 4D spatio-temporal reconstruction from the set of videos, including a deformable agent, the background scene, and a moving observer. This i... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback! We plan to expand on details in the additional page of the final version as well as the appendix. In [the response to Reviewer C9Gr](https://openreview.net/forum?id=fzdFPqkAHD¬eId=cNiL3khmUC), we also added a table of notations to improve the clarity. Our ... | Summary: The paper presents a method for learning interactive behaviors of various agents, including humans, cats, dogs and a bunny, by leveraging unstructured videos captured casually. The various videos are registered together in a common frame, offering a 4D reconstruction of the agent and the environment. Based on ... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback! Due to the complex nature of the problem, it is difficult to unpack all the details in the limited space. We plan to expand on details in the additional page of the final version as well as the appendix. In [the response to Reviewer C9Gr](https://openreview.n... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their feedback. We propose an approach to learn an interactive behavior model of agents from casual videos captured over a long time horizon. Reviewers note that we tackle a "challenging" problem (m6Ge, Kj9h) with “very interesting”, “effective” ideas (m6Ge... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SLIM: Style-Linguistics Mismatch Model for Generalized Audio Deepfake Detection | Accept (poster) | Summary: This paper proposed a new approach named Style-Linguistic Mismatch (SLIM) for generalizable audio deepfake detection. The authors claimed that a certain dependency between linguistic information and style information can generalize well for audio anti-spoofing tasks. Additionally, the proposed method can also ... | Rebuttal 1:
Rebuttal: Thank you for the time spent reviewing our manuscript and pointing us to another dataset that we believe would be a great fit for further evaluation of the proposed method. Below is our point-by-point response:
- “magnitude of stage 1 training data”
We acknowledge that our current approach is li... | Summary: The paper suggests a novel method for detecting synthesized speech. Namely, the framework introduced in the paper allows the detection of a statistically significant mismatch between the style (i. e. paralinguistic attributes) and linguistic characteristics of synthesized speech samples, which helps to differe... | Rebuttal 1:
Rebuttal: Thank you for the time spent reviewing our manuscript and for finding our work innovative. A point-by-point response to your questions can be seen as follows.
- ”Is the confidence of your detector connected with the severity of the artifacts in the synthesized speech samples? Do the mel spectrogr... | Summary: "SLIM: Style-Linguistics Mismatch Model for Generalized Audio Deepfake Detection" describes a motivation and systematic approach to disentangling different components of speaking characteristics, in order to perform audio deepfake detection. This paper demonstrates a working 2-stage training pipeline, numerou... | Rebuttal 1:
Rebuttal: Thank you for your time spent reviewing our work and for sharing your detailed comments which helped us to revise our work. A point-by-point response to the posted questions can be seen below:
- “What are the systems tested in Table 1?...”
As the samples referred to in Table 1 are part of the AS... | Summary: This paper proposes a new method for audio deepfake detection by first employing self-supervised pre-training on real samples only and then used to do real/fake classification. The proposed method achieves SOTA performance in both within-domain and cross-domain scenarios.
Strengths: 1. The proposed technique ... | Rebuttal 1:
Rebuttal: Thank you for your time reviewing our manuscript and for acknowledging our contribution to the field. We have provided a point-by-point response as follows:
- “The idea of capturing the mismatch between style and linguistics is promising, but it's unclear how this mismatch correlates with deepfak... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful comments and suggestions; we appreciate all reviewers’ positive feedback on our fundamental approach motivated by the style-linguistics mismatch modeling for deepfake speech detection, our experiments, and our overall paper presentation.
The reviewers’... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ReFT: Representation Finetuning for Language Models | Accept (spotlight) | Summary: The paper introduces Representation Finetuning (ReFT) - a family of methods to learn interventions directly on model representations, rather than model weights. The authors compare ReFT to Parameter Efficient Finetuning (PEFT), and find that it yields similar performance while being significantly more paramete... | Rebuttal 1:
Rebuttal: Thanks so much for raising these great questions and providing helpful feedback!
### RepE baseline.
We agree that gradient-free methods such as activation addition or RepE could be effective in steering models for tasks such as style transfer [4]. On the other hand, **we argue that it could be h... | Summary: This work proposes a novel method for fine-tuning language models (LM) called Representation Fine-tuning (ReFT), which updates only a small number of parameters. Unlike existing parameter-efficient fine-tuning methods such as LoRA, ReFT enables fine-tuning with minimal parameter updates by learning small inter... | Rebuttal 1:
Rebuttal: Thanks for assessing our paper to be a significant contribution, and for your question!
### ReFT with LMs other than LLaMAs.
**We addressed this question in our general responses** by exploring other model types such as Mistral and Phi! As shown by our initial results, both LoReFT and DiReFT wor... | Summary: The authors propose an alternative PEFT method based on representation intervention techniques that are used in interpretability research. They evaluate their method in a variety of settings including multiple architectures and finetuning dataset families.
Strengths: * The method presented by the paper uses a... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback and questions!
### LoRA with fewer parameters has been tried.
We want to clarify that **the baseline numbers we have in our tables are the best performance after hyperparameter tuning** done by the original LLM-Adaptor [3] paper. For instance, this adaptor b... | Summary: This paper proposes representation finetuning for efficient tuning or intervening for task-specific representations in models while keeping the base model frozen. They define LoReFT and unify several current representation intervention methods under their framework. They conduct extensive experiments on severa... | Rebuttal 1:
Rebuttal: Thank you for appreciating our work and raising interesting questions!
### Generalization of hyperparameters.
Yes, we try to challenge the generalizability of ReFT by testing whether a set of hyperparameters for one task transfers to another as we do hyperparameter search on separate dataset spl... | Rebuttal 1:
Rebuttal: We thank all reviewers for their useful comments. We remark on some of the shared questions here. All other questions are addressed in individual reviewer responses.
## Re: The significance of ReFT over LoRA and others.
Although in almost all other responses, we focus on comparing ReFT with othe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Rethinking Imbalance in Image Super-Resolution for Efficient Inference | Accept (poster) | Summary: In this paper, the authors propose a novel framework called Weight-Balancing Super-Resolution (WBSR) that reformulates the SR task as an imbalanced distribution transfer learning problem.
Strengths: The key contributions of the paper are: 1. Introduction of a Hierarchical Equalization Sampling (HES) strategy ... | Rebuttal 1:
Rebuttal: **Thanks for your positive evaluation and valuable suggestions.**
**Weaknesses:**
***[Q1:]: line 203 to 205 line of the article, the author introduces the reasoning method of gradient dynamic projection, but does not specifically illustrate the specific network used in this method within the art... | Summary: This paper rethinks the imbalance problem in image SR and proposes a plug-and-play weight-balancing framework. It combines a Hierarchical Equalization Sampling strategy and a Balanced Diversity Loss to reduce computational cost while keeping or improving SR performance. Extensive experimental results demonstra... | Rebuttal 1:
Rebuttal: **Thanks for your positive evaluation and valuable suggestions.**
**Weaknesses:**
***[Q1:]: The proposed method is of incremental contributions. This work is not the first to explore imbalance in image SR.***
**[A1:]**: We want to clarify the contributions and novelties of our work from both th... | Summary: This paper proposes a Weight-Balancing framework to address the imbalanced learning issues in image super-resolution. Two categories of imbalance are involved, including data distribution imbalance and model optimization imbalance. Experiments demonstrate the effectiveness of the proposed method.
Strengths: 1... | Rebuttal 1:
Rebuttal: **Thanks for your positive evaluation and valuable suggestions.**
**Weaknesses:**
***[Q1:]: Some grammatical errors should be corrected, e.g., "are used to accelerate inference have been widely xxx" in Line25-26. Please check the whole paper.***
***[A1:]***: Thanks for this suggestion. We will ... | Summary: To address imbalances and parameter redundancy problems, author proposed the Weight-Balancing framework (WBSR), which balances model learning without altering the original model structure or training data. The approach includes a Hierarchical Equalization Sampling (HES) strategy to handle data distribution imb... | Rebuttal 1:
Rebuttal: **Thanks for your positive evaluation and valuable suggestions.**
**Questions:**
***[Q1:]: To enhance generalization, consider testing the proposed methods on a broader range of datasets (e.g., different textures, lighting conditions).***
***[A1:]***: Following this suggestion, to validate the ... | Rebuttal 1:
Rebuttal: We appreciate all reviewers with their positive comments and valuable suggestions.
**Reviewer D78d (Rating: 7 - Accept)** gives positive comments on both our method and experimental results. The reviewer's concerns are on the applicability and generalization in some real-world scenarios. To re... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning | Accept (poster) | Summary: This paper proposes a novel Hierarchical Cluster-based Graph Auto-encoder (HC-GAE) for unsupervised graph representation learning. HC-GAE can reduce the over-smoothing problem and generalize to multiple downstream tasks.
Strengths: 1. The motivation is clear and easy to understand.
2. The proposed method seem... | Rebuttal 1:
Rebuttal: Q1: Both the motivations are not novel. Over-smoothing is a classic problem and Multi-task ability has also been discussed. The authors are encouraged to conduct experiments on link prediction task, and discuss the limitation on downstream tasks.
A1: We would like to further explain and emphasize... | Summary: The paper presents a Hierarchical Cluster-based Graph Auto-Encoder (HC-GAE) for improved graph representation learning. HC-GAE uses hard node assignment for encoding and soft node assignment for decoding; thus, it enables hierarchical compression and expansion of graphs. The authors argue their method can redu... | Rebuttal 1:
Rebuttal: Q1:The code should be released at the review stage to check reproducibility. Especially for empirical work, releasing codes is a prerequisite for acceptance to me.
A1: Thanks for the constructive suggestion. We have provided a demo to the reviewer, please see the official comment with the anonymi... | Summary: This paper develops a novel GAEs, namely the Hierarchical Cluster-based GAE (HC-GAE) model, to learn effective features for either node classification or graph classification. To extract the bidirectionally hierarchical structural features of the original graph, this paper first utilize the hard node assignme... | Rebuttal 1:
Rebuttal: Although this paper introduces a novel graph representation learning method, but some problems still need to be addressed or be clearer.
Q1: Why the HC-GAE model utilizes the hard and soft assignment for the encoder and decoder respectively? As I see, the author can only use any of these assignm... | Summary: The authors propose a new GNN-based representation learning method (HC-GAE), that can abstract effective local node features and global graph features. These features can be used for both node and graph classification. The new HC-GAE method consists of two main computational modules, they are the encoder assoc... | Rebuttal 1:
Rebuttal: Q1: I feel a little strange for the loss function. Because the function tends to minimize the differences of either the structures or the node features between the input (for encoder) and the output (decoder). As I see, if the authors use the output for the node classification, why you do not dire... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Diffusion Twigs with Loop Guidance for Conditional Graph Generation | Accept (poster) | Summary: The paper presents Twigs, a score-based diffusion framework for enhancing conditional generation tasks. It includes a central trunk diffusion process for graph structure and stem processes for graph properties. The innovative loop guidance strategy manages information flow between trunk and stem processes. Exp... | Rebuttal 1:
Rebuttal: Thank you very much for your many excellent suggestions! We have acted on all of these, and additionally, address all your questions and comments below.
**RE: Twigs cannot guide unseen conditions (W1) (Q2)**
Thanks for raising this point. Based on your suggestion we have added an experiment to ... | Summary: This study proposed a conditional generative model with guided diffusion. The authors introduce a new mechanism called loop guidance to include conditions. Empirical analysis includes small molecule generation on a diverse of datasets and properties, with both quantitative evaluation and visualizations.
Stren... | Rebuttal 1:
Rebuttal: Many thanks!
**RE: computational cost (W1)**
In Table 12 of paper, we present the inference times of our model compared to MOOD, showing that while we encounter a small overhead, we achieve superior performance.
In addition, we report below the average time (in seconds) required to train a ... | Summary: This paper introduces Twigs, a new score-based diffusion model for graph generation conditioned on graph-level properties. It employs two diffusion processes, one for graph data (trunk process) and one for graph-level properties (stem process). The underlying generation process corresponds to a factorization o... | Rebuttal 1:
Rebuttal: Many thanks.
**Re: related works (W1)**
The suggested works, including GDSS (Jo et al. 2022), GraphMaker (Li et al. 2024), and EDGE (Chen et al. 2023), are relevant to our research and will be included in our paper. Below we report the differences with our method. Refer to the table of the gl... | Summary: This paper proposes a novel score-based diffusion framework called Twigs that incorporates multiple co-evolving flows to capture complex interactions and dependencies for enriching conditional generation tasks. It consists of a central trunk process and additional stem processes, coordinated by a loop guidance... | Rebuttal 1:
Rebuttal: Thank you so much for your thoughtful comments. We address all your concerns, as described below.
**RE: Section 3 is written too generally and does not address the specific characteristics of the graph (W1)**
We maintain the section in a general format to accommodate multiple cases, specifical... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Back to the Continuous Attractor | Accept (poster) | Summary: The authors study continuous attractor networks, and their famous instability under noise. They show that continuous attractors, despite being unstable, are functionally robust, and analyse some behaviours when noise is introduced.
Strengths: The main thrust of the paper was very interesting and very novel. I... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their valuable comments. They have significantly enhanced the quality of our manuscript.
> signposting
We thank the reviewer for and agree with this feedback. We improved the text by:
- Changing the last paragraph of the introduction, summarizing each sect... | Summary: The manuscript investigates the stability and robustness of continuous attractors to small deformations in vanilla recurrent neural networks. Continuous attractors were once heavily studied in the context of working memory, but they are inherently fragile and susceptible to even small perturbations and lead to... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their valuable comments and suggestions (and appreciate the recent edit). They have significantly enhanced the quality of our manuscript.
### Weaknesses:
> Some more control experiments need to be added for proving the generality (See below).
We address t... | Summary: This paper studies the fragility of continuous attractors, which have been used to explain various computations or functions in the brain related to memory and navigation, to perturbations. The authors mainly focus their analyses on ring attractors, which have been used to model continuous-valued memory. Under... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their valuable comments and suggestions.
### Weaknesses
> The experiments and associated analyses focus solely on networks that approximate 1D ring attractors.
> This is quite simplistic, and at least for the numerical experiments, the authors could conside... | Summary: The study explores some bifurcations from continuous attractors in neuroscience models, revealing various structurally stable forms. Through fast-slow decomposition analysis, they uncover the persistent manifold surviving destructive bifurcations. Additionally, they analyze RNNs trained on analog memory tasks,... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their valuable comments and suggestions.
### Weaknesses:
>their theoretical investigation and results are limited to a few very simple systems, low-dimensional systems [...]
We respectfully disagree with the stated limitation-- the role of the analysis, a... | Rebuttal 1:
Rebuttal: We are grateful and encouraged that the reviewers found our work novel and interesting. Reviewers remarked that it is "a novel contribution and an important result to bolster the continuous attractor hypothesis", "fresh look, novel, original, and interesting [and] superb theoretical motivation", t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Bigger, Regularized, Optimistic: scaling for compute and sample efficient continuous control | Accept (spotlight) | Summary: This paper investigates the sample efficiency problem in continuous control. The authors propose the BRO algorithm, i.e., Bigger, Regularized, Optimistic. The authors find that strong regularization allows for effective scaling of the critic networks, which, paired with optimistic exploration, leads to quite g... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time reviewing our work and the suggestions on how to improve it. We are also very pleased that the reviewer found the experimental section solid. We leave our rebuttal below:
>missing baselines and references...
We thank the reviewer for suggesting these algorit... | Summary: This paper investigates how reinforcement learning (RL) can benefit from parameter scaling. The authors introduce BroNet, a variant of ResNet with LayerNorm, as a well-regularized network structure for the critic that improves performance when scaled. They also find that when the critic is properly regularized... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and valuable suggestions. We are also happy that the reviewer found the insights provided by our manuscript valuable. Please share our rebuttal below:
>As acknowledged by the authors, the study primarily focuses on state-based off-policy RL. The transferabilit... | Summary: The paper studies how to scale up RL algorithms in the continuous domain and introduces the BRO (Bigger, Regularized, Optimistic) algorithm, designed to enhance sample efficiency with (relatively) large models. The authors conduct extensive experiments to verify the effectiveness of factors like replay ratio, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and valuable feedback regarding our work. We are very pleased that the reviewer found our results on scaling interesting. Please find our rebuttal below.
>Usually, scaling up benefits more when a large amount of data is available, where large models can lead ... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful feedback. We are pleased that our work was well received, and that all reviewers recognized the potential significance of our work for the RL community, the scope of our experiments, and the significant performance improvements our method provides over p... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators | Accept (poster) | Summary: The paper proposes a novel method to address the accuracy degradation caused by downsampling on small AI processors. The authors observed that the input layer often has a small number of channels, leading to underutilization of the processors. To mitigate this issue, they introduce a technique involving patch-... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and effort in providing us with positive and thoughtful comments. We respond to your question in what follows. Please also refer to the *global response* we posted together.
---
*Question 1 & Weakness 1) How does the proposed method perform on more complex model... | Summary: This paper introduces DEX, a novel technique designed to enhance the efficiency of DNN inference on resource-constrained tiny AI accelerators by extending the data channels. This approach aims to improve both resource utilization and inference accuracy by incorporating additional spatial information from the o... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and effort in providing us with positive and thoughtful comments. We respond to your question in what follows. Please also refer to the *global response* we posted together.
---
*Question 1 & Weakness 1) What is the overhead of the channel expansion process in t... | Summary: Recent advancements in tiny ML accelerators, such as MAX 78000 and MAX 78002, have significantly boosted hardware processing power. On one hand, these accelerators feature 64 parallel processors with per-processor memory instances, enhancing CNN inference speed compared to traditional MCUs. On the other hand, ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and effort in providing us with positive and thoughtful comments. We respond to your question in what follows. Please also refer to the *global response* we posted together.
---
*Weakness 1) This approach appears suitable only for specific small devices.*
Pleas... | Summary: This paper indicates that current AI accelerators with limited data memory often require downsampling input images, which leads to reduced accuracy. Therefore, the proposed Data channel EXtension (DEX) includes additional spatial information from original images as informative input through two procedures: pat... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and effort in providing us with thoughtful comments. We respond to your question in what follows. Please also refer to the *global response* we posted together.
---
*Weakness 1) The proposed data channel extension requires the assumption that only a limited numbe... | Rebuttal 1:
Rebuttal: # Global Response
Dear Reviewers,
We appreciate all of you for your positive reviews and for highlighting the **strengths of our work**:
**ySD7:** (1) Easy to understand, (2) clearly written, (3) well-organized, and (4) demonstrates the effectiveness of the proposed method.
**UAVo:** (1) Pre... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SpikedAttention: Training-Free and Fully Spike-Driven Transformer-to-SNN Conversion with Winner-Oriented Spike Shift for Softmax Operation | Accept (poster) | Summary: The paper introduces a novel method for converting Swin Transformer and BERT into SNN without additional training, achieving high accuracy and energy reduction. Key innovations include fully spike-based encoding, trace-driven matrix multiplication, and an exponent-free spike-based softmax using winner-oriented... | Rebuttal 1:
Rebuttal: **Long Timestep of SpikedAttention)** Thanks for the comment about the total timestep. As you mentioned, SpikedAttention requires a longer timestep than a directly trained SNN. However, directly trained prior works [1,2] have 2x more weight parameters to achieve the accuracy of 80%. More parameter... | Summary: The paper presents SpikedAttention, a novel method for converting pre-trained transformers into spiking neural networks (SNNs). The method introduces two key techniques: trace-driven matrix multiplication and winner-oriented spike shift (WOSS) for softmax. These techniques enable the conversion of attention mo... | Rebuttal 1:
Rebuttal: **Impact of Timestep on Accuracy/Energy)** Thanks for the comment about the trade-off between the total timestep and the accuracy/energy efficiency. Regarding the accuracy, as discussed in Appendix E, the larger the timestep T, the smaller the base value of an one-spike SNN can be. A smaller base ... | Summary: This paper proposes a transformer-to-SNN conversion method without modifying its attention architecture. To minimize the energy consumption, the authors apply one-spike phase coding, Trace-driven matrix multiplication, and winner-oriented spike shift for softmax. They evaluate their conversion method on vision... | Rebuttal 1:
Rebuttal: **Neuromorphic Datasets)** Thanks for the comment on expanding experiments to the event-based dataset. In this work, we proposed an ANN-to-SNN conversion method for tasks that provide high accuracy using ANNs. Therefore, when submitting the paper, we did not train ANNs on event-based data to conve... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and insightful comments which have helped improve our paper. We address a few common points in this response. All other questions are addressed in reviewer specific responses.
**Meaning of ‘w/o ReLU’)** *( for Reviewer XrJp and Reviewer uL88 )* Since a sp... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Deep Learning for Computing Convergence Rates of Markov Chains | Accept (spotlight) | Summary: The authors proposed a novel computational method to estimate the convergence rate of general Markov Chains. They utilized neural network to verify if contration drift (CD) holds for a given Markov Chain. As an extension to a prior work [1], the authors provided further theoretical analysis of their methods, a... | Rebuttal 1:
Rebuttal: We sincerely thank you for your detailed feedback. In the following, we address the concerns (W1-4) and answer the questions (Q1-4).
W1. In Section 2, we only introduce necessary *analytical* concepts (e.g., random mapping representation, local Lipschitz constant, and contractive drift) to quickl... | Summary: The paper studies the problem of convergence rate analysis for general state-space Markov chains. They propose Deep Contractive Drift Calculator (DCDC), the first general-purpose sample-based algorithm for bounding the convergence of Markov chains. There are two components, a theoretical one that utilize an au... | Rebuttal 1:
Rebuttal: We sincerely thank you for your positive feedback. Please feel free to read the other rebuttals.
---
Rebuttal Comment 1.1:
Title: Thank you
Comment: Thank you for your positive feedback on my positive feedback. I'll read the other rebuttals. | Summary: #### Summary
The paper introduces the Deep Contractive Drift Calculator (DCDC), a novel sample-based algorithm for bounding the convergence rates of general state-space Markov chains to stationarity in Wasserstein distance. The method leverages deep learning to solve the Contractive Drift Equation (CDE), provi... | Rebuttal 1:
Rebuttal: We thank you for your valuable feedback and positive view about our paper. In the following, we address the concerns (1-4) and answer the questions (1-6).
1&4 *Computational and Sample Complexity*: The computational complexity, as with any typical deep learning method involving general non-convex... | null | null | Rebuttal 1:
Rebuttal: * We appreciate the feedback and comments of all of the referees. We note that two out of the three reports rate the paper with an evaluation of 8 (strong accept) whereas one of the referees has some concerns providing an evaluation of 4 (borderline reject).
* We try to focus most of the response... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Harmonizing Visual Text Comprehension and Generation | Accept (poster) | Summary: This work presents TextHarmony to simultaneously comprehending and generating visual text. The paper performs theoretical and experimental analysis about the performance degradation due to the inherent inconsistency between vision and language modalities. A MoE and LoRA-based module, Slide-LoRA, is then propos... | Rebuttal 1:
Rebuttal: Thank you for the valuable comments and the approval of contributions to our work. Your concerns are addressed as follows:
**W1: Compare TextHarmony with DreamLLM and Emu.**
In Table E, we compare TextHarmony with DreamLLM and Emu in terms of both visual comprehension and visual generation. As ... | Summary: This paper introduces a multimodal generative model (TextHarmony) for unified comprehension and generation of visual text. To overcome the performance degradation brought by modality inconsistency, the authors propose the slide-lora, which partially decouples the multimodal generation space. An image-text ca... | Rebuttal 1:
Rebuttal: Thanks for your time to review our paper, the valuable comments and the approval of contributions to our work. And we are looking forward to further discussions with you. Your concerns are addressed as follows:
**W1: The connection between this work and visual text is not clearly stated. ...**
... | Summary: TextHarmony is a versatile multimodal generative model designed to comprehend and generate visual text. Traditional methods struggle with the inconsistency between vision and language modalities, leading to performance issues. TextHarmony overcomes this with Slide-LoRA, which combines modality-specific and mod... | Rebuttal 1:
Rebuttal: Thanks for your careful review and valuable comments. We are looking forward to further discussions with you. Your concerns are addressed as follows:
**W1: The motivation ... is not clear ... first try ... may not be convincing ...**
Our motivation is more than just addressing gaps in unified v... | Summary: This work presents TextHarmony, a unified and versatile multimodal generative model proficient in comprehending and generating visual text. Simultaneously generating images and texts typically results in performance degradation due to the inherent inconsistency between vision and language modalities.
Strength... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper. If you have any further comments or suggestions, please let us know. Your concerns are addressed as follows:
**W1: Details of the Modal-Aware Gating are not given?**
As stated in **Line 123-127**, the Modal-Aware Gating is an MLP module containing two linea... | Rebuttal 1:
Rebuttal: Thanks to the ACs and reviewers for taking time and effort to review our manuscript. And we are also looking forward to further discussion. Below, we would like to address the common issues raised by reviewers.
**Common Issue 1: Performance compared to unimodal generation models, such as TextHa... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Differentiable Modal Synthesis for Physical Modeling of Planar String Sound and Motion Simulation | Accept (poster) | Summary: The paper presents a differentiable model that can synthesize musical string sound and simulate motion based on physical properties. The method uses finite-difference time-domain (FDTD) solver to obtain numerical solutions and take them as ground truth. Then a differentiable pipeline with neural network compon... | Rebuttal 1:
Rebuttal: We thank reviewer Hh9k for the extensive review. Below are the responses to your concerns. Each item in Weakness is labeled with a number following the W (from the top, W1, W2, ...)
- **W1 (The gap between the simulation and the real-world audio data)**
- We summarized some of the main diffe... | Summary: This paper proposes Differentiable Modal Synthesis for Physical Modeling (DMSP), which is a neural-model-based method to predict the vibration of nonlinear strings. DMSP takes the physical parameters for the differentiable equation as the input, predicts the mode frequencies and AM/FM effects, and finally outp... | Rebuttal 1:
Rebuttal: We appreciate reviewer ygxN’s effort in reviewing our paper. Below are the responses to your concerns.
- **W1 (Clarity in methodological details)**
- We clarify the mathematical definition of loss based on the reviewer's comments. We used $\ell^1$ distance as $\mathcal{L}_{f_0} = \|\hat{f_0} ... | Summary: A computational framework that can approximate the motion of nonlinear strings is proposed. The implementation is differentiable, so one can train then neural nets in the framework as usual.
Strengths: - The paper is written nicely such that I could follow the basic discussions even if I am not familiar with ... | Rebuttal 1:
Rebuttal: We thank reviewer gawP for the constructive review. We also thank you for appreciating the novelties made by the differentiable string motion synthesis. Below are the responses to some of your comments. Each item in Weakness is labeled with a number following the W (from the top, W1, W2, ...)
- *... | null | null | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for taking the time to review the paper and for their efforts to improve the quality of the manuscript with their constructive comments. We responded to each reviewer's comments and concerns individually, and the responses below address common points made b... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Theoretical Characterisation of the Gauss Newton Conditioning in Neural Networks | Accept (poster) | Summary: The authors derived upper bounds for the condition number of the outer product of the jacobian of the neural network output (the Gauss Newton matrix) in the case of deep linear networks, and non-linear networks with a single hidden layer with piecewise-linear activation functions. They empirically evaluate the... | Rebuttal 1:
Rebuttal: **Weakness 1**:
> The paper generally uses the same dataset (MNIST) for almost all the plots, so it's hard to tell if these good behaviour of their bounds hold on other, potentially harder to optimise, problems such as CIFAR-10.
**Answer**:
Thank you for this comment. Please note that we do have ... | Summary: This paper examines the condition number of the Gauss-Newton matrix [1] in neural networks. It shows that normalization techniques, such as Batch Normalization [2], initial normalization, skip connections [3], and appropriate layer dimensions, reduce the condition number and therefore enhance the training stab... | Rebuttal 1:
Rebuttal: **Weakness 1**:
> The paper is difficult to read and follow due to several typos and unclear ideas. The main contribution is not significant and heavily relies on Singh's work [4].
**Answer**:
We understand the concern of the reviewer regarding the potential overlap with Singh et al. [2021].
- We... | Summary: This paper characterizes the conditioning of Gauss-Newton (GN) matrix. The contribution of this paper is clear and straightforward: for deep linear networks, it establishes a bound on the condition number of GN matrix, which is further extended to 2-layer ReLU networks. These bounds could be useful in certain ... | Rebuttal 1:
Rebuttal: **Weakness 1**:
> My major concern is about the implications of the derived bounds. Specifically, I note that training/learning such as gradient descent learning dynamics [...] is not involved in deriving the bound. Thus it is hard to see the implication of these derived bounds since they are the ... | Summary: This paper is dedicated to the theoretical characterization of the condition number of the Gauss-Newton (GN) matrix in neural networks. By studying deep linear networks and two-layer nonlinear networks, the authors establish tight bounds on the GN matrix's condition number and extend this analysis to architect... | Rebuttal 1:
Rebuttal: **Weakness 1**:
> Convergence Rate Analysis in Figure 17: The network's optimal solution and the corresponding minimum loss differ under various settings, making it difficult to analyze the convergence rate from the loss changes. [...] Thus, it is hard to draw conclusions about the convergence spe... | Rebuttal 1:
Rebuttal: Dear Reviewers,
we would like to thank you for the time that you have committed to reviewing our work and for the questions and comments, which have helped to enhance our work considerably.
We are pleased to report that we were able to address almost all of your comments and questions (except o... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Localized Adaptive Risk Control | Accept (poster) | Summary: This paper proposed a novel localized adaptive risk control algorithm that provides not only average case risk guarantees but also worst-case guarantees. Simulations in several different applications are provided, demonstrating the improved performance when compared with adaptive risk control.
Strengths: The ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments. Below, we address each comment point by point:
- The focus of this work is on producing calibrated predictions, i.e., predictions with risk control guarantees, rather than on providing more accurate predictions. To this end, we compare standard L-... | Summary: This paper addresses the design and analysis of the localized version of adaptive risk control (L-ARC). In the first section, the problem of classical ARC is nicely introduced, showing the threshold updating mechanism and the convergence analysis of the resulting loss. Then, the problem setting, design, and ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments. Below, we address each comment point by point:
- The terms in the bound incorporate factors that relate the algorithm's guarantees to domain-dependent quantities, such as the maximum value of the loss $B$ and the maximum value of the kernel $\kapp... | Summary: The paper introduces Localized Adaptive Risk Control (L-ARC), an enhancement of Adaptive Risk Control (ARC). L-ARC updates a threshold function within a Reproducing Kernel Hilbert Space (RKHS) to provide localized risk guarantees while maintaining ARC's worst-case performance. Experiments show that L-ARC impro... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments. Below, we address each comment point by point:
- We aim to ease the implementation of the algorithm by providing code to reproduce the experiments. In the revised manuscript, we will also clarify the connection with online learning algorithms, ena... | Summary: This paper introduces an online calibration method to enhance the Adaptive Risk Control (ARC) framework. ARC traditionally adjusts prediction sets based on a scalar threshold to ensure long-term risk control and marginal coverage guarantees. However, as mentioned in the paper, it may unevenly distribute risk g... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful comments. Below, we address each comment point by point:
- The input space of the prediction model is commonly referred to as the feature space in the machine learning literature. We will be happy to clarify the terminology in the revised manuscript.
- A... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful comments. Multiple reviewers raised concerns about the linear memory requirement of L-ARC. In the original submission, we acknowledged this limitation and planned to leave the derivation and analysis of memory-efficient variants for future work. Nonethel... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes the Localized Adaptive Risk Control (L-ARC) scheme for learning to perform conformal prediction from online data. In the setting under consideration, the data is potentially non-i.i.d. and the scalar threshold parameter typically used by existing methods in the construction of the predictio... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments. Below, we address each comment point by point:
- From a methodological point of view, the main innovation lies in using functions from an RKHS to define prediction sets that are calibrated online and that ensure localized risk control. The RKHS fu... | null | null | null | null | null | null |
Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based Inference | Accept (poster) | Summary: This work introduces a new way to do active learning in simulation-based inference. This algorithm allows for the sampling of data points not only in regions of interest but also in regions that lead to high information gain. They then apply this algorithm to the problem of urban travel demand calibration, whi... | Rebuttal 1:
Rebuttal: Thank you for your time and effort providing detailed feedback on our work. Please find our responses to your questions and comments below.
Note: Where applicable, we prefix sections with `W-<x>`, `Q-<y>`, or `L-<z>` to reference itemized comments in Weaknesses, Questions, and Limitations, respec... | Summary: The paper considers the problem of efficient neural density estimation for simulation-based inference, in settings where we want to estimate the parameters of a model from which we can draw samples but cannot define a likelihood function. Essentially, the main contribution of the paper is to integrate an activ... | Rebuttal 1:
Rebuttal: Thank you for your time and effort providing detailed feedback on our work. Please find our responses to your questions and comments below.
Note: Where applicable, we prefix sections with `W-<x>`, `Q-<y>`, or `L-<z>` to reference itemized comments in Weaknesses, Questions, and Limitations, respec... | Summary: The paper addresses the problem of computational cost, or alternatively, of sample efficiency in simulation-based inference (SBI). By employing an active learning scheme in sequential neural posterior estimators (SNPE), the proposed method achieves improved sample efficiency, which is paramount when dealing wi... | Rebuttal 1:
Rebuttal: Thank you for your time and effort providing detailed feedback on our work.
Note: We prefix sections with `W-<x>`, `Q-<y>`, or `L-<z>` to reference itemized comments in Weaknesses, Questions, and Limitations, respectively.
**[W-1]** The discussion around EIG was principally intended to help set ... | Summary: This paper proposes an approach to performing neural simulation-based inference – specifically, sequential neural posterior estimation – in a simulation-efficient manner for complex and expensive simulation models. The idea is to use active sampling to sequentially generate datapoints from the simulator and to... | Rebuttal 1:
Rebuttal: Thank you for your time and effort providing detailed feedback on our work. Please find our responses to your questions and comments below.
Note: Where applicable, we prefix sections with `W-<x>`, `Q-<y>`, or `L-<z>` to reference itemized comments in Weaknesses, Questions, and Limitations, respec... | Rebuttal 1:
Rebuttal: We’d like to thank all reviewers for their time and effort in providing insightful feedback on our work. Please find our responses to your individual questions and comments in the rebuttal replies to each review.
Due to space constraints, we would like to address a common critique raised by many ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: A recent class of methods that have shown to perform well at simulation-based inference are based on modeling the posterior density as a neural network, using mixture density networks, normalizing flows, or other popular architectures. With enough data, these methods tend to produce salient estimates of the po... | Rebuttal 1:
Rebuttal: Thank you for your time and effort providing detailed feedback on our work. Please find our responses to your questions and comments below.
Note: Where applicable, we prefix sections with `W-<x>`, `Q-<y>`, or `L-<z>` to reference itemized comments in Weaknesses, Questions, and Limitations, respec... | Summary: The paper introduces ASNPE, a modification of sequential neural posterior estimation (SNPE) that uses active learning to determine the set of most informative simulation parameters. The method is benchmarked in a synthetic scenarios based on a real-world traffic network and outperforms domain-specific optimiza... | Rebuttal 1:
Rebuttal: Thank you for your time and effort providing detailed feedback on our work. Please find our responses to your questions and comments below.
Note: Where applicable, we prefix sections with `W-<x>`, `Q-<y>`, or `L-<z>` to reference itemized comments in Weaknesses, Questions, and Limitations, respec... | null | null | null | null |
Private Online Learning via Lazy Algorithms | Accept (poster) | Summary: This paper studies private online prediction from experts (OPE) and online convex optimization (OCO) problems and proposes a general transformation that converts lazy (non-private) algorithms into private algorithms. By applying it to existing lazy algorithms, they obtain improved regret bounds for both proble... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the feedback. We respond to the reviewer’s main comments below; we are glad to clarify further as needed during the discussion period.
Improvement for small $\epsilon$: It is true that in many applications, $\epsilon$ is set to be some constant number. But ... | Summary: This paper investigates private online learning, focusing on online prediction from experts (OPE) and online convex optimization (OCO). The authors propose a new transformation that translates lazy, low-switching online learning algorithms into private algorithms. Based on this transformation, their resulting ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the feedback. Please see our responses below; we are glad to clarify further as needed during the discussion period.
Adaptive adversary: we note that our main focus in this work is oblivious adversaries since the recent work of [AFKT23] studies adaptive adv... | Summary: The paper studies the differentially private (DP) variants of the classical online prediction form experts problem (OPE) and online convex optimization (OCO) problem. The main contribution is a (black-box) approach to transform lazy (i.e. slow-varying) online learning algorithms into private algorithms. The pa... | Rebuttal 1:
Rebuttal: Thanks for your review and feedback. Below, we address the main comments; we are glad to clarify further as needed during the discussion period.
1. Lower bounds: we acknowledge that the conditional lower bound is not ideal, but it is important to note that this is the first non-trivial (even cond... | Summary: This paper proposes a new mechanism that converts lazy online learning algorithms into private algorithms. Unlike previous private online algorithms that use individualized privatized methods, the paper's new mechanism is a black box private algorithm that can be applied to many popular non-private methods suc... | Rebuttal 1:
Rebuttal: Thanks for your time in reviewing and feedback. Please see our responses below; we are glad to clarify further as needed during the discussion period.
- Improvement for small $\varepsilon$: First, by modifying the parameter settings, we can recover the previous best results when $\epsilon \ge \Ome... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Reconstruct and Match: Out-of-Distribution Robustness via Topological Homogeneity | Accept (spotlight) | Summary: The paper proposes a method for improving domain generalization and test time adaptation by introducing a selective variant of slot attention, formulating the relationship between slots across images as topological homogeneity between hypergraphs constructed based on the slots, and thereby matching occurances ... | Rebuttal 1:
Rebuttal: We greatly appreciate the time and effort the reviewer has invested in reviewing our paper. However, **we must point out that the review contains many factual errors and misunderstandings, rendering most comments unacceptable due to their erroneous assumptions.** We will try our best to eliminate ... | Summary: This paper presents REMA which designed to improve the robustness of deep learning models against out-of-distribution (OOD) data. REMA employs a selective slot-based reconstruction module to dynamically map dense pixels into a sparse set of slot vectors, enabling the identification of major components from obj... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback, which we address below:
> **Q1-1: How sensitive is REMA's performance to the choice of hyperparameters such as the number of slots and attention iterations?**
As suggested, we have provided quantitative experimental results in **the attached P... | Summary: The authors propose an approach to tackle OOD generalization via a method that tightly combines learning-based feature extraction with graph-based relationship modelling to explicitly learn and represent the topological structure of image data, achieving competitive results across a range of OOD and test-time ... | Rebuttal 1:
Rebuttal: We are grateful for your insightful comments and appreciation of our work. We address each question in detail and provide further clarifications below.
> **Q1: Consistency in notation.**
Sorry for the confusion. We have modified the notation in Eq. (2) to make it consistent with other formulas, ... | Summary: A new methodology, REconstruct and MAtch (REMA) is introduced to learn a more robust and generalizable feature set in computer vision models. REMA relies on a slot attention module to learn sparse embeddings of features which characterize a target object, and this module is then coupled with a high-order rela... | Rebuttal 1:
Rebuttal: We are grateful for your insightful comments and appreciation of our work. We address each question in detail and provide further clarifications below.
> **Q1:Motivation for algorithmic design choices (SSR + HORR).**
(1) Although we aim to imitate the human vision process for OOD generalization,... | Rebuttal 1:
Rebuttal: We sincerely appreciate all four reviewers for their time and effort in providing feedback and suggestions on our work. We are glad that reviewers recognize our paper to be *novel* (jWEa, M7aK), *well-motivated* (M7aK, rhAB, EaaQ), and performing *extensive experiments and ablation studies* (jWEa,... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Long-range Brain Graph Transformer | Accept (poster) | Summary: This paper employs a random walk approach to capture long-range dependencies in the brain through a feature engineering scheme. The computed adaptive factors are incorporated into node features and used to train a Transformer model.
Strengths: 1. The idea of capturing long-range dependencies in the brain is r... | Rebuttal 1:
Rebuttal: We thank you for your detailed feedback and your questions. We hope we have well addressed your concerns. If there are any other issues remaining, we are pleased to address them further.
**W1.a**
In fact, we employ Pearson correlations as adaptive factors in random walk, without any modifcation. ... | Summary: This paper highlights a significant gap in the existing literature on brain network representation learning, specifically the inadequacy of current methods to effectively capture long-range dependencies, leading to limited an integrated understanding of brain-wide communication. To bridge this gap, the paper i... | Rebuttal 1:
Rebuttal: We thank you for acknowledging the novelty of the proposed method and for suggesting relevant analysis, which we have included in the global response. We hope to provide satisfying answers to the concerns raised. If there are any other issues remaining, we are pleased to address them further.
**W... | Summary: The study employs the adaptive long-range aware graph transformer (ALTER) to tackle the challenge of weak comprehension in whole-brain communication, which arises from the failure to capture long-range dependencies in brain networks. Initially, the study encodes long-range dependencies into long-range embeddin... | Rebuttal 1:
Rebuttal: We thank you for your detailed assessment of our work and for highlighting the merits of our approach, as well as the importance of the problem. We address all concerns below, if there are any other issues remaining, we are pleased to address them further:
**W1**
Brain networks are inherently den... | Summary: This work proposes Adaptive Long-range aware TransformER (ALTER), a brain graph transformer to capture long-range dependencies between brain ROIs utilizing biased random walk.
Strengths: 1. This work introduces a novel brain graph transformer with adaptive long-range awareness, which leverages the communicati... | Rebuttal 1:
Rebuttal: We thank you for the comments on our work. Below we address the questions raised. We hope we have well addressed your concerns. If there are any other issues remaining, we are pleased to address them further.
**W1**
As shown in Figure 1(a) of the global response, we have analyzed the output using... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful assessment of our work, as well as the useful feedback and actionable suggestions they provided. We are pleased that they found our work to be meaningful (reviewer u6uE) and reasonable (reviewer s7Az), that the experimental results are strong (review... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Efficient Leverage Score Sampling for Tensor Train Decomposition | Accept (poster) | Summary: This paper gives a better randomized alternating least squares algorithm for computing tensor factorizations. It's based on an exact characterization of leverage scores of the matrization of tensors via a suitable intermediate orthonormal representation. This is justified rigorously, and significant empirical ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and feedback, please find responses below:
**Weaknesses:**
> I'm a bit concerned about the setting of the experiments, which seem to be 3-dimensional dense tensors. My understanding is that a lot of the more complicated tensor instances are sparse and in h... | Summary: The authors proposed a leverage score sampling-based TT-ALS method to reduce the computational complexity of the traditional TT-ALS. Experimental results verify the performance of the proposed method.
Strengths: The paper is well written with good theoretical analysis and desired experimental performance. The... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and feedback, please find responses below:
**Weaknesses:**
> The contribution and novelty are not clearly stated compared with [Malik and Becker, 2021]. In [Malik and Becker, 2021], the leverage sampling was applied to TR. As TT can be treated as a special... | Summary: This paper presents an efficient algorithm to use leverage-score sampling to solve least squares problems arising as subproblems in a larger alternating least squares (ALS) algorithm for building an approximate Tensor Train (TT) decomposition. The paper reports empirical evaluation of the proposed algorithm, s... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and respond to the feedback below:
**Weaknesses:**
> [W1][Comparison against (Chen et al. 2023)]
The TensorSketch approach by Chen et. al. requires an **exponential** sample count in the tensor dimension q to achieve the $(\epsilon, \delta)$-guarantee on ... | Summary: This paper considers the problem computing the tensor train TT decomposition of large tensors and proposes a novel randomized approach for efficiently solving the problem. In particular, the Alternating Least Squares (TT-ALS) algorithm is considered and exact leverage scores sampling approach is proposed to ac... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and feedback, please find responses below:
**Weaknesses:**
> The randomized SVD approach seems to be more efficient in terms cost compared the the prosed method.
Randomized SVD cannot scale to large tensors. Indeed, to decompose a tensor using randomized ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their comments and feedback, which we all answered in the individual rebuttals below.
We summarize some of our answers to the main points raised by the reviewers (see individual rebuttals for details).
- [SVD-based vs ALS-based approach]: Overall TT-ALS is very pop... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Persistent Test-time Adaptation in Recurring Testing Scenarios | Accept (poster) | Summary: The paper proposes a novel method called Persistent Test-time Adaptation (PeTTA), aimed at addressing the gradual performance degradation of models when used for long-term test-time adaptation (TTA). Traditional TTA methods adapt to continuously changing environments but fail to account for the cumulative erro... | Rebuttal 1:
Rebuttal: 1. We agree with the reviewer that the real-world scenarios are significantly more complicated. Despite its simplicity, our $\epsilon$-GMMM model can *empirically demonstrate the behavior of a collapsing TTA model on a real-world CIFAR-10-C dataset* (see the similarity between Fig. 3(a) and Fig. 4... | Summary: This paper investigates the risk behind long-term test time adaptation. To achieve this, the authors simulate a long-term test data stream called Recurring Test-Time Adaptation by repeating a single period continual TTA setting for 20 times, and propose a Persistent TTA (PeTTA). Within the proposed algorithm, ... | Rebuttal 1:
Rebuttal: **Comments on the weaknesses part:**
1. *We respectfully disagree with the comment about the novelty since this study is not all about the anchor loss*. Indeed, we *acknowledged the anchor loss is not a new idea or our novel point* on line 751 (Appendix E5) and will include a proper citation to th... | Summary: The paper provides theoretical and empirical analyses on the error accumulation and model collapse in continuous TTA scenarios. From the analyses, the authors discover the risk of using constant key hyperparameters ($\alpha$ and $\lambda$ in RoTTA) and periodic reset of model parameter (in RDumb). They propose... | Rebuttal 1:
Rebuttal: **Comments on the Weakness Part:**
1. We would like to emphasize that recurring TTA serves as a *diagnostic tool* for catching the lifelong performance degradation of continual TTA, and even *in this simplest case, several SOTA continual TTA algorithms fail to preserve their performance*. This rai... | Summary: The authors proposed a practical TTA scenario called recurring TTA and, within this scenario, suggested the best-performing TTA methodology, which they named persistent TTA (PeTTA), measured by various benchmark performances.
Strengths: 1. The proposed recurring TTA scenario reflects the challenging and pract... | Rebuttal 1:
Rebuttal: **Comments on the Weaknesses Part:**
1. In Lemma 1, we mathematically showed that under Assumption 1, we have $\lim_{t \rightarrow \tau}\epsilon_t = p_1$. Furthermore, the convergence of $\epsilon_t$ to $p_1$, and the collapsing behavior when $\epsilon_t$ selected following Collary 1 are both emp... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful comments and valuable feedback on our work. During the rebuttal period, we have extensively conducted additional experiments: benchmarking the performance of EATA and the most recent continual TTA methods (ROID (WACV’24), and TRIBE (AAAI’24)). The persis... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Graph Convolutions Enrich the Self-Attention in Transformers! | Accept (poster) | Summary: To overcome the oversmoothing effects in general transformer settings, the author considers to treat the attention module as the graph filter and propose to use a polynomial graph filter (graph signal processing) techniques to alleviate oversmoothing. Specifically, the author considers the induced attention m... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer bzWT for the review and feedback, highlighting our strengths in
1. Clear idea applying GNN concepts to transformer models.
2. Analytical approach to high-pass and low-pass filters with polynomial approximation.
3. Extensive experiments across various transformer applica... | Summary: This paper proposes a novel approach to enhance the self-attention mechanism in transformers by drawing on graph signal processing principles. The authors reframe the standard self-attention as a low-pass graph filter and introduce a more generalized filter (GFSA) capable of capturing low-pass, high-pass, or c... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer k2id for the review and feedback, highlighting our strengths in
1. Easily understandable core idea with promising results across diverse transformer variants.
2. Versatile performance demonstrated across a wide range of tasks in multiple domains.
Below, we would like t... | Summary: This work proposes to enhance self-attention by considering the high-order power of the attention matrix inspired by graph convolution networks and graph signal processing, to overcome the oversmoothing issues of transformers.
To reduce the computational overhead, the authors propose to use a first-order Taylo... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer XKs2 for the review and positive feedback, highlighting our strengths in
1. Well-written paper.
2. Comprehensive experiments strongly support the advantage of GFSA.
Below, we would like to address each of your questions and concerns:
**Response to W1: confusing notat... | Summary: The paper proposed a graph-filter-based self-attention mechanism to improve its effectiveness. The authors conduct experiments in various fields, including natural language understanding, computer vision, automatic speech recognition, graph regression, and code classification, showing the generalization of the... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer Py4c for the review and feedback, highlighting our strengths in
1. Extensive experimentation across diverse domains demonstrating the generalizability of GFSA.
2. Clear and well-organized presentation of ideas.
3. Strong emphasis on reproducibility with detailed implem... | Rebuttal 1:
Rebuttal: Dear reviewers,
We sincerely appreciate your feedback and constructive comments on our paper. We are grateful for the recognition of several key strengths in our work:
1. Extensive experiments across diverse domains demonstrating the broad applicability of GFSA
2. Clear, well-organized presentati... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Exploiting Descriptive Completeness Prior for Cross Modal Hashing with Incomplete Labels | Accept (poster) | Summary: Cross-modal hashing (CMH) has attracted much attention due to its low computational and storage costs while maintaining high-dimensional cross-modal semantic similarity. To address the incomplete annotation challenge of CMH, this paper proposes a novel Prompt Contrastive Recovery method, PCRIL. The method incl... | Rebuttal 1:
Rebuttal: # Authors' Responses to Reviewer `MbLT`'s Comments
> Q1. The authors lack comparison with some recently proposed SOTA methods, no one within two years, so the experimental results are not convincing.
We further compare our method with:
- CMHML [1], a recent method investigating CMH with incompl... | Summary: This paper presents a novel cross-modal hashing method named PCRIL, which explores the indispensable but challenging problem of incomplete label recovery in multi-label learning. It conceives a CLIP-based prompting scheme and a complementary semantic propagation mechanism, enabling PCRIL to restore unknown lab... | Rebuttal 1:
Rebuttal: # Authors' Responses to Reviewer `zzeX`'s Comments
Thank you for your thoughtful review. We are pleased that you found our approach novel and technically robust. Your positive feedback on our contrastive recovery method and extensive experiments is greatly appreciated. Your insights are invaluabl... | Summary: The manuscript tackles the challenges of generating high quality hash codes for cross-modal retrieval in the presence of incomplete labels, which creates uncertainty in distinguishing between positive and negative pairs. To address the issue, a novel Prompt Contrastive Recovery approach called PCRIL is propos... | Rebuttal 1:
Rebuttal: # Authors' Responses to Reviewer `Sq2X`'s Comments
Thank you for your thorough review and insightful comments. We are delighted that you found our PCRIL approach innovative and effective in resolving incompleteness in cross-modal hashing. Your appreciation of our proposed prompt contrastive learn... | Summary: The authors propose a novel approach, Prompt Contrastive Recovery for Incomplete Labels (PCRIL), for cross-modal hashing with incomplete labels in this paper. They utilize a learnable CLIP prompt to encode selected anchor class combinations and employ a contrastive learning paradigm to construct multiple negat... | Rebuttal 1:
Rebuttal: # Responses to Reviewer `MwQQ`'s Comments
We sincerely appreciate your detailed review. We would thank your recognition of the originality and effectiveness of our approach, as well as your acknowledgment of our comprehensive analysis and clear presentation. Your feedback is highly valued and enc... | Rebuttal 1:
Rebuttal: # Global Reply to all reviewers
We would like to extend our gratitude to all the reviewers for their insightful comments and unanimous acknowledgement of our paper in the following aspects:
1. The task addressed by this work is both interesting and significant for real-world cross-modal hashing a... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: In this paper, to solve the problem of unknown labels in the task of cross-modal retrieval, the authors aim to progressively identifies promising positive classes from unknown label sets and recursively searches for other relevant labels.
Strengths: (1)Compared with existing related works, the proposed method... | Rebuttal 1:
Rebuttal: # Responses to Reviewer `FiXs`'s Comments
Thank you for your thoughtful review and positive feedback. We are pleased that you recognize the improvements and the effectiveness of our method. Your comments are greatly appreciated and please find our point-to-point response below
> Q1. The meaning o... | null | null | null | null | null | null |
Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices | Accept (poster) | Summary: The paper generalizes multiple existing structured matrices by means of Einsum. The scaling law of the structured matrices with different rank, compute intensity, and parameters-per-FLOPs is analyzed on GPT-2. Since the high-rank, non-parameter-sharing einsum operations obtain the best results, the paper propo... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful and supportive review. We agree that the large number of variables presents readability challenges and will update the paper to provide reminders about the meanings of the indices. We now provide several clarifications and new results inspired by your commen... | Summary: This paper proposes a general framework to cover different linear layers by continuous parametrization. The authors conduct extensive experiments to demonstrate several optimal design principles for linear layers, and further propose a novel sparse MoE architecture that improves upon existing works.
Strengths... | Rebuttal 1:
Rebuttal: We appreciate your feedback. Inspired by your comments, we have run additional experiments and provide clarifications below. We hope that you will consider these new results and clarifications in your final assessment.
**On the key contributions of our work:**
We highlight that this work provides... | Summary: In this paper, the authors explore the computational efficiency of various structured layers in language modeling tasks. Specifically, they propose a general parametrization of linear operators and conduct an empirical study on the conditions for scalable decomposition based on three key characteristics: rank,... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful feedback. Indeed, unifying existing structured approaches and performing extensive and well-controlled comparisons between them is an important contribution of this work. We now provide additional results and clarifications to your questions.
**On experiments with la... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for their feedback and questions. We provide a general response here and individual replies in separate posts below. Inspired by comments from reviewers, we include multiple new experiments encompassing new datasets, new architectures, and alternative MoE structures that... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Graph Neural Networks Need Cluster-Normalize-Activate Modules | Accept (poster) | Summary: The paper introduces a novel plug-and-play module named Cluster → Normalize → Activate (CNA) to enhance the performance of Graph Neural Networks (GNNs). The CNA module is designed to address the issue of oversmoothing, which occurs in deep GNN architectures and limits their ability to solve complex tasks. The... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments and finding our method creative, a significant advancement, and empirical evaluation thorough. We address your concerns next.
### Q1 (Theoretical underpinnings):
We agree that an improved understanding of the mechanisms driving CNAs strong empiri... | Summary: The authors propose a new normalization scheme they call "CNA." They propose clustering the nodes according to their features and computing the normalization statistics for each cluster.
This is essentially a batch norm with groups tailored for graphs.
This normalization is then augmented with a learnable acti... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments and for finding the writing satisfying. We address your concerns next.
### Q1 (Why CNA works):
We now provide theoretical underpinnings of why CNA works. Please have a look at the global response.
### Q2 (Empirical evaluation):
We already provid... | Summary: The paper describes a new updating rule based on a sequence of operation clustering, normalization and a learnable activation to replace the original plain relu-like update message passing and empirically show that such learnable updating function gains large performance improvement on existing benchmark datas... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments and for acknowledging our strong results, universality of the method, and clear writing. We will address the concerns next.
### Q1 (Additional theoretical analysis and comparison to other normalization techniques):
Please see our general response/... | Summary: This paper proposes a novel module, CNA (Cluster-Normalize-Activate), to address the oversmoothing problem in Graph Neural Networks (GNNs). The CNA module operates in three steps: clustering node features, normalizing them within clusters, and applying learnable activation functions to each cluster.
Strength... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments and for acknowledging that our experiments and analysis are solid and that our method is easily adaptable. We will address your concerns next.
### Q1 (Results on SAGEConv and GCNConv):
Thank you for taking a closer look. We have now run new experi... | Rebuttal 1:
Rebuttal: ### Global Response/Comment
We want to thank all reviewers for their time and effort in improving this work. We particularly appreciate your acknowledgment of the relevance of the issue, the novelty of the approach, and the extensive and convincing experiments. Your comments and questions helped i... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing | Accept (poster) | Summary: This work builds upon advances on equivariant and many-body architectures for the construction of neural network potentials. It lays out the formalism to substitute the conventionally-used spherical tensors in higher-rank models for Cartesian tensors. Taking as a reference the MACE architecture, the authors in... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive assessment of the manuscript and have addressed each point they raised below. All numerical results for the experiments conducted for this review are presented in Tabs. 1, 2, and 3 of the attached PDF. We also include Fig. 1, illustrating inference time and... | Summary: This paper introduces the use of higher-rank irreducible Cartesian tensors as an alternative to spherical tensors for equivariant message passing in machine learning interatomic potentials. The authors illustrate clearly on how to construct these tensors and their products, prove equivariance properties, and e... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive assessment of the manuscript. We have addressed each point they raised below. All numerical results for the performed experiments are presented in Tabs. 1–3 and Fig. 1 of the attached PDF.
**W1:** We added a large-scale data set that aims to assess the... | Summary: In this work, the authors proposed higher-rank irreducible Cartesian Tensor Product, and explored its usage in equivariant neural networks design in scientific applications such as molecular modeling. The authors firstly prove that irreducible Cartesian Tensor Product is equivariant to O(3) group, and further ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and comments. We have addressed each of their points below and included all new results in Tabs. 1–3 and Fig. 1 of the attached PDF.
**W1:** We agree that discussing these points would improve our work. Indeed, our approach includes TensorNet and ... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers,
We thank you for your time and effort in evaluating the manuscript and providing positive feedback and constructive suggestions. Below, we provide a general response to your comments, with more details available in the individual discussions. We will revise the manuscript accordin... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning predictable and robust neural representations by straightening image sequences | Accept (poster) | Summary: This paper presents a simple self-supervised learning objective which aims to "straighten" representation trajectories in latent space - maximizing cosine similarity of consecutive deltas of representation (i.e take three time steps, calculate the difference in representation between each pair of consecutive r... | Rebuttal 1:
Rebuttal: Thank you for your review and comments.
**Scope of experiments:** We acknowledge the limitations of our current experiments. In the global response, we explained why we did not use natural videos, and our plan for improvement.
**DINO v2 and MAE:**
- To give a partial answer to the reviewer’s qu... | Summary: the current manuscript introduces a self-supervised learning (SSL) objective inspired by biological vision systems. It proposes an objective that promotes the "straightening" of neural representations of image sequences, facilitating linear prediction. The proposed method is tested on small and synthetic datas... | Rebuttal 1:
Rebuttal: Thank you for your review and comments.
**General comments on novelty:** see global response.
**How straightening differs from the references mentioned by the reviewer:** References pointed out by the reviewer seem to focus on why and when the variance-covariance regularizer is useful, while our... | Summary: This is a very interesting paper that wants to show that robustness is a consequence of perceptual straightening during training -- both areas that have largely remained disconnected in vision. In particular because adversarial robustness is generally studied from a theoretical perspective, or empirical cat-an... | Rebuttal 1:
Rebuttal: Thank you for your review and comments.
**Qualitative assessment for adversarial images:** see global response.
**Regression results:** The purpose of training straightening on shuffled frames is to validate that straightening indeed makes use of the temporal correlations of inputs, and that if ... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their comments and questions. This global response addresses questions that were raised by multiple reviewers, and the other points are addressed in individual responses.
**Why didn’t we use a natural video dataset?**
- To link robustness and straightening, our primar... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MatFormer: Nested Transformer for Elastic Inference | Accept (poster) | Summary: The paper proposes Matformer, a technique to achieve elastic inference where one model is trained, encapsulating sub-models that can be extracted on demand. The main idea to achieve this is to apply a Matryoshka "self-stacking" of hidden states in the FNN blocks of transformers, which are randomly sampled at t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments and support. We clarify the main concerns below:
1."...unclear that the proposed approach would be better than training small..."
We clarify that MatFormer does not use more data or compute compared to the baselines trained separately and can b... | Summary: The paper introduces Matformer, an elastic modeling strategy for inference that provides flexibility for latency and cost requirements. The authors base their method on the recently proposed matryoshka representation learning (MRL) paradigm to introduce nested substructures in the transformer blocks. Specifica... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments and support. We clarify the main concerns below:
1. "Scaling ... will be difficult...Training .. intractable at model scales that are deployed today."
We clarify that MatFormer does not use more data or compute compared to the baselines trained... | Summary: This paper presents MatFormer, a nested Transformer architecture for elastic inference deployment constraints. It follows the principle of matryoshka representation learning and incorporate nested structure in the FFN modules of Transformers. Experiments show that MatFormer can (1) reliably obtain 582-850M mod... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation of our contributions, and answer the questions asked below:
--------
1. Can you add a discussion section with MoE?
**Answer**: We agree that a discussion section on MoE would be appropriate, and will include a discussion section on this in the final ... | Summary: The authors proposed a novel Transformer architecture called MatFormer to provide elastic inference across diverse deployment constraints. Specifically, the authors incorporate a nested Feed Forward Network (FFN) block structure within a standard Transformer model. During training, the authors optimize the par... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments and support towards the paper. We clarify the main concerns raised by the reviewer:
-----------
1. The idea of jointly optimizing a nested sub-structure is similar to Slimmable networks [A] and the supernet in Neural Architecture Search. More e... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Referring Human Pose and Mask Estimation In the Wild | Accept (poster) | Summary: This paper introduces a new task named as Referring Human Pose and Mask Estimation (R-HPM), which adopt text/point/scribble to represent a specific person and estimate its pose and segmentation mask. To achieve this goal, this paper proposes a new R-HPM dataset named RefHuman and a new method UniPHD to perform... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer DJJG for their comments and appreciation of our work. In response to the concerns expressed in Weaknesses and Questions, we provide the following answers:
> Does a point prompt contain only one point, while a scribble contains 12 points? This paper should provide a for... | Summary: In this paper the authors tackle the problem of in-the-wild human pose estimation in a “referring” setting where the goal is to determine the pose of a person referred to using either a text prompt or positional prompt. To achieve this, the authors annotate MS COCO dataset with over 50K annotated instances for... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer apkP for their comments and appreciation of our work. In response to the concerns expressed in Weaknesses and Questions, we provide the following answers:
> The motivation for obtaining pose in a "referring" manner is unclear. It might be more valuable to focus on text... | Summary: This paper proposes a new task called Referring Human Pose and Mask Estimation and introduces the corresponding RefHuman dataset, which is beneficial for research on human behavior comprehension. Additionally, the authors present a model that leverages three types of prompts for this task. The proposed UniPHD ... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer TbW6 for their comments and appreciation of our work. In response to the concerns expressed in Weaknesses and Questions, we provide the following answers:
> What is the relationship between $\mathbf{F}^{vl}$ and $\mathbf{P}^{'}$, and how do you enhance the template bas... | null | null | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers for their thoughtful and constructive feedback.
We are glad that **all** the reviewers recognize the importance of our newly proposed task of Referring Human Pose and Mask Estimation and appreciate the significance of our new dataset, RefHuman. The reviewers ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DeepStack: Deeply Stacking Visual Tokens is Surprisingly Simple and Effective for LMMs | Accept (poster) | Summary: The authors present a new architecture StackFormer - an effective and simple way to infuse fine-grained visual tokens from CLIP vision transformer to the early layers of LLaVA-1.5 and LLaVA-Next language models, without increasing the sequence length of visual tokens for LLMs. It doesn't require architecture ... | Rebuttal 1:
Rebuttal: We provide a detailed architecture figure in our **rebuttal pdf**. We recommend referring to Figure. 1 and Figure. 2 in our **rebuttal pdf** for a better understanding of the high-resolution token processing.
## q1-2: How to split high-resolution images into patch crops; and how to split high-res... | Summary: This paper proposes a new visual token organization method. Specifically, it proposes to stack visual tokens instead of the commonly used stringing. Experiments show that the proposed StackFormer can improve performance on TextVQA, DocVQA, and InfoVQA.
Strengths: - The proposed method is novel, different from... | Rebuttal 1:
Rebuttal: ## q1: Typo
Thank you for pointing it out, we have polished the representation and will update it in the next version.
## q2: Improvement for LLaVA-Next on MLMM benchmarks
Thank you for your valuable comments! We suspect that two main factors contribute to the results: (1) the image resolution, ... | Summary: This paper proposes a method to add more visual information to a MM-LLM without increasing the number of tokens processed by the model. The idea is simple, just add visual tokens to the existing hidden representation between each layer of the transformer. The approach is evaluated on many tasks and shows good ... | Rebuttal 1:
Rebuttal: Thank you for your comments! We are pleased that you find our work novel and simple! If you have any additional questions, please feel free to add detailed comments; we are happy to answer them and sincerely hope to address your concerns if any. | null | null | Rebuttal 1:
Rebuttal: First of all, we sincerely appreciate all your valuable comments and suggestions.
In this work, we proposed a new model called Stackformer to handle the long sequence of visual tokens in large multimodal models (LMMs). Unlike all previous works that string visual tokens into a long sequence, we i... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Pretrained Optimization Model for Zero-Shot Black Box Optimization | Accept (poster) | Summary: This paper focus on zero-shot black box optimization. They propose a pretrained optimization model (POM) to pretrain on a training dataset, achieving good results on BBOB benchmark and two robot control tasks. The authors design several parts including LMM and LCM for the POM to achieve the generation of sampl... | Rebuttal 1:
Rebuttal: **weakness 1**
We do not claim that the current version of POM is the best design, but it does show excellent performance in experiments. We also do not emphasize that POM must be under the scheme of mutate and then crossover. We just design LMM and LCM modules for solution generation according t... | Summary: This paper studies zero-shot optimizers for blackbox optimization problem. The core idea is to pretrain a hypernetwork that generate suitable optimization strategies on a subset of tasks; at test time, the hypernetwork can thus be deployed to propose the optimizer for a given unseen task. The key technical con... | Rebuttal 1:
Rebuttal: **weakness 1**
Thank you very much for your valuable suggestions. We think your suggestion can really help us enhance the readability of the paper. We have made the following changes to Section 3: 1) First, we briefly introduced the overall model architecture of POM and gave the overall model str... | Summary: The paper introduces POM, a neural-network-based evolutionary algorithm for black-box optimization. POM is trained on diverse optimization tasks to enable adaptation to new tasks. POM outperforms the baselines on BBOB benchmark and two robot control tasks.
Strengths: The proposed method performs better than t... | Rebuttal 1:
Rebuttal: We've carefully reviewed your feedback, addressed all queries, added the refs, and made revisions as per your suggestions. Your further review and acceptance of the updated manuscript would be highly valued.
**Weaknesses 1**
We have included references to [1-3] in the related work section of the... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
PrefPaint: Aligning Image Inpainting Diffusion Model with Human Preference | Accept (poster) | Summary: This paper tries to align diffusion models for image inpainting with human aesthetic standards via a reinforcement learning framework.
Strengths: 1. This paper proposes aligning diffusion models for image inpainting with human preferences by integrating human feedback through reinforcement learning, which imp... | Rebuttal 1:
Rebuttal: ## Reviewer G8Cx
### Q1. Differences from Other Diffusion Alignment
**Our work is significantly different from existing diffusion alignment work**. While some related works exist in the area of text-to-image tasks involving human preference, our work is **the first to align diffusion-based imag... | Summary: This paper makes the first attempt to align diffusion models for image inpainting with human preferences by integrating human feedback through reinforcement learning. The authors theoretically deduce the accuracy bound of the reward model, modulating the refinement process of the diffusion model to robustly im... | Rebuttal 1:
Rebuttal: ## Reviewer Hftn
### Q1. Differences from T2I Methods
We confirm that our method is **NOT** simply applying the text-to-image alignment scheme to image inpainting. The **technical novelty** of the proposed method primarily lies in modeling reward accuracy and adaptively controlling the regulari... | Summary: This paper is the first to use reinforcement learning in diffusion-based image synthesis. This significantly improves the quality since image synthesis is usually a one-to-many mapping, which may not be suitable for conventional learning methods. To generate reward functions for RL, this paper also gathers and... | Rebuttal 1:
Rebuttal: ## Reviewer yfx6
### Q1. Error of Reward Model \& Amplification Factor
We make statistics of reward estimation errors, and the results are shown in Fig. **S1** of the uploaded one-page PDF file. Here we make a table to briefly show the results. Although the proportion of very large error sample... | Summary: This paper attempt to align diffusion models for image inpainting with human aesthetic standards through reinforcement learning framework. To train the model, this paper construct a dataset containing 51,000 inpainted images annotated with human preferences. Extensive experiments on inpainting comparison and d... | Rebuttal 1:
Rebuttal: ## Reviewer 85gD
### Q1. Difference between the Proposed Method, Common Reinforcement Learning, and Supervised Fine-tuning
In the following, We first clarify that our method is **NOT** a straightforward practice of the common reinforcement learning on diffusion model alignment. Then, we address ... | Rebuttal 1:
Rebuttal: ## General Response
We thank all reviewers for their time and constructive comments. We sincerely thank Reviewer **yfx6** for the affirmation of the motivation and novelty behind our task, as evidenced by comments such as "Release a new dataset..." and " First incorporate RL with image inpainting... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
TrajCLIP: Pedestrian trajectory prediction method using contrastive learning and idempotent networks | Accept (poster) | Summary: The paper proposes a trajectory prediction approach for multi-agent configurations. The historical and future trajectories are encoded in terms of spatio-temporal interaction features (STIF) using Agentformer [28] and scene-agent interaction features (SAIF) using a transformer based on Fourier transform. The h... | Rebuttal 1:
Rebuttal: > Q1:The motivation for relying on an idempotent generative network over other generative models, e.g. GANs, is not well explained.
Further explanation on the starting point for choosing the idempotent generation framework. Our motivation is that, for trajectory prediction task, the trajectory fe... | Summary: This paper proposes to utilize contrastive learning for pedestrian trajectory prediction. A STIF encoder is used to extract spatial-temporal features and is trained with data augmentation. A SAIF utilizes the Fast Fourier Transform to extract the interaction information among the agents. The authors incorporat... | Rebuttal 1:
Rebuttal: > Q1:I have some doubts about the intuition of using CLIP between history and future trajectories.
Regarding further clarification on the unified encoding of historical and future trajectories using CLIP. In our method, the trajectory encoders are designed to capture motion characteristics rather... | Summary: This paper utilizes idempotent generative network to perform multiple tasks in pedestrian trajectory prediction and achieves state-of-the-art performance in those tasks, showing its great representation and generalization ability.
The proposed model has the following main components:
1. Spatio-Temporal Inter... | Rebuttal 1:
Rebuttal: > Q1: This is not the first work to use constructive learning.
We thanks reviewers for providing a new perspective. The mentioned two works, long-tail analysis[1] and FEND[2], both utilize contrastive learning, but our work differs in the problems it addresses with contrastive learning. These two... | Summary: This paper presents TrajCLIP, a novel method for pedestrian trajectory prediction that utilizes contrastive learning and idempotent neural networks. The authors propose an interesting approach to address some limitations of existing methods, particularly in terms of generalization and modeling complex trajecto... | Rebuttal 1:
Rebuttal: > Q1: Add a section discussing the computational requirements and runtime performance of TrajCLIP.
We appreciate you bringing up the model performance experiment. As the table illustrates, we have contrasted our approach with alternative techniques in terms of model size, computational complexity... | Rebuttal 1:
Rebuttal: Thank you to all reviewers for your valuable comments and recognition of the novelty of our work. We have provided further elaboration and clarification in response to the reviewers' feedback, along with additional experiments to supplement the explanation. We will address each reviewer's comments... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ExID: Offline RL with Intuitive Expert Insights in Limited-Data Settings | Reject | Summary: The paper introduces ExID, an offline reinforcement learning algorithm that enhances learning performance in limited data scenarios by combining domain knowledge in the form of simple decision trees with agent experience replay data.
Strengths: * Domain Knowledge Utilization: ExID incorporates domain knowledg... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. Please find our response below:
**W1: Discrete Action Space Limitation**
- We conducted experiments on discrete action space domains as many important real world problems use discrete action policies. For example navigation with actions forward or turning, f... | Summary: This paper studies offline RL when data is limited. The authors propose a domain knowledge-based regularization technique to learn from an initial tracker network and limited data buffer. The experiments verified the effectiveness of the proposal, which outperforms the classic RL baseline methods.
Strengths: ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. Please find our response below:
**W1: Limited novelty and comparison with traditional knowledge distillation**
- The domain knowledge considered in our setting is imperfect and is updated using expected improvement of RL policy in a completely offline manner... | Summary: The paper introduces a novel technique ExID, a domain knowledge-based regularization method, that adaptively refines initial domain knowledge to boost performance of offline reinforcement learning (RL) in limited-data scenarios. The key insight is leveraging a teacher policy, trained with domain knowledge, to ... | Rebuttal 1:
Rebuttal: Thank you for your appreciative and constructive feedback. Please find our response below:
**W1 Generalization to Continuous Domain**
- The proposed methodology can be extended to any continuous domain problem by using the regularization in Eq 4 : $\mathcal{L}(\theta) = \mathcal{L}cql(\theta) +... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and constructive feedback and for highlighting the following strengths.
**Strengths** – Novel and unexplored (Reviewer Cy8u), solid theoretical analysis (Reviewer Cy8u), simple and technically reasonable (Reviewer Cy8u, Nyzj), Practically applicable (Reviewe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Label Noise: Ignorance Is Bliss | Accept (poster) | Summary: This paper presents a theoretical framework for learning under multi-class, instance-dependent label noise. It introduces the novel concept of Relative Signal Strength (RSS) to measure the impact of noise and uses it to derive upper and lower bounds on excess risk. Notably, it proves the minimax optimality of ... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. The "strength" section of your review indeed summarizes our contributions. We also especially thank you for bringing up the point about the relation of RSS and KL divergence. In the next version, we will include the example you provided and explai... | Summary: The work provides a new insight on how to deal with instance-dependent label noise under the context of multi-class classification problem. Under certain conditions, they prove that training a classifier as if there is no noisy labels is the best course of action.
This idea is presented in details supported by... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We are especially glad that you enjoyed reading our paper.
> The key condition for ignoring the existence of noisy labels to work is to have A0=X (or A0 covers most of X), which in turns requires $\arg \max \widetilde{\eta}(x)= \arg \max \eta(x)$... | Summary: The author proposes a Label Noise Learning (LNL) method that assumes a noise transition matrix. The author introduces the concept of Relative Signal Strength (RSS), which is calculated as the ratio of the signal difference between the true prediction and the prediction under label noise. The author demonstrate... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper.
> I have given high marks to the author's novel perspective and the reasonable interpretations and proof methods presented... Therefore, I have assigned a rating of "weak accept". If the author provides more detailed treatment of the proposed me... | Summary: In this work, the authors use a new theoretical framework for analyzing learning under label noise in multi-class classification.
The proposed framework is based on relative signal strength (RSS), which measures noisiness data points in the training sets.
Based on RSS, the authors propose new upper and lower ... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper.
We are especially glad that you find our paper easy to read. Below we would like to address some concerns.
> It seems to me, that the analysis conducted under the Relative Signal Strength framework does not provide a lot of new surprising insight... | Rebuttal 1:
Rebuttal: We thank all reviewers for your time and effort. Our paper will be substantially better as a result of your comments and questions. Here we provide responses to questions that seem most likely to be of interest to all reviewers.
## Theory
> KL divergence vs. Relative Signal Strength (RSS)
We th... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper investigates multiclass classification under label noise, specifically instance-dependent label noise in which the noise can depend on the features as well (a.k.a. local noise). A minimax result is derived that lower-bounds the misclassification probability of a classifier. The paper finds a good em... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We appreciate your feedback and constructive criticism. In response to your comments, we would like to address your concerns regarding weakness of the theory and the experiments. We believe we can clarify many points.
## "Brittleness" of theory
> ... | null | null | null | null | null | null |
Over-parameterized Student Model via Tensor Decomposition Boosted Knowledge Distillation | Accept (poster) | Summary: In this work, the authors focus on the knowledge distillation (KD) task, using tensor decomposition to enhance the performance of the student model. Leveraging the principle of overparameterization, the authors employ the Matrix Product Operator (MPO), also known as tensor train matrix, to reformulate the orig... | Rebuttal 1:
Rebuttal: We sincerely thank you for the constructive comments and suggestions, which are very helpful in improving our paper. The following responses will be incorporated into the revised paper.
**Q1. The impact of over-parameterization on student model performance.**
**Reply:** Thank for your excellent ... | Summary: This paper introduces the Over-Parameterization Distillation Framework (OPDF), which addresses performance degradation in limited-parameter student networks after knowledge distillation (KD). OPDF proposes an overparameterized student model that utilizes the tensor-decomposition technique known as matrix produ... | Rebuttal 1:
Rebuttal: We sincerely thank you for the positive feedback along with constructive comments and suggestions, which are very helpful in improving our paper. We are also grateful that you recognized the strengths and contributions of our paper. Moreover, the following responses will be incorporated into the r... | Summary: The authors propose to start with an over-parameterised student model. This is realised using high-order tensors that can reconstruct the original parameter matrices. The idea is that this over-parameterised model will benefit more from knowledge distillation.
Strengths: The ideas is quite interesting/novel f... | Rebuttal 1:
Rebuttal: Reply to Reviewer fkms
We sincerely thank you for the constructive comments and suggestions. The following responses will be incorporated into the revised paper.
**Q1. Results on CV tasks are different from original TinyVit paper. Compared to the distillation results of the original method, OPDF... | Summary: This paper proposes a novel over-parameterization framework designed to enhance the effectiveness of knowledge distillation. This framework employs MPO as a tensor decomposition technique to expand small models into larger ones to give the student model more capacity. Moreover, to enhance the effectiveness of ... | Rebuttal 1:
Rebuttal: We sincerely thank you for the constructive comments and suggestions, which are very helpful for improving our paper. We are also grateful that you recognized the strengths and contributions of our paper. Moreover, the following responses will be incorporated into the revised paper.
**Q1. Additi... | Rebuttal 1:
Rebuttal: Global Response:
Dear Reviewers:
We would like to thank you for your constructive comments, which are very helpful in improving our paper. We have posted the point-to-point reply to each question/comment raised by you. And we have listed three additional tables in the *one-page PDF rebuttal file... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encoding | Accept (poster) | Summary: Paper addresses the lost-in-the-middle effect, observed in the past for some LLMs, with a method called Multi-scale Positional Encoding (Ms-PoE) where position is encoded using different scales for each attention head. More precisely, for each head a re-scaling ratio is substituting the position m of a token w... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer LVQT for supporting our work and providing constructive suggestions. To address Reviewer LVQT’s concerns, we provide point-wise responses below.
**[Q1: No Lost-in-the-Middle Effect]** Thank you for the insightful question. We have observed similar findings to tho... | Summary: This paper proposes a plug-and-play method named Ms-PoE to mitigate the lost-in-middle challenge of LLMs. Specifically, Ms-PoE leverages multi-scale position embeddings to enhance information awareness in different parts of the context. Without fine-tuning the model, Ms-PoE achieves an average accuracy gain o... | Rebuttal 1:
Rebuttal: We appreciate Reviewer xsrg for acknowledging our method is “efficient”, and the observation of attention heads is “insightful”. To address Reviewer xsrg’s concerns, we provide pointwise responses in the following.
**[Q1: Limited Improvements]** We respectfully disagree that our performance impr... | Summary: This paper addresses the 'lost-in-the-middle' issue in large language models (LLMs) by introducing Multi-scale Positional Encoding (Ms-PoE). This approach enhances LLMs' ability to handle relevant information in the middle of the context without fine-tuning or added overhead. Ms-PoE uses position index rescali... | Rebuttal 1:
Rebuttal: We thank Reviewer qA6u for acknowledging our work as “interesting and intriguing”. We provide pointwise responses in the following.
**[Q1: Limited Improvements]** We respectfully disagree with the claim that our improvements are limited. Our method offers a plug-and-play solution to enhance curre... | null | null | Rebuttal 1:
Rebuttal: We thank Reviewer qA6u, xsrg, and LVQT for their constructive suggestions and valuable questions. Additional supplementary materials are provided in the PDF, including:
- The attention patterns before and after rescaling **[Reviewer qA6u]**
- More results on LongBench. **[Reviewer qA6u, xsrg, LVQT... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention | Accept (spotlight) | Summary: This paper presents a sparse calculation technique for the attention mechanism in long-context large language models during the pre-filling stage. Specifically, the technique builds on the authors' observation of three patterns in the attention map: the A-shape pattern, the Vertical-Slash pattern, and the Bloc... | Rebuttal 1:
Rebuttal: 1. _"...generalizable... built on the observation of the attention patterns..."_
Thank you for your question. We address it from two angles: the generalization of MInference and the relative stability of dynamic sparse attention patterns across different examples.
For the generalization of MInfe... | Summary: A key challenge for LLM inference with processing long context lengths is time-to-first token for long prompts. This paper introduces a sparse attention method designed to accelerate prefill with long context lengths. They utilize a strategy that incorporates three different types of sparsity (A-shape, vertica... | Rebuttal 1:
Rebuttal: 1. _"...which may not generalize..."_
- To demonstrate the generalizability of MInference, we tested it on most open-source long-context LLMs, including LLaMA-3-8B/70B-1M, Yi-9B-200K, GLM-4-9B-1M, Phi-3-mini-128K, and Qwen2-7B-128K (**see general response PDF**). MInference consistently achieves ... | Summary: Targeting at the time-consuming profiling stage of long contexts, this paper proposed an efficient prefilling stage spare attention mechanism. It's based on the observation to common attention patterns. The proposed method can be integrated into most existing LLMs, such as LLama3 and Yi-9B. It achieves good pe... | Rebuttal 1:
Rebuttal: Thank you for your thorough review, and we apologize for any confusion caused.
1. _"Claim that 'we found the three patterns'"_
Thank you for your critique. We will revise the wording accordingly. Indeed, we discussed the importance of sparse attention in related works and how it inspired our res... | Summary: This paper proposes MInference, a method to accelerate the pre-filling stage for long-context LLM generation. The key method leveraged by MInference is dynamic sparsification, which consists of three sparse patterns observed in attention matrices: the A-shape pattern, the Vertical-Slash pattern, and the Block-... | Rebuttal 1:
Rebuttal: 1. _"For larger models and MoE"_
Thank you for your suggestion. We have added experimental results for **LLaMA-3-70B-1M**, as shown in Table 1, where MInference maintains excellent performance in larger models, significantly surpassing StreamingLLM and InfLLM[1], and nearly matching full attentio... | Rebuttal 1:
Rebuttal: We are grateful for the diligent efforts and insightful comments from the reviewers. Your suggestions have been incredibly valuable to our work. We will address and resolve these issues in our responses and in subsequent versions of our paper. Here we respond to some common questions and have incl... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Compositional Generalization Across Distributional Shifts with Sparse Tree Operations | Accept (spotlight) | Summary: Authors propose a new representation that they call Sparse Coordinate Trees. When applied to Differentiable Tree Machines, they make computation much more parameter and memory efficient. Due to clever design, the SCTs allow for much more efficient tree operations by bit-shifting, indexing, and addition. Becaus... | Rebuttal 1:
Rebuttal: # Reviewer mbkf
Thank you for the time you spent reviewing our paper. Addressing your feedback will make our submission much stronger.
## Weaknesses
A. We will make sure to improve the motivation for using left, right, and cons in the camera-ready version. The design of DTM was motivated by the ... | Summary: The paper proposes a novel way of representing sparse trees where nodes have vector attributes in a denser, tensorised format which they call Sparse Coordinate Trees (SCT). Essentially, the crucial component for SCTs is to represent the indices of the nodes according to their topological ordering, allowing for... | Rebuttal 1:
Rebuttal: # Reviewer Hmrt
Thank you for the extensive feedback you provided on our submission! Addressing your questions and the weaknesses you identified will significantly strengthen our work. We are pleased that you found our work “very well written”, “the suite of experiments is quite extensive”, and “t... | Summary: This work addresses the problem of compositional generalization in the domain of natural language processing. The authors highlight that incorporating tree structures into a models representation space is important for achieving compositional generalization. To this end, the authors build upon a recent method ... | Rebuttal 1:
Rebuttal: # Reviewer bgti
Thank you for the time you spent to understand our submission and provide valuable feedback! Addressing your concerns will make our paper substantially stronger. We are pleased that you found that our paper “addresses an important problem”, is “very well written”, contextualizes ou... | null | null | Rebuttal 1:
Rebuttal: # Global Rebuttal
We thank all of our reviewers for their careful analysis of our work. In this global response, we highlight points shared by multiple reviewers.
First, we are excited by the kind comments that reviewers provided concerning our paper. All three reviewers found our paper to be cle... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SimVG: A Simple Framework for Visual Grounding with Decoupled Multi-modal Fusion | Accept (poster) | Summary: This paper presents SimVG, which decouples multimodal understanding from downstream tasks and uses the pretrained model to perform feature extraction and multi-modal fusion. A dynamic weight-balance distillation (DWBD) module is proposed to enhance the token branch's ability. A text-guided query generation mod... | Rebuttal 1:
Rebuttal: **Q1:** Will the multi-modality encoder be trained or are the parameters frozen?
**A1:** The multi-modality encoder (MME) weights are trainable throughout the training process, but the learning rate of MME is set to 0.1 times of that of the other parameters.
**Q2:** Visualization of the feature ... | Summary: This manuscript introduces SimVG, a framework based on BEiT-3 that simultaneously encodes image, text, and object tokens. Additionally, it proposes a dynamic weight-balance distillation (DWBD) method to improve the simpler branch (MLP), thereby enhancing reasoning speed. The effectiveness of the proposed metho... | Rebuttal 1:
Rebuttal: **Q1:** The technical contribution of the proposed method appears insufficient. The approach primarily builds upon BEiT-3 by adding an object token and a fast MLP head with a distillation loss, which may seem more like an application of BEiT-3 rather than a novel contribution.
**A1:** Please refe... | Summary: Visual grouding is a typical task in vision and language domain. Existing methods only use limited downstream data to fit multimodal feature fusion, leading to significant performance degradation on complex texts. Therefore, it is necessary to decouple visual-language feature fusion from downstream tasks to pr... | Rebuttal 1:
Rebuttal: **Q1:** The writing needs improvement; for example, the motivation is not clearly and concisely described.
**A1:** We thank the reviewer for pinpointing this issue. We will try our best to improve the writting of the manuscript in the final version. Also, we outlines the main **motivations**, **i... | Summary: This paper introduces a transformer-based framework called SimVG for the visual grounding task, which, unlike CLIP-based models, decouples multimodal fusion from the downstream task into the model pretraining stage. SimVG modifies a recently proposed multimodal fusion encoder architecture (BEiT-3) to generate... | Rebuttal 1:
Rebuttal: **Q1:** The novelty of the paper is a bit limited to me since it basically borrows and applies the unified multimodal pretraining framework introduced in BEiT-3 to the visual grounding task. A clearer comparison between the proposed method and the BEiT-3 model is desired.
**A1:** **Firstly**, BEi... | Rebuttal 1:
Rebuttal: First of all, we would like to thank all the reviewers for your positive comments and valuable suggestions!
This rebuttal has two parts. First, please find our responses to some common concerns below. Then, we provide the response to each reviewer.
# Common concerns
## 1. Motivation
### 1.1. I... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Unmet Promise of Synthetic Training Images: Using Retrieved Real Images Performs Better | Accept (poster) | Summary: This paper studies whether training on synthetic images from generative model can **truly** surpass the baseline of training on the retrieved real images that are used to train the generative model. It provides several key insights: 1) retrieved real images are significantly superior to synthetic images across... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback! We are honored to hear that you enjoyed our paper, and are grateful you found our research question “timely” and “totally overlooked” by our community.
Your question—whether synthetic images can offer unique advantages for tasks with rare or complex concepts—... | Summary: There is a growing interest in training vision models using synthetic data. This paper explores the effectiveness of synthetic data compared to real images directly retrieved from image generator's training sets like LAION-2B. The experimental results indicate that, while synthetic data can be beneficial for s... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback! We are excited that you found our proposed baseline for measuring the utility of synthetic data to be “novel and inspiring.”
Your feedback prompted us to perform additional experiments to further validate our paper’s findings, and we believe the new results ... | Summary: The paper tries to answer the question of whether the progress of pretraining classification backbones with images obtained from generative models is due to the advances in generative image modeling or from the fact that these are implicitly sampled from huge image collections. To answer this question, the pap... | Rebuttal 1:
Title: Clarification request on references [1] and [2]
Comment: Thank you for your valuable review! We highly appreciate your feedback. To ensure that we can thoroughly address the review, could you please clarify what works [1] and [2] refer to? It seems that [1], [2] in the review text do not match refere... | Summary: This paper evaluates the performance of training machine learning models on synthetic images generated by the Stable Diffusion generative model compared to using real images directly retrieved from the LAION-2B dataset, which was used to train the generative model. The authors argue that while synthetic images... | Rebuttal 1:
Title: Clarification request on weakness point (1)
Comment: Thank you very much for your time and effort in providing us feedback! We highly appreciate your review. Could you please clarify your point (1) under the weakness section? We would love to address your points as throughly as possible. Specifically... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their detailed feedback. We will incorporate all suggestions in the next version of our paper. Thank you all very much for your invaluable help in improving our work!
Overall, we are thrilled to see that all reviewers found the research question we posed inte... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Trajectory Flow Matching with Applications to Clinical Time Series Modelling | Accept (spotlight) | Summary: This paper proposes a trajectory flow matching method for time series data, by using flow matching at each time points. To preserve the coupling of time series, the vector fields are conditioned on history lengths (or even more general $c$). The method also provides ways for model stability, irregularly sample... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive assessment of our paper. We appreciate the reviewer’s insight in the importance of continuous time series modeling. We are also interested in the fully continuous setting and would be interested in adapting ideas from functional flow matching [1] to this do... | Summary: This paper presents Trajectory Flow Matching, an extension of flow matching to time series. It can model irregular, sparsely sampled, and noisy time series. It trains in a simulation-free manner, bypassing backpropagation through the dynamics. The method is tested on ICU physiological time series, demonstratio... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their extremely positive response to our work. We are thrilled that you found our TFM to be a strong research contribution. Additionally, we are grateful for your suggestion regarding the future application of our model to periodic time series. We have some prel... | Summary: This paper presents Trajectory Flow Matching (TFM), a simulation-free training algorithm for neural differential equation models. This enables modeling of continuous physiologic processes using irregular, sparsely sampled, and noisy data, all with better scalability.
The authors provide theoretical proof that... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback that helps us further improve our paper. We have added a README, and impact statement on simulation-free training scalability in our manuscript.
We agree having explainability is important for others who may apply our method. We thank the reviewe... | null | null | Rebuttal 1:
Rebuttal: # Global Response
We thank the reviewers for their time evaluating our paper. We are grateful for the thoughtful comments, insights, and potential directions for future work. We have addressed each of the points raised and provided clarifications where necessary. Based on suggestions from the rev... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Expected Probabilistic Hierarchies | Accept (poster) | Summary: This paper considers the problem of hierarchical clustering. This problem is typically handled by discrete optimization approaches, which define a hierarchical clustering quality score (e.g. Dasgupta and TSD) and optimize it on a discrete search space. More recent approaches consider this problem from a probab... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comprehensive feedback. In the following, we address their comments.
**Comment:** While I appreciate the theoretical results for supporting the advantages of EPH over FPH, there is a lack of substantial technical improvements. Therefore, the novelty is limited.
**... | Summary: This paper proposes for hierarchical clustering a new method Expected Probabilistic Hierarchies (EPH) that is developed from the Flexible Probabilistic Hierarchy (FPH) method. Unlike FPH using Soft-Das and Soft-TSD, EPH provides an unbiased estimate on two new objectives called Exp-Das and Exp-TSD. EPH has add... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and address their comments below.
**Comment:** The technical contribution is limited since the framework of the algorithm and main techniques come from (Zügner et al., 2022).
**Response:** While our work builds upon the probabilistic hierarchies i... | Summary: This paper studies gradient-based methods for hierarchical clustering, presenting an interesting approach EPH which is both scalable and accurate. The approach uses a subgraph sampling approach for scalability and is interesting in its optimization of expected hierarchical clustering costs.
Strengths: This is... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable concerns. In the following, we address their comments.
**Comment:** It is a bit difficult for me to understand when a practitioner would choose this method over some algorithmic alternatives.
**Response:** EPH is particularly advantageous over alternativ... | Summary: This paper proposes a method for hierarchical clustering by optimizing expected clustering scores (DAS/TSD) over the distribution of hierarchies. They show theoretically that their proposed optimization objective is consistent with their discrete counterparts: that is, the solution for their proposed optimizat... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and address their concerns in the following.
**Comment:** The experimental results also only show a marginal improvement over the work that it builds on [1]. Based on the runtimes from Table 18, it seems that this marginal improvement comes at a g... | Rebuttal 1:
Rebuttal: We want to thank the reviewers for their valuable feedback and for acknowledging the clear writing (xKdT, AAtX, f9v5, YnBf), our extensive experiments (xKdT, f9v5, KKzQ, YnBf), and our methodological contribution including our theoretical analysis (xKdT, f9v5, YnBf).
We have addressed all comment... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper builds on a previous work [40] for probabilistic hierarchical clustering. Instead of using the “soft” version of Dasgupta cost and Tree-Sampling Divergence in the objective function, the paper proposes to use the expected value of the two cost functions and develops a sampling method for calculating... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comprehensive feedback. In the following, we address their questions and remarks.
**Comment:** The contribution looks incremental in this sense, even with some interesting and nicer theoretical properties over the previous work.
**Response:** While our work build... | null | null | null | null | null | null |
R$^2$-Gaussian: Rectifying Radiative Gaussian Splatting for Tomographic Reconstruction | Accept (poster) | Summary: The paper introduces R2-Gaussian, a framework for tomographic reconstruction using 3D Gaussian splatting (3DGS). This framework aims to address the limitations of traditional 3DGS in volumetric reconstruction, specifically for tasks like X-ray computed tomography (CT).
Strengths: - Identifying and addressing ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review.
> Q4.1: The performance in real-world clinical or industrial scenarios is not thoroughly examined. For instance, real X-ray images are given to reconstruct the CT scan.
We further evaluate our method on real-world data. We use FIPS [b], a public dat... | Summary: The paper aims to achieve high tomographic reconstruction performance with a limited number of views in a time-efficient manner.
To this end, the paper modifies 3DGS for X-ray projection by adjusting the rendering equation, correcting 2D projection errors, and using voxelizers for regularization.
The experimen... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review. The comments and suggestions are helpful in improving our paper.
> Q3.1: Novelty comparison w.r.t. X-Gaussian.
Our method demonstrates considerable novelty compared to the concurrent work X-Gaussian for the following reasons:
* **Broader task scope... | Summary: ### Motivation
- The authors propose to adapt 3D Gaussian Splatting (3DGS) to sparse-view tomographic reconstruction, i.e., to recover a radiodensity 3D volume from a small set of X-ray images and corresponding sensor information. This is relevant for various clinical and industrial applications.
### Contribu... | Rebuttal 1:
Rebuttal: We appreciate your positive review and valuable feedback.
> Q2.1: Did the authors preprocess the CT volumes?
Yes we convert raw volumes from HU to attenuation coefficients. Following [62, 7, 27], we then normalize voxel values to [0,1] for balanced evaluation across modalities. We will add more... | Summary: This paper presents a 3D reconstruction method for sparse-view computed tomography using 3D Gaussian Splatting. The core contribution is the reformulation of the volumetric rendering equation to include view-independent central density estimation. Additionally, the paper introduces a differentiable voxelizer t... | Rebuttal 1:
Rebuttal: We appreciate your recognition of our work and your valuable feedback.
> Q1.1: How does the proposed method compare to FDK when a large number of projections are used?
We further evaluate FDK and our method with 500 to 2000 views. Results in Tab. A show that our method outperforms FDK by a larg... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Thank you for your insightful comments and constructive suggestions. We appreciate Reviewers sKx6 and w7Ci for recognizing our paper's solid technical contribution, high impact on related areas, excellent evaluation, and good writing. We are grateful for the positive feedback from... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Rethinking the Diffusion Models for Missing Data Imputation: A Gradient Flow Perspective | Accept (poster) | Summary: This paper addresses two primary issues in Missing Data Imputation (MDI) using Diffusion Models (DMs): inaccurate imputation due to sample diversification and difficult training caused by the complexity of designing the mask matrix. The authors propose a novel approach, Kernelized Negative Entropy-regularized ... | Rebuttal 1:
Rebuttal: Thank you for your comments, our rebuttal organized point to point is posted as follows:
> W1 & Q2: Why we use RBF Kernel Function:
* The selection of the RBF kernel function was strategically driven by the need to satisfy the following condition: $\int{\nabla_{\boldsymbol{X}^{(miss)}}[u(\boldsymb... | Summary: This paper presents KnewImp, a kernelized negative entropy-regularized Wasserstein gradient flow imputation approach to numerical tabular data imputation. The authors argue that existing missing data imputation frameworks based on diffusion models suffer from two major limitations. Firstly, diffusion models pr... | Rebuttal 1:
Rebuttal: Thank you for your comments, our rebuttal is posted as follows:
> W1: Downstream Tasks
According to your suggestions, we added the performance on downstream classification task similar to Fig. 5 in the TDM paper [1]. **Please see Table 1 located in Section 2.1 from the pdf attached in common rebu... | Summary: This paper considered tackling the Missing Data Imputation (MDI) problem via diffusion models, which treats MDI as an generative problem. As DM-based methods focus on sample diversification rather than accuracy, which is the primary evaluation metric for MDI, the authors proposed one cost functional to discour... | Rebuttal 1:
Rebuttal: Thank you for your comments.
> W1: Contributions & Novelty of This Paper
* Our primary focus is on analyzing Diffusion Model (DM)-based Missing Data Imputation (MDI) using Wasserstein Gradient Flow (WGF, initially designed for functional optimization), **not merely integrating WGF into a generat... | Summary: The paper proposes a new algorithm for data imputation. The idea is to estimate the score function corresponding to the posterior p(x_miss/x_obs) using DSM and then infer the missing values using a WGF equivalence argued in this paper itself. These alternating steps are repeated until convergence. Simulations ... | Rebuttal 1:
Rebuttal: Thank you for your comments. Before reading our response, we think we should come to the following agreements:
1. Optimizing the instances $x$ from distribution $r(x)$ is optimizing this distribution $r(x)$, which is the basis of particle-based variational inference like Stein Variational Gradien... | Rebuttal 1:
Rebuttal: ## Overall Response
We are encouraged by the reviewers' acknowledgment of the strengths in our paper, such as its robust performance [Gtbe] [EhUg] [833r] [fs2D] [9XXU], comprehensive experimentation [Gtbe] [fs2D] [9XXU], and clear, concise presentation [Gtbe] [fs2D] [9XXU]. However, we also recogn... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper introduces Kernelized Negative Entropy-regularized Wasserstein Gradient Flow Imputation (KnewImp), a novel approach for imputing missing data in numerical tabular datasets. The proposed method addresses two significant challenges in diffusion model-based missing data imputation (MDI): inaccurate imp... | Rebuttal 1:
Rebuttal: Thank you for your insightful advice and valuable questions, we will respond to your concerns point by point.
## Weaknesses
> W1: Dense Mathematical
* We acknowledge that the theoretical concepts and mathematical formulations in our manuscript could be challenging for readers not extensively fami... | null | null | null | null | null | null |
MaVEn: An Effective Multi-granularity Hybrid Visual Encoding Framework for Multimodal Large Language Model | Accept (poster) | Summary: The paper presents a novel approach, termed MaVEn, which aims to enhance the performance of MLLMs in multi-image scenario by integrating discrete visual symbol sequences with traditional continuous representation sequences. This dual strategy is designed to bridge the semantic discrepancies between visual and ... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude for your insightful comments and the recognition of our work. We have carefully considered your suggestion and provide a detailed response below.
### 1. it would be recommended to conduct experiments comparing the performance of these different di... | Summary: This paper presents a Multi-granularity Visual Encoding framework (MaVEn) for better multi-image reasoning. MaVEn combines discrete visual symbols and continuous representation sequences, as well as designing a dynamic reduction mechanism to efficiently and effectively process and interpret information from mu... | Rebuttal 1:
Rebuttal: ### 1. Would the adjustments to the Visual Projector affect the performance of the Patch Selector?
The adjustments made to the Visual Projector in Stage 3 will not affect the performance of the Patch Selector. As shown in Figure 2(b) in our paper, the process is designed such that **we first use... | Summary: This paper introduces MaVEn, a Multi-granularity Visual Encoding framework that enhances Multimodal Large Language Models (MLLMs) in multi-image reasoning by combining discrete visual symbol sequences with traditional continuous representation sequences. Experimental results show that MaVEn significantly impro... | Rebuttal 1:
Rebuttal: Thank you for your recognition of our work and for your insightful questions.
### 1. Why not directly operate on continuous features to reduce feature redundancy ?
This is an excellent question. Directly reducing redundancy in continuous features can be approached in two main ways:
1. **Token... | Summary: The paper introduces MaVEn, a framework designed to improve Multimodal Large Language Models (MLLMs) in understanding and reasoning across multiple images. Unlike current MLLMs, which are mainly focused on single-image interpretation, MaVEn integrates both coarse-grained semantic concepts and fine-grained deta... | Rebuttal 1:
Rebuttal: ### 1. Not compare some of the latest methods
Thank you for your valuable comments. We have reproduced the results of mini-Gemini, MiniCPM-V, and InternVL and compared MaVEn with them. It is important to emphasize that our experiments were based on LLaVA 1.5. To validate our method's effectivenes... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude for your thorough and insightful reviews of our manuscript. We greatly appreciate the time and effort you have invested in providing valuable feedback and suggestions, which will undoubtedly help us improve the quality and clarity of our work.
We are... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes MaVEn, a novel multi-granularity hybrid visual encoding framework for multimodal large language models (MLLMs). MaVEn aims to improve MLLMs' capabilities in multi-image reasoning by combining discrete and continuous visual representations. The authors design a dynamic reduction mechanism to... | Rebuttal 1:
Comment: ## 1. The system can be a bit over complex for serving & compare computation complexity with other existing models.
Thank you for your insightful comments. We appreciate your concern and would like to address it comprehensively.
We acknowledge that the training process of our model might appear com... | null | null | null | null | null | null |
AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models | Accept (poster) | Summary: This paper introduces AlphaPruning, a novel framework for unstructured LLM pruning. The framework leverages HT-SR theory that utilizes the heavy-tailed shape of ESDs in layer-weight matrices to allocate layer-wise sparsity more effectively. By focusing on shape metrics rather than scale metrics, AlphaPruning d... | Rebuttal 1:
Rebuttal: ## Weakness
We present the differences between AlphaPruning and [1], as detailed below:
- **Different research focus.** Our study investigates post-training LLM pruning, whereas [1] studies model training.
- **The underlying principles of the two works are different.** [1] aims to balance layer ... | Summary: This work presents AlphaPruning, which prunes weight matrices of LLM models with different layer-wise sparsity levels based on the Heavy-tailed self-regularization theory.
Compared to pruning with uniform sparsity among layers, AlphaPruning alleviates performance degeneration when the sparsity level is high an... | Rebuttal 1:
Rebuttal: ## Weakness 1 and 2
AlphaPruning is grounded in heavy-tail self-regularization (HT-SR) theory, which we use to quantify the training quality of each layer and determine layer-wise sparsity. Here we provide a detailed overview of this theory and explain how AlphaPruning is built based on it. We wil... | Summary: This paper introduces Alpha Pruning, a novel approach for pruning large language models based on Heavy-Tailed Self-Regularization theory. Instead of applying a uniform pruning ratio across layers, Alpha Pruning utilizes PL_Alpha_Hill, derived from empirical spectral densities (ESDs), to assess how well-trained... | Rebuttal 1:
Rebuttal: ## Weakness 1
We provide a detailed explanation of the parts the reviewer suggested, including HT-SR theory, terms in the method, and Figure 1a. We will include these in the updated draft.
- **More details of HT-SR theory:** HT-SR theory [1-2] examines the empirical spectral density (ESD) of wei... | Summary: The paper introduces AlphaPruning, a novel method for pruning large language models (LLMs) using Heavy-Tailed Self-Regularization (HT-SR) Theory. AlphaPruning uses ESDs of weight matrices to determine layerwise pruning ratios. The method demonstrates the ability to prune LLaMA-7B to 80% sparsity while maintain... | Rebuttal 1:
Rebuttal: ## Weakness 1 and Question 1
**Motivation of our method and why property of $W^\top W$ can decide layer sparsity**. Our method is grounded in heavy-tail self-regularization (HT-SR) theory, which we use to quantify the training quality of each layer and determine layer-wise sparsity. The rationale ... | Rebuttal 1:
Rebuttal: We want to thank all the reviewers for the constructive feedback, which helps us improve our paper. Please refer to the attached PDF for our new experiments and see below for our responses to each comment.
Pdf: /pdf/1e7846f8aa2419be5a3924703a9cb73bc9d2a574.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
How Transformers Utilize Multi-Head Attention in In-Context Learning? A Case Study on Sparse Linear Regression | Accept (poster) | Summary: This paper empirically studies how the different heads works across different layers of the transformer: while the first layer uses all heads, the later layers mainly relies on a single head. In addition, the authors also propose a preprocess-then-optimize algorithm.
Strengths: This paper is easy to understan... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort you've invested in reviewing our work. We've addressed your questions and concerns as follows:
> 1. *The difference between our work and other works towards aligning multi-layer transformers with gradient descent, like [3,4,5].*
Thank you for your insi... | Summary: This work presents an analysis of how Transformers perform in-context learning by experimenting with a sparse linear regression problem setup. The author’s analysis combines both empirical and theoretical analysis. They first examine the properties of real models using pruning and probing methods. From this, t... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments! We sincerely appreciate the time and effort you've dedicated to providing thoughtful reviews. We've addressed your concerns as follows:
> 1. *The theoretical analysis is based on a simplified transformer maybe hard to apply the results to more practical rea... | Summary: This paper studies the mechanism of transformers under the in-context sparse linear regression problem. The authors reveal that the transformer pre-trained for this task has the first layer preprocessing the data, and the remaining layers implement gradient descent. More intriguingly, only one head in the seco... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort you spent on thoughtful reviews and comments. We address your comments below:
> **Q1**: *The P-probing lacks a controlled experiment. Should you also try regressing on the hidden states before the first layer? I understand that $h=1$ might be a controll... | Summary: This work seeks to provide a deeper exploration of the use of multi-heads, at different layers in a Transformer, to perform in-context
learning tasks. More specifically, the goal of the paper is to experimentally discover additional insights on the interactions of multi-headed attention across layers. Subseque... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort you spent on thoughtful reviews. We address your comments below:
> **Q1**: *The reviewer would like to better understand how the authors think about feed-forward layers with respect to the observations made by the authors regarding the necessity of mult... | Rebuttal 1:
Rebuttal: We sincerely appreciate the thoughtful reviews and comments provided by all reviewers. Below, we address the main points raised, details can be found in corresponding blocks for each reviewer:
- Reviewer fd5H focused on the role of MLP layers in our setting and their potential benefits for other ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
TARP-VP: Towards Evaluation of Transferred Adversarial Robustness and Privacy on Label Mapping Visual Prompting Models | Accept (poster) | Summary: This paper investigates the adversarial robustness and privacy aspects of models trained using the Language Model Visual Prompting (LM-VP) technique, which has not been done before. The results suggest that LM-VP models trained with transfer AT have advantages in AI security.
Strengths: - This paper connects ... | Rebuttal 1:
Rebuttal: **W1**. Generally, in existing work, the MIA success rate is typically larger than 50%. A value close to 50% indicates that the attack is invalid, as this is like a random guess. Thus, a successful defense against MIA would result in an MIA success rate close to 50%, as seen in Table 4 of [1], Tab... | Summary: This paper explores the trade-offs between adversarial robustness and privacy in deep learning models, highlighting that while AT improves robustness but it increases vulnerability to MIA. The authors introduce an ANF-based graph structure and CryptoANFNet, a neural network model for cryptographic problem-solv... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' recognition of our work. Our main contribution is that we first introduce a method that simultaneously enhances transfer adversarial robustness and privacy. As a new research prospect, we are happy to discuss any questions you may have. Additionally, we will also fully... | Summary: The works shows that LM-VP models can achieve the great adversarial robustness and privacy at the same time, different from full model adversarial training. Across different pre-trained models, the proposed transferred adversarial training achieves good classification accuracy and low MIA success rates.
Stren... | Rebuttal 1:
Rebuttal: **W1**. As per the reviewer’s suggestion, we conducted experiments on Tiny-ImageNet, which has a resolution of 64x64 and contains 200 classes. We select different pre-trained models and the results indicate that: The LM-VP model with transfer AT improves transfer adversarial robustness by 3%-24% a... | Summary: Adversarial robustness and privacy are important considerations in AI security, particularly in deep learning models. Adversarial training (AT) is effective in enhancing robustness against attacks, but it increases vulnerability to membership inference attacks (MIAs), compromising privacy. This trade-off betwe... | Rebuttal 1:
Rebuttal: **W1 and W2**. The main novelty of this work does not lie in the theoretical aspect. The main contribution of our work is actually to introduce a novel method to jointly improve the transfer adversarial robustness and privacy of LM-VP models. This issue has not been fully explored before and we a... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable comments on our work. We are very grateful to the reviewers for their recognition of our research topic and for their suggestions to improve our work. We give specific responses to each of the reviewers' comments. If there are further questions, we are hap... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective | Accept (poster) | Summary: This paper introduces Attraos, a new model for long-term time series forecasting (LTSF) that incorporates chaos theory and views time series data as observations from high-dimensional chaotic dynamic systems. Attraos utilizes attractor invariance, non-parametric Phase Space Reconstruction, and a multi-scale dy... | Rebuttal 1:
Rebuttal: Thank you very much for appreciating the technical novelty and efficiency achieved by our method. We are really sorry for missing several details, most of which delve into some details of the state-space model. Please allow us to provide you with a detailed response to questions 1-8.
* **Q1: $\mu(... | Summary: The paper introduces chaos theory into a long-term time series forcasting (LTSF) model called Attraos (a play on the words "attractor" and "chaos"). They propose a Multi-resolution Dynamic Memory Unit (MDMU) which is inspired by (and looks a lot like) the State Space Models (SSMs) used in the Mamba family of m... | Rebuttal 1:
Rebuttal: We sincerely thank you for appreciating the theoretical design of our model and its efficiency. We apologize for missing several details and would like to clarify as follows:
* **W1: Unclear presentation of results:** Your comment is really helpful. We have followed your comments to indicate the... | Summary: The paper introduces a novel approach, named Attraos, for Long-term Time Series Forecasting (LTSF) based on treating the observed time series as high dimensional chaotic dynamical system. The model first estimates an embedding of the data through Phase Space Reconstruction (Takens embedding) and then utilizes ... | Rebuttal 1:
Rebuttal: Thank you very much for appreciating our technical novelty and the SOTA performance achieved by our method. We are really sorry for missing several details. Here we endeavor to address your questions.
* **W1: Lack of discussion on model settings**:
**[why using this particular SSM]:** The pape... | null | null | Rebuttal 1:
Rebuttal: We commence by thanking the reviewers for their insightful comments. We are pleased to see that all the reviewers agree with some strengths of our paper, such as technique novelty(**Reviewer zUbZ, zqBV, qpQ4**), comprehensive evaluation (**Reviewer zUbZ, zqBV, qpQ4**), and efficiency (**Reviewer z... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Metric Flow Matching for Smooth Interpolations on the Data Manifold | Accept (poster) | Summary: This work proposes a metric flow matching algorithm, where interpolants are approximate geodesics learned by minimizing the kinetic energy of a data-induced Riemannian metric. This targets the trajectory inference problem, such as single-cell trajectory prediction.
Strengths: * This paper clearly addresses th... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their time and thoughtful review of our work, which gave us an opportunity to improve our work significantly. We are glad to hear that the reviewer found our work “well-written” and that it “proposes a solution that naturally connects the recently introduced... | Summary: This paper proposes metric flow matching, a variant of conditional flow matching where the interpolated distributions lie on the data manifold. They first learn trajectories between $p_0$ and $p_1$ which minimize a data-dependent kinetic energy, then use these trajectories instead of straight lines for conditi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and positive appraisal of our work. We are thrilled that the reviewer viewed our work to be “well-written, with great attention to detail” and that we “address an interesting problem” and is an “original contribution” that is a “useful addition to the generativ... | Summary: The authors introduce a method for trajectory inference based on conditional flow matching (CFM) that takes a formulation of the trajectories using Riemannian geometry. The Riemannian metric is built following the manifold hypothesis and prior work, resulting in a flow matching with a data-dependent metric enc... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback and constructive comments, which gave us an opportunity to improve our work significantly. We are pleased to see that the reviewer found our work “very well written and easy to follow” and that our idea is “elegant” and that “the method works well ... | Summary: This paper proposes an instantiation of flow matching where the interpolants are learned by minimizing the kinetic energy defined by a nonparametric metric defined over a set of data points (a weighted L2 norm of the velocity field). This metric is defined through another weighted normal distribution, with lea... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their detailed feedback and constructive comments, which gave us an opportunity to improve our work significantly. We are glad to hear that the main thrust of our work could be very “interesting” for higher dimensional ML applications. We kindly point the re... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful feedback and constructive questions that have helped us improve the submission significantly. We are glad to see that reviewers found our work “well-written, with great attention to detail” (R q2sv), that our proposed framework is “elegant” (R mgNS), “na... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset | Accept (poster) | Summary: The paper focuses on non-stationary learning of neural networks. The paper proposes a method that automatically adjusts a parameter that influences the stochastic gradient descent (SGD) update to account for non-stationarity.
Strengths: The problem tackled is relevant and the related work is clearly discussed... | Rebuttal 1:
Rebuttal: We thank Reviewer DfC5 for their feedback. Please find our response below.
> Figure 3, hard to read
Thanks to your feedback, we will modify the way we present the results, using the extended page limit for the camera-ready version. First, we will present **Soft Reset** method and compare it to b... | Summary: - The authors study a learning algorithm that can handle the non-stationarity of the data distribution.
- They propose a parameter drift model based on the Ornstein-Uhlenbeck process, which models a form of “soft parameter reset” adaptive to the data stream. The drift model has an adaptive parameter $\gamma_t$... | Rebuttal 1:
Rebuttal: We thank reviewer 4koq for their response. Please find our detailed answer below.
> why drift model...
In Section 3 and in Appendix D, we presented the reasons to use a drift model together with learning NN parameters. Figure 1 illustrates the high-level intuition in case of online Bayesian esti... | Summary: The paper proposes a method to effectively learn the neural network parameters in non-stationary environments. They propose a modified learning algorithm that adapts to non-stationarity through an Ornstein-Uhlenbeck process with an adaptive drift parameter. Drift parameter is used to track the non-stationarity... | Rebuttal 1:
Rebuttal: We thank the reviewer 6GY1 for their positive feedback. Please find our answer below.
> Section 2 - Simplification of equation (6) to (9) requires some explanation [page -5]
The equation 9 is obtained from 6 as follows. First, we linearise the log-likelihood function $\log p(y_{t+1} | x_{t+1}, \... | Summary: This work focuses on the problem of plasticity loss in non-stationary problems. This work proposes a new solution which adaptively drifts the parameters toward the initial distribution. The proposed solution is a form of soft resetting and can be seen as a meta version of L2-init where the degree of drift towa... | Rebuttal 1:
Rebuttal: We thank the reviewer Hmj6 for their feedback. Please find our detailed answer below.
> Hyperparameter sensitivity. Given that the proposed solutions introduce many new hyperparameters and the authors say in line 207 that one of the solutions is sensitive to the choice of hyperparameter, the pape... | Rebuttal 1:
Rebuttal: Dear reviewers, thank you for your feedback. In this section we provide answers to recurrent points from some of you.
# Computational Complexity
The following tables will be added to the Appendix together with a reference to this appendix at the end of Section 3.
**Notations**:
* P - number of... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LaKD: Length-agnostic Knowledge Distillation for Trajectory Prediction with Any Length Observations | Accept (poster) | Summary: This paper presents the LaKD method to improve trajectory predictions for variable input observation lengths.
LaKD incorporates two key ideas. The first idea is dynamic length-agnostic knowledge distillation. During training time, for each training sample, they augment it by randomly masking the input observa... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and efforts on evaluating our work. Here are my responses to your comments:
> **Comment 1**: The performance improvement compared to the naive Random Masking baseline is not very significant. I doubt whether it's worth the complexity to use this method in practic... | Summary: To tackle the length-agnostic trajectory prediction problem, the authors are motivated to utilize knowledge learned from both longer and shorter trajectories. They propose a plug-and-play self-distillation framework for trajectory prediction, which can be integrated with many different off-the-shelf trajectory... | Rebuttal 1:
Rebuttal: Thank you for your insightful review. The following are our responses to the points you have raised.
> **Comment 1**: I'm a bit confused about why the overall framework of bidirectional self-distillation works from an information theory / flow perspective.
Sorry for any confusion caused by our u... | Summary: The paper presents a length-agnostic knowledge distillation framework, which is motivated from knowledge transfer among trajectories of different lengths. The authors address knowledge conflicts during distillation from a dynamic soft-masking mechanism. The evaluation is conducted using Argoverse 1, nuScenes, ... | Rebuttal 1:
Rebuttal: We truly appreciate the reviewer of the constructive feedback. In light of these insightful comments, we would like to address them by providing the following clarifications.
> **Question 1**: The qualitative results do not clearly demonstrate the scenarios where the authors have been motivated. ... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful and constructive feedback. We really appreciate the reviewers thought our work to be "well-motivated" (Rudm, 4u3J, G7Yk), "easy to follow" (Rudm, 4u3J, G7Yk), "well-written" (Rudm, 4u3J, G7Yk), "novel" (Rudm, 4u3J, G7Yk), "a promising/good topic" (Ru... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A PID Controller Approach for Adaptive Probability-dependent Gradient Decay in Model Calibration | Accept (poster) | Summary: The submitted paper proposes a PID-based controller to ensure the consistent optimisation of model accuracy and model calibration. The controller with proposed Relative Calibration Error (RCE) dynamically adjust gradient decay rate to "control" model confidence. By applying a learning rate compensation mechani... | Rebuttal 1:
Rebuttal: **Answers to Questions**
* To the best of my knowledge, no existing work addresses model calibration using PID control. Most prior approaches apply PID concepts to optimization problems rather than model calibration. Our work, however, establishes a connection between model calibration and gradien... | Summary: The paper presents an approach to ensure consistent optimisation of both model accuracy and calibration. The authors used a PID-based controller for the task. The PID-based controller adjusts the gradient decay rate, which ultimately optimises the neural network by gradient descent. Further ablation studies ha... | Rebuttal 1:
Rebuttal: **Answers to Questions**
* In the experiments detailed in Sections 4.1 and 4.2, the hyperparameters of the PID controllers were determined through trial-and-error. In Section 4.3, we present ablation experiments that explore various P/I/D hyperparameters in the PID controller. As shown in Figure ... | Summary: The authors propose a method for improving the calibration of neural networks, which are known to be overconfident in their predicitions. Their method is based on modifying the softmax function to include a tunable hyperparameter- which they call the gradient decay coefficient- which is controlled throughout t... | Rebuttal 1:
Rebuttal: **Answers to Questions**
* We appreciate the reviewer's technical comment. We had previously considered using the Adam optimizer instead of SGD to achieve a stable gradient. In response to your question, we show additional ablation studies to evaluate the optimization performance when Adam replace... | null | null | Rebuttal 1:
Rebuttal: Some supplementary Figures and Tables.
Pdf: /pdf/b8e49d92a74d10b3f103a6bbe1602c702055fe92.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
AdjointDEIS: Efficient Gradients for Diffusion Models | Accept (poster) | Summary: This paper presents a way to optimize the initial seed or control parameters to a diffusion network to optimize a differentiable loss applied to the final samples drawn from a discretization of the diffusion SDE / probability flow ODE. Put differently the idea is to apply the white box integration trick from D... | Rebuttal 1:
Rebuttal: We thank reviewer 1FE9 for the helpful questions and interest in our work. We are happy the reviewer found the paper clear and comprehensive tackling an interesting problem. We address the questions raised by the reviewer below. We hope our responses help address the questions and are happy to ans... | Summary: In this paper, the authors proposed an accelerated method for differentials of pretrained diffusion models with respect to its latent valuables or parameters by making use of
1. the Taylor expansion of the log-SNR parameter, and
2. the exact integral formula of the derivatives related to the probability flow... | Rebuttal 1:
Rebuttal: We thank reviewer oW3N for the detailed questions and interest in our work. We are glad that the reviewer thought our work was well written. We address the questions raised by the reviewer below. We would be happy to provide additional clarification or to answer further questions about our work.
... | Summary: The paper proposes an adjoint sensitivity method -- AdjointDEIS -- for efficiently calculating gradients of diffusion SDE models. Current methods for naive backpropagation rely on discrete adjoints which are memory intensive. The authors introduce an approach based on the stochastic adjoint sensitivity method ... | Rebuttal 1:
Rebuttal: We thank reviewer jxa4 for the interest in our work and the insightful comments. We are encouraged the reviewer found the extensions from diffusion ODEs to diffusion SDEs as a strength of the paper. Below we respond to the question raised by the reviewer. We hope this helps address the questions a... | Summary: AdjointDEIS uses the method of adjoint sensitivity to compute gradients of diffusion models, which is more efficient and less memory intensive, and robust to the injected noise. This work proposes efficient solvers for both the adjoint probability flow ODE and the adjoint diffusion SDE. Experiments demonstrate... | Rebuttal 1:
Rebuttal: We thank reviewer vPC5 for the helpful comments and interest in our work. We agree with the reviewer that AdjointDEIS can be applied to more applications like inverse problems. Below we respond to the questions and concerns raised by the reviewer. We hope this addresses the questions and are happy... | Rebuttal 1:
Rebuttal: # General Response
We thank all the reviewers for all of their time and feedback on our submitted manuscript.
---
We are delighted to see that the reviewers appreciated the practical significance of our method, highlighting that it "can open the door to possibly novel applications of diffusion m... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Tactile DreamFusion: Exploiting Tactile Sensing for 3D Generation | Accept (poster) | Summary: This paper proposes to use the tactile sensing signal (height map and normal map) to improve the 3D generation quality, especially the geometric details. The authors use a 3D mesh generation guided by a normal-conditioned ControlNet to ensure the consistency between the visual textures and the tactile texture... | Rebuttal 1:
Rebuttal: Thanks for your time and valuable comments. We address the questions below:
### **Using texture reterival from a normal map database**
We agree with the reviewer that our work can be potentially extended to other high-resolution geometrical texture data such as the one from a normal map database... | Summary: This paper proposes a lightweight 3D texture field that ensures the consistency between visual and tactile textures while preserving photorealism. The experiments demonstrate that quantitative and qualitative results show good generation quality.
Strengths: 1. The authors pioneered the use of tactile sensing ... | Rebuttal 1:
Rebuttal: Thanks for your questions and comments. We would like to clarify that in this work, our main contribution is leveraging tactile sensing to enhance geometric details for 3D generation tasks. (1) We are the first to leverage tactile sensing to synthesize high-fidelity geometric details for 3D genera... | Summary: This submission addresses the long-standing challenge of enhancing geometric details in results produced by text-to-3D and image-to-3D pipelines. The approach introduces a novel method that leverages tactile normal modality to synthesize high-fidelity geometric details. Additionally, it employs attention maps ... | Rebuttal 1:
Rebuttal: Thank you for your encouraging comments and feeback. We answer each question as below:
### **Is the proposed method compatible with a purely text-to-3D pipeline?**
Yes, our method is compatible with purely text-to-3D backbones, such as RichDreamer (Qiu, et al., 2024). In our method, we generate o... | Summary: This paper proposes a method for generating 3D assets with detailed geometry through inputs from a tactile sensor. More specifically, given a bump-map as input from a tactile sensor (just a small patch is enough), the method uses it as regularization while maximizing the likelihood using a normals-conditioned ... | Rebuttal 1:
Rebuttal: Thank you for your encouraging and insightful comments. We’re pleased that you recognize our setup as “a novel and unexplored task.” Below, we address the individual comments.
### **Add albedo rendering results**
Due to space limit, we omitted the albedo renderings in the main paper. Thanks for p... | Rebuttal 1:
Rebuttal: We thank all reviewers for their efforts and feedback. The reviewers note that we solve “a novel and unexplored task” (zBVq) with an “innovative approach in utilizing tactile normal modality to enhance geometric details” (MUQX), provide “convincing” (zBVq) and “satisfying” (a9oZ) results, release ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Adaptive Important Region Selection with Reinforced Hierarchical Search for Dense Object Detection | Accept (poster) | Summary: The paper presents a novel RL-driven object detector guided by Evidential Q-learning. The main contributions are:
1. an adaptive hierarchical object detection paradigm supported by an RL agent to mimic human visual attention that performs searching in the top-down fashion;
2. an evidential Q-learning method d... | Rebuttal 1:
Rebuttal: **Q1: Performance comparison with [1] [2]**
Thank you for providing the references for these baselines. We would like to clarify that we focus primarily on improving the dense object detection performance by effectively discovering all objects (including the smaller ones) through leveraging the F... | Summary: The paper presents an innovative framework for dense object detection, called Adaptive Important Region Selection (AIRS). It introduces a method guided by Evidential Q-learning, which strategically identifies important regions within an image in a hierarchical manner. The method aims to reduce false positives ... | Rebuttal 1:
Rebuttal: **Q1: Intermediate results of RL masks during testing phase**
Thank you for the suggestion. Figure 1 in the attached PDF shows the RL masks that are projected to the removed false positive bounding boxes in the detection result. For more detailed description of the masking process, please refer ... | Summary: This article presents the method AIRS (Adaptive Important Region Selection) based on reinforcement learning paradigm to improve the performance of dense object detection in images.
It is highlighted that best SOTA object detectors either provide too many false positive detections in complex scenes, or fail at ... | Rebuttal 1:
Rebuttal: **Q1: Use other datasets with a big amount of small objects.**
Thank you for the great suggestion! First, we would clarify that COCO, Pascal VOC, and Open Images V4 are commonly used benchmark datasets to evaluate dense object detection models such as GFocal, DINO, FCOS etc. Therefore, we choose ... | Summary: Current state-of-the-art dense object detection techniques often generate numerous false positive detections in complex scenes, as they prioritize high recall. This study tackles this problem by introducing an Adaptive Important Region Selection (AIRS) framework. This framework builds on a pre-trained FPN-base... | Rebuttal 1:
Rebuttal: **Q1: It is not an easy-to-use framework. Can AIRS be used for end-to-end training of dense detector and how much is the training time.**
Thank you for the insightful question. We would like to clarify that our goal is to remove the false positive (fp) bounding boxes, which is orthogonal to any ... | Rebuttal 1:
Rebuttal: **General Response**
We would like to thank all the reviewers for their constructive suggestions and comments. Here, we summarize our responses to some common questions raised by multiple reviewers:
**Q1: Poor performance of AIRS in MS COCO Large Objects (Reviewers mhHL and Q21q)**
In this work... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper presents an adaptive hierarchical object detection framework for dense object detection by evidential Q-learning with specially designed reward function, searching through FPN based hierarchy in a top-down fashion. Theoretical analysis proves the upper bound of the action value error and extensive ex... | Rebuttal 1:
Rebuttal: **Q1: Clearer definition/description on what kind of "false positive" cases authors want to avoid and provide challenging visual examples to justify the claim.**
Thank you for the insightful question. A "false positive" detection covers two cases: i) a bounding box covering only part of an object... | null | null | null | null | null | null |
Boundary Decomposition for Nadir Objective Vector Estimation | Accept (poster) | Summary: This paper proposes a nadir point estimation method for evolutionary multi-objective optimization, named BDNE. BDNE decomposes the MOP into boundary subproblems and uses a bilevel architecture to optimize them. Theoretical analysis and empirical results demonstrate the effectiveness of the proposed method.
St... | Rebuttal 1:
Rebuttal: Thank you very much for your time and effort in reviewing our work. We are glad to know that you find our method is novel and has theoretical guarantees.
We address your concerns as follows.
## W1. Presentation.
**Transition from Section 2 to Section 3.**
Thank you for raising this concern. It i... | Summary: The authors model the task of computing the nadir objective vector as several bilevel optimization problems. A corresponding algorithm named BDNE is designed to estimate the nadir objective vector for black-box multi-objective optimization problems. BDNE scalarizes a multiobjective optimization problem into ... | Rebuttal 1:
Rebuttal: Thank you very much for your time and effort in reviewing our work. We are glad to know that you find this work is well organized, the analysis of existing methods is detailed and clear, and our method is demonstrated theoretically and experimentally.
we address your concerns as follows.
## W1. ... | Summary: This paper looks at the problem of finding the nadir objective vector in multi-objective optimization problems, which is important for both optimization and decision-making. This work first analyzes the drawbacks of existing techniques, and then proposes a new method for nadir objective vector estimation using... | Rebuttal 1:
Rebuttal: Thank you very much for your time and effort in reviewing our work. We are glad to know that you find this work is important and well-written, the analysis of existing methods is convincing, and our method has wide applicability.
we address your concerns as follows.
## Q1. Why is CMA-ES used for... | Summary: This work proposes bilevel optimization problems to align their optimal values with the nadir point. Some schemes are suggested to address potential flat fitness in upper-level optimization. An algorithm based on evolutionary computation is then proposed for black-box cases.
Strengths: 1. This work demonstra... | Rebuttal 1:
Rebuttal: Thank you very much for your time and effort in reviewing our work. We are glad to know that you find our method is concise, has broad applicability, and demonstrates its effectiveness theoretically and experimentally.
we address your concerns as follows.
## W1/W2. BDNE is a general approximate ... | Rebuttal 1:
Rebuttal: Dear AC and Reviewers,
We would like to thank you for the insightful comments and feedback on our paper. Overall, the reviewers agree that this work is important (NNkD) and well-written (NNkD, wp71), the analysis of existing methods is convincing (NNkD) /clear (wp71), the proposed method is conci... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Factorization Curse: Which Tokens You Predict Underlie the Reversal Curse and More | Accept (poster) | Summary: This paper focus on the "reversal curse", where models struggle to recall information when probed in a different order than encountered during training. The authors propose reframing this issue as the "factorization curse", which is a failure of models to learn the same joint distribution under different facto... | Rebuttal 1:
Rebuttal: Thank you for providing valuable feedback to improve our work. We appreciate you finding the factorization curse a novel concept to explain model information retrieval failures. We are happy you appreciate the development of WikiReversal and our extensive experiments. Based on your suggestions we ... | Summary: This paper addresses the reversal curse, where models trained on a relation in one direction (e.g. "A is the capital of B") cannot answer questions about the relation worded in the reverse order (e.g. "B's capital is [?]"). The authors frame this as a subcase of a broader problem, where models trained with a c... | Rebuttal 1:
Rebuttal: We thank the reviewer for carefully considering our work. We are glad you found the paper well written and that we tackle a well-defined problem with both theoretical and empirical insights. We are especially happy you appreciated the effort we put into crafting a more realistic benchmark for the ... | Summary: The paper extends the idea of the "Reversal Curse" from prior work and proposes ways to mitigate it by finetuning LLMs with a different objectives. To recall, the reversal curse is formulated roughly as follows: a model, when **finetuned,**(i.e. not prompted) in A is B statement, does not automatically general... | Rebuttal 1:
Rebuttal: We are glad you regard reliable knowledge retrieval as an impactful problem and view MLM-U as a consistent solution to the factorization curse. We are happy that you found the paper to be well-written and suggested several useful pieces of feedback. We address each below:
### Finetuning
First, we... | null | null | Rebuttal 1:
Rebuttal: We’d like to thank reviewers for their high-quality feedback and thoughtful suggestions for our work. We very much appreciate reviewers noted the importance of reliable knowledge retrieval in language models noting “LLM hallucination is one of the main roadblocks to their greater adoption, this is... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments | Accept (poster) | Summary: The paper titled "Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments" introduces the concept of self-healing machine learning (SHML). This framework aims to address performance degradation in machine learning models due to distributional shifts. Unlike traditional c... | Rebuttal 1:
Rebuttal: Dear reviewer xkmy,
Thank you for taking the time to review our paper. We're happy you think that SHML is a novel approach with a solid theoretical foundation and that has significant potential in high-stakes applications.
To address your concerns, we've expanded our evaluation with **five new ... | Summary: This paper proposes a new concept of self-healing machine learning, or SHML. The idea is based on understanding and addressing the reasons of performance drops in ML systems, thereby going beyond most common approaches that are labelled as reason-agnostic. The approach is based on a pipeline well illustrated i... | Rebuttal 1:
Rebuttal: Dear R-vweT,
we're glad you think our paper is important and easy to read.
---
# (A) Classification of reasons for performance drops
You're right that we don't provide an exhaustive classification of reasons for performance drops. We aim SHML to be a meta-level approach which flexibly adapts t... | Summary: The paper presents a self-healing framework for machine learning models called Self-Healing Machine Learning (SHML). Unlike previous methods, SHML autonomously diagnoses the causes of model degradation and suggests corrective actions based on these diagnoses. The authors formalize SHML as an optimization probl... | Rebuttal 1:
Rebuttal: Dear reviewer 4TYN,
Thank you for carefully reading our paper. We're glad that you think we address the problem of maintaining machine learning models autonomously and that this lowers the barriers for others. We respond to each point below.
---
# (A) Moving information from the Appendix to the ... | Summary: Model performance degradation on unseen data is a classic problem. Existing approaches solve the problem through a deterministic strategy: change model, retraining, etc. This paper proposes an adaptive way to decide the action after model degradation automatically and introduces a self-healing framework. The e... | Rebuttal 1:
Rebuttal: Dear Reviewer LnCp,
Thank you for your thoughtful feedback on our work on self-healing machine learning. We appreciate your recognition of the importance and novelty of our approach. We'll address your concerns in two parts, corresponding to both weaknesses.
---
# (A) Clarity of Section 3
The ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful and positive feedback!
We are encouraged by the unanimous recognition of our self-healing ML framework's importance and novelty. The reviewers consistently described our work as "important" (**R-LnCp**, **R-4TYN**, **R-xkmy**) with a "novel and interest... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Foundation Inference Models for Markov Jump Processes | Accept (poster) | Summary: The paper explores the possibility of using transformers to directly infer the parameters of a Markov jump process (MJPs) from a noisy time data set obtained from a time-dependent process. They first train the machine to correctly predict the model parameters by using synthetic datasets containing data obtaine... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the helpful comments and questions, which we know will improve the presentation of our work. Below we address each of them.
**@W1** (On how to apply the model): We kindly refer the reader to our general response above. We hope it addresses the reviewer’s co... | Summary: The authors propose a foundation model for Markov jump processes (MJPs) that is trained on sequences drawn from synthetic MJPs to predict the corresponding rate matrix and initial state distribution. High-level arguments are presented for the justification of model trained in such a way to be able to generaliz... | Rebuttal 1:
Rebuttal: We thank the reviewer for both the detailed review and the kind words about our work. Below we address each of the comments and suggestions.
**@Q1** (FIM success due to simplicity of the data): We completely agree with the reviewer. Indeed, we argued in Section 3, lines 119-127 of our paper, tha... | Summary: This study describes a foundation model for a specific stochastic process, called Markov Jump Process (MJP). The foundation model, called FIM, is trained with a large number of synthetically generated MJP data set. It is shown that the pre-trained FIM can make a zero-shot inference. The capability of FIM is de... | Rebuttal 1:
Rebuttal: Let us thank the reviewer for the detailed review, as well as for the proposed questions and weaknesses. However, let us remark here that some of the proposed weaknesses are somewhat vague. Accordingly, below we ask the reviewer to be more specific in (some of) their remarks.
**@W1** (On compari... | Summary: This work presents a framework for amortizing inference on Markov Jump Processes by learning a foundational model in a supervised fashion from synthetic data. Once learned, the "foundation model" is shown to be successful at zero-shot inference in MJP across a range of domains, out-performing SOTA models that ... | Rebuttal 1:
Rebuttal: First of all, we would like to thank the reviewer for taking the time to review our work and for the helpful comments and questions.
**@W1**: We thank the reviewer for pointing this reference out. We will include it into our related work section, together with other representative references on a... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their valuable comments and questions. We have carefully read and addressed all of them, for each reviewer separately, in the discussions below. We have however noticed a couple of common questions among (some of) the reviewers, namely
1. How is the input... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Rethinking Transformer for Long Contextual Histopathology Whole Slide Image Analysis | Accept (poster) | Summary: The authors provide LongMIL, a hierarchical and hybrid of local and global attention mechanism, to address the inherent low-rank bottleneck of MIL problems in computational pathology. Through extensive evaluations across feature encoders and subtyping/survival tasks, the authors indeed demonstrate the superior... | Rebuttal 1:
Rebuttal: Dear Reviewer UesU,
We appreciate your time and valuable feedback. We are glad that you found that our method is well-motivated and intuitive. Below, please find our point-to-point response to your comments:
> **W1: Novelty: Although the authors tried hard to distance from HIPT, I still consider... | Summary: This paper focuses on the issue of attention computation for long sequences in WSI (Whole Slide Image) images. The authors first analyze how the low-rank nature of the long-sequence attention matrix constrains the representation ability of WSI modeling. They then propose a method using local attention masks to... | Rebuttal 1:
Rebuttal: Dear Reviewer Ghwr,
We appreciate your time and constructive feedback. We are glad that you found that our analysis and method are valuable. Below, please find our point-to-point response to your comments:
> **W1 & W2: Although the paper identifies and analyzes the bottleneck issues in long-seq... | Summary: The authors point out that that MIL often has insufficient ability to offer accurate slide level classifications. There is a long (now) history of attempting to better consider sub-slide level context in the aggregation function. TransMIL, GNN-based methods all have provided attempts to this end.
The auth... | Rebuttal 1:
Rebuttal: Dear Reviewer GgQd,
We would like to express our sincere gratitude for your thoughtful review and insightful feedback on our manuscript. We appreciate your recognition of the thoroughness and approach taken in addressing the limitations of traditional transformer-based aggregators. Your positive ... | Summary: This work examines the problem of extrapolating Transformer attention to long sequences in WSI representation learning. The main technical contribution is in examining the low-rank bottleneck problem of Transformer attention for WSIs, and proposing LongMIL which introduces modifications via local attention mas... | Rebuttal 1:
Rebuttal: Dear Reviewer UUFY,
We appreciate your time and valuable feedback. We are glad that you found the formulation and analysis being novel and sound, and the figures and ablations being illustrative. Below, please find our point-to-point response to your comments:
> **W1: Main limitation of this work... | Rebuttal 1:
Rebuttal: Thanks to all the reviewers for your time and effort during the review process. We appreciate that you found our work insightful and solid.
We have responded to each reviewer individually, uploaded a rebuttal PDF, and collected the below response to general concerns. If you find our answers respo... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
How JEPA Avoids Noisy Features: The Implicit Bias of Deep Linear Self Distillation Networks | Accept (poster) | Summary: This paper presents a theoretical analysis of the JEPA and MAE SSL objectives for deep linear networks. Under a somewhat restrictive diagonal covariance assumption, the authors demonstrate that the critical time for learning a feature is dependent only on the input variance for MAE, while JEPA prioritizes lear... | Rebuttal 1:
Rebuttal: **Q.** Would the main theoretical results hold for deep predictor/decoder?
**A.** Our analysis can be directly extended to deep linear decoders/predictors without changing the results in any qualitative way. Nonlinear predictors however are non-tractable, and therefore beyond the scope of this wo... | Summary: The paper investigates the implicit bias of predictive self-supervised learning methods, specifically focusing on the Joint-Embedding Predictive Architecture (JEPA) and comparing it with the Masked Autoencoders (MAE). The study presents a theoretical analysis of the learning dynamics of these methods, revealin... | Rebuttal 1:
Rebuttal: **Q.** “How well do the theoretical results generalize to non-linear models and more complex data distributions?”
**A.** We have conducted additional experiments on ImageNet (in attached pdf), which are consistent with aspects of our theoretical predictions. (Please see the pdf for details of the... | Summary: Analyze learning in two self-supervised paradigms, JEPA and MAE, through the lens of learning dynamics in deep linear networks. Report on a qualitative difference in the order in which features are learned, thus demonstrating their different implicit bias.
Strengths: * Originality: the analysis of deep linea... | Rebuttal 1:
Rebuttal: **Q.** “Can you offer qualitative prediction from the theory to real-world systems implementing JEPA or MAE? What would be such prediction if the same deep architecture was trained on both JEPA and MAE loss, what would you expect to see differently in terms of the learned features?”
**A.** (Repro... | Summary: This paper aims to understand the implicit bias of on two paradigms of self-supervised learning, Joint Embedding Predictive Architectures (JEPAs) and Masked Auto Encoder (MAE). The authors introduce a tractable setting of deep diagonal linear networks and charaterize the learning dynamics of two objectives on ... | Rebuttal 1:
Rebuttal: **Q.** “More explanations would be helpful to understand the toy setting.”
**A.** Thank you for pointing this out, we plan to add more elaboration in the rebuttal to make it more accessible.
**Q.** “what's the motivation of choosing these two distributions?”
**A.** The motivation for choosing t... | Rebuttal 1:
Rebuttal: **General Response to all Reviewers**
We would like to thank all reviewers for their time and dedication in reviewing our paper, and for their support.
In response to several reviewers, we have conducted additional ImageNet experiments demonstrating phenomena consistent with our theory (describe... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors study a characteristic of two common approaches towards visual modeling in SSL--particularly MAE and JEPA. Previous works have demonstrated or identified empirically that JEPA architecture are more prone towards lower variance features whereas MAE optimize towards higher variance features. This wor... | Rebuttal 1:
Rebuttal: **Q.** “There was no mention of the moving average commonly used in JEPA...”
**A.** Indeed EMA (exponential moving average) is often used in practice to boost performance in a variety of SSL methods that employ self distillation, however we argue that the stop gradient operator is the crucial d... | null | null | null | null | null | null |
Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to Optimal | Accept (poster) | Summary: This paper considers the Bayesian optimization (BO) problem under an unknown length scale and upper bound of the RKHS norm.
The proposed algorithm LB-GP-UCB is designed to select length scale
from certain candidates set adaptively. Furthermore, the algorithm eliminates some candidate length scales if certain ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for reading our paper and for mentioning relevant related work. We address each question/concern below.
**Case of different length scales**
The method of Berkenkamp (2019) cannot really handle kernels with differing length scales across coordinates. While tech... | Summary: This paper proposes a novel Bayesian optimization algorithm for the setting with unknown kernel lengthscale. The proposed approach improves upon prior work by running a logarithmic array of algorithms on exponentially decreasing lengthscales in combination with a regret balancing scheme. The paper proves a reg... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for reading our submission, and pointing out the issues with writing. **We will correct the writing errors pointed out by the reviewer and try to break long sentences into shorter ones.** We agree that the algorithm is quite complex and it would be good to give ... | Summary: This paper introduces LB-GP-UCB (Lengthscale Balancing GP-UCB), a Bayesian Optimization (BO) algorithm that proposes a new tuning of the covariance function hyperparameters. A regret bound is derived, with logarithmic improvement over A-GP-UCB, the most similar solution in the literature. Some numerical experi... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for reading our submission and providing feedback on our paper. We address each question/concern below:
(1) The methods used by [13] and [28] are equivalent to the MCMC baseline we compared against, where the hyperparameters are marginalised from the acqusition... | Summary: This paper proposes an approach to deal with unknown hyper-parameters in Gaussian process upper confidence bound (GP-UCB) algorithms, a popular Bayesian optimisation (BO) strategy. The objective function is assumed to be a member of a reproducing kernel Hilbert space (RKHS) associated with a translation-invari... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for reading our submission and for their appreciation for our theoretical analysis and empirical evaluation. We address each question/concern below.
**Convergence of length-scales estimates**
In general, we consider the setting in which the function can arbitra... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for taking time to read our submission and provide insightful feedback, as well as asking interesting question. We answer to each of the reviewers individually below. | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Grid4D: 4D Decomposed Hash Encoding for High-Fidelity Dynamic Gaussian Splatting | Accept (poster) | Summary: This paper proposes using Hash Encoding to model the Deformation Field for dynamic scenes. The authors first decompose 4D encoding into four 3D encodings to avoid the losses caused by the low-rank tensor assumption. They also introduce an attention module to decouple spatial and temporal features. Since explic... | Rebuttal 1:
Rebuttal: Thank you very much for the constructive feedback. We hope that our response below will address your concerns.
**Q1: Citation of the two related works and writing problem.**
A1: We will add the citation of the mentioned related works and change the presentation in the final version.
**Q2: Shoul... | Summary: This paper proposes a Grid4D representation for dynamic scene rendering. It breaks low-rank assumptions on 4D-GS and propose a decomposed 4D hash-grid representation for encoding canonical 3D Gaussians. A attention module is used to aggregate the spatial-temporal features of 3D Gaussians. More training strate... | Rebuttal 1:
Rebuttal: Thank you very much for the constructive feedback. We hope that our response below will address your concerns.
**Q1: Comparison to SC-GS.**
A1: We conduct the comparison with SC-GS on the D-NeRF dataset. The comparison results with PSNR can be found in the following table, and the qualitative re... | Summary: The paper presents a grid-based method to compute deformed gaussians to render dynamic scenes from input images. It proposes to perform a 3D decomposition of the 4D input, using multiresolution hash encoding to access spatial (static) and temporal (dynamic) feature vectors. The static features are fed to an ML... | Rebuttal 1:
Rebuttal: Thank you very much for the constructive feedback. We hope that our response below will address your concerns.
**Q1: How to demonstrate the temporal coherence of the rendering results ?**
A1: To demonstrate the temporal coherence of our model, we provide some videos by displaying included images... | Summary: The paper introduces Grid4D, a novel dynamic scene rendering model that leverages hash encoding for 4D input decomposition, enabling high-quality and speedy rendering of dynamic scenes.
Unlike traditional plane-based methods that suffer from excessive feature overlap due to low-rank assumptions, Grid4D uses a... | Rebuttal 1:
Rebuttal: Thank you very much for the constructive feedback. We hope that our response below will address your concerns.
**Q1: Grid4D does not significantly improve the training speed in comparison to existing models.**
A1: Although our model has no improvement in training speed, our model can obtain a go... | Rebuttal 1:
Rebuttal: We thank four reviewers for their constructive comments on how to improve our paper. We will provide individual responses below. The qualitative results on the real-world Neu3D dataset, the qualitative comparison to SC-GS, and a simple video exhibition can be found in the following PDF.
Pdf: /pdf/... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A scalable generative model for dynamical system reconstruction from neuroimaging data | Accept (poster) | Summary: This paper proposes a SSM-based DSR algorithm, convolution SSM model (convSSM), to recover the underlying systems. To prevent exploding gradients, the model is trained by SGD with generalized teacher forcing (GTF), for both (pseudo-)invertible and signal convolved decoders. After validating and studying the pr... | Rebuttal 1:
Rebuttal: **Summary**
We thank the reviewer for taking the time to comment on and read our manuscript in detail, as well as for this supportive and positive evaluation. We provide new results and figures in an uploaded PDF, and replies to the questions and comments below. A full list of references is liste... | Summary: The authors introduce a novel algorithm for dynamic system reconstruction (DSR) suited for systems where current observables depend on an entire history of previous states, which notably includes fMRI signals (BOLD signals) and calcium imaging, as both signal are filtered with a response function. The algorith... | Rebuttal 1:
Rebuttal: **Summary**
We thank the reviewer for taking the time to comment on and read our manuscript in detail, as well as for this supportive and positive evaluation. We provide new results and figures in an uploaded PDF.
**Weaknesses**
[“*W1*”]
We apologize for the error in the table labeling. The s... | Summary: The paper proposes a teacher forcing (TF) mechanism for a latent variable model where the dynamics evolve according to a (deterministic) piecewise linear RNN model and the observations are linear projections of the latent space convolved with a filter (in the case of BOLD signals, the form of that filter is kn... | Rebuttal 1:
Rebuttal: **Summary**
Thank you for your detailed review! All refs. are found in the general rebuttal to all revs.
**Weaknesses**
[“*W1*”]
The conv. filter could in principle also be learnable by parameterizing its length and weights. Here our focus lay on incorporating biological prior knowledge on th... | Summary: This paper introduces two techniques, pseudo-inverse and deconvolution, for dynamical system reconstruction. The two techniques are used to help the teacher forcing for the latent sequence $z_t$ so that the learning can be more efficient. Experimental results show the effectiveness of the proposed ConvSSM + GT... | Rebuttal 1:
Rebuttal: **Summary**
We thank the reviewer for taking the time to comment on and read our manuscript in detail. We provide new results and figures in an uploaded PDF. A complete list of references can be found in the general rebuttal to all reviewers.
**Weaknesses**
[“*W1*”]
We see our major contribu... | Rebuttal 1:
Rebuttal: **General response**
We thank all reviewers for their positive and supportive feedback, for taking the time to review our work, and for providing many helpful comments and suggestions, which we address in detail below. We have prepared a PDF file with additional material and results.
Specificall... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Are We on the Right Way for Evaluating Large Vision-Language Models? | Accept (poster) | Summary: This paper investigates the evaluation of large vision-language models (LVLMs) and the currently used benchmarks. Within the paper two primary issues are identified: the lack of need for visual information, and data leakage. Based on these issues a new compiled benchmark is proposed MMStar that includes a set ... | Rebuttal 1:
Rebuttal: Thanks for your valuable suggestions. We address your concerns point by point:
**Q1: Given the 26.4% baseline for MMbench from [27], it is also surprising that the random choice value reported in this paper, for MMbench, is 0.0 in both Table 1 and Table 2.**
**A1:** The 26.4% in MMBench represen... | Summary: The current benchmarks used to evaluate Vision Language Models (VLMs) contain several flaws. In particular, a lot of questions can be answered without looking at the image at all. These benchmarks still being hard, the best proprietary models without looking at the images can obtain better scores than strong V... | Rebuttal 1:
Rebuttal: Thank you very much for your thorough review and appreciation of our work. Below, we address your concerns point by point:
**Q1: In the released dataset, the choices are directly integrated into the prompt. It would be good to also add a column with only the original question, and another column ... | Summary: In this paper, the authors examine current benchmarks for large vision-language models (LVLMs) and identify two main problems: 1) many samples do not require visual content, and 2) there is unintentional data leakage in LLM and LVLM training. To address these issues, they developed a multimodal benchmark calle... | Rebuttal 1:
Rebuttal: We are encouraged to see that you found our work intuitive, containing extensive experiments and empirical analysis, and well-written. We have endeavored to address your concerns as follows:
**Q1: The authors only consider multiple-choice questions for the MMStar benchmark. Including a wider vari... | Summary: The authors have identified two primary concerns with the benchmarks commonly used for large vision-language models (LVLMs). Firstly, many samples do not require visual content to answer the questions. Secondly, they noted unintentional data leakage during LVLM training. They assessed eight large language mode... | Rebuttal 1:
Rebuttal: We thank you for the positive comments on the novelty and meaningful impact of our findings and proposed benchmark. We detail your concerns and our corresponding responses below:
**Q1: The proposed metrics, Multi-modal Gain (MG) and Multi-modal Leakage (ML), are dependent on the base LLM utilize... | Rebuttal 1:
Rebuttal: We sincerely appreciate all reviewers for your time and efforts in the review. All detailed questions of each reviewer are answered accordingly in each column below. We hope these responses can address the reviewers' concerns adequately. Additionally, we provide the implementation details of the m... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Leveraging Drift to Improve Sample Complexity of Variance Exploding Diffusion Models | Accept (poster) | Summary: Diffusion models are powerful tools in generative modeling. As the paper pointed out, very few theoretical works in the literature
consider variance exploding diffusion models. Among those works, forward convergence rate $1/\text{poly}(T)$ is achieved compared to $\exp(-T)$ from the variance preserving models... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. We provide our response to each question below.
**Weakness 1: The universe error analysis for general $\tau$ and $\beta_t$.**
In this part, we first provide a universe complexity for general $\tau \in [1,+\infty)$ and $\beta_t\in [1,t^2]$ un... | Summary: The paper analyzes the Variance Exploding diffusion model under the manifold hypothesis. By a slight modification to the VESDE process, the authors propose a method whose convergence guarantees are better than prior best known rates in this regime.
Strengths: The rate obtained is state-of-the-art for Variance... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. We provide our response to each question below.
**W1: The real-world experiments on CelebA 256.**
We do experiments on the CelebA 256 dataset (a common face dataset) and show that our drifted VESDE can improve the results of pure VESDE **with... | Summary: This paper focuses on variance exploding (VE) based diffusion models and proposes a drifted VESDE forward process with an unbounded diffusion coefficient. This choice of coefficients allows an exponential-decay forward convergence rate, and the authors establish the first polynomial sample complexity for VE-ba... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. We provide our response to each question below.
**W1 & Q1: The different $\beta_t$ for reverse SDE and PFODE: the balance between different error terms.**
(a) We first recall the reverse beginning error term when considering the unified tange... | Summary: In this paper, the authors propose an analysis of the convergence of diffusion models under the manifold hypothesis in a similar setting as [1]. The main contribution is the extension of the analysis to the case of VESDE (Variance Exploding SDE) contrary to [1] which is limited to VPSDE (Variance Preserving). ... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and suggestions. We provide our response to each question below.
**W1: The real-world experiments.**
We do experiments on the CelebA 256 and show that our drifted VESDE improves pure VESDE **without training** from the quantitative and qualitative perspectives. ... | Rebuttal 1:
Rebuttal: # The Real-World Experiments and Discussion (CelebA 256)
Once again, we thank all reviewers for their valuable suggestions on real-world experiments. In this part, we show that our conservative drifted VESDE can improve the quantitative results (IS (higher is better), and Aesthetic score [1] (1-1... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
In-Context Symmetries: Self-Supervised Learning through Contextual World Models | Accept (poster) | Summary: This paper proposes ContextSSL, a novel self-supervised learning framework designed to enhance the existing joint embedding architecture by incorporating task-specific context. The main idea is to dynamically adapt symmetries by leveraging context in SSL. Consequently, ContextSSL can adapt to varying task symm... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and feedback that helps us improve the work. We believe that there are a few confusions that have resulted in the given rating. We have endeavored to address your concerns as concretely as possible and ask for your careful consideration of our clarifications. ... | Summary: This work proposes to employ context modules to learn general representations such that invariance and equivariance to specific augmentations do not bias the representations. The method utilizes a module to learn to be both invariant or equivariant based on the context of the input augmentations, thus producin... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thorough and insightful review, along with their positive feedback regarding the significance of our work for the SSL community, extensive evaluations and ablations, and our writing. In response to their review, have endeavored to address your concerns as concretely as... | Summary: This paper focuses on the problem of symmetry discovery in self-supervised learning. In particular, the goal is to learn models that are either sensitive to certain features like rotations and lightning or invariant to them, depending on the task. The authors propose to learn a world model that models transfor... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for the time they put in to review our work. We are glad to see that they recognize several strengths in our work, including the novelty of our approach, comprehensive empirical evaluation using many baselines, and conducting thorough ablations. Below, we share our ... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and expertise in evaluating our paper. Their perceptive remarks and constructive feedback have been valuable in improving our work. In response, we have made several key revisions to address their concerns and have conducted additional experiments to enhan... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Neur2BiLO: Neural Bilevel Optimization | Accept (poster) | Summary: This paper proposes two approximate methods for solving constrained, mixed-integer, non-linear bilevel optimization problems. The core idea is to convert the bilevel problem into a single level problem by clever use of neural networks trained offline by solving single level optimization problems. The upper-lev... | Rebuttal 1:
Rebuttal: Thank you for the review. Below, we provide responses to each of the weaknesses and questions.
**Weaknesses**:
- Indeed, none of the problems we study in our experiments contain coupling constraints. We have not found bilevel optimization benchmarks with coupling constraints in the literature.... | Summary: The paper tackles the bi-level optimization problem (BiLo) in general. BiLO can be seen as the problem of a leader computing a strategy (x) to commit to, such that the leader’s objective is optimized subject to the follower’s best response (y) to the committed strategy. The paper provides two ML-based approach... | Rebuttal 1:
Rebuttal: Thank you for the review.
We do acknowledge that the effectiveness certainly does depend on how well the value functions can be approximated. Through our experiments, we do indeed demonstrate that this is relatively easy for the problems studied and note that these are already challenging prob... | Summary: The paper develops a neural method to solve bi-level optimization problems. It begins with a motivating application and then proposes NEUR2BILO, which utilizes two layers of neural networks to approximate solutions for the upper and lower levels.
Strengths: I found the work conducted to be substantial, suppor... | Rebuttal 1:
Rebuttal: Thank you for the review. Below, we provide responses to each of the weaknesses.
1. For the first weakness, we will discuss two separate points.
- We acknowledge that this paper focuses on relatively specialized bilevel optimization problems/literature. We will add some basic references o... | Summary: The paper studied bilevel optimization problems with discrete decision variables. The proposed framework, Neur2BiLO, adopts a learning-based approach to solve such problems, which is based on a trained neural network to approximate the leader's or follower's value functions.
Strengths: The paper studied an im... | Rebuttal 1:
Rebuttal: Thank you for the review. Below, we respond to each of the weaknesses and questions.
**Weaknesses**:
- Next to the performance guarantees we provide in the paper, we believe that we have conducted a thorough numerical validation. In total, we test on 2250 instances with ranging instance sizes ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Overcoming Brittleness in Pareto-Optimal Learning Augmented Algorithms | Accept (poster) | Summary: In the burgeoning field of learning-augmented online algorithms, the ideal case is to design an algorithm that achieves a competitive ratio (CR) as a function of prediction errors, without knowing the prediction error in advance. To tackle this problem, many existing works focus on two extreme metrics: consist... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We comment first on “weaknesses”.
**1**. There are indeed other problems for which Pareto-optimal algorithms are brittle. Examples include 1-MAX search [19], [38] which is a much simpler version of one-way trading, online bidding [6], [23] and searching for a hidden ... | Summary: The authors consider learning-augmented algorithms for the one-way trading problem. In this problem, we are given a budget of 1 and a sequence of exchanges rates between 1 and M that are revealed in an online manner. Whenever an exchange rate is revealed, we have to decide whether to exchange a fraction of our... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Below we respond to “weaknesses”.
**1**. In regards to the prediction being tied to single values, please see *Point 2* in the *global response*. As we explain, while the model is indeed more amenable to single predictions, it can apply to more complex prediction set... | Summary: The paper considers the learning-augmented one-way trading problem. In the problem, we are given a starting budget equal to 1 and a sequence of exchange rates $p_1,...,p_n \in [1,M]$ arriving online. When each $p_i$ arrives, we need to. decide the amount to be exchanged to the secondary currency. Our goal is t... | Rebuttal 1:
Rebuttal: Thank you for your feedback. In regards to weaknesses/questions, our responses are below.
**1**. We address this issue in *Point 2* of the *global response*, which we also include below for convenience.
The concept of a profile is inherently applicable, and at the very least, to the class of onl... | Summary: In the context of learning augmented algorithms, two widely used metrics are robustness (i.e: the performance when the prediction is adversarially chosen) and consistency (i.e: the performance when the prediction is perfect).
This work analyzes the interplay between these two metrics in the one way trading p... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Please allow us first to comment on the “weaknesses”.
**1**. For the experiments, we chose a relatively simple profile in order to be able to compare our algorithms to the known Pareto-optimal one, in a very clear and meaningful manner. Nevertheless, we agree with you... | Rebuttal 1:
Rebuttal: We respond to some points brought up in the reviews.
**1**. **Experimental evaluation on complex profiles**. For the experiments, we chose a relatively simple profile in order to be able to compare our algorithms to the known Pareto-optimal one, in a very clear and meaningful manner. Namely, the... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Simple Image Segmentation Framework via In-Context Examples | Accept (poster) | Summary: This paper proposes a generalist segmentation model, dubbed as SINE, for a variety of segmentation tasks.
The general idea is to harness the in-context examples, and to alleviate the task ambiguity.
Specifically, an in-context interaction module, a matching Transformer and a Hungarian algorithm is devised to... | Rebuttal 1:
Rebuttal: >W1,Q1,Q2: Motivation does not match methodology design properly.
The motivation and methodology design are related and clear (supported by **sZmi,rhP3**).
- The goal of In-Context Fusion is to establish the correlations between reference and target (see Line 163-164), understanding the complex... | Summary: The paper proposes an image segmentation framework using in-context examples. To eliminate ambiguity from the in-context examples, multiple output masks are predicted. It uses a pre-trained image encoder to extract features from target and reference images, pools these features into ID and semantic tokens usin... | Rebuttal 1:
Rebuttal: We thank you for your comments and the approval of our motivation and practical utility of SINE. We address your concerns here.
___
>W1: The novelty of the overall idea and network structure is limited.
To address the reviewer's concerns, we first discuss the contributions and academic insigh... | Summary: The paper proposes a generalist model for image segmentation named SINE, which unifies multiple image segmentation tasks into the common formulation of visual in-context learning. This work aims to identify and model the task of object reidentification to reduce ambiguities within the in-context examples. By i... | Rebuttal 1:
Rebuttal: We thank you for your comments and the approval of our motivation and performance. We address your concerns here.
___
>W1: Discussion of more ambiguity in visual prompts.
Thanks for your helpful suggestions. SINE is the first work to highlight task ambiguity in the visual prompts of in-context ... | Summary: SINE aims to resolve the problem of task ambiguity in in-context segmentation, where previous models struggled to accurately infer tasks based on in-context examples alone. This ambiguity arises because traditional models often fail to distinguish between different segmentation tasks like semantic segmentation... | Rebuttal 1:
Rebuttal: We thank you for your comments and the approval of our task formulation. We address your concerns here.
___
>W1: Concerns on Generalizability.
Fig. 1 in the attached PDF shows SINE's capability in handling complex interaction relationships.
- In Fig. 1(a), the reference consists of multiple ima... | Rebuttal 1:
Rebuttal: # **General Response**
We thank the reviewers for recognizing that our paper points out an open research problem, i.e., the ambiguities in visual prompting (**nRMP**). The motivation on task ambiguity is clear (**rhP3**), and we effectively address task ambiguity (**sZmi,nRMP**). Our method is ra... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Mixture of Experts Meets Prompt-Based Continual Learning | Accept (poster) | Summary: The paper titled "Mixture of Experts Meets Prompt-Based Continual Learning" explores the integration of prompt-based continual learning methods with mixture of experts (MoE) architectures. The paper proposes a novel gating mechanism called Non-linear Residual Gates (NoRGa) to enhance the performance of prompt-... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and insightful comments. Below, we provide a point-to-point response to these comments and summarize the corresponding revisions in final version.
__Q1: Comparing NoRGa with other state-of-the-art continual learning methods that do not use prompts would hi... | Summary: The paper explores the theoretical underpinnings and practical implications of prompt-based methods in continual learning, aiming to enhance our understanding and optimize their effectiveness. It introduces a novel perspective by connecting prefix tuning with mixture of experts models, revealing insights into ... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and insightful comments. Below, we provide a point-to-point response to these comments and summarize the corresponding revisions in final version.
__Q1: The paper could benefit from clearer explanations regarding the core elements of prompt-based methods, ... | Summary: This paper introduces an extension to prefix tuning, by introducing non-linear residual gating - a simple extension over existing prefix tuning. This non-linear residual gating is supported with theoretical efficiency guarantees (polynomial versus exponential) to better estimate the optimal parameters. When ap... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and insightful comments. Below, we provide a point-to-point response and summarize the corresponding revisions in the final version.
__Q1: The placement as a continual learning contribution__
A1: Our contributions encompass the introduction of a novel con... | Summary: The topic of this paper is about the prompt-based continual learning. The authors give a theoretical analysis on these prompt-based continual learning methods, and utilize a Mixture-of-Expert (MoE) architecture characterized by linear experts and quadratic gating score functions. They develop a gating mechanis... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and insightful comments. Below, we provide a point-to-point response to these comments and summarize the corresponding revisions in final version.
__Q1: Comparison to Different Parameter-Efficient Fine-Tuning Methods and Theoretical Analysis__
A1: Thank y... | Rebuttal 1:
Rebuttal: **General Response:**
We thank all reviewers for their valuable feedback and suggestions, which have significantly contributed to the enhancement of our manuscript. We are encouraged by the endorsements that:
1. Our reveal relationship between self-attention, prefix tuning and mixture of experts ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Discovering Preference Optimization Algorithms with and for Large Language Models | Accept (poster) | Summary: This paper introduces a method of searching for offline RL objectives by using LLMs to generate and refine objectives. They demonstrate that several objectives discovered using this method are able to achieve higher evaluation scores than existing objectives (e.g. DPO) on a variety of benchmark tests. They pro... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and extremely helpful review. We’re glad that the reviewer finds our approach to be both novel and useful, though we agree that more analysis would improve the paper significantly.
> The majority of insights about DiscoPOP are presented as hypotheses or... | Summary: The paper proposes DiscoPOP, an algorithm for discovering preference optimization loss functions using Large Language Models (LLMs). The authors propose an LLM-driven objective discovery process by iterative prompting LLMs by previously evaluated performance metrics. Experiments on various benchmarks demonstra... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their concise feedback. We’re happy that the reviewer finds the paper innovative and the results impressive. We understand the reviewer has concerns around prompt sensitivity. We would like to point the reviewer to a feel ablations we’ve run on this.
In App... | Summary: The paper proposes an algorithm to discover preference objective functions using LLM for LLM preference optimization. Authors conduct experiments with the discovered objectives on multiple datasets and demonstrate that the discovered objective functions generally perform better than baselines. The authors also... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough and in-depth review. We are pleased that the reviewer finds our approach interesting and effective. We address the weaknesses outlined by the reviewer individually below.
> The objective function is discovered with a different placement of $\beta$
Thank ... | Summary: This paper proposes a novel approach to improving LLMs by using an automated system to discover new optimization algorithms. Traditionally, enhancing LLMs has relied heavily on hand-designed loss functions, but this research employs an LLM to iteratively generate and refine these functions itself. This paper i... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback. We’re glad the reviewer finds the approach and results promising and effective.
> does proposed LLM-driven discovery method generalize to other domains?
In our submission, we demonstrated that it works well on CIFAR-10 in the small case st... | Rebuttal 1:
Rebuttal: We are grateful to the reviewers for their insightful feedback. There is broad consensus amongst the reviewers that our approach is novel and effective.
$\color{red} R1$ (ePhP): “the new discovered algorithm in preference optimization achieved SOTA, proving its effectiveness.”
$\color{green} R2... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Frozen-DETR: Enhancing DETR with Image Understanding from Frozen Foundation Models | Accept (poster) | Summary: This paper proposes Frozen DETR, which leverages frozen foundation models as feature enhancers to improve the performance of the DETR object detection framework. By integrating feature maps from models like CLIP into the pyramid feature maps and feeding them into the encoder, Frozen DETR enriches the contextua... | Rebuttal 1:
Rebuttal: # To Reviewer C9T7
**Q1: It would be beneficial to provide a detailed analysis of the computational cost.**
**RE**: We provide analyses of the computational cost in Table 3, Table 4, and Table 5 in the main text. Besides, more discussion can be found in the rebuttals for all reviewers above.
**... | Summary: This paper incorporates frozen foundation model backbones into DETR pipelines. Specifically, the paper concatenates the output of the class token of the foundation models with the query vector in the decoder. Also, it concatenates the patch tokens with the feature pyramid from the Image backbone to improve acc... | Rebuttal 1:
Rebuttal: # To Reviewer QbuY
**Q1: The idea of feature fusion seems incremental from the perspective of technical contributions. By looking at the design, the feature fusion of the frozen foundation model is the only principle claim of the paper.**
**RE**: We respectfully disagree with this point. We woul... | Summary: This paper focuses on enhancing the performance of query-based object detection models. By inserting a foundation model into the DETR framework and treating it as a plug-and-play module instead of a backbone, the performance of query-based detectors can be significantly improved. The detection performance of D... | Rebuttal 1:
Rebuttal: # To Reviewer RksL
**Q1: When switching from R50 to Swin-Large or even ViT-Large, does this approach of inserting a frozen CLIP still lead to noticeable improvement?**
**RE**: Yes. We conduct experiments with Swin-L based on the Co-DETR detector. In this experiment, we equip the Co-DETR with DFN... | Summary: This paper explores using frozen vision foundation models to enhance object detection performance without fine-tuning. The authors demonstrate that foundation models, although not pre-trained for object detection, can significantly improve detection accuracy by leveraging their high-level image understanding c... | Rebuttal 1:
Rebuttal: # To Reviewer V83J
**Q1: A few suggestions on writing**
**RE**: Thanks for your helpful and detailed advice. We will carefully revise our manuscript. We rename the detector DINO as DINO-det and the self-supervised foundation model DINOv2 as DINOv2-FM.
***
**Q2: EVA-CLIP-18B is equipped with a ... | Rebuttal 1:
Rebuttal: # To all reviewers
We thank all reviewers for their helpful and insightful feedback and are encouraged they find that our method is innovative (Reviewer V83J), the experiments are very comprehensive (Reviewer RksL and C9T7), and the proposed method achieves promising results (Reviewer V83J, RksL,... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MKGL: Mastery of a Three-Word Language | Accept (spotlight) | Summary: This paper proposes a method to leverage LLMs as knowledge graph completion systems. New tokens that correspond to (potentially multi-word) concepts and relations are introduced to the model’s vocabulary, and then the LLM’s embeddings for the tokens composing those concepts/relations are aggregated and upscale... | Rebuttal 1:
Rebuttal: Thank you very much for your detailed and insightful comments. We have addressed your concerns below and hope our responses provide clarity:
### Weaknesses:
1. **Reproducibility: can the authors provide the variance in performance of the method?**
Thanks for your suggestion. We follow the e... | Summary: This paper proposes what seems to be an elaborate GNN+LLM+GNN sandwich of a model for doing knowledge base completion, having a GNN pipeline to form KB-informed token embeddings, passing those to a LLM (lllama-2 ) into a knowledge base completion prompt template, and then passing that output into another GNN-... | Rebuttal 1:
Rebuttal: Thank you very much for your constructive and detailed comments. We appreciate the opportunity to provide further clarifications.
### Weaknesses:
- **Why is the proposed model better than the prior works? It is better to illustate each modules step-by-step with clear figures. The name of "Retri... | Summary: The authors introduce a SOTA method for allowing LLMs to incorporate information from knowledge graphs, relying on Knowledge Graph Language token embeddings to retrieve context, and then score it using a retriever that helps form a distribution over the possible entities to be incorporated.
Strengths: Creativ... | Rebuttal 1:
Rebuttal: We are grateful for your encouraging comments and insightful suggestion. We hope the following response address your concern:
### Weaknesses:
- **Could use a longer and more detailed discussion section; ends a little too abruptly.**
Thank you for your insightful suggestion. We also agree th... | Summary: The paper proposes MKGL, a novel approach to integrate LLMs with KGs by instructing them in a specialized KG Language (KGL). KGL is a three-word language that mirrors the structure of KG triplets. The authors introduce a KGL context retriever and a score retriever, both based on LoRA, to efficiently encode tex... | Rebuttal 1:
Rebuttal: Thank you very much for your helpful feedback and constructive suggestions. We have carefully integrated them into our paper.
### Weaknesses:
- **Include more details of the methodology, e.g., the implementation of the multi-layered PNA for KG information retrieval.**
Thank you for your sug... | Rebuttal 1:
Rebuttal: Dear all reviewers:
We sincerely appreciate the time and effort you have dedicated to reviewing our paper.
We would like to express our gratitude to Reviewers XXej for suggesting the inclusion of more details in the related work and methodology sections. We have incorporated these suggestions i... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Mission Impossible: A Statistical Perspective on Jailbreaking LLMs | Accept (poster) | Summary: The paper introduces a new theoretical framework that views prompts as combinations of concepts and queries, allowing for a detailed analysis of how and why LLMs can be manipulated into producing unsafe responses. It proposes a statistical metric for alignment, focusing on quantifying the safety of the model's... | Rebuttal 1:
Rebuttal: Dear reviewer sD4q,
Thanks for your detailed review! Here are our responses to your concerns.
*1. ...(E-RLHF's) effectiveness in more complex conversational scenarios, where inputs can evolve over a series of interactions, remains untested.*
- That is a great point that we had discussed interna... | Summary: The paper presents a statistical framework that provides a theoretical analysis of the jailbreaking problem in language models. The authors first examine the PAC-Bayesian bound to demonstrate that there is a non-trivial probability for LLMs to mimic harmful behavior if such information is present in the pre-tr... | Rebuttal 1:
Rebuttal: Dear reviewer UXGw,
Thanks for your comprehensive review! Here are our responses to your concerns.
- **(1) The novelty of our framework.** To the best of our knowledge, we are the first to offer a theoretical analysis on jailbreaking from a statistical perspective. To tackle this problem and prov... | Summary: The paper addresses a very important issue of our time, the safety of LLMs. LLMs are already used in various applications and will be present in more applications to come, as e.g. Microsoft, Apple and Google are integrating LLMs in their applications and operating systems. Hence, the question on how to make th... | Rebuttal 1:
Rebuttal: Dear reviewer T1QG,
Thanks for your thorough review! We are pleased to provide further explanations on our E-RLHF proposal, its theoretical foundations, and its specific relation to LLMs.
*1. The definitions, theorems, etc. are all presented and motivated very nicely, but could also apply to an... | Summary: The paper provides a theoretical insight about LLM jailbreaks using PAC-Bayesian bound for pretraining LLMs. It assumes that there always exists the harmful data in the mixture, and as the model is trained on this mixture, the model will probably produce the responses in harmful zone (it has a specific definit... | Rebuttal 1:
Rebuttal: Dear reviewer qvRD,
We sincerely thank you for reading our work in great detail! Here are our responses to your concerns.
*1. E-RLHF is an inaugural and simple form of expanding the safety zone of LLM; I think there could be more sophisticated and effective ways, and I hope the authors will add... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for taking the time to review our paper and providing valuable feedback. We appreciate the recognition of our established theoretical framework and the acknowledgment of the nuanced formulation of our results from all reviewers. We had to traverse several conceptua... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Functional Bilevel Optimization for Machine Learning | Accept (spotlight) | Summary: The authors propose a functional view of bilevel optimization in machine learning, in which the inner objective is often strongly convex for many problems of interest. The authors prove the resulting optimization algorithm's convergence and benchmark their approach for regression and model-based RL tasks.
Str... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback. We will correct the typos, increase the font size in the figure captions, and add a link to the discussion of the assumptions as suggested. Additionally, we will include a discussion section in the main paper (provided in the general response) to address limit... | Summary: The paper proposes a functional approach to bilevel optimization for machine learning, focusing on inner-level problems defined over function spaces rather than traditional parametric settings. This allows the application of the proposed method to machine learning tasks without requiring the strong convexity t... | Rebuttal 1:
Rebuttal: **Additional comparisons**. As suggested by reviewer *MXrv* and in addition to the comparisons already made with most widely-used bilevel algorithms (AID, ITD, and variants) and SoTA methods for each problem (DFIV for the IV problem and MLE for model-based RL), we now additionally include a compar... | Summary: This paper introduces a novel functional perspective on bilevel optimization, where the inner objective is defined over a function space. The authors developed functional implicit differentiation and functional adjoint sensitivity, which together facilitate the establishment of a gradient-based algorithm in th... | Rebuttal 1:
Rebuttal: **Error analysis**. Thank you for pointing out these references, we will make sure to discuss them. We agree that the result of Thm. 3.1 does not provide an explicit dependence of the errors on the inner-level optimization. Providing such dependence would require introducing another level of techn... | Summary: This paper offers a new functional point of view for bilevel optimization problems in machine learning. This functional approach allows the use of an overparameterized neural network as inner prediction function while previous works have used an inner objective that is strongly convex with respect to the param... | Rebuttal 1:
Rebuttal: Thank you for thoroughly reading our work and giving us helpful feedback. Taking your feedback into account, we will simplify the notation and include a notation table. We agree that this could help the reader get a quick grasp of the mathematical objects considered. We will also include a discuss... | Rebuttal 1:
Rebuttal: # General comments
We thank the reviewers for their useful feedback. We now list the main changes made to the paper.
## Discussion section (limitations and perspectives).
We agree that such a section is important, and propose to include the following discussion:
### Discussion and concluding ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Poisson Variational Autoencoder | Accept (spotlight) | Summary: The paper proposes a variation on variational auto-encoders with a Poisson distribution over the latents. To make the model differentiable they use a differentiable sampling of the latent variables where the indicator function is replaced with a continuous approximation. They take inspiration from biological n... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and thoughtful comments.
> A significant weakness is a lack of study of the effect of the temperature parameter in the Poisson re-parameterization.
We agree. Please see our new rebuttal results, where we performed extensive experiments to address this point. ... | Summary: This work introduces VAEs with Poisson-distributed latent variables and a Poisson reparameterization for efficient training. This approach has theoretical and empirical connections with sparse coding and behaves more similarly to biological networks that rely on discrete spike counts.
Strengths: To the best o... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and thoughtful comments.
> Posterior collapse in VAEs (in the sense of "some latent dimensions are not used") is not necessarily an issue per se. If this is an issue in the specific scenarios considered in this paper, I think this should be clarified.
We agre... | Summary: Inspired by biological neurons, a new type of variational autoencoder, the Poisson variational autoencoder ($\mathcal{P}$-VAE) is proposed. The $\mathcal{P}$-VAE uses discrete latent states with Poisson priors, and learns sparse discrete representations of the data similar to sparse coding methods. The authors... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and thoughtful comments.
> My main concern for this work is in its limited impact.
We hope that we have addressed this concern in the general rebuttal. We developed P-VAE with neuroscience applications in mind, where we feel it will have a big impact. However... | Summary: In this paper, the authors propose a VAE model, the Poisson-VAE ($\mathcal{P}$-VAE), with a Poisson distributed prior and approximate posterior such that it works with Poisson distributed latents and demonstrate its sparse coding abilities on the van Hateren dataset and its efficacy on downstream classificatio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and thoughtful comments. We are also glad the reviewer finds our results "neat" and "elegant"!
> How do we know that the approximate posterior is Poisson?
During training, we use non-zero temperatures in our reparameterization algorithm. As a result, during t... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and insightful feedback. We believe addressing the reviewers' comments will substantially improve our paper. We plan to include these changes in the form of two major components: **(i)** further discussion of the work significance; and, **(ii)** additional res... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation | Accept (poster) | Summary: The authors extend AUC optimization techniques to pixel-level long-tail semantic segmentation and propose a general pixel-level AUC loss function. They decompose the loss function into inner-image and inter-image terms to decouple the interdependency, and calculate bounds to theoretically prove the effectivene... | Rebuttal 1:
Rebuttal: We deeply appreciate your time and effort in providing us with such constructive comments. We would like to respond to them as follows:
> **Q1:** In the tail-class memory bank module, how do you ensure that pasted tail pixels do not completely cover any category?
**A1:** The number of head clas... | Summary: This paper explores AUC optimization methods for pixel-level long-tail semantic segmentation, addressing complex dependencies and space complexity challenges. The authors propose a novel pixel-level AUC loss function and conduct a dependency-graph-based theoretical analysis to enhance generalization. They also... | Rebuttal 1:
Rebuttal: We deeply appreciate your time and effort in providing us with such constructive comments. We would like to respond to them as follows:
> **Q1:** Why did the authors choose the segmentation perspective? Would a contrastive classification approach achieve similar results?
**A1:** Thank you for th... | Summary: This paper investigates AUC optimization within the context of pixel-level long-tail semantic segmentation (PLSS), a complex task due to intricate loss term coupling and extensive memory requirements. Initially, the authors demonstrate the potential of AUC for PLSS from a theoretical perspective by addressing ... | Rebuttal 1:
Rebuttal: We deeply appreciate your time and effort in providing us with such constructive comments. We would like to respond to them as follows:
> **Q1:** The notations in this paper should be carefully defined. Some key symbols are used repeatedly, such as 𝑁 representing both batch size (In Alg.1) and s... | Summary: This paper introduces AUC optimization into the domain of long-tailed semantic segmentation. Specifically, the authors developed a pixel-level AUC loss function tailored for long-tailed semantic segmentation tasks and introduced a tail-class memory bank to address the memory demands. Additionally, the authors ... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments, and we would like to make the following response.
> **Q1:** On COCO, the performance improvement of Tail Class seems to be limited. Could the author explain the reason?
**A1:** Please refer to `C-A1` in `General Response`.
> **Q2:** Fig. 6(a) shows the mem... | Rebuttal 1:
Rebuttal: **General Response**
Dear SAC, AC, and reviewers,
Thank you for your invaluable feedback. Based on your comments, we have revised the details and now offer a summary of our responses.
- **Additional Experiments:**
1. Different sampling methods for the Tail-class Memory Bank
2. Different AU... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
RegExplainer: Generating Explanations for Graph Neural Networks in Regression Tasks | Accept (poster) | Summary: The authors focus on the interpretability of GNN in graph regression tasks. They propose a novel explanation method called RegExplainer as a plug-in to existing explanation methods, such as GNNExplainer and PGExplainer. They also tackle mutual information estimation (graph information bottleneck), distribution... | Rebuttal 1:
Rebuttal: Dear reviewer 4dqr, thank you for taking the time to review our work and providing feedback. In the following, we aim to address your questions and concerns.
A-1a: Thank you for your suggestion. We will include detailed descriptions of the datasets in future versions of the paper. In the appendix... | Summary: This work proposes a method to generate instance-level GNN prediction explanations specifically for graph regression tasks. This method addresses distribution shifting, a problem in regression, by using mix-up for contrastive learning. The work is evaluated on four datasets, both synthetic and real-world.
Str... | Rebuttal 1:
Rebuttal: Dear reviewer X7ek, thank you for taking the time to review our work and providing feedback. In the following, we aim to address your questions and concerns.
> How does this method perform with/against classification explanation models? As the explainer can be modularly applied to existing traine... | Summary: The authors propose an explanation method to interpret the graph regression models. The techniques are built upon the information bottleneck theory and contrastive learning. The authors show that their explanations are accurate in five graph regression datasets.
Note: If authors address my concerns in questi... | Rebuttal 1:
Rebuttal: Dear reviewer B4J7, thank you for taking the time to review our work and providing feedback. We appreciate your thorough review and aim to address your questions and concerns in detail. Due to character limitations, we will provide a detailed response to the remaining points in the official commen... | Summary: The paper introduces XAIG-R, a novel explanation method for interpreting graph regression models. It addresses distribution shifting and decision boundary issues, leveraging the graph information bottleneck theory (GIB) and self-supervised learning.
Strengths: - Intuitive and clear presentation and illstrrati... | Rebuttal 1:
Rebuttal: Dear reviewer LTFm, thank you for taking the time to review our work and providing feedback. In the following, we aim to address your questions and concerns.
> Limited Discussion on Computational Efficiency
A1: Thank you for pointing this out. We are glad to supplement our analysis of computatio... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We sincerely appreciate your time, consideration, and valuable comments, which have been instrumental in refining our work. If you have any further questions or concerns regarding our response or the current draft, please let us know. We are more than happy to discuss them in deta... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper addresses the challenge of interpreting graph regression models, a fundamental yet less explored task in graph learning compared to classification tasks. Existing explanation techniques are predominantly designed for classification, resulting in a gap for regression tasks. Based on the recent advance... | Rebuttal 1:
Rebuttal: Dear reviewer KBtu, thank you for taking the time to review our work and providing feedback. In the following, we aim to address your questions and concerns.
> Some of the model designs are not moviated consistently over a pronounced challenge. The paper seems to be a combination of Mixupexplaine... | null | null | null | null | null | null |
Stochastic Optimization Schemes for Performative Prediction with Nonconvex Loss | Accept (poster) | Summary: A nice paper that studies nonconvex performative prediction optimization. Proposed a new stationarity notion and demonstrated convergence for SGD with greedy deployment.
Strengths: 1. Extending the convergence measurement from the strongly convex case to the nonconvex case and proposing the stationary perform... | Rebuttal 1:
Rebuttal: > Performative prediction problem is less motivated, i.e., there lacks a icon application such that the problem can only be formulated as a performative prediction problem and cannot be formulated in other forms even considering the special structure of the problem. The numerical experiments lacks... | Summary: The paper studies convergence of stochastic gradient descent in a performative prediction context. The main result shows that SGD converges to an analogue of performative stability, which the paper terms “stationary performative stability” (up to a bias term). The results characterize the rate of convergence a... | Rebuttal 1:
Rebuttal: > I think some of the discussion in Section 3.1 could be simplified. Instead of assuming the chi squared divergence condition, one can get Lemma 3 by assuming that D(theta) is Lipschitz in TV distance (i.e. $||D(\theta) - D(\theta')|| \leq \epsilon ||\theta - \theta'||$), together with C2. The chi... | Summary: This paper studied the `performative prediction’ that means when predictive models are used to make consequential decisions like policy making, it can trigger actions that influence the outcome they aim to predict. And we know a system with unlimited positive feedback will eventually be destroyed. On the optim... | Rebuttal 1:
Rebuttal: > The experiment setting is relatively simple but it's a minor issue since this is a theoretical work and the experiment is showcasing the concept.
We chose a simple experiment setting to demonstrate the effects of key parameters such as sensitivity strength $\epsilon$ and lazy deployment period ... | Summary: This work studied performative prediction problems in nonconvex regimes and proposed the first algorithm, SGD-GD, with convergence guarantees in this case, it was further extended to a lazy deployment scheme so that the algorithm is bias-free.
Strengths: 1. First convergence analysis of gradient-based algorit... | Rebuttal 1:
Rebuttal: > The definition of SPS, as the authors mentioned, only considers the gradient regarding the loss function, while missing the gradient over the distribution parameter, which may not perfectly reflect the stationarity convergence of the objective function.
This is a valid observation. However, we ... | Rebuttal 1:
Rebuttal: ### General Response
We thank all the four reviewers for their careful reviews and valuable suggestions. We summarize our general responses and proposed improvement to the paper as follows:
- Our work provides one of the first convergence theories on SGD-greedy deployment scheme applied to perfor... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LinNet: Linear Network for Efficient Point Cloud Representation Learning | Accept (poster) | Summary: The submission #308 entitled "LinNet: Linear Network for Efficient Point Cloud Representation Learning" introduces a linear network designed for efficient point cloud representation learning. To achieve this task, the authors propose a novel disassembled set abstraction (DSA) module and a linear sampling strat... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and constructive comments. In the following, we address your concerns carefully.
**W1: One negative point is the significant memory footprint of the approach, as mentioned in the limitations section.**
**A:** Thank you for addressing the concern regarding the... | Summary: In this work, the authors propose an efficient learning framework for point cloud representation learning. For the computational intensive local aggregation operation, this work proposes Disassembled set abstraction (DSA) to aggregate local features in terms of the spatial distributions of points in a simple a... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and constructive comments. In the following, we address your concerns carefully.
**W1 & Q4: Why the proposed DSA and hash-based searching operations can improve the performances so greatly? (Higher than transformer-based methods)**
**A:** Thank you for your i... | Summary: The paper proposes a novel lightweight backbone network model for input point cloud data suitable for global and local per-point feature extraction. It relies on two main ideas: (1) the separate processing of point coordinates and features (and a further combination of these two streams of features before the ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and constructive comments. In the following, we address your concerns carefully.
**W1: The improvements (except for the NuScenes dataset) are not particularly distinctive.**
**A:** Thank you for your comments.
Firstly, for small-scale classification tasks,... | Summary: This paper proposes a method for point cloud segmentation and classification. The main contribution is making the local aggregation dependent on the anchor point. The approach demonstrates improvements of one to two percent on S3DIS and NuScenes datasets compared to existing methods.
Strengths: - The architec... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and constructive comments. In the following, we address your concerns carefully.
**W1: The paper's writing seems overly complex with lots of unnecessary jargon making it difficult to follow the core ideas and contributions.**
**A:** We sincerely apologize for... | Rebuttal 1:
Rebuttal: We thank all reviewers for their positive comments about the novelty (7sY3, zyd3), motivation (J8xV, zyd3), writing quality (7sY3, zyd3), and experiments (J8xV, 7sY3, aBR6, zyd3) of this work.
As suggested by the four reviewers, we conduct sufficient additional experiments on our LinNet and dem... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Roles of LLMs in Planning: Embedding LLMs into Planning Graphs | Reject | Summary: This work creates a hybrid LLM and classic planning algorithm, by integrating a LLM into the GraphPlan algorithm. The GraphPlan is an algorithm that solves a relaxed planning problem (forward expansion), and then traverses the created graph to find a valid plan (backtracking). Both steps are expensive. In the ... | Rebuttal 1:
Rebuttal: [Motivation of corrupted domain models]
Response #4.1: In real-world applications it is often difficult to design complete domain models (without corruption) provided for classical planners to solve real-world planning problems [16]. It is an open challenging problem to design effective approach t... | Summary: The paper investigates how large language models (LLMs) can be integrated into established planning frameworks, specifically graph-based planning. The authors propose a novel framework called LLMs4Plan, which incorporates LLMs at two critical stages of the planning process: action selection during graph expans... | Rebuttal 1:
Rebuttal: [Comparison with LLM+P]
Response #3.1: LLM+P cannot be directly compared because its input and output are different from our LLMs4Plan. The input and ouput of LLMs4Plan are in pddl format, while the input and output of LLM+P are in NLP form. The role of LLMs in LLM+P is more like a kind of semanti... | Summary: There have been debates about the fundamental planning abilities of LLMs in planning tasks. To achieve more reliable performance, several recent works have embedded an LLM into a search framework (e.g., MCTS, BFS) and viewed LLMs as heuristics. Along this line, this work take a closer look at the roles LLMs ca... | Rebuttal 1:
Rebuttal: [Restriction in the use of classical planning]
Response #2.1: Thanks. We think extending our approach to other domains of planning is not an issue that we need to worry about, as any planning domain that can be expressed in natural language form or can be expressed in natural language form through... | Summary: The paper aims to investigate integrating large language models (LLMs) into classical planning frameworks to enhance the planning effectiveness. The authors proposed a novel method named LLMs4Plan which integrates LLMs into action selection and mutual constraints solving within the graph-based planning framewo... | Rebuttal 1:
Rebuttal: [Insight of integrating LLMs into graph planning]
Response #1.1: Thanks. The insight of using LLMs in graph planning is analogous to one of the general ways humans figure out solutions to planning problems, i.e., first looking ahead and then searching back. Given planning problems, human usually c... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Adversarial Environment Design via Regret-Guided Diffusion Models | Accept (spotlight) | Summary: This work focuses on Unsupervised Environment Design (UED), a problem setting whereby a teacher designs environments for a student, learning to solve the task. This area of research has been in focus recently due to its ability to train more general agents in an open-ended setting. The authors look to build on... | Rebuttal 1:
Rebuttal: We appreciate Reviewer a2dv for the valuable feedback and review. Below is our response to the reviewer's comments and questions.
### Weak 1: About the x axis for the plots
We agree that using total steps is disadvantageous for replay methods. However, we want to point out that if we use student u... | Summary: This work applies regret-guided diffusion models to the UED setting in order to generate adversarial environments that preserve diversity.
Strengths: * The contributions are well motivated and appear to be novel.
* The writing is generally clear and concise.
* The paper is contextualized well within prior lit... | Rebuttal 1:
Rebuttal: We appreciate Reviewer 1NF7 for the valuable feedback and review. Below is our response to the reviewer's comments and questions.
### Weak 1: Minor typo "challenging"
We agree that the word "challenging" in line 253 is redundant, so we will exclude it.
### Weak 2: About complexity metrics: shortes... | Summary: This paper proposes an approach for gradient directed, regret-based UED based on guiding a pre-trained diffusion model.
Strengths: This paper addresses a major shortcoming of prior UED approaches. In the past gradient-based UED approaches have been out-performed by sample-based or evolutionary approaches for ... | Rebuttal 1:
Rebuttal: We appreciate Reviewer TVkr for the valuable feedback and review. Below is our response to the reviewer's comments and questions.
### Weak1: ACCEL results in the bipedal walker domain
There are two main differences between the original ACCEL paper and our experiments. First, the domain of enviro... | Summary: This paper proposes a diffusion model with differentiable regret estimate for unsupervised environment design. The authors write a diffusion process to model environment parameters where the process is described in terms of a scoring function and derivative of the regret. The scoring function is pre-trained on... | Rebuttal 1:
Rebuttal: We appreciate Reviewer rCfY for the valuable feedback and review. Below is our response to the reviewer's comments and questions.
### Weak 1: Is the diffusion process critical for the success? Can you train PAIRED with a differentiable regret?
The proposed algorithm critically relies on both the ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Nuclear Norm Regularization for Deep Learning | Accept (poster) | Summary: The paper proposes a method to regularize the nuclear norm of the Jacobian of a function, e.g., one that represents a neural network. This method builds on prior art and includes the authors’ novel contribution as follows: The authors reference prior art for an equivalent problem formulation of nuclear norm re... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review of our manuscript. We are glad you appreciate the originality and significance of our method for Jacobian nuclear norm regularization.
*"In the representation learning application, we only see the method’s efficacy on a single image. Could there be a way to qu... | Summary: The paper proposes a computationally tractable method to induce low-rankness of a neural network's Jacobian. The method essentially generalizes the max-norm from Renni and Srebro, 2005, to more general compositions of functions, as it is common in neural networks. The method is made computationally efficient b... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review of our manuscript. We are glad that you appreciate our technical contributions and the practical utility of our method.
*"One of the key arguments of the paper is that the method scales to high-dimensional problems and very large neural networks. The applicati... | Summary: The authors present an efficient method for regularizing the Jacobian of deep networks such that it is low-rank. This work is motivated by the fact that penalizing the Jacobian by the nuclear norm regularization is in general a computationally difficult task, as it needs to (i) actually compute the Jacobian an... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review of our manuscript. We are glad that you appreciate our theoretical contributions and view our method as well-motivated. In this rebuttal, we will address the questions in your review. If you are satisfied with our answers, we respectfully ask that you raise you... | Summary: The paper describes an elegant and very efficient numerical scheme for minimizing a regularization term taking the form of the nuclear norm of the Jacobian $\\|Jf [x]\\|_*$ of a function $f$ at an input $x$. The numerical scheme can be applied when the function is written as a composition of two functions $f=g... | Rebuttal 1:
Rebuttal: Thank you for your thorough review of our paper. We would be pleased to make the edits you have suggested for clarity and fix the typos you have pointed out in the camera-ready version of our paper. We have updated our proof of Theorem 3.1 to address your major issues; as we cannot upload revised ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful reviews of our submission. We have attached a PDF containing four figures:
1. For Reviewer 82sN, we have included a figure (top-left) comparing time per training step at batch size 1 for the denoising problem (11) using our regularizer and a naive Pytorch implementat... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
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