title string | paper_decision string | review_1 string | rebuttals_1 string | review_2 string | rebuttals_2 string | review_3 string | rebuttals_3 string | review_4 string | rebuttals_4 string | global_rebuttals string | dataset_source string | conference_year int64 | review_5 string | rebuttals_5 string | review_6 string | rebuttals_6 string | review_7 string | rebuttals_7 string | review_8 string | rebuttals_8 string |
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A New Neural Kernel Regime: The Inductive Bias of Multi-Task Learning | Accept (poster) | Summary: This paper analyzes the solutions to a multi-task training objective that entails finding a two-layer ReLU network that interpolates all training data and has minimum l2 norm of the first- and second-layer weights, in the overparameterized setting in which the number of neurons is larger than the total number ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful review of our work and the concerns brought up. Below, we address each of the weaknesses brought up in a point-by-point manner.
1. **[Multi-variate case]** The results for the multivariate case (which also apply to the univariate setting) are more of an app... | Summary: The paper shows that multi-task training can benefit the single tasks, even if the tasks are unrelated. Assuming a particular setting of training a 2-layer neural network that finds a path norm interpolation solution on univariate data, the multi-task optimizer is unique and is given by the piecewise linear fu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful review of our work and for their feedback. Below, we address each of the comments individually.
1. **[Clarification on Theorem 3.1]** We thank the review for this clarifying question about Theorem 3.1. Lemma 3.2 says that, given any solution which interpola... | Summary: The authors investigate the properties of solutions to multi-task shallow ReLU neural network training problems. It reveals a novel connection between neural networks and kernel methods, particularly focusing on interpolating training data while minimizing the sum of squared weights in the network. The finding... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback, but it seems the reviewer has a **misunderstanding** about the results of our paper. Below, we address each of the comments individually.
1. **[1-task vs. 2-task solution]** Our result does indeed demonstrate the difference in solution between learning 1 t... | Summary: Using the piece-wise linear data interpolation problem, the authors in this paper studied the solution obtained in single task learning and that in multi-task learning where interpolation functions are jointly obtained for multiple problems. Both numerical and empirical results are provided to show that muti-t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review and positive score. Our analysis does focus on two-layer networks, but the conclusions of the multivariate analysis can be used to reason about the behavior of functions learned at layers within a deeper network. A ReLU layer in a deep network can be viewed ... | Rebuttal 1:
Rebuttal: ## General response to reviewers
We thank the reviewers for their helpful feedback and careful review as well as the AC.
Most reviewers agreed that our results are novel and provide new insights on multi-task learning with neural networks. In particular,
- Reviewer XF9Q noted that our main result... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Tree of Attributes Prompt Learning for Vision-Language Models | Reject | Summary: This paper proposes Tree of Attributes Prompt learning (TAP). Unlike previous works that rely on unstructured class descriptions, this approach distillates structured knowledge graphs associated with class names from LLMs. Text/vision prompts and vision-conditional pooling module are designed to extract instan... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback and the positive assessment of our work! We address the detailed concerns below.
### W1: LLM Robustness
Thank you for highlighting this concern. We note that since our use of LLM is mainly retrieving simple facts without requiring complex capabilities, the... | Summary: This paper proposes a new method called "Attribute Prompt Learning Tree (TAP)" to improve the performance of CLIP on zero-shot and few-shot classification tasks. The authors leverage large language models (LLMs) to generate more descriptive text prompts and introduce a hierarchical tree-like structure to syste... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments! We address the detailed concerns below. We hope that our responses will reflect positively on your final decision.
### W1: Computational and Time Costs
Thank you for highlighting the concern. We would like to clarify how TAP manages these costs effectively c... | Summary: This paper propose a method that aiming to align the vision modality with not only the category name but also the whole concept subgraph the noun represents in the knowledge graph. This is achieved by adding a bunch of attributes branches attached to this concept. The authors argue that this integration of att... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback and the positive assessment of our work! We address the detailed concerns below.
### W1: Potential for Hallucinated Content from LLM
Thank you for your insightful comment. We considered using Wikipedia for description generation; however, this approach sti... | Summary: The TAP method structures textual descriptions in a hierarchical “concept-attribute-description” format, effectively creating a knowledge graph from large language models (LLMs) for each category name. This structure allows for a more comprehensive and detailed understanding of the visual content. The paper re... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments! We address the detailed concerns below. We hope that our responses will reflect positively on your final decision.
### W1: Human Review Requirement
We appreciate the reviewer highlighting the concern regarding the human review process in our method. We apol... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for your valuable comments. We first reply to questions raised by multiple reviewers and then other questions from every reviewer.
Q1. Model's generalizability. (Reviewer bYva, ZE3g, 1xvn)
To evaluate the generalizability of our model, we conducted an additional... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes a new prompt tuning method for adapting the vision-language model. The authors design the tree of attribute prompt learning to substitute the categorical description for adapting the vision-language model. A vision-conditional pooling module is proposed to extract instance-specific text fe... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments! We address the detailed concerns below.
### W1: Marginal Performance Improvement
We appreciate the reviewer's observation.
**a. Generalizability in Base-to-Novel Experiments:**
The base-to-novel experiment is crucial for evaluating the generalizability of ... | null | null | null | null | null | null |
Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models | Accept (spotlight) | Summary: The authors propose a new framework to optimize prompting for various tasks. The frameworks consists of a buffer, where high-level problem-solving templates are stored. A query is first processed by extracting its key aspects.Templates are selected using embeddings of the templates and the extracted key aspect... | Rebuttal 1:
Rebuttal: *We thank Reviewer cwbi for the positive review and valuable feedback. We are glad that the reviewer found that our proposed BoT is more flexible and more efficient, and our method shows clear advantages over existing approaches. Please see below for our responses to your comments.*
**Q1: What e... | Summary: This paper proposes a novel reasoning procedure for LLM, named "buffer of thoughts". The core idea is that for each task, first extract a template describing how the task should be solved, then store all these templates in a "buffer". As the LLM receives a new query, a retrieval procedure is applied to extract... | Rebuttal 1:
Rebuttal: *We thank Reviewer upCD for the positive review and valuable feedback. We are glad that the reviewer found that our BoT makes good contributions to the improvements of LLMs, and our method significantly improves LM's reasoning ability in various tasks. Please see below for our responses to your co... | Summary: This paper proposes a new approach called Buffer of Thoughts (BoT) to improve LLMs reasoning abilities. BoT addresses this by creating a "meta-buffer" that stores general problem-solving “thought” templates across different tasks. When a new query is given as input, BoT retrieves a relevant thought from the me... | Rebuttal 1:
Rebuttal: *We thank Reviewer yiEf for the positive review and valuable feedback. We are glad that the reviewer found that the proposed thought template and meta-buffer is novel, and our method achieve notable improvement across various tasks. Please see below for our responses to your comments.*
**Q1: How ... | Summary: The paper introduces "Buffer of Thoughts" (BoT), a novel framework designed to improve the reasoning abilities of large language models (LLMs) by incorporating a 'thought-augmented' approach. This framework uses a component called 'meta-buffer' to store high-level thoughts—concise, distilled reasoning strategi... | Rebuttal 1:
Rebuttal: *We thank Reviewer hZUQ for the positive review and valuable feedback. We are glad that the reviewer found that the proposed framework is novel, and greatly enhances the accuracy, efficiency, and robustness of LLMs across multiple reasoning tasks. Please see below for our responses to your comment... | Rebuttal 1:
Rebuttal: Global response
We sincerely thank all the reviewers for their thorough reviews and valuable feedback. We are glad to hear that the proposed framework is novel (all reviewers) and effective (all reviewers) in enhancing the reasoning abilities of LLMs, the paper is well-written and easy to follow ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Noise Balance and Stationary Distribution of Stochastic Gradient Descent | Reject | Summary: The paper studies the effect of rescaling symmetry in SGD and shows SGD tends to favor solutions with balanced gradient noises. The authors then derive an exact solution of the stationary distribution of a toy model trained by SGD. The derived solution shed lights on problems observed in deep learning such as... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We will answer the weaknesses and questions below.
**Weaknesses:**
**The results of the paper are interesting and important,...The language should also be made more precise.**
Thank you for your suggestions. We will do our best to refine our language in the revision... | Summary: For ReLU networks trained by gradient flows, it is classical that a type of Minkowski inner product between the coefficients of consecutive layers is preserved. The authors demonstrate a monotonicity of the same quantity for stochastic gradient descent in continuous time. They use this to study the invariant d... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. We will answer the weaknesses and questions below.
**Weaknesses:**
**Important quantities are defined throughout the plain text. I understand that...**
This is a good question. Theorem 3.1 holds generally for an arbitrary network with the rescaling symm... | Summary: This paper tries to analyze the specific features that carry the noise of SGD (through a continuous model). The authors show that there is a certain 'law of balance' across the layers when some invariance is assumed. Going further, they derive a toy model to push their study, showing that there is an analytic ... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We will answer the weaknesses and questions below.
**The law of balance is an interesting phenomenon, yet...**
Thanks for raising this point. We stress that the law of balance applies to high-dimensional problems as well. In the high-dimensional case, the convergenc... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for their constructive feedback, which has helped us greatly improve our manuscript. We are encouraged to see that all reviewers agree that our contribution is "good."
To address the concerns of the reviewers, we will make the following additions and changes to the ma... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MTGS: A Novel Framework for Multi-Person Temporal Gaze Following and Social Gaze Prediction | Accept (poster) | Summary: This paper handles the problem of predicting human social labels and gaze heatmap simultaneously for all people in the input image sequence, which improve the accuracy of gaze following based on human social cues. Specifically, they first calculate person tokens through a person module, and then design an inte... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback, and for their positive evaluation of our paper. We answer their questions and comments below.
**Clarification of elements**
The Pairwise Instance Generator was drawn in the figure to illustrate that the social gaze decoders take a pair of person tokens a... | Summary: Paper proposes a novel framework which solves multiple gaze prediction tasks, gaze heatmap, in-out frame classification, social gaze classification for multi-person in one-pass simultanously. It also contributes a new dataset by extending existing datasets annotations. Comprehensive experiments were conducted ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback, and for noting our submission as a good paper. We answer their questions and comments below.
**Novelty of method**
We refer the reviewer to our discussion on the novelty of our architecture in the overall response.
**Dataset is an extension of existin... | Summary: This paper presents a novel framework for multi-person temporal gaze following and social gaze prediction. The authors propose an architecture that jointly predicts gaze targets and social gaze labels for all people in a scene. It uses a transformer-based model that processes both frame tokens and person-speci... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback, and for highlighting our paper as a very solid study. We answer their questions and comments below.
**Novelty of method**
We refer the reviewer to our discussion on the novelty of our architecture in the overall response.
**Generalization to new domain... | Summary: This paper focuses on social gaze prediction in videos. An approach based on ViT has been proposed, combining three modules including person module, interaction module, and prediction module. The authors also summarised the current shortcomings of the existing datasets and have introduced a new dataset compris... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback, and for raising valuable discussion elements. We answer their comments and questions below.
**Significance of improvements**
We agree with the reviewer that differences of 0.001 can be negligible, however, differences in 0.01 are significant as discussed... | Rebuttal 1:
Rebuttal: We thank the reviewers for their feedback on our paper, which presents the following contributions:
- A novel **temporal, multi-person architecture** that jointly models gaze following and social gaze prediction.
- A new **dataset, VSGaze**, which extends and unifies annotations across multiple ga... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ConStat: Performance-Based Contamination Detection in Large Language Models | Accept (poster) | Summary: Authors propose a new definition of contamination: "artificially inflated and non-generalizing benchmark performance" rather than "the inclusion of benchmark samples in the training data". They develop ConStat, a statistical method that reliably detects and quantifies contamination by comparing performance bet... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review and insightful questions. We are happy to hear that they found our motivation, presentation, and organization very good, and the performance of our method great. Below, we address their questions.
**Could you elaborate more on the effect of the synt... | Summary: The paper proposes a performance-based approach to detect data contamination in large language models. First, a set of reference models is evaluated on the original benchmark and a proxy of it. Next, a difficulty correction function is fitted to find the relationship between the performance from the original b... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review and insightful questions. We are pleased to hear that they found our paper well-written, our experiments comprehensive, and the applicability of our method to closed-source models interesting. Below, we address their questions.
**Should contaminatio... | Summary: The authors introduce a definition for “contamination” based on its outcome rather than its cause, unlike many previous approaches. The authors propose ConStat, a novel method for quantifying the contamination of a model on some benchmark and demonstrate that it outperforms other methodologies for detecting co... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review and insightful questions. We are happy to hear that they found our paper a pleasure to read and that our research can be used to build trust in our evaluations. Below, we address their questions.
**How did you ensure that the generated dataset had a... | Summary: This paper targets the problem of contamination detection of LLMs by proposing ConStat, a performance-based statistical approach.
- Instead of detecting the inclusion of test samples as contamination, the authors define interpretation as "abnormal" performance on benchmarks.
- ConStat builds on this definitio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review and insightful questions. We are happy to hear that they find our paper a joy to read, our definition novel and interesting, and our experiments comprehensive and solid. Below, we address their questions.
**Is measuring contamination relative to a s... | Rebuttal 1:
Rebuttal: $\newcommand{R}{\textcolor{green}{E2e6}}$ $\newcommand{S}{\textcolor{blue}{tnxc}}$ $\newcommand{T}{\textcolor{purple}{hKkW}}$ $\newcommand{X}{\textcolor{red}{UkYs}}$ $\newcommand{Y}{\textcolor{brown}{xz9Q}}$
We thank the reviewers for their detailed reviews and insightful questions. We are pleas... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper introduces a new performance-based definition of data contamination, shifting the focus from the cause of contamination to its effect on performance. The paper also presents ConStat, a statistical method that detects and quantifies contamination by comparing performance on primary and reference bench... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review and insightful questions. We are pleased to hear that they appreciate the extensive experiments and find the new definition clear and easy to understand. Below, we address their questions.
**Does an abnormally high performance on your reference benc... | null | null | null | null | null | null |
DEFT: Efficient Fine-tuning of Diffusion Models by Learning the Generalised $h$-transform | Accept (poster) | Summary: The authors propose a method for conditional generation using diffusion models, named DEFT. The idea is to combine a fixed, pre-trained unconditional model with an additionally learned conditional correction term to generate conditionally. The authors provide extensive experiments to demonstrate the effectiven... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback, we will incorporate the following discussion in a revised version of the manuscript.
### Q1: Do you claim to be the first to come up with the idea of learning a small conditional corrector to make an unconditional model conditional?
**A1**: Thank y... | Summary: The authors propose a novel conditional diffusion sampling strategy for solving inverse problems. Previous conditional diffusion-based inverse solvers are heuristically motivated, lack a unifying framework, and suffer from sensitivity to hyperparameters and heave computation of the Jacobian of the trained scor... | Rebuttal 1:
Rebuttal: Thank you for this valuable feedback on the presentation - this is very helpful for clarifying our work. We have made updates to the manuscript to address the points you suggest for clarification. The error in Table 1 will be fixed in the camera-ready version. In detail:
### Q1: How does Doob’s h ... | Summary: The paper tackles the problem of utilization of generative modelling to solve inverse problems. The main highlight is that the authors developed a technique that can solve inverse problems without the need for backward pass through the generative model. Hence enabling deriving the prior knowledge from even clo... | Rebuttal 1:
Rebuttal: Thank you for pointing us to further baselines beyond the ones we provide that would provide an interesting axis of comparison to our method.
### Q1: Comparison against FreeDoM and MPGD
**MPGD** [1]: MPGD proposes three variants: MPGD w/o projection, MPGD-AE and MPGD-Z. MPGD-Z requires a latent ... | Summary: The paper proposes a new framework for fine-tuning unconditional diffusion models for conditional generation based on Doob’s $h$-transform. By utilizing a small set of observations and ground truth samples, the algorithm can learn the conditional $h$-transform that is used to
Strengths: - The work is a novel ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments. We ran additional experiments, the complete results are available in Table 1 in the 1-page PDF.
### Q1: How does DEFT perform on OOD samples?
**A1**: We evaluated DEFT, trained on ImageNet (Section 4.1), on a subset of 200 images of the ImageNet-O ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable and thorough feedback, we will revise the paper accordingly. Below, we will address the main points and describe improvements we made to our submission.
#### Strengths and contributions
We appreciate the recognition of our work's **novelty** (reviewers `no... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Credal Learning Theory | Accept (poster) | Summary: This paper extended traditional statistical learning theory which usually considers a fixed underlying data-generating distribution to a case where the underlying distribution is assumed to be from a convex set of distributions. Several excess risk bounds (finite realizable, finite unrealizable, and infinite h... | Rebuttal 1:
Rebuttal: *The writing can be improved. For example, the first paragraph is basically the notation and a review of ERM.*
We thank the reviewer for their input. In the updated version, we will strive to further improve our writing. We believe, however, that the first paragraph is needed to set up the notat... | Summary: This is a paper on learning theory with a focus on machine learning. The authors consider a setup with several training sets available. Given an additional (test) set, the authors assume that the distribution generating these new data coincides with one of the distributions generating the training set or is a... | Rebuttal 1:
Rebuttal: *In which sense are the results in the paper not a simple corollary of those in Liang's LNs?*
We thank the reviewer for this deep question, and for giving us the opportunity to be clearer about this topic.
Theorem 4.1 is an immediate extension (not a corollary though) to the credal case of Lian... | Summary: This paper introduces a novel learning framework termed “Credal Learning Theory” which extends the traditional statistical learning theory to handle variations in data distributions, especially the topic “domain generalization”. The authors propose using credal sets, which are convex sets of probability distri... | Rebuttal 1:
Rebuttal: *Provide real-world examples demonstrating that the constructed credal set encompasses most of the potential distributions we need to consider in these scenarios, thus illustrating the practical relevance of the credal set.*
We thank the reviewer for the question, and for giving us the opportunit... | Summary: The paper develops a so called credal learning theory that uses convex sets of probability distributions (also known as credal sets) to model the uncertainty of the data-generating distribution. As in the classical statistical learning theory, the paper derives new theoretical bounds on the risk of the models ... | Rebuttal 1:
Rebuttal: *In my opinion, the presentation is quite dense, the notation appears to be quite heavy in places and the paper is not easy to follow. I think it's important to illustrate some of the key concepts introduced in sections 3 and 4 with some examples. Tables 1 and 2 as well as Figure 2 are good but th... | Rebuttal 1:
Rebuttal: **We first answer to Reviewer sJhN's last question**
*Are there any analyses of Computational Complexity that are specific to the use of credal sets as an approach to infer models? Can such an approach be implemented for limited but large datasets?*
We thank the reviewer for their question, the ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
InterControl: Zero-shot Human Interaction Generation by Controlling Every Joint | Accept (poster) | Summary: This paper focuses on text-to-motion synthesis, specifically, to generate motion of multiple interacting people. The method can potentially work with an arbitrary number of people and models inter-person interactions as pairs of joints that can be either in contact or not, separated by a certain control distan... | Rebuttal 1:
Rebuttal: Thanks for the constructive review. We will revise our paper according to the insightful suggestions in our final version. We are happy that our paper is regarded as ‘clear’ and ‘well-written’ and our framework is ‘meaningful’. We have tried our best to clarify questions and we hope that our respo... | Summary: This paper tackles the challenge of generating human interaction motions involving a flexible number of characters. To simplify the representation of these interactions, the authors propose a joint-pair contact/avoid representation. Given an interaction description, a large language model (LLM) generates motio... | Rebuttal 1:
Rebuttal: Thanks for the insightful review. We will revise our paper according to the constructive suggestions in our final version. Please refer to General Response for comparison with OmniControl and InterGen, and the explanation of penetration and unsmooth motion issues.
**Q1: How does the contact joint... | Summary: The paper introduces InterControl, a method designed to address the task of controllable human motion generation and zero-shot human interaction generation. By leveraging the prior knowledge of LLM, InterControl can generate human interactions involving any number of people in a zero-shot manner. Specifically,... | Rebuttal 1:
Rebuttal: Thanks for the insightful review. We will revise our paper according to the constructive suggestions in our final version. Please refer to General Response for comparison with OmniControl and InterGen, and the explanation of penetration and unsmooth motion issues.
**Q1: Examples of avoidance.**
... | Summary: ### Summary
The paper "InterControl: Generating Human Motion Interactions by Controlling Every Joint" aims to generate interactions between multiple people based on text descriptions, with precise joint control. It leverages a pre-trained single-person motion diffusion model and extends it to multi-person sce... | Rebuttal 1:
Rebuttal: Thanks for the detailed review. Please refer to general response for more common questions.
Q1: Similarity to OmniControl and GMD.
A1: (1) Our method could control all joints while GMD only controls root joint. (2) We focus on zero-shot interaction generation and use controllable single-person m... | Rebuttal 1:
Rebuttal: We sincerely thank reviewers’ effort for our paper and the insightful review for us to improve our paper. We carefully read all reviews and address common concerns point by point here.
**Q1@VAgJ, HwRy and 7GL6**: Comparison with InterGen.
**A1**: Firstly, our method is fundamentally different f... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Disentangling Interpretable Factors with Supervised Independent Subspace Principal Component Analysis | Accept (poster) | Summary: Here the authors propose an extension of PCA that decomposes datasets into multiple independent subspaces that are encouraged to reflect provided covariates of interest. To enforce these dependencies, the authors leverage the Hilbert-Schmidt Independence Criterion (HSIC).
Strengths: In short, my views of the ... | Rebuttal 1:
Rebuttal: ## **Response to Reviewer debZ**
Thank you for your positive feedback and insightful comments! We appreciate your thorough review and would very much like to address any remaining concern.
### **W1. Impact of $\lambda$ selection and an auto-selection pipeline**
Thank you for the suggestion. We c... | Summary: This paper proposed a linear dimensionality reduction and subspace extraction method based on Hilbert-Schmidt Independence Criterion (HSIC) and Supervised PCA. In specific, several interpretable subspaces, which are independent from each other, are disentangled from the data observations and the leaned subspac... | Rebuttal 1:
Rebuttal: ## **Response to Reviewer QSK9**
Thank you for your thoughtful review of our work. We address your main points below:
## **Weaknesses**
### **W1: Theoretical analysis and subspace recovery**
We acknowledge the limitations in our theoretical analysis. However, sisPCA makes minimal assumptions abo... | Summary: The work proposes a new method that determines the factors of variation bonded to different labels in a supervised way, obtaining a method akin to supervised PCA but in several independent subspaces thorough the Hilbert-Schmidt independent criterion (that it’s employed to maximize both the independence between... | Rebuttal 1:
Rebuttal: ## **Responses to Reviewer UeQu**
Thank you for your constructive feedback on the strengths and weaknesses of our work. We address your main concerns below:
## **Weaknesses**
### **W1: Limited novelty**
While our method extends PCA, it offers significant contributions by uniquely combining superv... | Summary: This paper presents a method for distentangling multiple independent linear latent subspaces that align with a set of response variables. This uses the Hilbert-Schmidt Independence Criterion (HSIC) to measure the dependence each subspace and the targe variable providing supervision for that subspace, a concept... | Rebuttal 1:
Rebuttal: ## **Response to Reviewer PWKM**
Thank you for your summary. We appreciate your evaluation that our technical contribution is novel enough to warrant presentation at NeurIPS. | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful comments. We first address the two main points raised by multiple reviewers, followed by responses to individual comments.
### **M1: Highlight the technical novelty (Reviewers PWKM and UeQu)**
We appreciate your acknowledgment of our paper’s technical c... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fast Graph Sharpness-Aware Minimization for Enhancing and Accelerating Few-Shot Node Classification | Accept (poster) | Summary: The paper introduces an innovative approach to improve the generalization capabilities of Graph Neural Networks (GNNs) in few-shot node classification tasks. The authors propose a novel algorithm, Fast Graph Sharpness-Aware Minimization (FGSAM), which incorporates sharpness-aware minimization (SAM) techniques ... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and reviews. We provide a point-by-point response below. Hope this can address your concern and make things clearer.
> **Q1:** What is the standard deviations for the results in table 4? Are the improvements significant?
**Response to Q1:** The detailed stand... | Summary: This paper focuses on efficient graph neural network (GNN) training in few-shot node classification (FSNC) problem by extending sharpness-aware minimization (SAM) for reducing the computational cost and improving the generalization of GNNs on unseen classes. The training phase is accelerated by perturbing the ... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and reviews. We provide a point-by-point response below. Hope this can address your concern and make things clearer.
> **W1:** There are related work on applying SAM to few-shot tasks (Sharp-MAML) [1] and performing SAM every k steps [2], and the innovation of... | Summary: The paper proposes a method for few-shot learning on graphs leveraging sharpness-aware minimization (SAM) from the vision community. The paper explains SAM as a technique for gradient perturbation during training to push the parameters to "flatter" regions of the loss space in hopes of achieving better general... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive feedback and valuable comments on our manuscript. We found that your comments are really valuable to further improve our manuscript. Below, we address each of your concerns and questions:
> **Q1:** I think the paper is very good and clear in most cases. T... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Beyond Accuracy: Ensuring Correct Predictions With Correct Rationales | Accept (poster) | Summary: This paper introduces a novel method called dual-correct predictions, designed to train models to make accurate predictions based on correct rationales, thereby improving their safety for deployment. Additionally, the authors develop a unique dataset containing structured rationales that explicitly outline the... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and instructive reviews. Below we provide point-to-point responses.
**Q1. Detailed explanation of Equation 4.**
In Eq.4, **to achieve correct predictions**, our backbone objective ensures the correct alignment of image and text embeddings in a shared space (Li... | Summary: This paper proposes a method for incorporating rationales into the predictions of foundation models. The goal is to build models that make "dual-correct predictions": predictions that are correct because the model "reasons" using the correct rationale. To do this, the authors collect a new rationale dataset th... | Rebuttal 1:
Rebuttal: We appreciate your encouraging comments and recognition of our contributions. Below we provide point-to-point responses.
**Q1. Since the method and problem are framed generally, there should be more experiments across other domains (e.g. text) and models (e.g. diffusion models).**
Indeed, in thi... | Summary: Machine learning models are mostly commonly evaluated based on accuracy, yet highly accurate models can conceal issues with the underlying reasoning. For example, models can predict the right label while predicting it for the wrong reason. To improve the ability of foundation models to make good predictions fo... | Rebuttal 1:
Rebuttal: Thank you for your encouraging comments. Below we provide point-to-point responses.
**Q1. Whether dual-correct prediction problem is the same as the Right for the Right Reason?** \
No, our dual-correct prediction problem is different from the Right for the Right Reason in [1]. **1) Explanation gr... | Summary: The goal of the paper is to train Vision Transformer models in a way that they make the "right predictions for the right reasons". The paper follows up on prior work in ML explainability that extracts "rationales" for model predictions. The idea of the paper (which is also considered in some prior works) is to... | Rebuttal 1:
Rebuttal: Thank you for your detailed and instructive reviews. Below we provide point-to-point responses.
**Q1. Why can we trust the rationales generated by GPT-4?** \
**1) Sufficient knowledge.** Existing studies prove that GPT-4 has expert-level expertise in commonsense [1] and domain knowledge [2]. **2)... | Rebuttal 1:
Rebuttal: Dear Reviewers, Area Chairs, and Program Chairs,
Thank you for your time and effort in reviewing our paper. We appreciate the reviewers find we `"does a nice job"` (WT2A) on studying an `"important topic"` (VWuj) which is `"critical"` (VWuj) for building trust in models, our research problem of d... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
2DQuant: Low-bit Post-Training Quantization for Image Super-Resolution | Accept (poster) | Summary: The paper presents 2DQuant, an innovative low-bit post-training quantization technique for transformer-based image super-resolution that significantly advances the state-of-the-art by introducing a two-stage optimization process. This process includes a novel Distribution-Oriented Bound Initialization strategy... | Rebuttal 1:
Rebuttal: ## Reviewer4 EXmU
> Q1:The proposed DOB and DQC are much similar with DBDC and PaC, but only change weight compression to symmetric quantization, could you give more details about the differences?
A1:
In fact, our DOBI and DQC are quite different from DBDC and Pac. The specific differences are a... | Summary: This paper introduces a novel two-stage post-training quantization (PTQ) method aimed at compressing image super-resolution (SR) models for efficient deployment on edge devices. The authors address the challenge of accuracy degradation in low-bit quantization by proposing the 2DQuant method, which combines Dis... | Rebuttal 1:
Rebuttal: > Q1a: The contribution lacks novelty... For example, the distribution of weights and activations is well studied by previous works.
A1a: First, the study of distribution is not our core contribution or innovation, it is one of our experimental observations. While our main contributions are 1) t... | Summary: This paper present a practical post-training quantization method for SR transformer, namely 2DQuant. The 2dquant mainly rely one two techniques, the first is Distribution-Oriented Bound Initialization (DOBI) determine the quantization range initially, and the second one is distillation Quantization calibration... | Rebuttal 1:
Rebuttal: > Q1:As presented in section 3, the authors use fake quantization during the design of the method, but didn’t mention if it can be replaced by the real quant, which can be implemented and bring real acceleration in hardware. The author should discuss if the proposed method can achieve real quantiz... | Summary: The authors propose a low-bit post-training quantization (PTQ) method, 2DQuant, for image super-resolution. 2DQuant is a dual-stage low-bit PTQ method. They first investigate the weight and activations. They propose Distribution-Oriented Bound Initialization (DOBI) by using different searching strategies to ge... | Rebuttal 1:
Rebuttal: > Q1: ... how about the acceleration when deploying the quantized models into device?
A1: In the field of quantization research, when we apply the same quantization method to the same neural network module, the speedup ratio of the model does not change due to variations in the quantizer paramete... | Rebuttal 1:
Rebuttal: Dear Reviewers and Area Chairs,
We appreciate all reviewers (R1-6qcS, R2-Xp5V, R3-DoGx, R4-EXmU) for the constructive reviews and positive feedback to our 2DQuant. Your expertise and insightful comments help us to further improve our paper.
We are pleased that:
- R1 and R2 acknowledge the pract... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Feint Behaviors and Strategies: Formalization, Implementation and Evaluation | Accept (poster) | Summary: This paper proposes a method to generate feint behaviors and strategies so that the agent can obtain temporal and spatial advantages when competing with opponents. Specifically, this paper first describes the characteristics of feint behaviors at action-level and proposes a Feint behavior template generator ca... | Rebuttal 1:
Rebuttal: ## [Reviewer 8F3g]
### - Whether the actions generated by the Feint policy model are really Feint behaviors?
The actions generated by Feint policy model are indeed Feint behaviors. As discussed in Appendix E, the Feint policy model is constraint by intended high-reward behaviors and corresponding ... | Summary: The authors present a comprehensive formalization of feint behaviors in competitive multi-player games. The paper introduces a method for the automatic generation of feint behaviors using Palindrome-directed templates and combines them with high-reward actions in a Dual-Behavior Model. This formalization is in... | Rebuttal 1:
Rebuttal: ## [Reviewer R6PG]
### - In Section 4.2.1, the two reward functions, $R^i$, in Eqs (1) and (2). Are they the same?
Yes, the $R^i$ in Eqs(1) and Eqs(2) are the same, which are the game environment reward given a state $s_t$, the agent action $a_t^i$, and opponents actions $a_t^{-i}$. We do not in... | Summary: The paper presents a comprehensive approach to formalizing and implementing feint behaviors in multiplayer games. It introduces a new method for the automatic generation of feint behaviors using Palindrome-directed templates and combines these with high-reward actions in a Dual-Behavior Model. The paper furthe... | Rebuttal 1:
Rebuttal: ## [Reviewer CkiB]
### - Apply appropriate smoothing to the curves for Figure 4
We thank the reviewer for this suggestion. We will add appropriate smoothing to the curves for Figure 4 for better visualization in our revision.
### - How does the complexity of the Palindrome-directed templates affe... | Summary: This paper introduces the first comprehensive formalization of Feint behaviors in multi-player games. The authors present a novel approach to automatically generate Feint behaviors using Palindrome-directed templates and combine them with intended high-reward actions in a Dual-Behavior Model. The formalization... | Rebuttal 1:
Title: Further clarification and apology for not reponse individually through initial rebuttal
Comment: We sincerely apologize for not creating an individual response during the rebuttal period, where we assume all relevant questions are addressed in the General Question part showing to all reviewers. We th... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive feedback, and address all concerns.
We address the following general questions here, and we address individual questions separately under corresponding reviews.
## General Questions
### [Reviewer: Wu2K, CkiB, R6PG] Lack of Discussion (and Identifica... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Vector Quantization Prompting for Continual Learning | Accept (poster) | Summary: This paper presents VQ-Prompt, a prompt-based continual learning method using Vector Quantization (VQ) to enhance task knowledge representation and overcome catastrophic forgetting. VQ-Prompt incorporates VQ into the end-to-end training of discrete prompts, optimizing the prompt selection process with task los... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable and insightful comments.
**W1. There is no constraint when calculating the similarity score \alpha and prompt key K, which may lead to corrupt prompt learning, i.e., for most test samples from different tasks, the selected prompts are similar, and... | Summary: The representations learned by large pre-trained models have led to many Continual Learning approaches based on these models. Specifically, prompt-based approaches train a small set of learnable parameters (prompts) to guide a fixed pre-trained model for a particular task. One key component in these approaches... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable and insightful comments.
**W1. One of the authors' motivations is learning discrete prompts, which they even mentioned as a contribution. However, even after reading the explanation between lines 50 and 61, the contribution of having discrete prom... | Summary: This paper introduces Vector Quantization Prompting (VQ-Prompt), a novel prompt-based method designed to mitigate catastrophic forgetting in the sequential learning scenario of Class Incremental Learning (CIL). VQ-Prompt utilizes Vector Quantization (VQ) to facilitate end-to-end learning with a set of discrete... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable and insightful comments.
**W1. Considering Prompt Learning for Continual Learning (CL) assumes that there is a good and generalizable pretraining that could be transferable (tuned) to the different downstream tasks. Moreover, considering the way t... | Summary: Prompt-based continual learning has emerged to address catastrophic forgetting in sequential task learning by using a pre-trained Vision Transformer (ViT) enhanced with learnable "prompts." These prompts contain task-specific knowledge and guide the model in generating task-relevant outputs. During inference, ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable and insightful comments.
**W1. The performance improvements are meaningful, but not significant - particularly on specific tasks, such as CIFAR-100 and CUB-200 benchmarks.**
We respectfully disagree with the assessment that the improvements are n... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning Transferable Features for Implicit Neural Representations | Accept (poster) | Summary: This paper introduces STRAINER, a novel training framework for Implicit Neural Representations (INRs). Unlike traditional INRs, which are trained on a single signal, STRAINER aims to learn transferable features that can be effectively utilized for fitting new signals from a similar distribution. The method inv... | Rebuttal 1:
Rebuttal: We thank the reviewer for careful and thorough evaluation of our work. We appreciate the reviewer’s kind comments on STRAINER’s originality in learning transferable representations for INRs, extensive evaluation of STRAINER on datasets, and the detailed analysis provided by STRAINER on feature tra... | Summary: This paper proposes STRAINER, where implicit neural representations (INRs) are trained for a class of similar objects, with the INRs for all objects having a shared, instance-agnostic encoder and instance-specific decoders. The paper also examines INR training dynamics. STRAINER is evaluated on 2D image regres... | Rebuttal 1:
Rebuttal: We thank the reviewer for careful and thorough evaluation of our work. We appreciate the kind comments made on the novelty of our method and performance over Siren/meta-learning baselines. We welcome the reviewers suggestion on comparing with more baselines, and are thankful for the references pro... | Summary: The research focuses on learning transferable features using a SIREN model, where encoder and decoder sub-networks are utilized to optimize weights for encoding and decoding. The paper evaluates the main claims made in the abstract and introduction, confirming that the goals are clearly stated and the method e... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful and thorough evaluation of our work. We appreciate the kind comments on STRAINER being able to learn fast at test time, efficient recovery of high frequency components, and its ability to learn more transferable input space subdivision. We address the comments... | null | null | Rebuttal 1:
Rebuttal: We are thankful to the reviewers for their careful and thorough evaluation of our work. We first provide key contributions of our work and then provide additional experiments and results as requested by reviewers.
__Summary of paper:__
STRAINER provides a framework to learn powerful and trans... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Many-Shot In-Context Learning | Accept (spotlight) | Summary: This paper explores the effectiveness of in-context learning with hundreds to thousands of examples, bringing the number of examples closer to the range one might use for supervised training methods. Experiments are performed on a large number of tasks and benchmarks using Gemini 1.5 as the LLM, in each case ... | Rebuttal 1:
Rebuttal: Thanks for the detailed comments and questions, which we address below and would strengthen our work.
In the **[rebuttal pdf](https://openreview.net/attachment?id=4BVPccQZEN&name=pdf)**, we have added results on *runtime differences, many-shot performance of 1.5 Flash and frontier LLMs, ablated ... | Summary: In this work, the Authors investigate the performance of large language models on in-context learning (ICL) tasks when provided with a large number - in the order of hundreds or thousands - examples (many-shot ICL regime), enabled by recent increases in context window sizes.
The Authors demonstrate significan... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments and questions. Our detailed response follows:
> **While authors include some results with GPT-4-Turbo and Claude-3-Opus, a more comprehensive comparison across models would strengthen the generalizability**
While we do not fully address this lim... | Summary: Owing to the significant increases in context window lengths, the paper analyzes the efficacy of the Gemini 1.5 Pro LLM in the many-shot in-context learning (ICL) setting, where hundreds to thousands of exemplars can be provided to the model at inference time. ICL has generally been restricted to the few-shot ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments, which we try to address below. We have added a many-shot ICL comparison for Gemini 1.5 Flash with frontier LLMs to understand the role of model size in the **[rebuttal pdf](https://openreview.net/attachment?id=4BVPccQZEN&name=pdf)**.
> **unsure o... | Summary: This work conducts a comprehensive study on in-context learning (ICL). The experiments range from few-shot to many-shot scenarios, with up to 2048 in-context examples. It was observed that as the number of examples increases, the performance generally improves, even matching the performance of fine-tuned model... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and questions, which we address below. We have added new results on 1.5 Flash and inference time of many-shot in the **[rebuttal pdf](https://openreview.net/attachment?id=4BVPccQZEN&name=pdf)**.
> **Experiments are mostly limited to 1.5 Pro .. different mo... | Rebuttal 1:
Rebuttal: We thank all the reviewers R1 (XEKv), R2 (mj1R), R3 (kkH9), and R4 (2oDD) for their valuable feedback! All reviewers are in favor of acceptance and found the paper to be **comprehensive, very well-written, significant contribution to ICL, broad scope and applications, novel annotation-free ICL met... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Neural expressiveness for beyond importance model compression | Reject | Summary: In this paper, the author proposes "Expressiveness," a metric that measures the dissimilarity of feature maps produced by different filters. Subsequently, the author introduces NEXP, a technique to prune filters based on their expressiveness. The proposed method is tested on tasks such as image classification ... | Rebuttal 1:
Rebuttal: **W1.** We have adjusted the notation formatting in 3 and especially in "Generalization of concepts at a structural level" to improve clarity. Below is an analytical list of all changes made in the paper:
W1.a. Changes emphasized on the "Generalization of concepts at a structural level":
- Lines ... | Summary: This paper works on weight pruning for CNNs. It proposes an evaluation metric, i.e., "expressiveness", to evaluate whether a neuron/groups of neurons should be pruned or not. The metric focuses on the neurons' ability to redistribute informational resources. As the evaluation of expressiveness requires data sa... | Rebuttal 1:
Rebuttal: **W1 and Q1.** NEXP is designed to measure the redundancy of activation structures based on their expressiveness, where the finest granularity can be considered that of a single neuron, and thus it can be adjusted to any activation structural component, e.g., convolutional filters (as demonstrated... | Summary: This paper propose a new structured pruning approach NEXP. It works by computing the dissimilarity score of the feature activations across samples and removing those filters with smaller variances. Experimental results on several models and datasets demonstrate the effectiveness of the proposed approaches.
St... | Rebuttal 1:
Rebuttal: **W1.** Kindly refer to the response in W1 for the rebuttal to reviewer's 4Yxj comments.
**W2.** The proposed pruning metric, as demonstrated in 3.2 and illustrated in eq.11, requires a total of ${\frac{N(N-1)}{2}}$ combinations, unlike the $N^{2}$ combinations in an $N$ by $N$ matrix, as highlig... | Summary: This paper aims to handle network pruning problem. Specifically, it proposes to use a new importance measurement, called expressiveness, to decide the pruning process. It jointly considers the model state to leverage on the proposed measurement. In addition, it can also combined with typical importance based p... | Rebuttal 1:
Rebuttal: **W1.** The submitted version of the paper features a brief conclusion section to adhere to the 9-page limit of the NeurIPS format, prioritizing space for other sections. We agree that an extended discussion in the conclusion is vital for the paper's overall remarks and readability. Below, we prov... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their thoughtful feedback and their comprehensive suggestions.
*(Each review has been addressed separately)*
Pdf: /pdf/5f82d053074bca98ce8afaacc8ce6c9a27145e3d.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DiffLight: A Partial Rewards Conditioned Diffusion Model for Traffic Signal Control with Missing Data | Accept (spotlight) | Summary: This paper proposes a conditional diffusion model named DiffLight, which is able to unify traffic data imputation and decision-making for TSC when data is incomplete. Specifically, it proposes a partial reward conditional diffusion method to avoid the negative effects brought by the padded values of missing da... | Rebuttal 1:
Rebuttal: We are delighted that the reviewer found our motivation interesting and reasonable. Thank you for your positive and insightful comments. We respond to each of the points below:
> [W1 & Q1] Explanation of Diffusion Communication Mechanism
Thank you for your comments. We have made a brief introduc... | Summary: The paper introduces "DiffLight," a novel approach combining traffic data imputation and decision-making for Traffic Signal Control (TSC) under scenarios with missing data using a diffusion model framework. It employs partial rewards conditioned diffusion and a spatial-temporal transformer architecture to addr... | Rebuttal 1:
Rebuttal: We highly appreciate your high-quality and valuable suggestions. We provide the point-by-point response as follows:
> [W1 & Q1] Comparative analysis
Thank you for emphasizing the gap in the comparative analysis. We apologize for our unclear expression. Actually, all the baselines were implemente... | Summary: The paper proposes a conditional diffusion framework to address the traffic signal control (TSC) problem under conditions of missing data. Utilizing Partial Rewards Conditioned Diffusion (PRCD) with classifier-free guidance, for both data imputation and decision making. The authors employ the DDIM sampling met... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and insightful comments. We respond to each of the points as follows:
> [W1] Selection for datasets
Thank you for your comment. We appreciate the opportunity to elaborate on this. We would like to point out that DiffLight is designed to address the traf... | Summary: This paper focuses on traffic signal control under the condition of missing data, presenting DiffLight, a conditional diffusion model that integrates traffic data imputation and decision-making tasks. It introduces a partial rewards conditioned diffusion method to handle missing rewards (PRCD), employs a spati... | Rebuttal 1:
Rebuttal: We appreciate the reviewer recognizing the importance of our work and thank you for the detailed comments. Please find the point-by-point responses to the reviewer's comments below.
> [W1] Discussion on MissLight
Thank you for highlighting the importance of a direct discussion between DiffLight ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Efficient Lifelong Model Evaluation in an Era of Rapid Progress | Accept (poster) | Summary: The paper presents the idea of Lifelong Benchmarks as a way to deal with the problem of model overfitting, both at the individual model level and at the community level. The authors present a framework, Sort & Search (S&S), as a way to deal with the ever increasing benchmarking cost: not only does each benchma... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thorough evaluation and positive, enouraging feedback. We seek to address the reviewer’s concerns and questions below:
> S1 It's a bit unclear, however, where these new samples and models come from and how the datasets actually grow. If a researcher/engin... | Summary: To mitigate models from overfitting to the standardized benchmark itself, new samples can be added to the test set, resulting in a Lifelong Benchmark. However, when a new sample is added, all existing models must be evaluated on the added sample. When a new model is added, it must be evaluated on all existing ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thorough evaluation and their detailed feedback. We seek to address the reviewer’s concerns and suggestions below:
> W1, For Figures 2,3,4, the plots display MAE values larger than 0.1. In Section 4.6, the paper states that this aleatoric error is irreduc... | Summary: The paper presents a novel approach to addressing overfitting in standardized machine learning benchmarks by introducing Lifelong Benchmarks, which are designed to expand continuously, thereby providing a more dynamic evaluation environment. The authors propose the Sort & Search (S&S) framework for efficient m... | Rebuttal 1:
Rebuttal: > W1 The paper would benefit from explicitly stating the assumptions about the data samples and models within the main text. I have included specific questions regarding the data samples in the questions section to help clarify these points.
We thank the reviewer for this point. We have sought to... | Summary: The paper aims to improve the efficiency for evaluating large-scale lifelong benchmarks with the rapidly growing number of machine learning models and samples. To address this issue, the authors propose the Sort & Search framework, which avoids the need to evaluate all samples upon adding new models (or evalua... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thorough evaluation and positive feedback. We’re pleased that the reviewer recognised our work’s originality, noting it "targets a practical and pressing problem", "experimental results demonstrate significant compute reduction" and "with clear examples an... | Rebuttal 1:
Rebuttal: We thank the reviewers for finding our work to be ***important in the era of large models*** (Reviewers jvk8, T9R1, bEaJ, tnhy), to have ***strong mathematical formulations and theoretical results*** (Reviewers oB2P, bEaJ, T9R1), and to be ***tackling an important pressing problem with sound empir... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors propose *lifelong benchmarks*, a solution to the high cost and saturation problems of the current evaluation paradigm. They try to predict which samples will be harder to classify to select a subset that can efficiently serve as a proxy for estimating model performance on the full set, and also use... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback. We seek to address the reviewer’s concerns and suggestions below:
> W1 Simple classification tasks aren't the domain we're most concerned about evaluating models on efficiently. It is unclear how this would extend to harder domains such as LM eva... | null | null | null | null | null | null |
MultiOOD: Scaling Out-of-Distribution Detection for Multiple Modalities | Accept (spotlight) | Summary: This paper first proposes a MultiOOD benchmark for multimodal OOD detection. It then further proposes methods including A2D and NP-Mix for better tacking the multimodal OOD detection task.
Strengths: 1. I appreciate the formulation of the multimodal OOD detection benchmark.
2. This work presents extensive ex... | Rebuttal 1:
Rebuttal: Thanks for your insightful reviews, and we appreciate your valuable suggestions! We address your concerns and questions as follows:
>**Q1**: Discuss with other multi-modality works in related works.
**A1**: Thanks for your insightful suggestion! As shown in **Table 10** in our main paper, we in... | Summary: This paper introduces MultiOOD, a new benchmark for multimodal out-of-distribution (OOD) detection. The authors propose two new techniques: 1) Agree-to-Disagree (A2D), which encourages discrepancy between modality predictions during training, and 2) NP-Mix, a novel outlier synthesis method. Extensive experimen... | Rebuttal 1:
Rebuttal: Thanks for your insightful reviews, and we appreciate your valuable suggestions! Please find the responses to your questions below:
>**Q1**: Lacks important baselines (e.g. ensemble of multiple singleOOD methods for each modality). This also relates to the question of "why we should study MultiOO... | Summary: The paper introduces a novel OOD detection benchmark for multimodal data (called MultiOOD), covering diverse dataset sizes and modalities. Based on this benchmark, the authors first demonstrate the Modality Prediction Discrepancy phenomenon, which means that
the discrepancies of softmax predictions are shown ... | Rebuttal 1:
Rebuttal: Thanks for your insightful reviews and great support of our paper! We provide the responses to your questions as follows:
>**Q1**: In the video modality, what features do you extract to characterize the stream? Do you extract per-frame features, or per-video features.
**A1**: Thanks for your val... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their encouraging and insightful comments. We have carefully read through them and provided global and individual responses, respectively. In global responses here, we first add evaluations for a general question on the **ensemble of multiple unimodal OOD m... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Semi-Open 3D Object Retrieval via Hierarchical Equilibrium on Hypergraph | Accept (poster) | Summary: This paper extends the open-set 3D object retrieval problem to a semi-open situation where hierarchical semantic labels are considered. The authors leverage the multi-level category information with a proposed hypergraph-based Hierarchical Equilibrium Representation (HERT) framework. This framework consists of... | Rebuttal 1:
Rebuttal: **Response for Reviewer vaz9**
We sincerely thank you for the valuable comments and advice, which provided important guidance for the presentation of this paper and clarified the direction for future work.
1. **About the writing and symbols (Answer for Weakness 1)**:
We apologize for these typ... | Summary: The paper introduces a novel framework called the Hypergraph-Based Hierarchical Equilibrium Representation (HERT) for semi-open 3D object retrieval. The proposed framework addresses the practical scenario of semi-open environments where the training and testing sets share a partial label space for coarse categ... | Rebuttal 1:
Rebuttal: **Response for Reviewer oLzP**
We sincerely thank you for the valuable comments and advice, which provided important guidance for the presentation of this paper and clarified the direction for future work.
1. **About the framework (Answer for Weakness 1)**:
Based on your suggestions, we have r... | Summary: This paper introduces a more practical Semi-Open Environment setting for open-set 3D object retrieval with hierarchical labels, in which the training and testing set share a partial label space for coarse categories but are completely disjoint from fine categories. A novel framework, HERT, is proposed for this... | Rebuttal 1:
Rebuttal: **Response for Reviewer YDem**
We sincerely thank you for the valuable comments and advice, which provided important guidance for the presentation of this paper and clarified the direction for future work.
1. **About the technical contribution (Answer for Weakness 1 and the Questions)**:
a) **... | Summary: This paper introduces a Semi-Open Environment setting for open-set 3D object retrieval, addressing the limitation of existing methods that only consider single-layer labels and assume no overlap between training and testing sets. The authors propose the Hypergraph-Based Hierarchical Equilibrium Representation ... | Rebuttal 1:
Rebuttal: **Response for Reviewer nF63**
We sincerely thank you for the valuable comments and advice, which provided important guidance for the presentation of this paper and clarified the direction for future work.
1. **About coarse labels and datasets (Answer for Weakness Major 1, 3, Minor Q1, and the Q... | Rebuttal 1:
Rebuttal: We thank all reviewers for your insightful feedback and for your valuable time and effort. We try to answer all the questions and weaknesses of each reviewer in the rebuttal section below. The attached PDF contains our additional experimental results and figures.
Pdf: /pdf/25cd80128441175bfbfbd46d... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Generative Modelling of Structurally Constrained Graphs | Accept (poster) | Summary: The authors proposed ConStruct, a graph generative framework that enables hard-constraining graph topological properties that hold upon edge deletion throughout the entire sampling trajectory. Specifically, the authors model the forward (data-to-prior) process using an edge absorbing noise model, and they pred... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the importance of the topic under consideration and for the insightful comments. We address the reviewer's concerns in the following points:
**W1**: We agree with the reviewer and do also envision the extension to joint node-edge constraints, e.g. valency c... | Summary: The paper presents a novel diffusion model, ConStruct, to generate graphs that follow certain pre-specified properties. ConStruct involves an edge-absorbing forward process and a projected edge-addition reverse process to sample graphs that satisfy pre-specified constraints. The novelty of their method comes f... | Rebuttal 1:
Rebuttal: We appreciate the detailed comments and pertinent questions. We address the raised concerns below:
**W1**: We acknowledge that our model is tailored for the task of constrained generation, which requires the explicit and unambiguous definition of the constraint, as reflected in the paper's title.... | Summary: This paper presents ConStruct, a framework incorporating structural constraints into graph generation models using a discrete graph diffusion process. By introducing an edge-absorbing noise model and a projector operator, ConStruct ensures generated graphs meet specific properties like planarity or acyclicity,... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s constructive feedback and requests for clarification. We have addressed the raised questions below:
**W1**: An example of other purely structural constraints that ConStruct does not cover by default are edge-insertion invariant properties, *i.e.*, properties... | null | null | Rebuttal 1:
Rebuttal: # Global Rebuttal
We kindly thank all the reviewers for their time and valuable feedback on our work.
As a brief overview, our paper presents ConStruct, the first graph constrained diffusion framework to fully run in the discrete setting. ConStruct guarantees the satisfaction of edge-deletion i... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Optimal Transport-based Labor-free Text Prompt Modeling for Sketch Re-identification | Accept (poster) | Summary: This article extends image-based person and vehicle reid task to sketch field. First, authors introduce a novel sketch-based reid framework named OLTM, which utilizes text information to achieve modal alignment. Additionally, in sketch-based reid, authors apply the VQA model to generate textual attributes of p... | Rebuttal 1:
Rebuttal: **W1: Computational complexity.**
**A:** Please refer to **[Respones to nGMK:W1, edWJ:W6, beT2:W1]** in **Common Response**.
**W2, W3, W4: Minor issues.**
**A:** Thank you for your thorough review and for highlighting these minor issues. We have revised the manuscript to address the mentioned i... | Summary: This paper focuses on sketch based person ReID. Especifically, a labor free text method with OT is proposed, which achieve a large performance improvement on two public databases. Overall, I think the idea of this paper is interesting and the experiments show its superiority.
Strengths: 1. The proposed method... | Rebuttal 1:
Rebuttal: **W1, Q2: Minor issues.**
**A:** Thanks for your nice comment. We have identified all errors and corrected them in the manuscript.
**W2, W3, Q4: Optimal transport.**
**A:** Thanks for your constructive comment. Due to space constraints, a detailed description of optimal transport (OT) and matri... | Summary: This paper proposes an optimal transport-based labor-free text prompt network for sketch re-identification. The authors address two primary challenges: the expense of text annotation and cross-modal interaction, leveraging generated text attributes for multi-granularity modal alignment. The experimental result... | Rebuttal 1:
Rebuttal: **W1: Triplet Assignment Loss.**
**A1:** Thank you for your inquiry. We have included visual analysis in **Figure 1 of PDF**, which illustrates the convergence curve and sample distances during training. **Figure 1(a)** shows that conventional triplet loss converges prematurely. In contrast, our ... | Summary: This article proposes a framework for pedestrian re identification in sketch images based on optimal transportation theory, which utilizes visual question answering pre training models to address the current issue of high text annotation costs and only focuses on global feature representation. This framework u... | Rebuttal 1:
Rebuttal: **W1: Computational complexity.**
**A1:** Please refer to **[Response to nGMK:W1, edWJ:W6, beT2:W1]** in **Common Response.**
**W2: Overfitting.**
**A2:** We sincerely appreciate your valuable comments regarding overfitting, as this is also a concern for us. Consistent with the approaches in [1... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' valuable time in providing constructive feedback. We have thoroughly reviewed the comments and made the necessary responses and corrections. If our responses do not fully address the reviewers' questions, we are open to further discussion.
We are honored that the revi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning to Embed Distributions via Maximum Kernel Entropy | Accept (poster) | Summary: This paper focuses on distributional regression (classification) that carries out supervised learning over a collection of datasets, where one instance (subject to one label) is a dataset that can be considered a distinct empirical distribution. The paper proposed a novel learning objective, maximize quantum e... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you very much for your thoughtful review.
Regarding the topic of unsupervised learning datasets, the Leukemia experiment conducts unsupervised kernel learning on the entire set of available distributions without accessing the corresponding labels. In the experiments describe... | Summary: The authors consider the distribution regression problem. The authors propose to learn an embedding function (i.e., embedding support data points into a sphere) and leverages the kernel embedding (into RKHS) and kernel functions (within RKHS space). The authors propose an algorithmic approach to learn such emb... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you very much for your thoughtful review.
Before addressing your specific questions, we want to emphasize that the leukemia diagnosis dataset used in the main part of our paper is a real dataset. It was collected during clinical studies, is sufficiently diverse, and represen... | Summary: This work studies the distribution regression problem. The inputs are distributions, and the goal is to learn embeddings for these inputs. The authors propose the following method: First map the distributions to the kernel mean embeddings with respect to a embedding kernel $k_{emb}$, and then conduct a kernel ... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We are grateful for your meticulous review and constructive feedback.
Since our initial submission to ICML, the manuscript has undergone significant improvements, particularly in the experimental section, prompted by critiques akin to those presented by the Reviewer.
We concur th... | Summary: The authors propose a method for learning kernel embeddings for distributions via maximizing a Renyi entropy objective. They relate their objective to the distributional variance of the embeddings, and explain why this can lead to good embeddings for downstream tasks. Empirical evaluations show good results on... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you very much for your thoughtful review.
Upon reading the Reviewer's comments we were confused by what exactly the Reviewer contends when referring to embeddings learned by SGD via Cross-Entropy (CE). Our methodology is inherently unsupervised, whereas CE intrinsically nece... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes a new unsupervised way to learn kernels for distribution regression through entropy maximization. In addition, they also propose a geometric interpretation which is very interesting. The learnt kernels are general and have shown on some experimental settings to perform better than standard ... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you very much for your thoughtful review.
* We appreciate the insightful comments from the Reviewer. We will incorporate the Bayesian approaches they highlighted into our extended discussion in Appendix C. We have dedicated this appendix to a thorough review of related liter... | null | null | null | null | null | null |
Testing Semantic Importance via Betting | Accept (poster) | Summary: The paper presents a method to test feature importance when a model makes its decision. The proposed method is based on hypothesis testing. The features concerned in this work is human-interpretable ones. For example, when CLIP makes a "cat" prediction to the image, the features tested are: "whiskers", "pointy... | Rebuttal 1:
Rebuttal: ## Thank you for your comments!
We thank the reviewer for their encouraging comments. Here, we address each weakness point individually, and we are looking forward to discussing more.
> *if* really works on input features from user, is a very important contribution.
We do want to stress that ou... | Summary: Recently, there has been a lot of interest in understanding the inner workings of deep neural networks. Most existing works learn semantic concepts that are inherently understandable to the user. Often, each semantic concept comes with an associated score, and in many cases, it is hard to interpret the scores.... | Rebuttal 1:
Rebuttal: ## Thank you for your comments and questions!
We now address each point raised by the reviewer individually, and we are looking forward to clarifying any outstanding questions.
---
### **Diverse datasets and models**
We thank the reviewer for their suggestions, which have significantly strengt... | Summary: The paper defines statistical importance of semantic concepts for black-box models such as CLIP via conditional independence.
This is motivated by the fact that users would be interested to know how they should interpret two concepts with different importance scores, and if the difference in such two concepts... | Rebuttal 1:
Rebuttal: ## Thank you for your comments and questions!
Here, we address each point individually, and we are looking forward to discussing with the reviewer.
---
### **Limited experiments and transferability across different vision-language models**
This is a great question, and we thank the reviewer fo... | Summary: The paper discusses the need for precise statistical guarantees in feature importance, especially for semantic concepts, to ensure transparency and avoid unintended consequences.
It introduces a framework using conditional independence for testing semantic importance and demonstrates its effectiveness on synt... | Rebuttal 1:
Rebuttal: ## Thank you for your comments and questions!
We address each point individually, and we are looking forward to discussing with the reviewer to answer any outstanding questions.
---
### **Comparison with SOTA**
We thank the reviewer for this comment, which has significantly strengthened our ex... | Rebuttal 1:
Rebuttal: ## Thank you for your comments!
We sincerely thank all reviewers for their valuable comments and suggestions, which have strengthen our experimental results and the presentation of our contributions.
**Following all reviewer comments, we have significantly extended our experiments on real world... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Mixed Dynamics In Linear Networks: Unifying the Lazy and Active Regimes | Accept (poster) | Summary: In this paper, the authors study the training dynamics of a two-layer linear network $A = W_1 W_2$ and show that for a wide range of network width $w$ and initialization scale $\sigma^2$, the dynamics of $A$ can be approximated by the self-consistent equation:
$\dot{A} \approx -\sqrt{ A A^T + \sigma^4 w I } \... | Rebuttal 1:
Rebuttal: Thanks for the thoughtful review. Regarding the weaknesses you mention:
1. We will improve the readability of the proofs, thanks to the error/typos
that you and the other reviewers have found.
2. We agree that this intermediate width regime is of particular interest,
and it is probably the regim... | Summary: This paper derives a formula for the training dynamics of two-layer linear networks, encompassing the lazy regime, the active regime, and the mixed regime.
Strengths: - In constract to previous works, the authors reveal the existence of mixed dynamics for training two-layer linear networks, which combine the ... | Rebuttal 1:
Rebuttal: Thanks for the thoughtful review. Regarding your questions:
- Note that we view the mixed dynamics as part of the active regime,
what we show is that the short lazy dynamic period that always appears
at the beginning plays an important role, so it is useful to think
of the active regime as a mix ... | Summary: This paper consider the GD dynamics for two layer linear network. The authors introduce an approximated dynamics which interpolate between the lazy and the balanced regime. The authors also showed the phase diagram based on the above dynamics for low-rank matrix factorization problem.
Strengths: 1. This paper... | Rebuttal 1:
Rebuttal: Thanks for the thoughtful review. Regarding the weaknesses you raise:
1. Proving this is non-trivial and would require some work to be proven
directly, the intuition is that one needs to take a learning rate
of order $\sigma^{-1}$ to get finite size updates to $A_{\theta}$,
but the change to the ... | Summary: The paper studies gradient descent dynamics in two-layer linear networks. It is shown that in the wide-hidden-layer regime with standard random initialization there is a simple self-consistent differential equation describing the network output (Theorem 1). This equation generalizes both lazy training regime a... | Rebuttal 1:
Rebuttal: Thanks for the thoughtful review. Regarding the weaknesses you mention:
It is indeed true that there are many solutions to the set of equations
we obtain, and most of the work in the proof is to prove that one
approaches the `right' solution. This part of the argument is quite
technical and canno... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Attention boosted Individualized Regression | Accept (poster) | Summary: The authors propose an individualised matrix regression method, where the individualised part is shown to be related to self-attention. The method is presented nicely, with theoretical and empirical results that indicate the usefulness of the method.
Strengths: The paper is well-written and easy to follow. Th... | Rebuttal 1:
Rebuttal: Thank you very much for your review.
# Main questions
>**Q1**: Personalised interpretations
**A1**: Thanks for your feedback. For real data, we show in Figure 2 the individualized coefficients of different samples and their significant internal relations and frame out corresponding regions in o... | Summary: The paper introduces a method for self-attention-based individualized regression and derives it's relation to transformers. The method is evaluated in a simulation setting and on an Alzheimer Brain MRI dataset.
Strengths: - Interesting theoretical treatment of individualized regression and its connection to t... | Rebuttal 1:
Rebuttal: Thank you very much for your review.
>**Q1**: Only applied to tiny datasets with image size 48 x 48 and only two experiments.
**A1**: Thank you for your feedback. The MRI scans are preprocessed to be of size $113\times 137\times 113$ and we further resize the extracted slices to the size $48\tim... | Summary: This paper proposed an individualized regression method and applied it to medical image analysis. The method can handle matrix-valued data and does not require additional information on sample similarity. The authors also analyzed its relationship to the attention technique. Finally, the proposed method was ev... | Rebuttal 1:
Rebuttal: Thank you very much for your review.
>**Q1**: The method can only work for matrix-valued data, such as image data.
**A1**: Thanks for your comment. Matrix-valued data, particularly images, are pervasive in many practical applications, and our method aims to provide a novel solution for these sce... | Summary: The paper proposes an approach for regression where common model coefficients can be modulated by sample-specific data. In particular, here the approach is applied to images (or matrices), where sample-specific data is derived from patch similarities (measured through rotation correlation), reflecting intra-im... | Rebuttal 1:
Rebuttal: Thank you very much for your review.
>**Q1**: Is the performance of the method proposed significantly different from the other methods, both for the simulation and the real data case? (Claims of superiority are not supported by hypothesis tests between the proposed method and the other 4 methods.... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors present an interesting approach to individualization of regression for heterogenous data. They first set up an individualized model with additive homogenous and heterogenous components containing matrix-valued coefficient matrices to be learned. They nicely establish the equivalence of their model ... | Rebuttal 1:
Rebuttal: Thank you very much for your review.
>**Q1**: Why is there no anonymized link to code?
**A1**: Codes were uploaded with submission to the “Supplementary Material” part as a zip file. Due to the request for anonymity, they are not public at the moment. We will provide the public link in the camer... | null | null | null | null | null | null |
Global Convergence in Training Large-Scale Transformers | Accept (poster) | Summary: The paper considers the theoretical mean-field limit of Transformers where width and depth go to infinity and studies the approximation error and convergence properties of gradient flow with weight decay. Both a residual self-attention layer and a residual feedforward layer are approximated by an ODE which ave... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and suggestions. They are extremely helpful in improving our work and presentation. Below, we address your concerns point by point.
**Q1**: The error bounds in Theorem 3.1 are so large they may be impractical beyond initial training moments, with hidden cons... | Summary: The authors theoretically investigate the mean field limit of residual transformer models. As depth and width go to infinity, thanks to the residual structure, the forward pass can be modeled as an ordinary differential equation, and the training gradient flow converges to a Wasserstein gradient flow. The auth... | Rebuttal 1:
Rebuttal: We are grateful for your supportive comments, which are greatly helpful for us to improve our work. We address your questions as follows.
**Q1**: The content is intense and the reviewer suggests polishing the manuscript. Adding a summary of the main ideas in the manuscript and brief introductory ... | Summary: This paper analyzes gradient flow on Transformer networks. It is shown that for wide and deep Transformers, gradient flow converges to the Wasserstein gradient flow and reaches a global minimum.
Strengths: This is a well-written paper and it seems the results are strong and clean. However, I have very little ... | Rebuttal 1:
Rebuttal: Thank you for your recognition of our paper. Your comments provide valuable guidance on our presentation. In this work, we aim to take the initial steps towards studying the theoretical optimization guarantees of Transformer models and, for the first time, prove the global convergence property via... | Summary: This paper studies transformers in their mean field limit. They use a ResNet architecture with infinite depth. The residual blocks are made of two steps: one transformer step and one standard MLP. They also use infinite width for both steps.
Then, they show that the gradient flow in this limit is well posed, b... | Rebuttal 1:
Rebuttal: Thank you for your valuable suggestions. In the following, we give point-by-point responses to your questions.
**Q1**: One of the main weaknesses for a submission to NeurIPS is the format. The main paper is devoted to a (well done) explanation of main results, but insufficient explanations of the... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper presents a rigorous theoretical analysis of the global convergence properties of gradient flow in training Transformers in the setting of in-context learning. By constructing the mean-field limit, the authors show that as the model's width and depth increase to infinity, the gradient flow converges t... | Rebuttal 1:
Rebuttal: We are grateful for your constructive and helpful comments and suggestions. We address your concerns as follows.
**Q1**: Corollary 4.1 only implies asymptotic bounds for $L$ and $M$, instead of the linear and quadratic terms in the current version of Corollary 4.1.
**A1**: Thank you for the cruc... | null | null | null | null | null | null |
FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide Image Classification | Accept (poster) | Summary: This paper proposes a few-shot learning approach for WSI (Whole-Slide Image) classification. This approach, built upon Tip-Adapter, leverages a cache branch to memorize the knowledge from few-shot instances and then retrieve label information from the cached knowledge. In addition, a prior branch, which utiliz... | Rebuttal 1:
Rebuttal: $\textbf{Q1:}$ This paper is overall rough and sub-par in writing, requiring substantial improvements in clarity.
$\textbf{R1: }$ Thanks very much for pointing out the problem. We have revised the above errors as follows: “slice-label” has been corrected to “slide-label,”, “a efficient annotatio... | Summary: To address the challenges of expensive fine-grained annotation and data scarcity encountered in the clinical application of deep learning-based WSI classification methods, this paper proposes a novel and efficient dual-tier few-shot learning paradigm named FAST. Under this new paradigm, the authors introduce a... | Rebuttal 1:
Rebuttal: $\textbf{Q1:}$ The function $\phi(\cdot)$ in Figure 2 is not mentioned or explained in the paper, which may confuse readers.
$\textbf{R1:}$ We apologize for any inconvenience brought to you. We have added a description of the function \phi(\cdot) in Section 3.2, revising “The retrieval result is ... | Summary: In this article, the authors propose a novel few-shot learning paradigm for WSI classification. This paradigm is based on two branches: the first is a learnable cache model that utilizes both labeled and unlabeled instance data, and the second, the Prior Branch, leverages the prior knowledge of a pre-trained C... | Rebuttal 1:
Rebuttal: $\textbf{Q1:}$ The study lacks experiments with V-L models specific to the pathology field. Since CLIP is not originally based on pathology images, the authors should include comparisons using PLIP [1] and CONCH [2].
$\textbf{R1:}$ Thanks for your great suggestion on improving the quality of our ... | Summary: This paper investigates the issue of Whole Slide Images (WSI) classification, a study with practical value. It proposes a new working paradigm that is an improvement based on Tip-Adapter. Theoretically, this new paradigm can effectively address the problem and has strong scalability.
Strengths: This study has... | Rebuttal 1:
Rebuttal: $\textbf{Q1:}$ This paper lacks some important related work. The proposed method is based on the Tip-Adapter. While, there are many improvements based on Tip-Adapter, such as [1-4]. I think the experiments should include comparisons with these related methods, or at the very least, mention and bri... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for your valuable comments. These comments have greatly helped improve the quality of our manuscript. Next, we will reply to the questions raised by each reviewer individually. The figures and tables mentioned in our replies have all been uploaded in a single PDF f... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
UDPM: Upsampling Diffusion Probabilistic Models | Accept (poster) | Summary: This paper introduces a novel generative model called the Upsampling Diffusion Probabilistic Model (UPDM). UPDM aims to decrease the number of diffusion steps needed to generate high-quality images, resulting in a significantly improved efficiency compared to previous methods.
Strengths: 1. This paper is well... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the valuable comments. Our response is detailed below.
**Weaknesses**:
1) Will be fixed in the revised version. We thank the reviewer for the effort.
2) Although the three datasets we examined UDPM on are diverse, with CIFAR10 and AFHQv2 being multi-class ... | Summary: The paper discusses the Gaussian diffusion modeling at different dimensionality by incorporating downsampling in the forward process. As a solution, the authors propose a new model called Upsampling Diffusion Probabilistic Model (UDPM), which reduces the latent variable dimension before adding noise. The rever... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s valuable points. Our response to the reviewer’s comments are given below.
**Weaknesses**:
1) The number of steps is determined by the smallest noise resolution you want to start with. Then everything is fixed. From our experiments, we found out that beyond the 3 diff... | Summary: This paper proposes a new training and sampling scheme for a diffusion model. The motivation is to enhance the effectiveness and interpretability of the diffusion model. Building upon the methods of DDPM, this paper introduces an upsampling operation into the Markov process, enabling the model to denoise and u... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's insightful comments. Our response is provided below.
**Weaknesses**:
1) It is well known that diffusion models severely suffer from heavy computations to produce pleasing-looking images due to two aspects: (i) The large number of diffusion steps and (ii) the lar... | null | null | Rebuttal 1:
Rebuttal: In the following PDF file, we present the following ablation studies and results:
1) Additional demonstration of the interpretability of the model.
2) Ablation study on the contribution of each loss term.
3) Additional results on more a diverse dataset with higher resolution.
We hope after this ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Model Reconstruction Using Counterfactual Explanations: A Perspective From Polytope Theory | Accept (poster) | Summary: This paper presents a novel and valuable theoretical analysis leveraging polytope theory to provide novel insights into using counterfactual explanations for model reconstruction and significant contribution to the field of model reconstruction/interpretability of black box models. The key contributions are:
... | Rebuttal 1:
Rebuttal: We thank the reviewer for reading the paper and appreciate their detailed review.
**_CCA for other machine learning models:_** The proposed CCA algorithm, being implemented through a modified loss function, in its current form is limited to neural networks. However, the initial theoretical devel... | Summary: The paper proposed a model reconstruction methodology by using one-sided counterfactual explanations. After generating counterfactuals (assumed to be the closest counterfactual to the observation), the authors reconstruct the original model using a piece-wise linear approximation (with a bunch of hyperplanes).... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking out the time to review this paper, and appreciate their acknowledgement of the novelty of our work.
**_On significance and limitations of theoretical results:_** Deriving theoretical bounds for model reconstruction using counterfactuals under general non-convex de... | Summary: The paper presents an approach to mitigate the decision boundary shift problem of counterfactual-based model reconstruction algorithms. They leverage the fact that counterfactuals differ from ordinary instances since they exist relatively close to the decision boundary, to derive a novel loss function for mode... | Rebuttal 1:
Rebuttal: We are grateful for the detailed review and the important suggestions.
**_Dependency on counterfactual generating method:_** The performance of the proposed method does not depend on the specific counterfactual generating method, except for the proximity of the generated counterfactuals to the d... | Summary: This paper studies model reconstruction attacks by using the proximity of counterfactuals to the decision boundary. The authors aim to establish theoretical guarantees for such attacks. To this end, they characterize the number of queries required for the attacker to achieve a given error in model approximatio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review and greatly appreciate the positive opinion about our work.
**_On the variety of recourse methods:_** As per the suggestions of the reviewer, we have now implemented **ROAR** [Upadhyay et al.] and **C-CHVAE** [Pawelczyk et al.] methods and will include them... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful comments and suggestions. We are glad that our work has been recognized as an “in-depth analysis of a novel model reconstruction strategy” by Reviewer z1A7 and as a “good starting point for future works” by Reviewer VLao.
Model reconstruction using... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Ask, Attend, Attack: An Effective Decision-Based Black-Box Targeted Attack for Image-to-Text Models | Accept (poster) | Summary: The paper focuses on the vulnerability of image-to-text models to adversarial attacks, particularly in a decision-based black-box targeted attack scenario. The authors design a three-stage attack process: (i) Ask: Guides attackers to create target texts that fulfill specific semantic requirements. (ii) Identif... | Rebuttal 1:
Rebuttal: To Weakness 1：
Our technical contribution lies in carefully crafting specific designs to improve search efficiency and make our framework applicable in more difficult scenarios. Each design and its technical contribution is summarized as follows.
1) Improving search efficiency from different p... | Summary: The authors tackle the challenging problem of adversarial attacks on Image-to-Text Models, focusing specifically on the black-box attack scenario where an attacker has no access to the internal workings of the model, only its output. To address this challenge, they propose a novel framework called Ask, Attend,... | Rebuttal 1:
Rebuttal: To Weakness 1：
To underscore the significance of research into image-to-text black-box targeted attacks, we will add an example of societal harm and highlight a societal benefit: 1) Harm Example: Social media companies use image-to-text AI for content moderation of user-uploaded images. Black-bo... | Summary: This paper proposes a new adversarial attack, termed AAA (ask, attend, attack) attack towards image-to-text models. In the Ask stage, the attacker iteratively generates candidates to generate individuals closer to the target semantics in the feature space of the target model. During the Attend Stage, the attac... | Rebuttal 1:
Rebuttal: To Weakness 1：
Section 3.3 (Ask stage) aims to obtain prior knowledge for searching the target text. Prior knowledge shortens the search path from image to target text as much as possible, improving the search efficiency of Attack stage (line 137).
Specifically, we perform a random search with... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers, thank you for recognizing our work as novel, important, and experimentally adequate. And your comments have greatly improved the quality and clarity of our manuscript. We will address your concerns one by one.
Pdf: /pdf/132c92adf358b2e20ab9392c02b216be2b32cf2e.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Online Learning with Sublinear Best-Action Queries | Accept (poster) | Summary: In this paper, the authors studies the classical problem of online learning in the additional context of algorithms with predictions, or learning-augmented algorithms. In online learning, a learning algorithm must repeatedly select from a set of options, each associated with a loss function, and the goal is to... | Rebuttal 1:
Rebuttal: We thank the reviewer for the questions and suggestions, which we will implement in the final version of the paper.
**Question.** The authors discuss the potential of using noisy ... prediction regime in the future.
**Answer.** We thank the reviewer for this suggestion. In several applications,... | Summary: The paper’s title concisely summarizes the topic. They study both the “full feedback” setting, where the learner observes the entire loss vector (what the loss would have been for each possible action) on each time step, and the “label-efficient feedback” setting, where the learner only observes the loss vecto... | Rebuttal 1:
Rebuttal: We thank the reviewer for the questions and suggestions, which we will implement in the final version of the paper.
**Question 1.** Can you provide technical intuition for where the multiplicative power of the best-action comes from?
**Answer.** We are happy that the reviewer shares our enthusia... | Summary: This paper considers an online learning with actions model where the learner is allowed to make $k$ "best action queries". Such a query at step $t \in [T]$ will return $i_t^\ast \in \arg\min_{i \in [n]} \ell_t(i)$; an action that minimizes the loss at step $t$. The loss values are bounded (in $[0, 1]$ w.l.o.g)... | Rebuttal 1:
Rebuttal: We thank the reviewer for the questions and suggestions, which we will implement in the final version of the paper.
**Question.** In the equations after line 178, is the last equality an inequality using $T\eta \leq \hat k$?
**Answer.** Yes, thank you for pointing this out.
**Limitation.** The ... | Summary: This paper considers the standard prediction with expert advice setting of online learning, but with the twist that the learner may issue, up to $k$ times, a "best-action query" before making a prediction, in which case the identity of an expert incurring the smallest loss in the round is revealed to the learn... | Rebuttal 1:
Rebuttal: We thank the reviewer for the questions and suggestions, which we will implement in the final version of the paper.
**Weakness.** Perhaps another point of improvement ... best-action queries?
**Answer.** One other application of our model is in fraud detection. In this context, an online learnin... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Disentangling the Roles of Distinct Cell Classes with Cell-Type Dynamical Systems | Accept (spotlight) | Summary: The author developed a model named Cell-Type Dynamical Systems (CTDS) to capture the excitatory (E) and inhibitory (I) neurons’ electrical activity of rat frontal orienting fields (FOF) and anterior dorsal striatum (ADS) during an auditory decision-making task.
Strengths: The experiments are well-designed and... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive assessment of our work. We are glad that the reviewer found our work to be well presented, and our experiments to be comprehensive. Below are out detailed responses to the reviewers questions:
1. **Clarifications on figures 2D / 3CD:** Thanks for bringing ... | Summary: This work proposed the dynamical model with cell-type specific latent, especially focused on the excitatory (E) and inhibitory (I) neurons. They developed a cell-type dynamical system (CTDS), where E/I neurons will only have positive or negative effects. They apply this model into decision making takes, and CT... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive assessment of our paper and constructive suggestions for improving it. We are glad that the reviewer found our experiments to be extensive, and our causal perturbation results to be impressive. However, we feel that the reviewer has misunderstood one of our... | Summary: This work describes how a latent dynamical systems (LDS) model of neural activity can incorporate distinct excitatory (E) and inhibitory (I) latents. Doing so requires being careful to maintain sign constraints (Dale's law) in the transition matrix defining latent dynamics as well as the emission matrix defini... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed and insightful review. We are grateful that the reviewer found our work to be novel, technically solid, and of significance to the neuroscience community. Below are our responses to the reviewer’s comments and questions:
1. **Relevant literature on sign c... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their positive assessment of our work, and for their detailed comments and suggestions. We are delighted that the reviewers found our work to be well-written, of significance to the community, and our experiments to be well-designed and technically solid. We thank review... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold | Accept (poster) | Summary: There have been previous attempts at doing finetuning on positive synthetic examples to improve LM reasoning, but the performance gain from these attempts are generally quite limited (and possibly even negative). In this paper, the authors take a different approach which also accounts for negative examples and... | Rebuttal 1:
Rebuttal: Thanks for the feedback and for the positive assessment of our paper! We are grateful to you for the comments and kind words regarding the contribution of our paper. To address the questions, we have added a discussion of computational costs below and answer the other questions raised. **Please le... | Summary: The paper investigates how to effectively leverage synthetic data to improve reasoning capability in the GSM8K and MATH datasets. The authors identify that sampling correct synthetic data from a fine-tuned model is more sample-efficient but comes with the risk of overfitting artifacts in the synthetic data. In... | Rebuttal 1:
Rebuttal: Thank you for the feedback. To address the concerns, we add new results applying per-step RL to KTO and RFT as well, and compare computational costs. We also clarify the per-step DPO algorithm, advantage functions and Fig 7. **Please let us know if these responses address your concerns, and if so,... | Summary: This paper investigates the impact of synthetic data on improving the reasoning capabilities of large language models (LLMs). The authors conduct an empirical study followed by a theoretical formalization to understand when and how synthetic data helps or hurts model performance on reasoning tasks.
Key findin... | Rebuttal 1:
Rebuttal: Thank you for the feedback. To address your concerns, we clarify and add several new results to show how per-step RL on negative data can fix issues caused by accumulation of errors from training on multiple generations of positive synthetic data, and also respond to the other concerns on choice o... | Summary: This paper studies the effect of synthetic data on improving the reasoning abilities of LLMs. The authors have compared with multiple approaches including SFT, RFT, DPO, per-step DPO, etc., and characterized the model performance w.r.t. different scales of synthetic data under each training regime. Several pra... | Rebuttal 1:
Rebuttal: Thank you for the review! To address the concerns, we add many results for RFT, spurious correlations, per-step RL, and advantage estimation, which we believe improve the quality of the paper. We will use the 1 extra page in the final to incorporate them, along with a conclusion, and clarification... | Rebuttal 1:
Rebuttal: We thank all reviewers for their detailed feedback, and in particular would like to highlight the the positive assessment of our work by Reviewer iuJa: **“contains quite a lot of insights, and is substantially better than most papers on synthetic data that I’ve seen in the past”**.
To address the... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Score-based 3D molecule generation with neural fields | Accept (poster) | Summary: In this study the authors propose a novel FuncMol model for molecular generation. The proposed approach is based on continuous space neural fields and involves the joint training of a molecular neural field along with molecular modulation codes. The implementation of a neural empirical Bayes technique allows t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and questions. A general rebuttal is posted above. Below we address the reviewer's individual questions.
**1. MolDiff Baseline.** MolDiff shows that incorporating bond information in point cloud-based approaches improves the quality of the generated samples... | Summary: This paper proposes a new representation for 3d molecules, and the representations are low-dimensional, compact, and scalable. Based on the representation, this paper proposes a new score-based generative model, FuncMol, which shows competitive reuslts on GEOM-drug and scales up to CREMP. Besides, FuncMol adop... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and positive feedback. A general rebuttal is posted above. Below we address the specific issues raised by the reviewer.
**“Why does FuncMol not outperform GeoLDM and VoxMol?"** It is challenging to explain why these models perform differently, as they make d... | Summary: This paper introduces FuncMol, a method that leverages recent work on implicit neural representations for unconditional 3D molecule generation. The key idea is to (1) parametrize continuous atomic densities through a neural field network and molecule-specific modulation codes and (2) use a score-based generati... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. A general rebuttal is posted above. Below we address the reviewer's specific questions.
**Issues with overfitting/memorization.** By further investigating this issue, we realized that memorization is likely caused by a degenerate latent space—as ... | Summary: This study proposes a neural field model that treats molecular data as continuous atomic occupancy fields. The model learns a latent code that can be used to predict the atomic occupancy in discretized grids. The authors then perform score-based generative modeling using neural empirical Bayes and show higher ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive and valuable feedback. A general rebuttal is posted above. Below we address the reviewer's concerns.
**1. Meaningful representation of latent space.** See "Meaningful representation of latent space [yg8b]" on the main Rebuttal. Overall, the additional experi... | Rebuttal 1:
Rebuttal: We thank the reviewers for the helpful comments. The reviewers agree that the paper has good soundness, presentation and contributions. They also acknowledge that our approach is novel, addresses limitations of point-cloud and voxel representations, is scalable, has faster sampling and is competit... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Connectivity Shapes Implicit Regularization in Matrix Factorization Models for Matrix Completion | Accept (poster) | Summary: The paper studies implicit regularization in matrix completion, a problem that estimates missing entries from a partially observed matrix. In particular, in this paper, matrix completion is formulated as an optimization problem of the form: $\min_{A, B} f(A,B) = \|M - AB\|_S^2$ where $S$ is the subset of obser... | Rebuttal 1:
Rebuttal: * **Point 1: Why should we care about data connectivity (in the sense of this paper)? I think the motivation of this study needs to be further justified.**
* Reply: We appreciate the reviewer's question regarding the importance of data connectivity in our study. As the title of our paper suggests... | Summary: This study empirical reveals that the connectivity of observed data significantly influences the implicit bias and identifies a hierarchy of intrinsic invariant manifolds in the loss landscape, providing a preliminary framework for understanding the mechanisms behind implicit regularisation in matrix factorisa... | Rebuttal 1:
Rebuttal: * **Point 1: Have you verified your findings on high dimensional matrix with experiments?**
* Reply: We sincerely appreciate the reviewer's important question regarding the scalability of our findings. While our main results focus on smaller matrices for clarity of presentation, we have indeed co... | Summary: This paper studies the training dynamics of matrix factorisation for matrix completion, optimising the vanilla mean-squared-error loss function via gradient descent with small initialisation. The authors characterise the observation pattern of the underlying matrix via the connectivity of its associated bipart... | Rebuttal 1:
Rebuttal: * **Point 1: I find this work hard to fault. While the theoretical results could be criticised for being fairly complex, having spend some time understanding them, the payoff makes it absolutely worthwhile.**
* Reply: We are deeply grateful for the reviewer's careful examination and understanding... | Summary: This paper attempts to present a unified understanding of when and how matrix factorization models have different implicit regularization effects. Their key finding is that connectivity of the observed data plays an important role: (i) in the connected cases, the model learns the lowest-ranked solution and (ii... | Rebuttal 1:
Rebuttal: * **Point 1: I am unsure of how restrictive the assumptions used for the theoretical results are which are used to prove Theorem 2.**
* Reply: We appreciate the reviewer's concern regarding the restrictiveness of our assumptions. We have taken steps to clarify and justify these assumptions:
1.... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We sincerely thank all reviewers for their thoughtful and insightful comments. We have carefully addressed every comment, and we believe that the reviewers' collective feedback has significantly improved the manuscript. To address the common concerns raised, we have made the follo... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper systematically investigates the implicit regularization of matrix factorization for solving matrix completion problems. The authors find by empirical results that the connectivity of observed data plays a crucial role in the implicit bias, with a transition from low nuclear norm to low rank as data ... | Rebuttal 1:
Rebuttal: * **Point 1: In some cases, an extremely small initialization is required, which may potentially impact the training speed.**
* Reply: Our empirical analysis in Appendix B.4 (Fig. B6) demonstrates the relationship between observation magnitude differences and the required initialization scale. To... | null | null | null | null | null | null |
Exploring the Edges of Latent State Clusters for Goal-Conditioned Reinforcement Learning | Accept (poster) | Summary: This paper considers goal selection in goal-conditioned reinforcement learning. The core idea of this paper is to group states with small temporal distance into clusters, and then select goals that are on the clutter boundaries. In addition, the method gives priority to goal states that are accessible to the a... | Rebuttal 1:
Rebuttal: We appreciate your insightful feedback and constructive comments!
**R1. State Dimensionality in the Experiment Benchmarks**
The maximum state dimensions in our test suite exceed 29. Block rotation and pen rotation involve an anthropomorphic robotic hand with 24 joints. In total, the action space... | Summary: The paper introduces a cluster edge exploration (CE2) algorithm, which is implementing the “Go-Explore” principle in a – to the best of my knowledge – novel manner. Key idea is to use clustering of the state space latents – go to one of these clusters and then explore from there. As a main result, exploration ... | Rebuttal 1:
Rebuttal: We appreciate your insightful feedback and constructive comments!
**R1. The temporal distance network seems tedious to train additionally**
The temporal distance network can be trained efficiently using supervised learning from replay buffer trajectories to predict action steps from the current ... | Summary: This paper develops an approach for frontier exploration in the context of model based reinforcement learning. The key idea of the paper (inspired by prior work Go-Explore) is to cluster a group reachable states in the latent space and keep track of the current frontier, such that new goals can be sampled clo... | Rebuttal 1:
Rebuttal: We appreciate your insightful feedback and constructive comments!
**R1. One of the main weaknesses of the paper is that the delta in terms of core algorithmic contribution beyond go-explore is limited.**
Our algorithm, CE$^2$, tackles the core challenge in the Go-Explore mechanism: how to select... | Summary: This paper presents a method called "Cluster Edge Exploration" (CE2) to perform goal selection in goal-conditioned reinforcement learning and enable efficient exploration. Concretely, the method builds on the Go-Explore principle, which learns separate policies for exploration and goal-reaching.
The main idea... | Rebuttal 1:
Rebuttal: We appreciate your insightful feedback and constructive comments!
**R1. It seems that some of the tasks haven't been trained to convergence. This makes it harder to draw definitive conclusions on the method's sample efficiency or final performance.**
We thank the reviewer for the suggestion. In ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the valuable feedback and suggestions from the reviewers. This global rebuttal includes a PDF file with updated training results and ablation study findings over extended training steps for our benchmarks (suggested by Reviewer NgsZ). Our method consistently outperforms the... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors propose a method for model-based exploration based on exploring reachable trajectories. The expand upon the Dreamer algorithm by optimizing the encoder to encode a notion of distance between state and optimizing for a likely reachable goal when training in the learned world model. The method is eva... | Rebuttal 1:
Rebuttal: We appreciate your insightful feedback and constructive comments!
**R1. One thing I would have like to see is some analysis of the additional computation time required to optimize for the goal states versus other methods. If it is prohibitively expensive, then the wall time could still be much lo... | null | null | null | null | null | null |
Knowledge-Empowered Dynamic Graph Network for Irregularly Sampled Medical Time Series | Accept (poster) | Summary: The paper aims to handle difference and correlation between multiple variables in the irregularly sampled medical time series data. By proposing a model named KEDGN with textual medical knowledge and dynamic variable graphs, it is able to outperforms baselines on a variety of datasets. Comprehensive experiment... | Rebuttal 1:
Rebuttal: # Responses to Reviewer n8KK (1/1)
Dear reviewer n8KK,
Thank you for your valuable feedback on the supplement of relevant literature of the article. Here is the response:
**W1. The idea of learning dynamic graphs that model variable relationships is quite common in literatures. It would be bet... | Summary: This work considers the irregular timestamp contained in current medical data. They employ a density-aware mechanism to the time-varying correlations among variables.
Strengths: This work addresses an important topic in this field and is well-organized and written. The experiments are sufficient to prove thei... | Rebuttal 1:
Rebuttal: # Responses to Reviewer 68iN (1/1)
Dear reviewer 68iN,
Thank you for your valuable feedback on the formula details and completeness of the paper. Here is the response:
**W1. $Z^{t}$ 's definition is not clear**
A1. We apologize for the oversight. If there are no observations at the preceding/... | Summary: Throughout this article, the authors have described an approach (KEDGN) to tackle Irregularly Sampled Medical Time Series. In this task the data is composed of multiple variables represented with time series. The sample rate of a time series can be irregular and two time series can have their samples taken at ... | Rebuttal 1:
Rebuttal: # Responses to Reviewer yFgU (1/1)
Dear reviewer yFgU,
Thank you for your valuable feedback on the formula details of the article. Here is the response:
**W1. Details of the Proposed Model could be better explained by giving the intuition behind the equations. (Especially for 4.3.2 and 4.4)**
... | Summary: This paper investigates the problem of irregularly sampled medical timeseries classification. The core idea is to use PLM to obtain semantic embeddings for variables, which are used to form a variable correlation graph. Then, the variable correlation graph is dynamically adjusted with the observations, based o... | Rebuttal 1:
Rebuttal: # Responses to Reviewer vHK1(2/3)
**Q1. The function of activation function in equation (7). **
A1. On one hand, as you mentioned, this activation function reflects the dynamics of variables, such as time decay or exponential increase. On the other hand, this activation function serves a normali... | Rebuttal 1:
Rebuttal: # Global Response
We sincerely thank all the reviewers for their consistent positive feedback regarding the significance of our work, the novelty of our approach, the thoroughness of our experiments and analyses, and the quality of our presentation. Additionally, we greatly value the reviewers' i... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Bayesian Approach to Data Point Selection | Accept (poster) | Summary: The paper proposes a method for data point selection (DPS) in the setting where only little data from the target (or meta) distribution is available, together with a lot of data from the training distribution. DPS aims to find the weights of data points in the training distribution such that the model paramete... | Rebuttal 1:
Rebuttal: ## [Re: Graphical Model and Derivations]
See the General Rebuttal:
* [Re-1: Derivations for Eq 4]
* [Re-2: Graphical Model and Derivations with Weight NN]
## [Re: Hyperparameter Analysis]
See [Re-3: Hyperparameter Analysis] in the General Rebuttal
## [Re: Ensuring the Non-negative Weights]
Line... | Summary: This work proposes a Bayesian approach to the data point selection task. Specifically, the authors introduce the important weights to each training point and derive the posterior joint probability of the instance-wise weights and network parameters. The parameters and weights are then sampled iteratively based... | Rebuttal 1:
Rebuttal: ## [Re: Derivations for Eq 4]
See [Re-1: Derivations for Eq 4] in the General Rebuttal
## [Re: Eq5 and Normalizing Constant]
No. The normalising constant in Eq.(4) is $p(D_m|D_t)$ (please see our derivations, especially, Eq.(R4) in our rebuttal above), and this normalising constant has no depend... | Summary: This paper proposes a new Bayesian method for Data Point Selection (DPS), called BADS. DPS aims to select training data points that optimize performance on downstream tasks. Instead of relying on bi-level optimization (BLO), BADS frames DPS as a posterior inference problem. The method uses a weight network to ... | Rebuttal 1:
Rebuttal: ## [Re: Convergence Analysis]
We provide a theorem showing that our SGLD algorithm converges to the true posterior to some extent. Our analysis is based on (Zou et al. 2021) where we make some adjustments for our case.
**Assumption 1** (from Assumption 4.3 of (Zou et al. 2021))
There exists $m>... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their thoughtful and detailed feedbacks.
## [Re-1: Derivations for Eq 4] @Reviewer **JeEz**, **oLJs**, and **5MKv**
From the graphical model in **Fig. 1 in PDF**, $D_m \perp (w, D_t) \ | \ \theta$ is the only conditional independence assumption we make.
$$
... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening | Accept (poster) | Summary: This paper proposes CS-GNN, a subgraph GNN approach utilizing graph coarsening and graph Cartesian products. This novel and flexible subgraph GNN can effectively generate and process any desired bag size. The paper also discovers new permutation symmetries in the produced node feature tensor during generalized... | Rebuttal 1:
Rebuttal: We appreciate your positive feedback! We are pleased to hear that you find our method novel with solid theoretical foundations. We're also glad that you find the empirical results promising. Below, we address your specific comments and questions.
**Q1:** *The size of the coarsened graph, though ... | Summary: The authors introduce a novel graph learning method that leverages message passing over product graphs. Specifically, message passing is performed over both the input graph and its product with a coarsened version of itself, which can be derived through techniques such as node clustering. Additionally, the aut... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback. We are pleased you appreciated the novelty of our idea and the identification of the basis of equivariant layers for our product graphs. Below, we address each of your points.
**Q1:** *Key definition*
**A1:** We emphasize that the coarsened graph is not exp... | Summary: The paper is very well written. Existing subgraph GNN methods are mentioned. Then a novel subgraph GNN framework is formulated. The authors discuss the equivariance properties of this new subgraph GNN formulation followed by an experimental evaluation of the proposed method.
Strengths: * Strong mathematical f... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback. We appreciate your positive remarks on our method's novelty, strong mathematical formulation, and clear writing. Below, we address your specific questions.
**Q1:** *The goal of our work and baselines*
**A1:** Let us clarify. The goal of this work is to redu... | null | null | Rebuttal 1:
Rebuttal: We are grateful to all reviewers for their feedback and constructive comments and happy to see that our work was positively received in general.
In particular, the reviewers all recognized the novelty and importance of our proposed method:
- “The topic of subgraph GNNs is interesting” (**DujM**)
... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
IPM-LSTM: A Learning-Based Interior Point Method for Solving Nonlinear Programs | Accept (poster) | Summary: This paper introduces IPM-LSTM, an approach integrating Long Short-Term Memory (LSTM) neural networks with Interior Point Methods (IPMs) to solve Nonlinear Programs (NLPs). The key innovation lies in approximating solutions to linear systems within IPMs using LSTMs, aiming to accelerate the convergence of clas... | Rebuttal 1:
Rebuttal: Thank the reviewer for reading our manuscript and providing constructive coments. Please refer to the Author Rebuttal for a clarification of the two-stage framework proposed in our work.
### Weakness
>*1. The decision to use the L2O approach for solving a least squares.......*
Thanks for raisin... | Summary: This paper introduces a method called IPM-LSTM, which integrates machine learning techniques into interior point methods (IPM). Specifically, the authors propose training a RNN model, LSTM, to quickly approximate the solution of linear systems within IPM. This approach is numerically validated on several conve... | Rebuttal 1:
Rebuttal: Thank the reviewer for reading our manuscript and providing constructive coments. Please refer to the Author Rebuttal for a clarification of the two-stage framework proposed in our work.
> *My main concern about this paper is Assumption 1, which requires the accuracy of the linear system solution... | Summary: This paper proposed to replace the linear system solver used in the inner loop of interior point method (IPM) with an LSTM for solving general non-linear programs. The LSTM is trained in a unsupervised manner to minimize a unconstrained least square objective derived from the KKT conditions. The proposed frame... | Rebuttal 1:
Rebuttal: Thank the reviewer for reading our manuscript and providing constructive coments. Please refer to the Author Rebuttal for a clarification of the two-stage framework proposed in our work.
>*1. The linear system solver used for IPOPT is unclear, which significantly impacts the solver's overall perf... | null | null | Rebuttal 1:
Rebuttal: Before addressing the reviewers' comments, we would like to first clarify the two-stage framework proposed in our work.
- **Stage I**: we **utilize IPM-LSTM to produce a high-quality approximate solution** (which might neither be feasible nor optimal but presumably well-centered). The IPM-LSTM is ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Context and Geometry Aware Voxel Transformer for Semantic Scene Completion | Accept (spotlight) | Summary: This paper proposes CGFormer for semantic scene completion. It generates distinct voxel queries for different input images, instead of simply predefining a set of trainable parameters. The deformable cross attention is extend to 3D pixel space, avoiding sampling the same features for different projecting point... | Rebuttal 1:
Rebuttal: > Q1. Parameters and performance comparison between CGFormer and Symphonize.
To compare the performance of CGFormer with Symphonize with a comparable number of parameters, we revisited the design of our CGFormer and replaced the EfficientNetB7 with ResNet50 and the Swin blocks in the TPV branch w... | Summary: The authors present Context and Geometry Aware Voxel Transformer (CGFormer) for the semantic scene completion task. Their method extends the baseline VoxFormer with a Context-Aware Query Generator (CAQG), 3D deformable attention layers, a depth refinement block, and a dynamic fusion of voxel and TPV features. ... | Rebuttal 1:
Rebuttal: > W1 (a). Performance gain of the context-aware queries.
Compared to methods that do not use temporal inputs or rely on much larger image backbone networks, CGFormer achieves the highest performance in 12 out of 19 categories on both the test and validation sets, as shown in Tables R2 and R3 of t... | Summary: This manuscript studies the problem of road scene semantic scene completion from RGB images. The architecture and benchmarking frameworks follow a widely accepted literature. The innovation proposed here is better queries that are informed of the geometry and semantics of the input scene, a cross-attention var... | Rebuttal 1:
Rebuttal: > W1. Ablation on the number of the cross-attention and self-attention layers.
We present the results of different configurations of cross-attention and self-attention layers on the semantickitti validation set in the table below. As shown in this table, the performance improves gradually with th... | Summary: This paper proposes a state-of-the-art Semantic Scene Completion method called CGFormer. It introduces a Context and Geometry Aware Voxel Transformer that dynamically generates queries tailored to individual input images, addressing depth ambiguity through a 3D deformable cross-attention mechanism. The networ... | Rebuttal 1:
Rebuttal: > W1. Parameter and performance comparison with Symphonize.
| Model | IoU↑ | mIoU↑ | Parameters (M) ↓ | Training Memory (M) ↓ |
| --- | --- | --- | --- | --- |
| EfficientNetB7, Swin Block | **45.99** | **16.87** | 122.42 | 19330 |
| ResNet50, Swin Block | **45.99** | 16.79 | ... | Rebuttal 1:
Rebuttal: We appreciate the valuable comments of reviewers, which have greatly contributed to enhancing the quality of our paper. We are glad that the reviewers recognized various strengths of our work, including the clear motivation [wDiN] [Lm1y], interesting idea [jg8M], comprehensive experiments [ryRH, j... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agents | Accept (poster) | Summary: In this paper, the authors propose a novel benchmark for evaluating the capability of LLMs and Code Agents in generating test cases from GitHub issues. The benchmark includes real-world issues, patches, and golden tests from popular Python repositories. The authors claim that Code Agents outperform traditional... | Rebuttal 1:
Rebuttal: We thank Reviewer ueFW for their critical perspective and for raising interesting questions, which we address below.
**Can you discuss the effect of possible data contamination on your results and how it might be mitigated?**
We thank the reviewer for raising this point. In short, we see no sta... | Summary: This paper introduces SWT-BENCH, a novel benchmark for evaluating automated test generation capabilities of AI models, particularly Code Agents. The authors adapt existing code repair datasets and methods to the task of test generation, proposing new metrics such as fail-to-pass rate and change coverage. Their... | Rebuttal 1:
Rebuttal: We thank Reviewer pxq3 for their detailed review, insightful questions, and helpful suggestions. We are happy to hear they appreciate the originality, quality, clarity, and significance of our work and in particular our comprehensive and rigorous evaluation across methods and models. Below, we add... | Summary: This paper focuses on automatic test generation using large language models (LLMs) and code agents. The authors introduce a benchmark called SWT-BENCH, which aims to analyze the performance of LLMs and agents in generating unit tests given a task description. The key contributions include:
1) Creating the SWT... | Rebuttal 1:
Rebuttal: We thank Reviewer Ky1F for their detailed review, insightful questions, and helpful suggestions. We are happy to hear they appreciate the potential impact of our work as well as the extensiveness of our empirical evaluation and its analysis. Below we address their remaining questions.
**Can the ... | Summary: This paper re-purposes SWE-Bench, a previous benchmark on repository-level code generation, to SWT-Bench, a benchmark for test generation by asking LLMs to generate tests to reproduce the issues for various GitHub repos and see if such tests can capture the bugs before the gold-standard patches are applied and... | Rebuttal 1:
Rebuttal: We thank Reviewer i5fN for their review and valuable questions. We are happy to hear they consider the problem we study important and under-explored, our work thorough and interesting and our results impactful. Below, we address their remaining concern.
**Is your paper over-claiming and implying ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their detailed, insightful, and overwhelmingly positive reviews.
We are encouraged to see that the reviewers consider the problem we investigate under-studied and important (PhkB, i5fN, pxq3, ueFW), our benchmark impactful (PhkB, i5fN, Ky1F, pxq3, ueFW), our empirical ev... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: Authors present a new benchmark for the SWE tasks of generating tests corresponding to an issue in a codebase with unit tests. They propose to repurpose the SWE-bench dataset for this task. They also evaluate LLM based prompting and agentic approaches (SWE-Agent and AutoCodeRover) with metrics like code change... | Rebuttal 1:
Rebuttal: We thank Reviewer PhkB for their detailed review, insightful questions, and helpful suggestions. We are happy to hear they appreciate the importance and novelty of the problem we study, the importance of our contributions, the extent of our analysis, and the quality of our exposition. Below we add... | null | null | null | null | null | null |
Coded Computing for Resilient Distributed Computing: A Learning-Theoretic Framework | Accept (poster) | Summary: This paper focuses on coded computing for machine learning and derives loss-minimizing encoding and decoding functions.
Strengths: Please see the “Questions” section.
Weaknesses: Please see the “Questions” section.
Technical Quality: 3
Clarity: 3
Questions for Authors: My review is as follows:
Major:
T... | Rebuttal 1:
Rebuttal: We express our gratitude to the reviewer for their valuable comments and feedback. In the above, we have offered a general response to the reviewers, addressing some of their common concerns. Here, we provide additional responses to the remaining questions raised.
> Major:
>The biggest thing that... | Summary: The authors consider the problem of improving the reliability of distributed computing architectures by encoding the input data before it is processed by worker machines such that a good approximation of the desired output can be reconstructed using only a subset of the workers’ outputs. They utilize learning ... | Rebuttal 1:
Rebuttal: We express our gratitude to the reviewer for their valuable comments and feedback. In the above, we have offered a general response to the reviewers, addressing some of their common concerns. Here, we provide additional responses to the remaining questions raised.
> LeTCC is proven to have a reco... | Summary: This work proposes a learning theory-based novel framework for coded computing with a focus on distributed machine learning applications. The proposed method sends mixtures of input samples to the worker nodes that compute the desired results on the mixtures. An encoder and a decoder functions are fitted at th... | Rebuttal 1:
Rebuttal: We express our gratitude to the reviewer for their valuable comments and feedback. In the above, we have offered a general response to the reviewers, addressing some of their common concerns. Here, we provide additional responses to the remaining questions raised.
> The experimental section does ... | Summary: The paper deals with coded distributed computing. I need to note that it is very popular research area now with a vast number of papers. But the authors are right when mention that the majority of papers utilizes standard algebraic codes (such as Reed-Solomon codes). The main problem of such approach is that t... | Rebuttal 1:
Rebuttal: We express our gratitude to the reviewer for their valuable comments and feedback. In the above, we have offered a general response to the reviewers, addressing some of their common concerns. Here, we provide additional responses to the remaining questions raised.
> «We develop a new foundation f... | Rebuttal 1:
Rebuttal: # General Response to Reviewers
We appreciate the reviewers' constructive feedback. Here, we provide a general response to their common questions.
**Experiments.** In our revised version, we present a more comprehensive evaluation by incorporating the statistical properties of our experiments. ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Vision Mamba Mender | Accept (poster) | Summary: The paper introduces a novel post-hoc optimization strategy for existing Vision Mamba architectures, termed Vision Mamba Mender, aimed at enhancing the performance of Mamba models in visual recognition tasks. The authors seek to identify and rectify flaws in the Mamba model’s mechanisms from both external and ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s recognition of the novelty and interest of our proposed method and the acknowledgment of its contribution to the interpretability research of the Mamba model. Below are our responses to each of your comments.
---
> **Q1**: Before reading the methodology section, the ... | Summary: The papers addresses the limitations of Mamba based models in vision through a post-hoc optimization scheme that address external state flaws and internal state flaws, identified through respective internal and external state correlation analysis. The introduced corrective measures improve performance on image... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and comments. We are pleased to hear that you find the motivation of this paper clear and that it holds significant importance for the vision community. We are also glad that you consider the correlation analysis proposed in the paper to be novel and detailed, an... | Summary: This paper analysis Mamba model from a post-perspective. It introduce a state correlation analysis method to establish the correlation between hidden states and predicted results, and analysis the external state flaws and internel state flaws. Furthermore, this manuscript propose repair method to handle these... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and comments. We are pleased that you find our research topic both interesting and novel, and that you believe the analysis of the Mamba model is well-motivated. We are also gratified that the experimental results have met your approval. Below are our responses t... | Summary: 1. Addressing the main issue in the existing model:
Mamba, despite its success in long sequence tasks, faces mixed opinions and challenges in visual tasks due to inherent flaws and suboptimal performance. Understanding these flaws and optimizing Mamba's performance in the visual domain are critical research... | Rebuttal 1:
Rebuttal: Thank you for your comments and the positive feedback. We have carefully reviewed each of your comments. Although some comments may not fully align with our research objectives, we are nonetheless very appreciative of your feedback. Below are our responses to each of your comments.
---
> **Q1**:... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Thank you for the time and effort you have invested in reviewing our paper. We are particularly grateful for your recognition of the novelty and originality of our work. We are also pleased that our approach to analyzing and optimizing the Mamba model from a post-hoc perspective h... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance | Accept (poster) | Summary: This paper investigates the "zero-shot" performance of multimodal models like CLIP and Stable-Diffusion. By analyzing 34 models and 5 pretraining datasets, the study finds that these models require exponentially increasing amounts of data to achieve linear improvements in performance. Additionally, the study s... | Rebuttal 1:
Rebuttal: > **W1: unify table descriptions**
Thank you for pointing out this discrepancy, we apologise for the oversight and have fixed this in the paper.
> **W2: connections to existing work**
Thank you for this question. We have included a brief related work section in the main paper in sec 7, describi... | Summary: The authors evaluate CLIP's zero-shot performance and its correlation to the pretraining data. Their experiments, based on the open LAION datasets, show that the limitations of Vision-Language Models (VLMs) in downstream tasks such as image generation and zero-shot recognition are linked to the frequency of co... | Rebuttal 1:
Rebuttal: > **W1: Dips in accuracy and consistency of log-linear trends?**
We thank the reviewer for raising this concern.
- **Analysis of drops in high freq concepts.** We provide some intuitions on why there are some drops in the trend at high freqs for CC-3M and CC-12M, we investigated which concepts o... | Summary: This paper examines the relationship between the frequency of concepts in pretraining data and the performance of downstream tasks associated with those concepts. Extensive experimental results reveal a log-linear relationship, suggesting that exponential increases in data are necessary to improve zero-shot mo... | Rebuttal 1:
Rebuttal: > **Q1: Dips in accuracy at high freqs**
Thank you for the suggestion. We look into CC-3M and CC-12M, the pretraining datasets corresponding to which we see dips in accuracy on the classification tasks. From our analysis, we hypothesise two main reasons for these performance dips:
- **Concept am... | Summary: This work explores the extent to which zero-shot generalisation really occurs in large-scale in model that were trained on web-scale datasets. The approach taken relies on identifying concepts that are present in train and test data and evaluating concept frequencies and per-concept performance. From extensive... | Rebuttal 1:
Rebuttal: > **W1: Why not simply scale up models?**
Thank you for raising this point.
- **Train-test similarity as a control factor.** We agree models with higher ImageNet accuracy perform better on the long-tail. Note however that results in fig 6 are not normalized for *“train-test similarity”*. Normaliz... | Rebuttal 1:
Rebuttal: **General Response to all reviewers**
We thank all the reviewers for finding our work ***interesting and important*** (Reviewer oRi7), ***clearly written and well presented*** (Reviewers oRi7, s2XF, WCHb, KUaM), ***containing extensive empirical evidence*** (Reviewers oRi7, s2XF, KUaM), and for f... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Compact Language Models via Pruning and Knowledge Distillation | Accept (poster) | Summary: This paper empirically explores compressing language models with pruning and knowledge distillation. It summarizes the best practices of pruning and distilling language models, which are supported by extensive experiments.
Strengths: 1. This paper is well-written, and the best practices are easy to follow, wh... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their encouraging comments and insightful feedback. Please find our responses below:
> **... it is better to highlight the difference between the final choice in this paper and the approaches in previous work like Sheared LLaMa.**
To help with this compar... | Summary: In this paper the authors explore compression of LLMs via pruning and Knowledge Distillation. They try out a variety of approaches for pruning as well as the retraining step and provide a comprehensive analysis of best practices for getting compact LLMs from their larger counterparts. The authors explore pruni... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for pointing out the strengths of the paper and providing valuable feedback.
> **I would have liked some form of qualitative study on comparing the generations from the models. Some form of human evaluation on say 25-50 long form generation examples would be g... | Summary: This paper proposes Compact Language Models via Pruning and Knowledge Distillation, which combines various tricks and methods to compress a 14B model to 8B while achieving better performance than training from scratch.
Strengths: The paper conducts extensive experiments, comparing the latest baselines, and th... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their encouraging comments and insightful feedback. Please find our responses below:
> **Will the authors open-source the code? If the code and data are open-sourced, I would raise my score.**
**Data:** more details on the composition of the Nemotron-4 dat... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their insightful feedback and comments. We have posted individual responses for each review, making every effort to provide additional results to support our responses within this limited rebuttal period. We hope our rebuttal addresses all reviewer concerns.
Pd... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Generating Code World Models with Large Language Models Guided by Monte Carlo Tree Search | Accept (poster) | Summary: This paper explores a novel and promising direction of model-based RL, proposing to represent the dynamic model and the reward model using code world models, which are Python programs that can be executed and rollout environments. With such code world models, we can learn a policy to maximize the return predic... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We appreciate that you found our motivation compelling and our framework effective. We also appreciate your recognition of our unique perspective on the offline setup of the code world model idea. In the following we address your concerns and we are confident ... | Summary: The authors present a search algorithm based on Monte Carlo tree search to synthesize programs with LLMs. The contribution includes formulating a search space that is compatible with the functionalities of a LLM: the actions in the search tree include generating new lines of code, fixing bugs, and improving cu... | Rebuttal 1:
Rebuttal: Thank you for the thorough review and the valuable suggestions to improve the clarity and soundness of our work. We appreciate your recognition of the power of GIF-MCTS and of the usefulness of the ablation studies on our method. We detail in the following how we are going to address your comments... | Summary: This paper primarily investigates the application of LLMs to synthesizing world models for reinforcement learning environments, where the world model is expressed as a Python program implementing the state transition and reward functions. Concretely, starting from available environment simulation code (e.g., a... | Rebuttal 1:
Rebuttal: Thank you for the kind words about our work. We are glad that you found it interesting and believe it will inspire future works! We agree that continuous physics simulations are particularly challenging and we also were not particularly surprised by that, but we will be looking to address this in ... | Summary: This paper proposes Generate, Improve, and Fix with Monte Carlo Tree Search (GIF-MCTS) for generating Code World Models (CWMs) using Large Language Models (LLMs). The authors present code representations of reinforcement learning (RL) environments, enabling the application of LLM algorithms for code generation... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our work and provide your detailed feedback. We are grateful for your comments and we hope that the following will clarify the details and novelty of our work.
**1. Pre-training:**
> Reliance on pre-training. I didn’t fully understand why learning the world... | Rebuttal 1:
Rebuttal: We thank all the reviewers for engaging with our work and providing their precious feedback. In addition to our individual answers, we include the following material:
**Example of generated Code World Model.** Reviewers u7M2 and 3vW3 asked for examples of Code World Models (CWMs), being intereste... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning | Accept (poster) | Summary: This paper studies robust offline RL with function approximation under the specific setting of $d$-rectangular linear DRMDPs, where the nominal environment is a linear MDP with simplex feature space. The authors propose two learning algorithms and establish instance-dependent upper bounds for the suboptimality... | Rebuttal 1:
Rebuttal: We thank the reviewer for your positive feedback on our work. We hope our response fully addresses all your questions.
---
### 1. Requirement of the number of trajectories $K$ to be poly(d,H).
This is an interesting question. We acknowledge that our current analysis requires the sample size $K$... | Summary: This paper proposes minimax optimal and computationally efficient algorithms with linear function approximation in the context of distributionally robust offline RL. The authors incorporate multiple new techniques in theoretical analysis, e.g., variance information, suboptimality and estimation uncertainty dec... | Rebuttal 1:
Rebuttal: We thank the reviewer for your valuable time and effort in providing detailed feedback on our work. We hope our response will fully address all your questions.
---
### 1. Computational tractability.
We would like to note that we discussed the computational tractability of our algorithm on Line ... | Summary: This paper considers the distributionally robust Markov decision process (or sometimes robust MDP in the literature). In particular, it considers the linear RMDP with $d$-rectangularity TV uncertainty set, which decouples the uncertainty set from the state-action pair. Two algorithms are proposed to solve this... | Rebuttal 1:
Rebuttal: We thank the reviewer for your positive feedback on our work. We hope our response will fully address all your questions.
---
### 1. Comparison with Ma et al. (2022).
We acknowledge that Ma et al. (2022)’s work is most closely related to ours. However, we note that there are several technical f... | Summary: This paper presents a theoretical study of distributionally robust MDPs. Their findings show that function approximation is both different and harder in robust RL as compared to offline RL. They show matching information theoretic lower bounds for their novel algorithm.
Strengths: 1. The paper has positioned ... | Rebuttal 1:
Rebuttal: We thank the reviewer for your positive feedback on our work. We hope our response fully addresses all of your questions.
---
### 1. More motivation on practical scenarios where DRMDPs are applicable.
In many real-world applications, the agent only has access to a **single** source domain, whic... | Rebuttal 1:
Rebuttal: ## Overall Response
We would like to thank all reviewers for their insightful and detailed reviews and comments. We have addressed the comments from the reviewers and revised the manuscript accordingly. In the following, we would like to provide overall responses to several common questions rais... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MSPE: Multi-Scale Patch Embedding Prompts Vision Transformers to Any Resolution | Accept (poster) | Summary: 1. They analyze the resolution adaptability in ViT models, identify the patch embedding layer as the crucial component, and provide a low-cost solution.
2. They propose Multi-Scale Patch Embedding (MSPE), which enhances ViT models by substituting the standard patch embedding layer with learnable, adaptive con... | Rebuttal 1:
Rebuttal: Thank you for providing us with your valuable feedback and suggestions. We appreciate your input and have carefully considered your questions. Below, we provide detailed responses to each of them:
> #### **W1: Assuming $\mathcal{X} \sim \mathcal{N}(0, 1)$ in Image Processing**
We completely agre... | Summary: The paper introduces Multi-Scale Patch Embedding (MSPE), a novel approach to enhance Vision Transformers (ViTs) by allowing them to adapt to variable input resolutions without resizing images to a fixed resolution. MSPE uses multiple variable-sized patch kernels and selects the optimal parameters for different... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We have carefully considered your questions and would like to address them as below:
> #### **W1: Experiments of Positional Encoding Strategies**
Thanks for your comment. MSPE employs the vanilla position encoding approach, a learnable $(N, DIM)$ vector where... | Summary: This paper proposes to substitute the standard patch embedding with multiple variable-sized patch kernels. This eliminates the need to resize the original image. Extensive experiment results are shown to demonstrate the benefits.
Strengths: The problem is well defined and the proposed method is sound. Convinc... | Rebuttal 1:
Rebuttal: Thank you for providing valuable feedback and suggestions. We greatly appreciate your input and completing the necessary experiments. Here is our detailed response to your questions:
>**Q: Experimental Results on More Than 10x Resolution**
Our method can be directly applied to high-resolution im... | Summary: The paper aim to address the challenge of adapting ViTs to variable input resolutions, which is a critical issue often overlooked in real-world applications. The authors propose a new method named Multi-Scale Patch Embedding (MSPE), which enhances the patch embedding layer by incorporating multiple variable-si... | Rebuttal 1:
Rebuttal: We greatly appreciate your insightful comments and suggestions, as they have been helpful in refining and enhancing our work. We have thoroughly reviewed all of your points and have addressed your concerns as outlined below:
> #### **W1: Technical Contribution**
Thank you for your kind feedback.... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their detailed and perceptive comments, which have significantly refined and improved this paper.
We are grateful that the reviewers appreciate our paper in various aspects, including its well-defined problem and theoretical analysis [tsgU, RdsV, 9L8r], simple... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
UMB: Understanding Model Behavior for Open-World Object Detection | Accept (poster) | Summary: The paper presents Understanding Model Behavior (UMB), a framework for Open-World Object Detection (OWOD) that not only detects unknown objects but also analyzes the decision-making with text attributes. UMB first prompts large language models to generate text attributes. Then, it models the empirical, in-dist... | Rebuttal 1:
Rebuttal: We sincerely appreciate the valuable feedback you provided on our work, particularly regarding the comparisons with recent works, the ablation experiments, and the content discrepancies. Below is our detailed response to each of your comments:
**Comparison with Recent Works**: The references [1]... | Summary: This paper aims to understand the model’s behavior in predicting the unknown category. First, the authors model the text attribute and the positive sample probability, obtaining their empirical probability, which can be seen as the detector’s estimation of the likelihood of the target with certain known attri... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable feedback on our work, particularly regarding the concerns related to the detailed technical design. Below is our detailed response:
**OOD Score**: To detect unknown objects, we proposed a method that models the probability of an object being predicted as fore... | Summary: This paper proposes a new solution for the challenging task of Open World Object Detection (OWOD) by exploring the reasons behind non-classification and then using the textual attributes of unknown objects to infer the most similar known category. Evaluation results on multiple real-world application datasets ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback, which will help us improve the quality of our manuscript. Below, we address each of the points raised.
**Limitation Discussion**: We appreciate the reviewer's suggestion to enhance the limitation discussion with visualizations of failure exam... | Summary: This paper introduces a new open-world object detection model (UMB) aimed at understanding the model's behavior when predicting unknown categories. By modeling text attributes and positive sample probability, the paper proposes a joint decision-making method based on empirical probability, in-distribution prob... | Rebuttal 1:
Rebuttal: We greatly appreciate your valuable comments on our work, particularly your concerns about similarity verification, resource consumption, and deployment. Here are our detailed responses:
**Similarity Verification**. Ideally, we would like to annotate every unknown object in the RWD dataset and id... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable feedback, which has been instrumental in guiding us to improve our work. In the attached document, we have provided additional experiments, including examples of detection failures (Figure 1), a comparison of more metrics (Table 1), further ablation studies (T... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Aligning Model Properties via Conformal Risk Control | Accept (poster) | Summary: The paper connects conformal risk control to define prediction sets containing results that satisfy a property. In short, assume that function $f$ (a trained model) does not satisfy some property $\mathcal{P}$, we can define a prediction interval around results of $f$ s.t. those intervals satisfy $\mathcal{P}$... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for a positive and confident assessment of our paper and appreciate the clear attention to detail shown by the reviewer. Both the strengths and weaknesses/questions/limitations make it evident to us that the reviewer invested significant time understanding the c... | Summary: This article offers an alternative approach to alignment by testing whether trained models follow specific properties. This is done, for instance, on monotone or concave functions. A small modification for the use case is made to the Conformal Risk Control setting to allow vectorial parameters. Using CRC and p... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for what we believe is an overall positive assessment of our paper. We especially appreciate that they find the topic interesting and our approach novel. We also agree this is a standout strength of this work. Additionally we thank the reviewer for observing tha... | Summary: The authors propose a way to solve the problem of alignment in Machine Learning using Conformal Risk Control. They first expand the previous work of conformal risk control to multidimensional parameters $\boldsymbol{\lambda}$, then used this extension to propose a way to test if a function belongs to a certain... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their positive assessment of our paper. We appreciate the recognition of the solid theoretical foundations, the clarity of our methodology, and the overall readability of the paper. We also value the opportunity to address your concerns and clarify our contr... | Summary: This paper proposes a method to post-process a pre-trained model to align with a subset of hypotheses on which the specific desired behaviors can be attained. The proposed method relies on proximity oblivious testers to give detection for the misalignment, based on which a conformal risk control process is use... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their time and effort in reviewing our paper and providing valuable feedback. We appreciate that the reviewer acknowledges the generality and importance of the problem we considered, as well as the flexibility of our approach concerning the properties that c... | Rebuttal 1:
Rebuttal: We thank all reviewers for their positive feedback and critical assessment of our paper. We aim to address remaining concerns and reinforce the contributions of our work. This response focuses on key points that may have been underemphasized, aiming to further convince the reviewers who gave us Ac... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Representational Learning | Accept (oral) | Summary: The paper introduces the $r$-loopy Weisfeiler-Leman ($r$-$l$WL) test, an innovative hierarchy of graph isomorphism tests, and the corresponding GNN framework, $r$-$l$MPNN. This new approach extends the counting capabilities of previous algorithms, specifically allowing the counting of cycles up to length $r+2$... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review, for acknowledging the originality and rigor of our paper, and for voting to accept. We address each point individually below. “W/Q” numbers the weakness or question, followed by our response.
---
> **W1**: “Some of the mathematical proofs are compl... | Summary: The paper proposed a hierarchy of graph isomorphism tests and a corresponding GNN framework, $r$-$\ell$-MPNN while showing the ability to count homomorphisms of cactus graphs.
Strengths: The strengths of the paper are:
* The ability to count homomorphisms of cactus graphs without any additional explicit subst... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review, acknowledging our contributions, and voting to accept our paper. We address each point individually below. “W/Q” numbers the weakness or question, followed by our response.
---
> **W**: “The weaknesses of the paper is that while the paper is well w... | Summary: In this paper, the authors propose a loopy version of the Weisfeiler-Lehman (WL) algorithm. This version utilizes an extended notion of neighborhood, incorporating paths between standard neighboring vertices to update vertex coloring. By parameterizing the length $r$ of these paths, we obtain $r$-$l$WL. The re... | Rebuttal 1:
Rebuttal: We thank you for your thorough review and valuable suggestions. We include a theoretical and experimental comparison with $k$-OSAN in an updated manuscript and believe the points below address the Reviewers' questions adequately.
---
> **W1**: “The motivation for focusing on cactus graphs is unc... | Summary: This introduce $r$-loopy Weisfeiler-Leman, a new hierarchy of graph isomorphism test and a corresponding GNN framework. It achieves good cycle counting power and surpuss $k$-WL in some cases. The power of r-lWL is examined in various synthetic and real-world datasets.
Strengths: 1. Strong theoretic results wi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review, acknowledging our contributions, and voting to accept our paper. We address each point individually below. “W/Q” numbers the weakness or question, followed by our response.
---
> **W**: “More datasets [1, 2] can be included to evaluate expressivity... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Stability and Generalization of Adversarial Training for Shallow Neural Networks with Smooth Activation | Accept (poster) | Summary: The paper studies generalization bounds for two-layer networks trained using several variants of adversarial training. The main results are a bound on the stability of the algorithm, which in turn provides a bound on the generalization and the robust accuracy.
Strengths: - As far as I know, the results presen... | Rebuttal 1:
Rebuttal: ### W1: Regarding $m\geq \eta^2 T^2$
Reviewer has a correct understanding of why stability holds, i.e., the change of the weights when doing GD is very small compared to the number of neurons. We utilize the weakly convex robust loss (see Lemma 4.1) as well as $m\geq O(\eta^2 T^2)$ to establish ... | Summary: Adversarial Training is a popular method to train models that enhance robutness to adversarial examples. In recent years, a lot of papers study the generalization of adversarial training in various models. In this paper, the authors study the generalization of adversarial training for a special shallow neural ... | Rebuttal 1:
Rebuttal: ### W1: The model studied in this work is a special neural network. By fixing the weights of last layer, it has only one layer of trainable parameters, it is unclear if the results shown in this special neural network can be generalized to regular neural netowrk.
It is known since the early 90s t... | Summary: This work studies the stability of adversarial training in two-layer neural networks in binary classification problems. The authors study gradient-descent-based adversarial training, with nearly-optimal adversarial perturbations, in two-layer neural networks with a frozen second layer, and they obtain guarante... | Rebuttal 1:
Rebuttal: ### W1: Detached from practice?
Our is a theoretical result and theory always lags practice and rarely matches the practice perfectly. Having said that, we would like to remind the reviewer that it is known since the early 90s that training neural networks is computationally hard, even for two l... | Summary: This paper uses uniform stability to analyze adversarial training on wide shallow networks when the adversarial perturbations are $\beta_1$-optimal. Assuming there exists a robust network near initialization, in expectation the best network iterate has test loss that scales with $1/\sqrt{T}$. The results for G... | Rebuttal 1:
Rebuttal: ### W1: The bounds are in expectation and not high probability bounds.
We can give high probability bounds based on [r1], which relates high probability generalization bounds with algorithmic stability, but they are looser than the bounds in expectation. We had those originally in the paper but... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Optimization Algorithm Design via Electric Circuits | Accept (spotlight) | Summary: This paper addresses the design of new optimization algorithms (centralized and distributed) which lend themselves better to theoretical analysis than existing optimization algorithms, which are tailored more towards establishing fast worst-case convergence guarantees. The novel part is that the authors borrow... | Rebuttal 1:
Rebuttal: Thank you for your positive review and thoughtful questions.
We are glad that the reviewer found our framework to be "quite useful for convergence analysis of optimization algorithms".
**W1.**
We thank the reviewer for this precise point. In the revised paper, we will clarify (in the abstract ... | Summary: This paper presents a methodology of designing an electric circuit whose continuous-time dynamics converge to the solution to its corresponding optimization problem (Theorem 2.2). Furthermore, the paper presents the discretization scheme of the continuous-time dynamics, generating convergent optimization algo... | Rebuttal 1:
Rebuttal: Thank you for the positive and constructive feedback. We are glad that the reviewer found our "unified framework of implementing various optimization problems into an electric circuit" as a strength of the paper.
**W1.**
Thank you for bringing these references to our attention. We will include... | Summary: This paper proposes the use of RLC circuits to design optimization algorithms. The authors prove that circuit dynamics in continuous time converge to the solution of the optimization problem. By specifically designing the RLC components, this approach recovers many existing algorithms. The authors also introdu... | Rebuttal 1:
Rebuttal: Thank you for the constructive comments. We are pleased that you found our framework to be "a novel and interesting perspective on algorithm design." Additionally, we're glad that you recognized our automatic discretization package as a strength of our work.
**W1.**
To clarify, the role of th... | Summary: This paper presents a novel framework for designing optimization algorithms with electric RLC circuits. It contains two stages:
1. design an appropriate circuit whose equilibrium is the solution to the optimization problem.
2. discretize the continuous-time dynamics of the circuit to form a discrete-time algor... | Rebuttal 1:
Rebuttal: We are happy to hear that the reviewer found our proposed framework "novel" and "practically useful".
**W1.**
This is further addressed below.
**W2.**
The assumption of strong convexity is made to prove the well-posedness of the circuit ODE. We clarify that this assumption is not necessary f... | Rebuttal 1:
Rebuttal: # Common Response
We thank the reviewers for their thoughtful comments and suggestions. We are pleased that the reviewers generally find our framework novel and valuable. We address the reviewers' specific questions in the individual responses. | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Spectral-Risk Safe Reinforcement Learning with Convergence Guarantees | Accept (poster) | Summary: This paper considers spectral-risk safe RL algorithm with convergence guarantee. In particular, the paper considers a constrained MDP approach where the objective is to maximize the expected cumulative reward subject to the constraint that the spectral-risk measure is below a certain threshold. The main challe... | Rebuttal 1:
Rebuttal: Thanks for the detailed review and valuable feedback on our paper.
We appreciate the effort to review our work.
Below is our response to the reviewer's comments.
**Weakness (convergence rate):**
Thanks for pointing out the need for convergence rate analysis.
We have addressed the convergence rat... | Summary: This paper proposes a spectral risk measure-constrained RL algorithm, called spectral-risk-constrained policy optimization (SRCPO). This algorithm leverages the duality of spectral risk measures and treats the risk-constrained RL problem as a bilevel optimization. In this bilevel optimization problem, the oute... | Rebuttal 1:
Rebuttal: Thanks for the thorough review and insightful comments on our paper.
We appreciate the effort to review our work.
The following is our response to the reviewer's comments.
**Weakness 1 (inner and outer problems)**
As the reviewer commented, handling risk by separating the given problem into th... | Summary: In this paper, the authors have considered the framework of risk-constrained reinforcement learning (RCRL) by tackling risk-measure-based constraints. The non-linearity of risk meaures makes the convergence of RL schemes challenging. In this paper, a spectral risk measure-constarined RL algorithm is proposed.... | Rebuttal 1:
Rebuttal: Thanks for the valuable feedback on our paper. We appreciate the effort in reviewing our work. We have carefully considered the reviewer's comments. Below, we address the concerns the reviewer has raised:
**Q1 (performance gap):**
In the general response above, we analyzed the performance gap c... | Summary: The paper provides the new spectral-risk-costrained policy optimization algorithm, that uses the duality of spectral measures, and bilevel optimization approach to address the risk constrained reinforcement learning problem, solving the inner primal problems and outer dual problem. The paper provides global co... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thanks for the positive review and valuable feedback on our paper. We appreciate the effort in reviewing our work. Below, we respond to each of the points mentioned.
**Weakness 1 (line 88 and 168)**:
$F\_X$ is the cumulative density function (CDF) of the random variable $X$. We w... | Rebuttal 1:
Rebuttal: # General Response
We appreciate all the reviewers for their insightful comments and suggestions.
In this response, we will address the common concern raised by the reviewers: convergence rate analysis and performance gap.
**Convergence rate**
We will analyze the convergence rate of the propos... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Leveraging Hallucinations to Reduce Manual Prompt Dependency in Promptable Segmentation | Accept (poster) | Summary: The paper introduces an innovative approach called ProMaC (Prompt-Mask Cycle) to improve promptable segmentation. The primary goal of ProMaC is to reduce the dependency on instance-specific manual prompts by leveraging hallucinations from Multimodal Large Language Models (MLLMs) to generate more accurate and t... | Rebuttal 1:
Rebuttal: We thank the reviewer for valuable feedback and for appreciating the idea of our method, strong justification of each component of our framework and extensiveness of our experiments. Following are the responses regarding your concerns.
> *Inconsistent Performance: In scenarios where hallucination... | Summary: The paper focuses on promptable segmentation, aiming to minimize the need for manually designing prompts. Specifically, it explores the hallucination issue in MLLMs and finds that hallucinations can reveal valuable contextual information, which could be largely beneficial to promptable segmentation tasks, espe... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive evaluation of our work and for acknowledging the originality of the proposed method as well as its significance for future research. We are glad that the reviewer finds the results impressive and the idea of this paper is quite interesting.
> *It would be be... | Summary: The paper introduces the Prompt-Mask Cycle generation framework (ProMaC), which innovatively uses hallucinations from Multimodal Large Language Models to refine segmentation prompts and masks. This method contrasts with traditional approaches by leveraging rather than eliminating hallucinations, enhancing task... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive evaluation of our work and for acknowledging the value and insight of the proposed method.
> *Could they provide comparison results between the predictions of the ProMaC method and the ground truth across iterations? This would offer a more visual representa... | Summary: The paper introduces an iterative Prompt-Mask Cycle generation framework (ProMaC) with a prompt generator and a mask generator. The prompt generator uses a multi-scale chain of thought prompting, initially exploring hallucinations for extracting extended contextual knowledge on a test image. These hallucinatio... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading and insightful comments. Following are the responses regarding your concerns.
> *The authors should clearly explain how this paper utilizes the hallucinations.*
As we explained in Line 143-150, we utilize hallucination to bootstrap a scene-understand... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their uniformly positive evaluations and valuable feedback. We appreciate fruitful suggestions of the reviewers that helped to improve the overall presentation of our work. We are encouraged by the positive comments from reviewers for the following: (i) The idea of t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SpikeReveal: Unlocking Temporal Sequences from Real Blurry Inputs with Spike Streams | Accept (spotlight) | Summary: The SpikeReveal paper uses a blurry RGB image and leverages pulse data captured by a spike camera of the corresponding scene to guide the image deblurring task. In terms of network design, the authors cascade a blind-spot network for denoising, a super-resolution network, and a deblurring network, employing a ... | Rebuttal 1:
Rebuttal: We are grateful for your detailed feedback and suggestions, which have helped us identify key areas where our manuscript can be improved.
***1. [Definition of t_s] There is an issue with the definition of t_s in line 109. When the first spike occurs, there is no previous spike to reference. Ther... | Summary: The work focuses on improving image sharpness from blurry inputs using spike cameras with high-motion capture rates. Addressing limitations of supervised learning in real-world scenarios, the authors introduce a self-supervised framework for spike-guided motion deblurring. Validation through extensive experime... | Rebuttal 1:
Rebuttal: Thank you for dedicating your time to provide constructive criticism and recommendations for our article.
***I'm curious about how the order of applying BSN (Blur to Sharp Network) and EDSR (Enhanced Deep Super-Resolution) influences model performance. Specifically, I wonder if this sequence coul... | Summary: This work proposes a spike-guided self-supervised image deblurring algorithm that combines the high spatial resolution of RGB cameras with the high temporal resolution of spike cameras to obtain sharp RGB images in real-world scenarios. The self-supervised network addresses performance degradation issues found... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and for pointing out potential issues and improvements in our paper.
***1. While many existing image-based deblurring algorithms perform adequately, the introduction of spike cameras presents challenges such as aligning the two modalities. The authors have no... | Summary: This paper combines the RGB camera and the spike camera for image deblur. The key contributions consist a self-supervised learning framework for deblur and a real-world dataset RSB.
Strengths: This paper presents a novel self-supervised framework for image deblur with spike camera. The network design is inter... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort you have taken to review our manuscript.
***1. The originality is marginal. The whole framework is similar to [36]. Please clarify more on the difference between this paper and [36].***
Paper \[36\] is an ICCV23 publication on a self-supervised evnet-b... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive comments and positive feedback. We are pleased that our paper has been recognized as "well-written" [G1PY,VbQb] with a "pioneering approach" [RXBh] and our network design found "interesting" [G1PY]. Our proposed S-SDM is acknowledged for effectively ad... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Evaluating the design space of diffusion-based generative models | Accept (poster) | Summary: This paper analyzes both the training and sampling errors of diffusion models. The analysis sheds light on two practically relevant design choices, which are the noise level weighting (during training) and discretization (during sampling).
Strengths: - Figure 1 presents a clear qualitative picture of how to c... | Rebuttal 1:
Rebuttal: We greatly thank the reviewer for all the helpful comments. Sorry for confusions created by our initial submission. We hope the itemized responses below, can do a better job in making things clearer, and will further accomplish this goal in a revision.
> All the theoretical results are very hard ... | Summary: This work develops theoretical bounds on the training error and sample complexity of score-based diffusion models, which further imply a complete convergence analysis (by combining the both). In addition, the derived bounds shed light on the efficient hyper-parameters selection, including re-weighting function... | Rebuttal 1:
Rebuttal: We deeply thank the reviewer for all the helpful comments and sincerely appreciate the positive evaluation. Here are our itemized responses:
> W1
We sincerely appreciate your comment and have done a major revision of the paper in accordance.
> W2
Thank you for this advice. It is in general h... | Summary: This work studies the training and sampling process and then achieves the end-to-end analysis for diffusion models. For the optimization process, this work uses an over-parameterized NN and gradient descent to prove the convergence rate. For the sampling process, this work provides VESDE results and explains t... | Rebuttal 1:
Rebuttal: We greatly thank the reviewer for all the helpful comments. We especially appreciate the comment "...the first one to analyze the optimization process with a deep NN...the first step to explain the “bell shaped” weight...". Please kindly see our itemized replies below:
>Weakness 1
We agree that t... | Summary: This paper provides a full error analysis (considering both training and sampling) for a class of score-based SDE diffusion models, and using the results to understand why and when certain time and variance schedules are preferred.
Strengths: - The theoretical contributions of the paper are excellent
- Overal... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for all the helpful advice and comments and deeply appreciate the positive evaluation.
>Weakness 1: The stated theorems are quite dense and are not easy to parse...
We greatly appreciate the comment and will add more intuitive explanation or informal versions of ... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for the helpful comments. We have improved our convergence theory, and the new version now uses a much more relaxed assumption, where the original version $$d=\Theta(m)$$ is replaced by
$$d=\Omega({\text{poly}}(\log (nN))).$$
Note in the old version, the i... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Articulate your NeRF: Unsupervised articulated object modeling via conditional view synthesis | Accept (poster) | Summary: This paper presents an unsupervised method to learn the pose and part-segmentation of articulated objects with rigid parts using conditional view synthesis. Their approach learns the NeRF of both states and extracts 3D voxels for optimizing the pose and segmentation.
Strengths: Using 3D Voxel instead of mesh ... | Rebuttal 1:
Rebuttal: Thanks for reviewing our paper.
1. **Alternating optimization:** ```This method separates the optimization of pose and segmentation, first optimizing the pose and then optimizing the segmentation. In my opinion, this is not as effective as jointly optimizing them, since the segmentation results c... | Summary: This paper introduces a novel method for decomposing articulated objects and predicting their articulation. The pipeline is trained without supervision. Initially, a "static NeRF" of the object's initial state is obtained. The method then employs part-aware rendering to optimize the pose-change tensor and obje... | Rebuttal 1:
Rebuttal: Thanks for reviewing our paper.
1. **Artifacts for visualization:** ```There are still many artifacts that can be seen in the visualization results```
The artifacts during rendering are indeed caused by the imperfect segmentation in the NeRF space, though they do not harm the pose and joint esti... | Summary: This paper presents an unsupervised framework that jointly learns articulations and part segmentations of objects with rigid parts from multi-view images.
Specifically, they proposed a two-stage approach. In the first stage, a static NeRF is fitted to one of the object states. In the second stage, the optimiz... | Rebuttal 1:
Rebuttal: Thanks for reviewing our paper!
1. **Problematic heuristic**: ```Using pixel difference as the heuristic for tagging moving parts is potentially problematic```
We do not use RGB pixel values but tag the moving parts based on the opacity difference (the alpha channel for RGBA images), which won’t... | Summary: The paper proposes a method for modeling articulated objects with neural radiance fields (NeRF). It employs a stage-wise training schema, first building a NeRF of the object in a reference configuration. Then a segmentation in parts and relative pose-changes are learned in an alternating fashion. A modified re... | Rebuttal 1:
Rebuttal: Thanks for the review!
1. **Typos and other errors**: Thanks ! We will fix them in the final version.
2. **Clarity:** ```explaining the steps in Section 4.2, provide dimensions of matrices U, F, the formula in L.167-168 ... how the viewpoint v' is used```
We use homogeneous coordinates for repr... | Rebuttal 1:
Rebuttal: # Global comment
We sincerely thank all reviewers for their valuable and insightful comments. We are particularly encouraged by the positive feedback received and appreciate the opportunity to address the concerns raised. We are happy to address these common issues comprehensively in the following... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Computational Aspects of Bayesian Persuasion under Approximate Best Response | Accept (poster) | Summary: The paper considers the problem of BP under \delta-best responses of the receivers. This means that there might be multiple actions that are BR to a specific signalling scheme. This creates non trivial problems of the algorithmic problem of computing the optimal signalling scheme. The paper provides poly-time ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments.
**Need for a new reduction**: the fundamental reason that the two problems are not necessarily "comparably hard" is that the computational complexity depends crucially on (1) the representation and (2) additional special structure. It is certainly true tha... | Summary: This paper studies a variant of the Bayesian persuasion problem where, instead of best-responding, the receiver $\delta$-approximately best responds to the sender. Specifically, upon receiving a signal, the receiver takes the $\delta$-optimal action that is worst for the sender on the induced posterior belief.... | Rebuttal 1:
Rebuttal: Thank you for your detailed and insightful comments.
**Suggestions**: Thank you for the helpful suggestions, we will revise our paper accordingly.
**Running time of the QPTAS**: the running time generally depends on the specific algorithm chosen to solve the LP given in Theorem F.1. Algorithms ... | Summary: This paper studies the Bayesian Persuasion problem under the condition that the receiver may respond suboptimally. The authors provide a few computational results on the problem from its computational hardness to the approximation algorithms.
Strengths: The paper considers an important and realistic problem o... | Rebuttal 1:
Rebuttal: Thank you for your comments.
We respectfully disagree with the view that our paper is "almost a copy-paste" of the work by Gan et al., and that the results are "identical". While we recognize the high-level similarities between Stackelberg games and Bayesian persuasion, we believe that the commu... | Summary: This paper studies Bayesian persuasion settings under approximate best response, where the receiver may choose suboptimal actions based on their beliefs. The authors develop efficient algorithms to compute an (almost) optimal sender commitment. First, they show the failure of the revelation principle. Furtherm... | Rebuttal 1:
Rebuttal: Thank you for your detailed and insightful comments.
**QPTAS and hardness not explained**: we agree this is suboptimal. We made a hard choice here due to the strict page limit. There is a high-level description of the QPTAS in Appendix F. We find the proof of the hardness result particularly i... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Separate and Reconstruct: Asymmetric Encoder-Decoder for Speech Separation | Accept (poster) | Summary: This paper proposes a novel transformer based architecture, sepreformer, for speech separation based on a siamese decoder network that operates on separated speech signals in the encoded space.
Strengths: A major strength of the paper is it's state of the art performance on a variety of datasets, showing as h... | Rebuttal 1:
Rebuttal: **Q1: The first major weakness is the presentation quality of the paper. Starting with the abstract, which goes right into a discussion of feature length and computation without setting up the problem and overview. I think a lot of the high level picture is missing, especially related to the parti... | Summary: This paper presents a novel approach to time-domain speech separation, departing from the conventional chunk-based dual-path processing. The authors introduce an asymmetric encoder-decoder architecture, where the encoder analyzes features and splits them based on the number of speakers. A Siamese decoder recon... | Rebuttal 1:
Rebuttal: **Q1: Transformer Usage: While the use of Transformer blocks is highlighted, similar architectures have been successfully employed in previous works like Sepformer, raising questions about the uniqueness of this contribution.**
A1: Thank you for your comment. We agree that the usage of Transforme... | Summary: The paper proposes SepReformer, an efficient time-domain separation network. The model is an encoder-decoder architecture that splits the output features of the encoder based on the number of speakers before feeding them to the decoder. Both encoder and decoder networks are comprised of transformer blocks that... | Rebuttal 1:
Rebuttal: **Q1: Testing on clean data. It would be interesting to see how the proposed model performs on noisy datasets.**
A1: Thank you for your comment. We performed experiments on noisy and noisy-reverberant datasets in Table 4 to show our models generalizability power. Please refer to it.
**Q2: Evalu... | Summary: The authors propose a neural network architecture to separate a speech mixture containing 2 speakers. The proposed U-net based architecture replaces the inter and intra chunk processing - a popular method for speech separation - with global and local attention mechanisms. They further propose a mechanism to r... | Rebuttal 1:
Rebuttal: **Q1: The authors have shown good results on a bunch of datasets, and the separated audios in the supplemental file are of high quality. But all these results are based on simulated data, so it makes you wonder how well the model would do with real data. It’d be great if the authors could show how... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their helpful comments and suggestions. We sincerely appreciate the time and effort they have dedicated to reading and commenting on the paper. Below, please find our point-by-point response to all the comments. We believe that it is greatly improved by... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Noise-Aware Differentially Private Regression via Meta-Learning | Accept (poster) | Summary: The paper proposes a meta learning gaussian dp algorithm. It uses the same framework as ConvCNP but replaces the encoder during train and meta test with a noisy encoder obtained by adding gaussian noise. The paper is not particularly well written and I have trouble following the notation in the main paper and ... | Rebuttal 1:
Rebuttal: > The delta value of 10^-3 is large compared to the literature. Setting it at 1/N puts it into the well-known privacy violating regime where one can return a random record with no noise, violating the privacy of someone with probability 1.
This would be true if we considered any $(\epsilon, \delt... | Summary: This paper equips meta learning framework with DP guarantees. Specifically, datasets are split into context and target subsets respectively, where an encoder learns good representations from abundant context data, and is able to generalize to a limited amount of target data that may be sensitive. Since the out... | Rebuttal 1:
Rebuttal: > I am not familiar with the meta-learning framework (and related works), so it's likely I misunderstood some parts and had confusion about the meta-testing part.
Meta-learning has two phases, meta-training and meta-testing. You should view meta-testing as analogous to supervised learning: the in... | Summary: Authors propose DPConvCNP, a meta-learning model with a functional DP mechanism. This model is a modification to the SetConv procedure, applying clipping and work from [Hall et al. 2013] with a tighter Gaussian mech. analysis from [Dong et. al 2022] to conduct a sensitivity analysis and privatize the algorithm... | Rebuttal 1:
Rebuttal: > Q1 : My main question/concern with the work is how important is leveraging a powerful hyperparameter tuning library (like BayesOpt from Optuna) to adjust hyperparameters for the empirical performance of the proposed method DPConvSet?
We only use Optuna and BayesOpt on the DP-SVGP baseline. We d... | null | null | Rebuttal 1:
Rebuttal: Some reviewers had questions regarding how meta-learning differs from standard supervised learning, and the consequences of these differences from the point of view of privacy. To summarise, meta-learning has two phases, meta-training and meta-testing. Meta-testing is analogous to training in supe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Enhancing Chess Reinforcement Learning with Graph Representation | Accept (poster) | Summary: The paper introduces a new variant of AlphaZero, AlphaGateau, with a neural network architecture based on GNNs, that allows the model to generalize across board sizes. The new architecture outperforms the original AlphaZero architecture under certain conditions.
Strengths: The paper follows a promising direct... | Rebuttal 1:
Rebuttal: # Remarks
> the paper does not provide any information on how the two models compare when they are fully trained. AlphaGateau seems to reach a performance plateau within 100 steps, but the AlphaZero model trained by the authors would likely keep on improving for orders of magnitude more training ... | Summary: The paper explores a novel approach to reinforcement learning for Chess by utilizing a graph-based representation of the game state instead of the traditional grid-based representation. This method is based on GNNs and aims to overcome the limitations of CNNs used in previous models like AlphaZero. Specifica... | Rebuttal 1:
Rebuttal: > 1. How does AlphaGateau compare to other graph neural network-based reinforcement learning models, such as those presented by Ben-Assayag and El-Yaniv (2021) [1]?
We are not aware of GNN-based RL models that can be applied to chess besides a slightly adapted version of ScalableAlphaZero (from B... | Summary: The authors demonstrate a GNN that works on chess and is amenable to generalization.
Strengths: # originality
This is the first chess GNN approach that performs well that I'm aware of in the literature, and requires some invoations in the edge representation.
# quality
The results look good, although the ... | Rebuttal 1:
Rebuttal: # Remarks
> The results look good, although the computational limitation of the work mean that a larger test would be good to demonstrate that this model scales up.
We are aware that the reduced scale compared with the original AlphaZero is an issue, we have since ran a fine-tuning experiment wi... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and thoughtful opinions.
As shown by reviewer abiC, we were unclear in our terminology and cause confusion when compared to Silver et. al. (2017) with regards to what we called steps, leading to a first impression of our experiments being smaller than they ac... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
RandNet-Parareal: a time-parallel PDE solver using Random Neural Networks | Accept (poster) | Summary: This paper proposes a new method for sequentially predicting and correcting numerical simulations by introducing random neural networks. The RandNets are single-layer feed-forward neural networks and only the output layer is for training. The numerical experiments have shown the improved training efficiency an... | Rebuttal 1:
Rebuttal: We would like to thank you for recognizing that our method shows improved training efficiency and scalability compared to the baseline methods. We also appreciate you acknowledge that our paper is well-written and well-organized, contains theoretical developments, and exemplifies RandNet-Parareal ... | Summary: This paper proposes a method to accelerate the simulation of partial differential equations (PDEs) by converting them into systems of ordinary differential equations (ODEs). It then utilizes a framework that merges the random neural network and the parareal approach, termed RandNet-Parareal. For validation, th... | Rebuttal 1:
Rebuttal: We would like to thank you for acknowledging that our method succeeds in accelerating the solutions of complex PDEs. We appreciate you recognize the efficacy of our approach and see no limitations beyond those we mentioned in the paper.
In the paragraphs below, we replied in detail to your questi... | Summary: The authors introduce a numerical algorithm that computes the solution to a large system of ordinary differential equations (ODE) "parallel in time". The main idea of the solver is based on the existing "Parareal", which introduces parallelism by running a sequential, fast, and inaccurate ODE solver and then c... | Rebuttal 1:
Rebuttal: We thank you for the critical assessment of our work. We identified these main criticisms: (a) lack of a computational complexity study; (b) comparison to a GP with full kernel and lack of alternatives to the GP solution; \(c\) use of a suboptimal fine solver _F_. We addressed (a) conducting a det... | Summary: The paper introduces their method RandNet-Parareal, which is a method to solve differential equations. Their method can be categorized as a Parallel-in-time technique that aims at parallelizing solvers in the temporal domain. They extend the Parareal algorithm by using RandNets which learn the difference betwe... | Rebuttal 1:
Rebuttal: We appreciate that you recognize how the proposed method yields a significant speedup compared to competing ones. We believe to have successfully replied and tackled all your questions (Q) and weaknesses comments (W). We hope that you reconsider your score based on the new provided information.
... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their constructive feedback and suggestions. We are happy to read that the paper "is well-written and well-organized" (awZW), "very well-written" (Eu5i), "contains clear descriptions and robustness study" (Eu5i) and that the proposed method is a "new approa... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Pearls from Pebbles: Improved Confidence Functions for Auto-labeling | Accept (poster) | Summary: This paper introduces a threshold-based auto-labeling (TBAL) method called Colander to maximize TBAL performance by finding the optimal labling confidence function and thresholds. In order to find the optimal confidence function, Colander treats the auto-labeling objective as an optimization problem that maxim... | Rebuttal 1:
Rebuttal: We appreciate your feedback and acknowledgment of the paper's strengths. Our response:
**Clarification on thresholds**
The vector $\mathbf{t}$ denotes the thresholds over $k$ classes. $T^{k}$ stands for the space of threshold vectors $\mathbf{t}$. Because we solve a relaxed version of the optimi... | Summary: This paper proposes a novel auto-labeling method, called Colander. In contrast to existing works, Colander models the objective of finding an optimal confidence function as a constrained optimization problem (the confidence function should have maximum coverage whilst obtaining a sufficiently low error rate; b... | Rebuttal 1:
Rebuttal: Thank you for your insightful review and for recognizing the key strengths of our paper. Our responses:
**Details and effects of hyperparameter selection**
Due to space constraints, we deferred a detailed discussion on hyperparameter search to Appendices C.3 to C.7. We plan to incorporate more ... | Summary: This paper discusses threshold-based auto-labeling functions aimed at identifying a large subset of unlabeled instances where the auto-labeling error remains below a specified threshold. The authors observed that standard temperature-scaled softmax scores are inadequate for effectively thresholding labeled ins... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback and for noting the strengths of our paper. Our responses are the following:
**On ST/AL works in the experiments**
Our response involves the following 3 points:
1. The focus of our paper is to study confidence functions for existing TBAL techniques [1,2,3... | Summary: This paper addresses the challenges of overconfident scores in threshold-based auto-labeling (TBAL) systems. It critiques existing confidence scoring and calibration methods and introduces Colander, a new post-hoc method tailored to optimize TBAL performance.
Strengths: - The paper has a good identification o... | Rebuttal 1:
Rebuttal: Thank you for your positive and encouraging review. We are delighted with your assessment and recognition of our work’s contributions. Our response:
**Dependence on validation data.** Yes, Colander, like other post-hoc methods, relies on validation data to learn the confidence function. In fact... | Rebuttal 1:
Rebuttal: We thank all of the reviewers for their insightful and positive feedback. We have used their suggestions to improve our draft, added experiments, and improved the clarity of our work. Before providing individual responses, we (1) summarize the strengths highlighted by reviewers, (2) provide a pair... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper introduces a Colander for threshold-based auto-labeling (TBAL) pipeline. Colander trains a flexible confidence function using latent information of the classifier model, instead of fixed confidence functions. The optimization problem of Colander is based on the objective of TBAL, that is, maximizing... | Rebuttal 1:
Rebuttal: We appreciate the detailed review. We have updated our work to account for points on notation and writing. Our response:
### Contributions
We believe the reviewer has missed our paper's key contribution, specifically **with respect to prior work on TBAL [1]**. Our work **does not reiterate [1]**. ... | null | null | null | null | null | null |
Implicit Multimodal Alignment: On the Generalization of Frozen LLMs to Multimodal Inputs | Accept (poster) | Summary: The authors propose an exploration of the Implicit Multimodal Alignment (IMA) phenomenon in frozen Large Language Models (LLMs): when exposed to perceptual tokens (e.g. from image or audio features), authors show that those tokens are implicitly aligned on text tokens, even so the LLM has only been trained on ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the detailed feedback on our work. The comprehensive comments reflect a significant effort in reading our paper and providing feedback, which we highly appreciate. We recognize the reviewer's intention to help us further improve our paper and are grateful fo... | Summary: The paper explores how frozen Large Language Models generalize to multimodal inputs without the need for extensive re-training. It introduces the concept of Implicit Multimodal Alignment, which suggests that despite the distinct representations of perceptual and textual tokens, there exists a form of implicit ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the feedback. In the following, we answer the reviewer’s concerns.
## **Weaknesses:**
- **The concept of alignment within neural networks, although well-explored here, does not offer a groundbreaking methodological advance. The novelty lies more in the appl... | Summary: This paper conducts an in-depth study on the generalization capabilities of LLM when handling multimodal inputs without multimodal fine-tuning. It reveals the implicit multimodal alignment (IMA) effect between perceptual and textual tokens within LLMs and finds that this effect is closely related to the model ... | Rebuttal 1:
Rebuttal: We thank the reviewer for very positive feedback about the paper and appreciate finding it novel, supported by an extensive series of experiments and insightful messages. This feedback encourages us to push further for similar interesting work. In the following we try to address all the reviewer's... | Summary: This work aims to understand multi-modality representation within MLLMs. It provides some interesting findings about how LLMs generalize to non-textual tokens and what helps LLMs to generalize to multimodal tokens. Additionally, several implications are proposed based on these findings.
Strengths: 1. The auth... | Rebuttal 1:
Rebuttal: The authors would like to thank the reviewer for the positive feedback and appreciate finding the paper interesting, insightful and well presented. In the following we try to address all the remaining concerns:
## **Weaknesses**
### About experiments on α-SubNet.
- **Clarification about Figure ... | Rebuttal 1:
Rebuttal: - We would like to thank the reviewers for the very detailed and positive feedback. We appreciate finding our work original (vaQc, hn2C, dcgF, 69kn), provide valuable and interesting insights (vaQc, hn2C, dcgF, 69kn), supported by extensive experimentation (hn2C) and well presented (vaQc). We find... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Autonomous Driving with Spiking Neural Networks | Accept (poster) | Summary: Although SNNs have potential of neuromorphic computing in sustainable and safety-critical autonomous technology, they still lack evidence in complex real-world computer vision applications. In this work, the authors propose a unified end-to-end SNN called SAD that consists of three models to generate safe traj... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough and constructive feedback on our manuscript. We appreciate the positive comments on the novelty and relevance of our work, as well as the recognition of its potential impact in the field of autonomous driving. We have carefully considered all the points rai... | Summary: This paper introduces Spiking Autonomous Driving (SAD), an end-to-end spiking neural network (SNN) designed for autonomous driving. SAD integrates perception, prediction, and planning into a unified neuromorphic framework. It achieves competitive performance on the nuScenes dataset and significantly outperfo... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and insightful comments on our manuscript. We appreciate the time and effort you've invested in providing valuable feedback. We have carefully considered your points and are pleased to address them below.
>I wonder if the numbers in your proposed SNNs are spikes... | Summary: The authors adapted ST-P3 into a version with a binary spiking neuron and then stated that autonomous driving based on a spiking neural network can address the energy challenges. The authors stated that this neuromorphic technology can be a step toward sustainable and safety-critical automotive technology.
St... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough examination of our work and their insightful comments. We acknowledge the challenges highlighted in your review and appreciate the opportunity to address them. In the following responses, we aim to clarify our contributions, explain our methodological choic... | Summary: This paper presents an end-to-end SNN model for the autonomous driving to address the energy challenges. This model consists of three main modules: perception, prediction, and planning. The model is evaluated in the nuScenes dataset.
Strengths: 1. This paper introduce the first SNN designed for end-to-end aut... | Rebuttal 1:
Rebuttal: We appreciate the time and effort the reviewer has dedicated to evaluating our work. Your feedback is valuable in helping us improve and clarify our research.
>The novelty is limited. The paper just uses the SNN to autonomous driving and does not provide any special designs.
Response: We appreci... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper presents Spiking Neural Network to reduce the energy consumption by normal neural network in autonomous driving. The network includes end-to-end architecture including perception, prediction and planning. It evaluates the model using nuScenes dataset and achieves comparable performance in three modu... | Rebuttal 1:
Rebuttal: Thank you for your insightful questions regarding the practical implementation of SNN in self-driving systems. While it's true that SNNs are not yet widely deployed in commercial self-driving operations, our research aims to pave the way for their future adoption by demonstrating their viability a... | Summary: This paper introduces a unified Spiking Autonomous Driving (SAD) system based on Spiking Neural Networks (SNNs). The system is trained end-to-end and comprises three main modules: a perception module that processes inputs from multi-view cameras and constructs spatiotemporal bird's-eye views; a prediction modu... | Rebuttal 1:
Rebuttal: We sincerely appreciate the thoughtful comments and questions raised by the reviewers. Your feedback has provided us with valuable insights that will help improve our manuscript. We are grateful for the opportunity to address these points and clarify certain aspects of our work. In the following r... | null | null | null | null |
Robust Guided Diffusion for Offline Black-box Optimization | Reject | Summary: The paper proposed RGD, a novel method for integrating classifier guidance into classifier-free guidance diffusion models for solving offline MBO problems. Experiment results and ablation studies validate that the method outperforms state-of-the-art baselines and each proposed component is resonable.
Strength... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you very much for your thorough and constructive feedback. Your insights are immensely valuable and provide essential guidance as we seek to enhance the quality and clarity of our manuscript. We truly appreciate the time and effort you have invested in reviewing our work, and... | Summary: In this paper, the authors proposed to combine both classifier guidance and classifier-free guidance for offline black-box optimization. In addition, the authors propose a Proxy Refinement procedure by minimizing KL divergence between the Proxy distribution and diffusion distribution regarding $y$.
Strength... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We greatly appreciate your insightful feedback. Your suggestions are crucial for enhancing our manuscript, and we are dedicated to meticulously revising our work in accordance with your recommendations.
## Weakness
> Technical contribution seems to be incremental. Employing diffu... | Summary: The paper proposes a framework called Robust Guided Diffusion for the problem of Offline Black-box Optimization. The key idea is to formulate the solution as conditional generation of high-performance designs using a diffusion model which has explicit guidance from a proxy (surrogate) model. This proxy model i... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your thoughtful feedback. We are committed to incorporating your suggestions in our revisions.
## Weaknesses
> One major premise in the paper is that proxy guidance conditional generation is more robust.
Let's clarify some concepts. Our discussion from Line 26 to 3... | Summary: The paper introduces a robust guided diffusion framework for offline black-box optimization, combining proxy and proxy-free diffusion for conditional generation. Key improvements include proxy-enhanced sampling and diffusion-based proxy refinement to address out-of-distribution issues. Experiments on the Desig... | Rebuttal 1:
Rebuttal: ## General Reply
Dear Reviewer,
Thank you for your valuable feedback. Your insights are instrumental in improving our paper, and we are committed to thoroughly revising our work based on your suggestions.
## WEAKNESSES
> The paper lacks comparison with relevant approaches like ICT [1] and Tri-... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We appreciate your detailed evaluation and insightful comments on our manuscript. Acknowledging your feedback, we have addressed one primary concern highlighted in your reviews within this response.
## Adversarial Sample Identification
> (Reviewer a4Cg) i) Algorithm 1, Line 4, h... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper introduces a new method, named RGD, for Offline Black-box Optimization (BBO). RGD incorporates an improved proxy to guide the previous proxy-free method (i.e. DDOM[4]). Key technical innovations includes (1) improving the robustness of the proxy function against adversarial samples by consistency reg... | Rebuttal 1:
Rebuttal: ## General Reply
Dear Reviewer,
We sincerely appreciate the time and effort you have invested in providing such a constructive review of our manuscript. Your insights and suggestions are invaluable, and we are truly grateful for the guidance you have provided. We are fully committed to carefully... | null | null | null | null | null | null |
Boosting Alignment for Post-Unlearning Text-to-Image Generative Models | Accept (poster) | Summary: This paper presents a novel perspective on model unlearning for text-to-image generative models, considering it a constraint optimization problem. It introduces a new loss function for the unlearning process, which is a combination of the remaining loss and the forgetting loss. The paper's key technical contri... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback. We have provided detailed responses to your questions and concerns below. Should you have any remaining concerns or questions, we would welcome further discussion. If our responses have adequately addressed your initial concerns, we would be grateful if you ... | Summary: This work addresses the issue in generative models where powerful models may be generating harmful or undesired contents that should be unlearned. This work proposes to balance the unlearning objective and the text-image alignment on the remaining data, by identifying a gradient direction that achieves a monot... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback. We have provided detailed responses to your questions and concerns below. Should you have any remaining concerns or questions, we would welcome further discussion. If our responses have adequately addressed your initial concerns, we would be grateful if you ... | Summary: This paper addresses the problem of unlearning in generative text-to-image models. They formulate a training objective that improves upon the commonly-used one that simultaneously minimizes the loss on the retain set while maximizing it on the forget set (referred to as GradDiff here); i.e. gradient descent on... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback. We have provided responses below and hope they clarify the points you raised. Should you have any remaining concerns or questions, we would welcome further discussion. If our responses have adequately addressed your initial concerns, we would be grateful if ... | Summary: This paper tackles the approximate machine unlearning task of the target class and concept removal from diffusion models. This work endeavors to improve the existing literature’s output quality and text alignment after unlearning. Firstly, A concept of the restricted gradient is proposed, allowing monotonic de... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback. We have provided detailed responses to your questions and concerns below. Should you have any remaining concerns or questions, we would welcome further discussion. If our responses have adequately addressed your initial concerns, we would be grateful if you ... | Rebuttal 1:
Rebuttal: **General response**
We thank the reviewers for their thoughtful feedback. We are glad the reviewers find that:
- **Our paper addresses an important problem in machine unlearning** [h3BB, RWKV, DUTP]
- **Our paper is well-written and clearly presented** [h3BB, RWKV, DUTP]
- **Our proposed method... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
UNION: Unsupervised 3D Object Detection using Object Appearance-based Pseudo-Classes | Accept (poster) | Summary: This work presents an unsupervised 3D object detection method named UNION, which exploits LiDAR, camera, and temporal information jointly for generating pseudo bounding boxes to train existing object detectors. In addition, the authors introduce an appearance-based clustering method to generate pseudo class la... | Rebuttal 1:
Rebuttal: We thank you for your valuable feedback.
**Qualitative results of pseudo-bounding box generation**
We provide qualitative results of the intermediate outputs of the UNION pipeline and the final generated pseudo-bounding boxes **in Figure 2 in the rebuttal PDF**.
This figure shows sample 2 from s... | Summary: This paper explores the challenge of unsupervised 3D object detection, introducing UNION. UNION leverages camera, LiDAR, and temporal information jointly to train 3D object detectors without relying on self-training. The approach demonstrates strong performance particularly on the nuScenes dataset. Additionall... | Rebuttal 1:
Rebuttal: We thank you for your valuable feedback.
**The method's novelty**
UNION extracts object proposals by spatially clustering the non-ground points from LiDAR.
Subsequently, the velocity of each object proposal is estimated using self-supervised scene flow, and the cameras are leveraged to encode it... | Summary: The paper introduces UNION, an unsupervised 3D object detection method designed to detect both static and dynamic objects without manual labels. UNION utilizes spatial clustering and self-supervised scene flow to generate object proposals and employs visual appearance encoding to distinguish between static and... | Rebuttal 1:
Rebuttal: We thank you for your valuable feedback.
**Different image encoders**
See global rebuttal text: 'Different image encoders'.
**Cyclist performance for multi-class detection**
From the pseudo-classes of multi-class UNION, there are 1, 1, 3, and 4 pseudo-classes assigned to the cyclist class for ... | Summary: This paper introduces UNION, a novel method for unsupervised 3D object detection that leverages object appearance-based pseudo-classes. This paper addresses the challenge of training object detection models without manual annotations by using spatial clustering and self-supervised scene flow to generate static... | Rebuttal 1:
Rebuttal: We thank you for your valuable feedback.
**First method to do unsupervised multi-class 3D object detection**
Our submission did not compare to [8] as it was not peer-reviewed back then.
Now that it has been published at CVPR'23, we will add the paper to our related work section and Table 1 (the ... | Rebuttal 1:
Rebuttal: We thank the reviewers (R1:LFQD, R2:ZwY5, R3:Gw7r, R4:JvDj) for their valuable and detailed feedback.
We appreciate that our method UNION was overall well-received e.g. 'represents a creative fusion of existing ideas applied to the problem of 3D object detection' (R1), 'is quite novel' (R2), and '... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Transformers need glasses! Information over-squashing in language tasks | Accept (poster) | Summary: This paper presents an in-depth analysis of decoder-only Transformers, focusing on their limitations in handling information propagation. The authors identify two key phenomena: "representational collapse" due to "over-squashing" (line in Graph Neural Networks). These issues lead to a significant loss of infor... | Rebuttal 1:
Rebuttal: We are happy to hear that you believe our paper offers valuable insights into Transformers, has a very strong theoretical analysis, and excellent presentation. We would like to address your questions.
**(Q1)** *Could you please include some statistics of difference of tokens' representations on ... | Summary: The paper provides a theoretical and empirical analysis of the final representations of transformers, revealing the phenomenon of representational collapse.
Strengths: - The paper is well-written.
- It highlights a problem in transformers that causes them to fail on a large set of tasks (assuming this extends... | Rebuttal 1:
Rebuttal: We thank you for pointing out that our paper is well written and that you found it interesting and with real-world relevance. We would like to address your questions on the breadth of tasks affected and on positional encodings.
**(Q1)** *What do the authors believe is the breadth of tasks affect... | Summary: In the paper, the authors first discuss a phenomenon occurring in LLMs that they call "representational collapse". They provide empirical evidence of the phenomenon in state-of-the-art language models and they provide a theoretical justification for it. They then show that decoder-only transformers exhibit wha... | Rebuttal 1:
Rebuttal: We are happy that you found our paper well organized, well written, and the over-squashing phenomenon interesting. We would like to address your questions on positional encodings and on the prompting.
**(Q1)** *[...] it seems to me that they only consider relative positional embeddings and not a... | null | null | Rebuttal 1:
Rebuttal: We are delighted to see that our paper has been well-received by all reviewers, with comments on the high quality of the writing and presentation.
We would like to summarize the improvements we have made to our paper. We have added a supplementary one-page PDF with the additional experiments in o... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Global Distortions from Local Rewards: Neural Coding Strategies in Path-Integrating Neural Systems | Accept (poster) | Summary: This paper develops a model meant to explain recent experimental work showing that the orderly receptive fields of grid cells in the rodent brain can be distorted by spatial landmarks. Here, the model is a biologically-inspired ANN which is trained to path-integrate, with non-uniform weighting across the envir... | Rebuttal 1:
Rebuttal: Thank you for taking the time to read and review our paper. Your feedback was invaluable, as it highlighted that we had not properly emphasized several crucial aspects of our work. In particular, we had not sufficiently explained the generality of our theoretical framework and the fact that our ex... | Summary: The overarching goal of this paper is to provide a theoretical framework to understand the experimentally observed effects of rewards on grid cells, which have historically been known to encode spatial information. To do so, they focus on path-integrating RNNs whose units behave like grid cells post training. ... | Rebuttal 1:
Rebuttal: Thank you very much for your thorough review. We appreciate your attention and address your comments and questions below.
**Main concern: link between theory, and firing patterns observed in piRNN experiments versus real experiments**
Thank you for raising the point that distortions of firing pa... | Summary: The paper investigates the phenomenon of grid cell distortion in rewarded environments, a topic of interest in neuroscience. The authors propose a theoretical framework to understand how the 2D firing fields of grid cells deform while preserving their topological structure in high-dimensional neural space. By ... | Rebuttal 1:
Rebuttal: We thank you very much for the thorough review and for your time. We address your comments and questions below.
**On real neuroscience experiments:**
You make the very important point that linking the theory with “real” data from rodent brains is important to increase the confidence of this work... | Summary: This paper provides a mathematical theory of how spatially local reward distortions can lead to global representational distortions in grid cells.
Strengths: The paper is extremely well-written, self-contained, nicely motivated from biology, and mathematically elegant. As someone who does not know the grid ce... | Rebuttal 1:
Rebuttal: We thank you for the thoughtful review, and address your comment and questions below. We look forward to hearing from you in case you have any additional comments!
**Answer to weakness on Originality/Novelty:** We agree that the originality and novelty of our approach could have been made clearer... | Rebuttal 1:
Rebuttal: We thank the four reviewers for their time and attention. For this rebuttal, we ran additional experiments to draw a more complete picture of the phenomena of firing patterns distortions, and expanded on our theory to address reviewers’ concerns. Along the way, we also found some new exciting resu... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Cluster-Learngene: Inheriting Adaptive Clusters for Vision Transformers | Accept (poster) | Summary: Cluster-Learngene is an innovative approach for initializing Vision Transformer models. It works by inheriting adaptive clusters from a large pre-trained ancestry model. The key features of this method include:
Adaptive Clustering: Cluster-Learngene adaptively clusters the attention heads and feed-forward netw... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive feedback. Below, I will respond to your questions.
> Q1: Lack of comparison to other clustering methods.
R1: Your question is thoughtful. In **Section 4.3.1**, we have compared clustering methods such as k-means and provided different values for th... | Summary: This paper seeks to address the issue of overgeneralizing the applicability of large pre-trained models in deep learning, particularly when faced with task-specific resource constraints. The authors propose Cluster-Learngene, a novel method that clusters critical internal modules from a large ancestry model an... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive feedback. Below, I will respond to your questions.
> Q1: The paper lacks a complexity analysis or specific time cost assessments for Cluster-Learngene, which are crucial for understanding its computational efficiency.
R1: For the largest model, DeiT... | Summary: The paper introduces Cluster-Learngene, a novel approach for initializing Vision Transformers (ViTs). This method clusters attention heads and position-wise feed-forward networks (FFNs) from a large "ancestry" model to form a condensed initialization core, termed "learngene." By leveraging the density characte... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive feedback. Below, I will respond to your questions.
> Q1: Insufficient Downstream Task Evaluation
R1: Thanks for your suggestion. We conduct additional segmentation experiments on the ADE20K [A]. We set the base learning rate to $10^ {−3}$ and trai... | Summary: This paper introduces Cluster-Learngene, a weight initialization method for initializing downstream models of various sizes with pre-trained models. The proposed method is based on Learngene and includes the following two improvements: MSA and FFN centroid, which extract critical parameters and reduce redundan... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive feedback. Below, I will respond to your questions.
> Q1: Section 3.1 and L183-196 seems to have a messy formatting of formulas;
R1: We appreciate the reviewer's keen observation regarding Section 3.1 and lines 183-196. We apologize for any inconven... | Rebuttal 1:
Rebuttal: > Q1: Experiments focused on image classification: The experimental evaluation is primarily on image classification tasks. Testing on a broader range of vision tasks like object detection or segmentation would demonstrate the generalizability of the approach better.
R1: Thanks for your suggestion... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Bandit-Feedback Online Multiclass Classification: Variants and Tradeoffs | Accept (poster) | Summary: The work studies multiclass classification with a finite label set $\mathcal{Y}$ and a model class $\mathcal{H}$ of $\mathcal{Y}$ -valued functions. The analysis focuses on optimal mistake bounds as opposed as optimal regret. In particular, the goal is to find tight relationships between optimal regret bounds,... | Rebuttal 1:
Rebuttal: Response to weakness:
The main goal of the paper is to measure the role of natural and well-studied resources given to the learner/adversary, and such bounds are inherently relative. However, it is well-known that $\mathsf{opt}_{\operatorname{full}}^{\operatorname{det}}(\mathcal{H})$ (and up to... | Summary: This paper studies the mistake bounds of multiclass classification with adversaries. This paper provides the mistake bound gaps between the bandit feedback setting and full information setting, between the adaptive and oblivious adversaries for randomized learners under the bandit feedback setting, and between... | Rebuttal 1:
Rebuttal: Response to weakness #1:
As stated in lines 310-313, we are mainly interested in the experts setting as a means for proving Theorem 1.1. We believe that the paper already explores a sufficient range of problems and variations within the setting of learning hypothesis classes. Therefore, we intent... | Summary: This paper considers online multi class classification in the adversarial setting with a focus on the worst-case expected number of mistakes as a performance measure. The paper studies the effects of information (i.e. feedback provided to the learner), adaptivity (of the adversary) and randomness (of the learn... | Rebuttal 1:
Rebuttal: Contribution of Theorem 1.1:
We first want to make sure that the contribution of Theorem 1.1 is completely clear.
In the summary, the following statement is made with respect to Theorem 1.1: “The results give upper and lower bounds on the mistakes which are tight to logarithmic factors.”
However,... | Summary: This paper studies online multi-class classification in the mistake bound model. It focuses on understanding how various resources affect the optimal mistake bounds of the learner. These resources concern feedback models (bandit feedback vs full information), adversarial models (adaptive vs oblivious), and lea... | Rebuttal 1:
Rebuttal: Response to weakness #1:
It is indeed an interesting open question to find a more natural and efficient algorithm for the experts setting. This question is discussed in detail at the open questions section.
Response to weakness #2:
We agree that some important concepts and discussions appear ... | Rebuttal 1:
Rebuttal: We thank the reviewers for taking the time to carefully read our work, and for their thoughtful comments and suggestions to improve it. We will make our best efforts to incorporate their valuable suggestions in the next version of this paper. We respond to specific issues raised by each reviewer i... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Evidence of Learned Look-Ahead in a Chess-Playing Neural Network | Accept (poster) | Summary: The author examine if a chess NNs encode future moves in their activations. The results suggest that Leela predicts future self-play moves and that the activations can be manipulated to change the predicted moves.
Strengths: # originality
Linear probes for difference concepts are well known in the chess lite... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback!
> looking a puzzles where Leela gets the next move wrong would be a good first step, i.e. what is the accuracy of your move predictor when the model is wrong, or can you patch to get the correct move.
We focus on puzzles that Leela gets right simply so we ... | Summary: **Update after rebuttal:** To me personally, the authors' response clarifies all open questions and misunderstandings and adds interesting new results. I remain convinced that the work is ready for publication and interesting to the NeurIPS audience, which means my score remains 'Accept'.
The paper investigat... | Rebuttal 1:
Rebuttal: Thank you for your careful review and great suggestions!
> Throughout the paper I was wondering why the policy network and not the value network was used.
Great question, there was no particular reason for this choice except that we decided to focus on only one head for the purposes of expositi... | Summary: This paper conducts a mechanistic interpretability analysis of Leela and shows evidence that it learns to look ahead in the network.
Strengths: This paper provides a convincing answer to an important question: are networks like Leela's analogues of System 1-style pure intuition, or do they encode some amount ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback and questions! We answer them below and clarify potential misunderstandings.
> the claim that "look-ahead or other sophisticated algorithms should pick up on the importance of this difference, but shallow heuristics should mostly ignore it." required more ju... | Summary: This paper closely examines, using various forms of activation patching and probes, the inner workings of the state-of-the-art policy network of Leela Chess Zero (for the game of Chess). The authors find several different pieces of evidence that suggest it is likely that the network has learned to carry out so... | Rebuttal 1:
Rebuttal: Thank you for your review and insightful questions!
We agree that “existence proof” was poor wording on our part and will rephrase that to “evidence” in both places, thank you for the suggestion. Thanks also for the minor comments, which we’ll incorporate.
> is it definitively possible to draw a... | Rebuttal 1:
Rebuttal: Thank you for your detailed reviews and suggestions! We’re encouraged that you found the results *“interesting and inspiring”* (abVe), thought the paper gave a *“convincing answer to an important question”* (SpQ5), and said it has *“great experimental work, with convincing evidence, important cont... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper try to discover and analyze the evidence of learned look-ahead in Chess playing network. They take experiments on filtered Chess puzzle dataset and the policy network of Leela chess policy. The claims are mainly analyzed from three perspectives, including Activation patching, attention head analysis ... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and suggestions!
**Policy vs value network (2.3):** Great suggestion, thank you! We have now run all our experiments on Leela’s value head and will include these in the paper. We used the logs odds of the win probability as the effect size for patching experimen... | null | null | null | null | null | null |
CV-VAE: A Compatible Video VAE for Latent Generative Video Models | Accept (poster) | Summary: The paper introduces CV-VAE, a 3D VAE that is trained through latent space regularization using an existing two-dimensional VAE decoder. This design facilitates seamless integration with 2D VAE-based diffusion models. The concept, while simple and straightforward, is notably efficacious and offers practical ut... | Rebuttal 1:
Rebuttal: **W1: Flickering problems of CV-VAE**
Our optimization loss is a trade-off between compatibility and better reconstruction quality. Therefore, there is still a domain gap between CV-VAE and diffusion models, which leads to color shifts or flickering problems. This offset can be alleviated by furt... | Summary: This paper designs a 3D VAE consistent with the 2D VAE, whose output mode can losslessly switch between 2D and 3D while retaining the characteristics of both the 2D VAE and 3D VAE. This allows for obtaining performance similar to the original 2D VAE while increasing the frame count of the 3D output.
Strengths... | Rebuttal 1:
Rebuttal: **W1： More examples to explain the model is not just learning simple replication**
Thank you for your suggestion. **(1)** Due to the size limitation of the paper (50MB), we did not include the text-to-video results as videos in the pdf. The visual results were provied in the supplementary materia... | Summary: This paper proposes a new video VAE, starting with pretrained image VAE. It includes several techniques that are still capable of handling images and do not suffer from significant computational overhead. First, they use pretrained weights of 2D VAE (from Stable diffusion) by using their weights as initializat... | Rebuttal 1:
Rebuttal: **W1: Comparison between recent video autoencoders**
**(1)** There might be a missunderstanding. We have compared with the latest Video VAE in Table 1: VAE-OSP, which was released in May 2024. (2) MAGVIT2 [2] and CMD [3] are SOTA methods, but they have not released their weights. Following the s... | Summary: This paper focuses on a compact video VAE suitable for both image and video generation tasks. The motivation stems from the absence of spatial-temporal compressed continuous latent 3D VAE models for video generation. To effectively utilize 2D VAE and seamlessly integrate image generation models, the authors pr... | Rebuttal 1:
Rebuttal: **W1: Fairness of training and evaluation data**
Thank you for your comments on the fairness. **(1)** Unified training data. Since the VAE models in Table 1 are trained on different datasets and some of them did not release their training and dataset details, such as VAE-OSP, it is difficult to ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful analysis and feedback, we are glad the reviewers find that
* Ours proposed question are valuable
* "Designing a 3D VAE with 2D latent consistent characteristics is very meaningful, and this paper successfully achieves this " - Reviewer U7Gm.
* "... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Globally Convergent Variational Inference | Accept (poster) | Summary: The paper studies an alternative objective for variational inference, the expected forward KL divergence. Under some technical assumptions, convexity is shown, which facilitates global optimization. Moreover, a tractable surrogate objective is presented and it is shown that the approximation error can be made ... | Rebuttal 1:
Rebuttal: Thank you very much for your review. Based on your comments, we will add some clarifying sections to motivate the key intuition further. We aim to address your main points below.
> *How restrictive are the assumptions?*
We believe our assumptions are mild enough to apply to many practical settin... | Summary: This work addresses a common problem of non-convexity while approximating posterior distributions using variational inference. Although using ELBO as a variational objective is popular, this paper considers a forward KL(FKL) divergence objective. Its first main contribution is to show that when the variational... | Rebuttal 1:
Rebuttal: Thank you for your review. Below, we attempt to address your main points.
> *The idea of using an expectation with P(X) also seems interesting...is this idea novel?*
Although it's not novel (e.g. Section 2.1), we do think it is underappreciated. Our main contribution lies in the analysis of this... | Summary: The authors established the global convergence of a particular VI method, which is based on forward KL and variational family parameterized by neural network. The analysis techniques are extended from widely studied NTK for two layer neural network. The authors also conducted experiments to verify the theoreti... | Rebuttal 1:
Rebuttal: Thank you very much for the review. Below, we try to answer your main questions and concerns.
> *What is the difference between the analysis in this paper and existing analysis of two layer neural network in supervised learning setting...which has been widely studied?*
Firstly, let us highlight ... | Summary: The authors study convergence of forward-KL variational inference in the neural posterior estimation (NPE) setting with an exponential family variational distribution, whose natural parameters are produced by a neural network. For this setting, it is known that the forward-KL is convex in the natural parameter... | Rebuttal 1:
Rebuttal: Thank you for the detailed review. We will incorporate your main suggestions as outlined below.
> *The result stated in Lemma 1 is well known...please consider citing a textbook or relevant review paper*
Although we agree that Lemma 1 follows easily from the standard properties of the exponentia... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and helpful comments. We are encouraged by your largely positive feedback and appreciate your thoughts on areas of improvement.
We have responded to each of your points individually in the review-specific rebuttals. In this global rebuttal, we wish to emp... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Conformalized Multiple Testing after Data-dependent Selection | Accept (poster) | Summary: This paper addresses the problem of conformalized multiple testing following data-dependent selection procedures. To manage the distorted distribution resulting from the selection process, the authors propose adapting the calibration set according to the selection rule. Under the assumption that the selection ... | Rebuttal 1:
Rebuttal: > Weakness 1: The methodological novelty is limited: the idea of adapting the calibration set with the selection strategy to retain exchangeability is not very novel. Although dealing with conformal prediction, [1] also constructs the reference set based on the selection rule. The investigated sel... | Summary: This paper proposes a method for multiple testing in the conformal setting that outputs the largest possible rejection set with FDR control contained with a data-dependent selection. The proposed method involves two steps:
(1) constructing selective conformal p-values (i.e., p-values solely use points in the ... | Rebuttal 1:
Rebuttal: > Weakness 1: Comparison with self-consistent adjustment
Thank you for the valuable suggestion. We have incorporated theoretical and empirical comparisons with your proposed method. And these discussions will be added in the future version of our work.
- From the theoretical point of view, we ha... | Summary: This paper considers the problem of sample selection among a pre-specified group. The authors formulate this problem as a multiple testing problem and develop a procedure based on conformal inference, in which special treatment is adopted to find a specific calibration set that is exchangeable to the selected ... | Rebuttal 1:
Rebuttal: > Weaknesses for technical contribution: As introduced in the paper, the technical difficulty of sample selection among the selected units with FDR control lies in (1) constructing valid p-values in the presence of selection and (2) dealing with the dependency between p-values for FDR control. The... | Summary: The authors study the validity of Benjamini-Hochberg like procedure on conformal p-values computed on data selected with a particular rule. Assumptions on the rules (and examples verifying them) are mentioned and guarantees demonstrated in those cases. Experiments on synthetic and classical real data and also ... | Rebuttal 1:
Rebuttal: > Weakness 1: the lack of references to Vovk's work in particular, having studied conformal p-values and conformal testing for a long time.
**To W1**: Thank you for the nice suggestions. We will incorporate more Vovk's work to enhance the clarity of the review. There are several literature we pla... | Rebuttal 1:
Rebuttal: # Response to All Reviewers
Dear reviewers, thanks for your great efforts and valuable comments on our paper! We are glad that the reviewers found our paper "considers an interesting problem", "provides an interesting approach" and "provides extensive theoretical proof". Multiple testing after dat... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
GO4Align: Group Optimization for Multi-Task Alignment | Accept (poster) | Summary: The paper designs a multi-task optimization method, namely GO4Align, to address task imbalance by aligning optimization processes across tasks. It proposes an adaptive group risk minimization strategy, formulated as a bilevel optimization problem where the lower-level optimization is a task grouping optimizati... | Rebuttal 1:
Rebuttal: *We sincerely thank # Reviewer sLBS for their insightful comments. The following mainly addresses the concerns and answers questions.*
---
**1. Clustering methods.**
In our main experiments, we employed standard K-means for instantiation. K-means is a widely used clustering approach that worked ... | Summary: This paper proposes a multi-task optimization method designed to address task imbalance by aligning optimization processes across different tasks. To accomplish this, the authors developed an adaptive group risk minimization strategy, which includes two key techniques: (i) dynamic group assignment, clustering ... | Rebuttal 1:
Rebuttal: *We sincerely thank # Reviewer 9PzS for their insightful comments. The following mainly addresses the concerns and answers questions.*
---
**1. The phenomenon of task imbalance and application scenarios.**
Thanks for your kind suggestion. This phenomenon of task imbalance describes that some ta... | Summary: The paper proposes GO4Aligh, a multi-task optimization approach, which targets the task imbalance issue. Specifically, the authors devise two objectives in multi-task optimization: 1) the first is the dynamical group assignment, which can attribute similar tasks to the same cluster and distinguish different on... | Rebuttal 1:
Rebuttal: *We sincerely thank # Reviewer AXsM for their insightful comments. The following addresses their concerns and provides answers to their questions.*
---
**1. GO4Align versus FAMO.**
We thank the reviewer for the comment. GO4Align and FAMO are both strong candidates for handling task imbalance in... | Summary: This paper presents an approach for multi-task learning aiming to reduce interference among tasks by adaptive loss weighting. Instead of task-specific weights as in existing works, the authors proposed to group similar tasks and all tasks in the same group share the same weight. The paper also includes a new d... | Rebuttal 1:
Rebuttal: *We sincerely thank # Reviewer FqZ5 for their insightful comments. The following addresses their concerns and provides answers to their questions.*
---
**1. Motivations.**
Thank you for acknowledging the two technical contributions to our paper. We will clarify the motivations for both in the ma... | Rebuttal 1:
Rebuttal: Dear Reviewers and Area Chair:
We sincerely thank you for your time, insightful suggestions, and valuable comments. We are encouraged by your support and positive reviews of our work:
+ Neat/innovative idea of adaptive task grouping in MTO **[# Reviewers FqZ5/AXsM]** ;
+ Manuscript well-written/s... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Private Algorithms for Stochastic Saddle Points and Variational Inequalities: Beyond Euclidean Geometry | Accept (poster) | Summary: This paper studies private SSP beyond Euclidean geometry. They prove a near optimal bound on SP-gap for geometry between $\ell_1 ,\ell_2$. This result is then extended to SVI.
Strengths: The results are solid improvement over previous work. The method on overcoming the generalization issue is novel and intere... | Rebuttal 1:
Rebuttal: 1. The exact instantiation of $\mathcal A_{emp}$ we use is given by Algorithm 2, Stochastic Mirror Prox. Lemma 3 shows that the implementation satisfies the needed relative accuracy guarantee. We will add the following comment to the ``Algorithm Overview'' section, line 179, to make this more clea... | Summary: This paper is quite far from my area, so please consider this review accordingly.
The paper addresses the problem of private Stochastic Saddle Points and Variational Inequalities. The primary contribution is extending previous work that focused solely on the L2/L2 setup to more general lp/lq settings, where t... | Rebuttal 1:
Rebuttal: We provide a definition of monotone operators in the preliminaries section, line 122. We can use the extra page allowed in the final version to provide more background.
With regards to the contribution of our work, while some algorithmic changes are needed in comparison to [BGM23], we emphasize t... | Summary: This work studied stochastic saddle point and variational inequality problems in potentially non-Euclidean cases. For stochastic saddle point problems, they proposed a recursive regularization framework, and provided the convergence guarantee and sample complexity for convex-concave problems. They further ext... | Rebuttal 1:
Rebuttal: 1. Assuming the parameter space is bounded is very common in SSPs due to the problems unconstrained domains incur. For example, even for simple bilinear losses, say $f(w,\theta) = \langle w, \theta \rangle$, an unbounded domain means the strong gap is *infinite* at any non-zero point.
2. It is in... | Summary: The authors study differentially private algorithms for stochastic saddle point (SSP) problems and stochastic variational inequalities (SVI). The proposed method relies on recursive regularization approach and obtain near optimal rates for settings were the parameters of interest are constrained to be in a bou... | Rebuttal 1:
Rebuttal: 1. We will add the following text to the ``Algorithm Overview'' paragraph (line 179), as well as additional comments:
*The saddle point problem defined in each round of Algorithm 1 is solved using some empirical subroutine, $A_{emp}$. This subroutine takes as input a subset of the dataset, $S_t$... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
BoostAdapter: Improving Vision-Language Test-Time Adaptation via Regional Bootstrapping | Accept (poster) | Summary: This paper proposes a training-free test-time adaptation approach for vision-language models. It combines the idea of entropy minimization with a training-free adaptor to enhance adaptation performance. The experimental results generally demonstrate the effectiveness of the proposed method.
Strengths: 1. The ... | Rebuttal 1:
Rebuttal: **Q1, Q9, Q12. Technical insights and difference with existing baselines.**
Please refer to Q1 in Global Response.
**Q2, Q3, Q4. Figure 2 (b), Figure 3 and typo.**
Thanks for your advice. We will revise the figures, rewrite the corresponding part, and fix the typo in the revision.
**Q5. Ablat... | Summary: This paper studies the problem of test-time vision-language model adaptation. The authors devise training-free method by maintaining a key-value memory for feature retrieval from both historical and boosting samples. The boosting samples are drawn from regional bootstrapping and capture the knowledge of the te... | Rebuttal 1:
Rebuttal: **Q1. Technical insights and differences with existing baselines.**
Please refer to Q1 in Global Response.
**Q2, Q5. Computation overhead and efficiency.**
Please refer to Q2 in Global Response.
**Q3, Q11. Number of augmented views.**
The analysis of the augmented views can be found in Tabl... | Summary: The paper focuses on gradient-free test time adaptation of CLIP model with ViT-B/16 and ResNet-50 backbones on out-of-distribution datasets. The authors take inspiration of augmentations from gradient-based test time methods and incorporate this concept of augmentations in gradient-free and memory (cache) base... | Rebuttal 1:
Rebuttal: **Q1. Computation overhead and efficiency.**
Please refer to Q2 in Global Response.
**Q2. Specific Datasets.**
As shown in Figure 5 in the appendix, BoostAdapter benefits from boosting samples to capture fine-grained knowledge of the test samples. We further provide more qualitative results o... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable feedback and are encouraged by the positive comments on our contributions, including
1. Soundness and Novelty:
- Training-free adaptation broadens real-world applicability (Reviewer a236).
- Innovative use of sample augmentations in the cache for... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search | Accept (poster) | Summary: This paper proposed ReST-MCTS* to assist the large language model to answer reasoning questions. A variant of MCTS, which utilizes the evaluation of current state as the value function, is employed to
automatically annotate the process reward of each intermediate node via sufficient times of rollouts. A self-... | Rebuttal 1:
Rebuttal: Thank you for acknowledging our contribution to LLM reasoning and raising valuable concerns and questions about various aspects of our work. We appreciate the time and effort you have dedicated to thoroughly assessing our work. To address your concerns and questions, we now provide a detailed re... | Summary: This paper proposes a novel approach for self-training large language models (LLMs) that combines process reward guidance with Monte Carlo Tree Search (MCTS). This method generates high-quality reasoning traces and per-step values to train policy and reward models, eliminating the need for manual annotation. E... | Rebuttal 1:
Rebuttal: Thank you for acknowledging our contribution to LLM self-training, clear definitions, and theoretical support and raising valuable concerns and questions about various aspects of our work. We appreciate your dedicated time and effort to thoroughly assess our work. We provide a detailed response to... | Summary: This paper introduces a novel approach for self-training large language models (LLMs) called ReST-MCTS*. This method integrates process reward guidance with Monte Carlo Tree Search (MCTS) to collect high-quality reasoning traces. These traces are then used to train policy and reward models without relying on m... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and concerns regarding novelty, the design of reward/value/PRM, and related aspects of our paper. We genuinely appreciate your dedicated time and effort to thoroughly assess our work. We have carefully considered your comments and have made the necessary respon... | Summary: The paper introduces ReST-MCTS*, a novel framework for self-training LLMs using MCTS combined with process reward guidance. The core innovation is in addressing the limitations of traditional self-training methods, which often include incorrect intermediate reasoning steps despite producing correct final answe... | Rebuttal 1:
Rebuttal: Thanks a lot for acknowledging the strengths of this work as an innovative self-training method, robust theoretical foundations, and extensive benchmarks. We have given a detailed discussion with related work, clarity issues, and the performance of SC.
```
W1: Concerns on lack of comparison betwe... | Rebuttal 1:
Rebuttal: Dear ACs and reviewers,
thank you very much for your valuable feedback. We list the main issues raised by reviewers and explain them below.
```
The motivation and reason behind the design of ReST-MCTS*.
```
First, the main reason **we design a different reward and value for MCTS lies in the defe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
NeuroClips: Towards High-fidelity and Smooth fMRI-to-Video Reconstruction | Accept (oral) | Summary: The paper introduces NeuroClips - a framework for reconstructing high-fidelity videos from fMRI data. It combines pixel-level and semantic-level visual learning through perception and semantic reconstruction pathways. The Perception Reconstructor (PR) ensures smoothness and consistency by creating a rough, con... | Rebuttal 1:
Rebuttal: We sincerely appreciate your careful review. In particular, you have provided us with more than twenty constructive suggestions and insightful questions, which is extremely precious at a time when the quality of reviews in the community is deteriorating. We value each of your suggestions and provi... | Summary: The proposed framework NeuroClips introduces a strong pipeline for fMRI-to-Video reconstruction in the field of Brain Visual Decoding. The Perception Reconstructor(PR) maintains the motion of the video and the Semantic Reconstructor(SR) ensures the semantic information of the video. Multi-fMRI Fusion raises up... | Rebuttal 1:
Rebuttal: We would like to thank you for taking the time to review our work. We value each of your suggestions and provide the following responses:
> **Weakness 1: Light Variations**
We highlight that **light variations** are also a feature that distinguishes video from images. Our initial hypothesis was ... | Summary: This paper proposes NeuroClips, a framework that decodes high-fidelity and smooth video from fMRI. NeuroClips uses a semantics reconstructor for video keyframes to ensure semantic accuracy and consistency, and a perception reconstructor for capturing low-level perceptual details, ensuring video smoothness.
St... | Rebuttal 1:
Rebuttal: We would like to thank you for taking the time to review our work. Your effort has ensured that our submission received adequate attention and review. We address the questions and clarify the issues accordingly as described below.
> **Weakness 1: NeuroClips Fails to Accurately Follow the Ground T... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers, AC, and SAC for their valuable time and selfless dedication. We are very pleased to see that the reviewers recognize the quality of our presentation (mcBd, oPVf), consider our experiments solid and extensive (mcBd, oPVf), and approve the novelty or soundness of Ne... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Off-policy estimation with adaptively collected data: the power of online learning | Accept (poster) | Summary: This paper presents an approach to estimating linear functionals of the reward function in contextual bandit settings from adaptively collected data. They consider the class of augmented inverse propensity weighted (AIPW) estimators and prove guarantees about the quality of the estimator in terms of the qualit... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed reviews. Below are point-by-point responses. We hope the reviewer can increase the score if our responses address your concerns.
1. Re "There is significant missing discussion of relevant related work on finite sample approaches for off policy evaluation in... | Summary: The paper investigates the challenge of estimating a linear functional of the treatment effect from adaptively collected data, commonly found in contextual bandits and causal inference studies. It introduces finite-sample upper bounds for the mean-squared error (MSE) of augmented inverse propensity weighting (... | Rebuttal 1:
Rebuttal: We thank the reviewer for the suggestions. Below are the point-by-point responses. We hope the reviewer can increase the score if the weakness listed in the review has been improved.
1. Re "it would be helpful if the authors could include some simulation experiments to verify the theoretical re... | Summary: This paper study the off-policy problem in the sequential decision setting with adaptively collected data. The authors propose to use augmented Inverse propensity weighting estimator to estimate the policy value and conduct extensive theoretical analysis on the estimator, including variance and mean square err... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments. Below are our point-by point responses to better position our paper and explain our contributions. We hope the reviewer can increase the score if the confusion about the paper and its contributions has been resolved.
1. Re "I am confused about the claim of... | null | null | Rebuttal 1:
Rebuttal: Please check the attached PDF file for preliminary experimental results to corroborate our theory in the paper.
Pdf: /pdf/634369da97f80b3c1a2b7cbd01f2762023100dad.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Beyond the Doors of Perception: Vision Transformers Represent Relations Between Objects | Accept (poster) | Summary: The paper aims to answer how Vision Transformers (ViTs) perform tasks requiring visual relational reasoning. The study focuses on two tasks: identity discrimination and relational match-to-sample (RMTS), showing that ViTs process information in two distinct stages: perceptual and relational. Further analyses u... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review! Find point-by-point replies below:
**Weaknesses**:
1. “Related work”: We completely agree! We will use the extra space in the final submission to expand our literature review section to include more insights from both the (vast) cognitive science literature a... | Summary: This paper examines how vision transformers (ViTs) process visual relations, focusing on same-different tasks. The paper finds that pretrained ViTs fine-tuned on these tasks often develop a two-stage processing pipeline: a perceptual stage that extracts object features into separate representations, followed b... | Rebuttal 1:
Rebuttal: Thank you for your detailed review! Find point-by-point replies below:
**Weaknesses**:
1. “Toy settings”: Fair point! To address this, we created a realistic same-different dataset using 3D models of objects and used it to evaluate our models, similarly to [1]. See Figure 2 in the supplemental PD... | Summary: The paper uses techniques from mechanistic interpretrability to analyze the algorithms implemented by pretrained ViTs to solve abstract visual reasoning tasks. The authors use two synthetic same-different tasks: discrimination and relational match-to-sample (RMTS), to analyze CLIP pretrained ViTs, DINO pretrai... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review! Find point-by-point responses below:
**Weaknesses**:
1. “Simple relations”: This is fair! With respect to “higher order relations”, we attempted to explore this using the Relational-Match-to-Sample (RMTS) task—an explicitly hierarchical version of the discrim... | Summary: This work studies ViTs' learning behavior with relational tasks by experimenting on 2 same-different tasks: discrimination and RMTS tasks. And the authors propose a dataset to analyze. And discovers that there are 2 stages of attention processing of CLIP ViTs by attention scores from patches to other patches.... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and questions. Find point by point responses below:
**Weaknesses**:
1. “...Literature review for context…”: We completely agree. Upon acceptance, we will use the extra space to expand our literature review section to include more insights from both the (vast... | Rebuttal 1:
Rebuttal: We thank all of the reviewers for leaving thoughtful, high-quality comments and questions on our manuscript. Here, we address some common themes found in multiple reviews, and also list all of the additional analyses that we have performed (or are planning to perform) to address any outstanding co... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Visual Prompt Tuning in Null Space for Continual Learning | Accept (poster) | Summary: This paper introduces the orthogonal projection into the visual prompt tuning for continual learning, which comprehensively considers the full operations of a transformer layer on the interference problem. Moreover, two sufficient consistency conditions for self-attention and an invariant prompt distribution c... | Rebuttal 1:
Rebuttal: **1:** The symbol "$\Rightarrow$" may cause confusion in Eq. (8). Our intention was to convey that the left equation can be *simplified to yield* the right equation, rather than the left equation *being a sufficient condition* for the right one. We will correct this to ensure a more precise expre... | Summary: This paper introduces the orthogonal projection into the visual prompt tuning for continual learning, which comprehensively considers the full operations of a transformer layer on the interference problem. They propose two sufficient consistency conditions for the self-attention and an invariant prompt distrib... | Rebuttal 1:
Rebuttal: **1:** In VPT-Deep [13], the output tokens corresponding to the input prompts of the current ViT layer will be replaced by new trainable prompts in the next ViT layer. Therefore, we do not need to compute the output tokens corresponding to the input prompts. According to the forward propagation of... | Summary: This work aims to eliminate the interference on previously learned knowledge for visual prompt tuning in the field of continual learning, so that catastrophic forgetting can be mitigated. To this end, it analyzes the conditions for keeping the output features unchanged in the transformer block that features th... | Rebuttal 1:
Rebuttal: **1:** We do not simplify Eq. (10) directly because it leads to difficulty in deriving the solution expressed in terms of $\Delta\mathbf{P}$. Eq. (10) introduces non-unique solutions and a quadratic term of $\Delta\mathbf{P}^2$, which is explained in detail as follows.
According to Eq. (3), we de... | Summary: This paper proposes a novel paradigm for continual learning based on prompt tuning. By deriving the constraints for orthogonal projection of prompt gradients in ViTs, the method aims to minimize forgetting during the learning process. Experiments on four benchmarks show that NSP² achieves superior performance ... | Rebuttal 1:
Rebuttal: **1:** In theory, it seems that orthogonal projection methods do not have the merits of backward knowledge transfer. However, this problem can be alleviated by two techniques in our approach. **(1)** We adopt a plasticity enhancement strategy which employs a hyper-parameter $\bar{\eta}$ to control... | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable feedback, with three reviewers (W3C1, 2TYW and 4B73) strongly supporting our work. We are encouraged that reviews think our paper:
- **the idea in this paper is novel and interesting** (by Reviewer W3C1);
- **the theoretical proof is solid** (by Reviewer 2... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Provable Benefit of Cutout and CutMix for Feature Learning | Accept (spotlight) | Summary: This paper offers a theoretical explanation for the effectiveness of two practically useful algorithms—Cutout and Cutmix—by applying typical feature learning analysis to multi-patched feature and noise data. The authors present negative results for ERM as a comparison and demonstrate positive results for Cutou... | Rebuttal 1:
Rebuttal: We would like to express our appreciation to the reviewer for your valuable and constructive comments. In the following, we address the points raised by the reviewer.
## **W1 & Q1. The use of smooth activation and generalization to non-smooth activation**
We note that our activation function di... | Summary: This work theoretically investigates why patch-based data augmentation methods for image recognition, namely, CutOut and CutMix, improve performance based on the framework by [Zou+ICML23].
Specifically, they showed that when a CNN is trained with CutOut and CutMix, it can focus on rare and extremely rare featu... | Rebuttal 1:
Rebuttal: We would like to express our appreciation to the reviewer for your valuable and constructive comments. In the following, we address the points raised by the reviewer.
## **W1. Confusion on the notions of features**
We have noticed that the notions of common features, rare features, and extremel... | Summary: - This paper investigates the effectiveness of patch-level data augmentation techniques, specifically Cutout and CutMix, in training two-layer neural networks.
- The study compares vanilla training, Cutout training, and CutMix training using a feature-noise data model.
- The findings show that Cutout can lea... | Rebuttal 1:
Rebuttal: We would like to express our appreciation to the reviewer for your valuable and constructive comments. In the following, we address the points raised by the reviewer.
## **W1. It only analyzes Cutout and CutMix**
As the reviewer would also agree, a complete theory is not built in a single day. W... | Summary: The paper aims to provide a novel theoretical insight into the training dynamics of data augmentation methods such as Cutout and Cutmix. It also supports theoretically why Cutout and Cutmix perform better than ERM by showing that the ERM training is unable to learn rare and extremely rare features and that ERM... | Rebuttal 1:
Rebuttal: We express our gratitude for your valuable comments. In the following, we address the points raised by the reviewer.
## **W1, Q1. The definitions of features**
Please refer to our global response. We describe these features in Section 2.4, but we acknowledge that discussion near Definition 2.1 ... | Rebuttal 1:
Rebuttal: Dear reviewers,
We express our gratitude for your time and valuable comments.
Before addressing concerns/questions raised by individual reviewers, we would like to re-emphasize the main intuition behind our theoretical framework and findings.
## **Motivation for feature and noise in our data di... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper theoretically analyzed the benefits of two data augmentation techniques, Cutout and CutMix, for feature learning of two-layer and two-neuron convolutional neural networks. The authors considered an ideal data distribution in high dimension with one feature patch, one dominant noise patch, and some b... | Rebuttal 1:
Rebuttal: We express our gratitude for your time and valuable comments. We also thank you for bringing to our attention the typo in (4) and unclear statement in Lemma B.3 (we intended to claim the “existence” of such $\gamma_s^{(t)}, \rho_s^{(t)}$ for each training method). We will fix/clarify this in our n... | null | null | null | null | null | null |
Graph Diffusion Policy Optimization | Accept (poster) | Summary: The paper studies the problem of learning graph diffusion generative models on arbitrary non-differentiable objectives using policy gradients. Authors argue that the recently proposed DDPO technique doesn't work well on the discrete, graph-related learning tasks and consider a modified objective and a correspo... | Rebuttal 1:
Rebuttal: Thank you for your insightful review and valuable questions. Below, we respond to the comments in ***Weaknesses (W)*** and ***Questions (Q)***.
***W1: Other RL techniques***
Thank you for your suggestions. Due to the multi-step generation process characteristics of DPMs, directly estimating mod... | Summary: This paper introduces graph diffusion policy optimization (GDPO), a policy gradient method for optimizing graph diffusion probabilistic models with respect to non-differentiable reward signals. By establishing the connection between a T-step denoising process and a T-step Markov Decision Process (MDP), policy ... | Rebuttal 1:
Rebuttal: Thank you for your insightful review and valuable suggestions. Below, we respond to the comments in ***Weaknesses (W)*** and ***Questions (Q)***.
---
***W1: Experimental settings***
Thanks for your suggestions. We will continue to polish our introduction to experimental settings, moving import... | Summary: This paper introduces Graph Diffusion Policy Optimization (GDPO), a novel approach to optimize graph diffusion models for arbitrary objectives using reinforcement learning. The key contributions are:
1. Formulating the denoising process of graph diffusion probabilistic models (DPMs) as a Markov decision proce... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and valuable suggestions. Below, we respond to the concerns raised in ***Weaknesses (W)*** and ***Questions (Q)***.
---
***W1: Theoretical Analysis, Scalability, and Failure Cases***
Thank you for highlighting these points. Despite considerable efforts, th... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers,
We thank all reviewers for their constructive feedback, and we have responded to each reviewer individually. We have also uploaded a **Rebuttal PDF** that includes:
$\\textrm{\\color{blue}Figure A}$: Some visualizations of graph data generated based on Reviewer gTVc's suggest... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Finding Transformer Circuits With Edge Pruning | Accept (spotlight) | Summary: The paper proposes Edge Pruning, a novel algorithm for circuit finding. They claim that it compares favourably to prior methods on GPT-2 small in terms of circuit metrics like faithfulness. They also claim it scales to the 13B model size. Finally, they apply their model to circuit-finding in a 13B model, and p... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and feedback. We are happy to find that you found our method original, our experiments convincing and the potential impact high. We respond below to some of the points raised in the review.
> KL divergence v/s Logit Difference for evaluation
We actually evaluat... | Summary: In this paper, the main focus lies in finding "transformer circuits" to perform a variant of structured pruning on transformers. While generic structured pruning removes neurons, the authors claim that it is a too-coarse approach and propose "edge pruning", where one output wired to more layers can be wired ju... | Rebuttal 1:
Rebuttal: Thank you for pointing us to the literature on Channel Pruning and NAS! We agree this is relevant and will include a discussion of these papers in our next draft. However, we believe there has been a misunderstanding regarding the goal and key contributions of our paper, which we would like to cla... | Summary: The authors propose "Edge Pruning" as an effective and scalable method for automatic circuit discovery. Edge Pruning consists of learning a binary mask over the edges of the computation graph in a transformer neural network. Edge pruning performs favorably compared to the prior art and the authors demonstrate ... | Rebuttal 1:
Rebuttal: Thank you for leaving a thoughtful and constructive review. It endears us to learn that you found our method novel, and our case study a good demonstration of its utility. We take this opportunity to respond to some of your questions here.
> Other methods on CodeLlama
We couldn’t run ACDC on Cod... | Summary: The paper proposes a method to automatically discover circuits within trained transformer models that are responsible for its behavior on a specific task. The automatic discovery of those circuits forms the first step to interpret the trained model. The transformer is visualized as a graph with each attention ... | Rebuttal 1:
Rebuttal: Thank you for providing valuable feedback and suggestions. We are happy to read that you found our method interesting and useful, and the methods convincing. We would like to respond below to some of the questions and concerns raised.
> Circuits may have varied behavior on tasks or input datapoin... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful comments. We are happy to see that the reviewers found our method interesting, the experiments convincing, and the potential impact high.
At the same time, two questions were raised by multiple reviewers. We would like to address them here.
> Can Edge ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Temporal-Difference Learning Using Distributed Error Signals | Accept (poster) | Summary: In the paper, the authors propose a novel reinforcement learning algorithm. The algorithm is based on Q-learning and uses a biologically inspired design to work with local error signals and updates, eliminating the need for the biological implausible backpropagation. The proposed algorithm, Artificial Dopamin... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their constructive feedback and detailed questions, and we’re glad to hear that you appreciate the novelty, clarity, and elegance of our work.
To address your specific questions and concerns:
**Clarifications on Section 3.1.**
We understand how this may be co... | Summary: This paper addresses the distributed credit assignment problem in biological reinforcement learning, where a naive implementation of backpropagation is implausible. The authors show that it is possible to update action values using a variant of the forwward-forward algorithm specialized for RL. They then valid... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their insightful feedback, and we’re grateful for the opportunity to improve and clarify our work through answering your detailed questions and concerns.
**There is a long tradition arguing that it is actually the dorsal striatal subregions (caudate and putamen... | Summary: In this work, the Authors aimed to propose a computational model that is consistent with classical functions assigned to select regions in the reward system in the brain. To this end, they build upon George Hinton’s Forward-Forward algorithm, extending it with the ability to robustly generate continuous (inste... | Rebuttal 1:
Rebuttal: Thank you for the review. We noticed that this review is nearly identical to an earlier review we received at a different conference. Building on the previous feedback, we have significantly updated our manuscript with additional experiments, ablation studies, and expanded discussions, including n... | Summary: An novel, biologically plausible RL algorithm based on DQN and Fast-Forward called Artificial Dopamine is introduce. The algorithm is inspired by Dopamine pathways in the human brain and uses synchronous, locally computed per-layer TD errors for training. Additionally, a novel recurrent network architecture is... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their constructive feedback, and we’re glad to hear that you appreciate the significance, scientific rigor, and clarity of our work.
We have fixed the typo on L116, and reworded L134 as “…in many practical applications with large or continuous state spaces…” to... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning Cut Generating Functions for Integer Programming | Accept (poster) | Summary: The paper is concerned with the interplay of learning theory and the branch-and-cut algorithm for solving mixed-integer programs (MIPs).
Concretely the authors analyse the problem of cut selection.
In general, cut selection asks for a given (class of) MIPs: What cut(s) should be added to the lp relaxation, to ... | Rebuttal 1:
Rebuttal: We greatly appreciate your thorough review and the thoughtful feedback. Regarding the experimental evidence, we acknowledge that our paper focuses primarily on theoretical aspects, and thus, the experimental section is somewhat preliminary. First, we agree that it was poor scholarship on our part ... | Summary: This paper studies the learning of generic classes of cut generating functions, which can be used as an algorithmic tool for solving integer programming problems. The paper presents a handful of cut generating function families, studies the learning complexity of these functions, and presents a computational s... | Rebuttal 1:
Rebuttal: Thank you very much for the thoughtful and encouraging review. Section 5 is meant to illustrate that our analysis extends to the setting where one wishes to select cutting planes tailored to instances using, say, a neural network mapping from instances to cutting planes. This was done in a more ge... | Summary: This work presents sample complexity results for learning parameters for certain classes of cut generating functions, along with some numerical experiments. These are functions that determine coefficients of cutting planes to help solve mixed-integer programming problems, and some of the most effective cutting... | Rebuttal 1:
Rebuttal: Thank you very much for the insightful review and providing detailed feedback. Regarding your questions:
1. This is an good point. The two families of cut-generating functions considered in this paper are indeed parametrized geometrically. We will include additional explanations and figures to cl... | null | null | Rebuttal 1:
Rebuttal: The attached PDF includes figures of a 2-dimensional cut generating function, related to reviewer yCxh's question (point 2).
Pdf: /pdf/ac03b8b7624a88381b4ac2449a951c09eece8a19.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Any2Policy: Learning Visuomotor Policy with Any-Modality | Accept (poster) | Summary: This paper aims to enable robots to understand tasks and their environments using multiple modalities such as text, audio, images, video, and point clouds. To accomplish this, the authors introduce Any2Policy, a versatile framework designed to process multi-modal inputs at the instruction and observation level... | Rebuttal 1:
Rebuttal: We thank reviewer for the comments and reference reviewer with identifier 5TLy as R4. Comment n of reviewr m is denoted as RmCn.
**[R4C1]**
> While the authors have conducted extensive ablations on real-world experiments, replicating these results in practice may be challenging for follow-up rese... | Summary: This paper proposes the simultaneous fusion of image, text, point cloud, video, and audio—four modalities—in robotic manipulation tasks, while also considering the integration of information from both instruction and observation. Through the transformer architecture and cross-attention mechanism, embodied alig... | Rebuttal 1:
Rebuttal: Rebuttal:
We thank reviewer for the comments and reference reviewer with identifier RRf1 as R3. Comment n of reviewr m is denoted as RmCn.
**[R3C1]**
> The technical contribution of this paper is limited. Although the paper analyzes some difficulties in the process of fusing multimodal informa... | Summary: This submission develops a new model that can handle many different modalities as input for instruction and observations (video, text, image, pointcloud etc.). Different modalities are encoded via different frozen encoders (with projection layers kept trainable) to a shared representation to then be passed int... | Rebuttal 1:
Rebuttal: We thank reviewer for the comments and reference reviewer with identifier QY6x as R2. Comment n of reviewr m is denoted as QY6x.
**[R2C1]**
> The performance of AnyPolicy looks very similar to EmbodiedGPT in the Franka kitchen setting in Figure 4. The Figure 4 caption appears to be wrong
Thank ... | Summary: The paper aims to enhance the generalizability of robotic agents by enabling them to handle tasks using diverse modalities such as text, audio, images, and point clouds. The authors introduce a multi-modal system named Any-to-Policy, which utilizes a versatile modality network to adapt various inputs and conne... | Rebuttal 1:
Rebuttal: We thank reviewer for the comments and reference reviewer with identifier h8z5 as R1. Comment n of reviewr m is denoted as RmCn.
**[R1C1]**
> More details on the datasets.
We concur with the reviewer's suggestion to provide additional details in our paper. To address this, we have included in t... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Improving Neural Network Surface Processing with Principal Curvatures | Accept (poster) | Summary: The paper promote to use the principal curvature as the surface presentation that can be better used in the modern neural network architectures. To support their hypothesis, the paper compares three different representations: HKS, SHOT descriptor and extrinsic coordinates in the use by PointNet++, Delta Net an... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the time they have spent on reviewing our work.
However, we are puzzled by several statements they have made, making it difficult to give a meaningful rebuttal. We will respond, point by point, as best as we can:
> "... [section 3] takes up too much spac... | Summary: This paper proposes to use surface curvatures as input to neural networks to improve performance on 3D tasks. The main hypothesis is that as the curvatures are intrinsic properties of the surface, they will enable more effective learning for the relevant tasks. The paper carries out some evaluation comparing p... | Rebuttal 1:
Rebuttal: We thank the reviewer for their work and time.
We clarify misunderstandings and respond to the questions raised below:
> "The main hypothesis is that as the curvatures are intrinsic properties of the surface, they will enable more effective learning for the relevant tasks."
The idea that curva... | Summary: The paper proposes to use curvature instead of previous surface descriptors in neural networks that process shapes. They show that the principal curvatures and/or mean curvature are better surface descriptors for three very different neural network pipelines, and is much faster to compute. They point out previ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments, and for their very valuable remarks.
We acknowledge that our results raise some interesting and important questions that are not answered in the work, as pointed out in the 'weaknesses' section. However, we do not see these as weaknesses of the ... | null | null | Rebuttal 1:
Rebuttal: We would like to first thank the reviewers for their time, and thoughtful comments and questions that will certainly help improve the revised paper. In particular we thank the reviewers for acknowledging the qualities of our work:
\begin{equation*}
\text{"... it is impressive that the benefit ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Efficient Sketches for Training Data Attribution and Studying the Loss Landscape | Accept (poster) | Summary: The paper proposes efficient sketching algorithms designed to address memory constraints in large-scale models. The key idea is to eliminate the multiplication of a dense projection matrix multiplication (large matrix materialization) found in the existing sketching algorithms such as in FJL and FastFood. The ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their careful reading of our paper and the many valuable suggestions to improve the presentation. We also appreciate the reviewer's acknowledgment that this work is likely to be of interest to the NeurIPS community, with potential applications across various dom... | Summary: The authors present a framework for scalable gradient sketching and HVP sketching. They introduce three algorithms AFFD, AFJL, QK and provide guarantees for sketching. The paper focuses on three applications: training data attribution, intrinsic dimension computation, and Hessian spectra analysis. These are al... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read the paper, give feedback on the presentation and appreciating the theoretical part of the paper.
**Answers to Questions**
```Which components of the 6 contributions in the introduction are the main contributions of the paper?```: The main contrib... | Summary: This paper introduces new methods for sketching high-dimensional gradients and HVPs. These are important building blocks for tools like training data attribution and Hessian spectrum analysis. Their methods introduces both empirical and theoretical improvements over prior methods, and are used to demonstrate n... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their insightful feedback and positive assessment of our work. We commit to include [1 & 2] suggested in the review to the Related Work.
**Answers to Questions**
```Would benefit from ... there's a newer version.```: We acknowledge the need for improved clar... | Summary: Gradient information is useful for various tasks such as training data attribution and intrinsic dimension analysis, but often suffers from huge compute/memory costs, limiting its practical utility. This paper focuses on several popular sketching approaches (e.g., FJL, FFD), points out lookup-based memory as t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments, taking the time to read the paper, and finding the insights provided by our sketching algorithms valuable.
**Answers to Specific Questions**
1. We focused on models that don't require partitioning weights across devices. For larger models, sketc... | Rebuttal 1:
Rebuttal: We extend our gratitude to the reviewers for their insightful comments and suggestions. We particularly appreciate the encouraging feedback, acknowledging the practical relevance and timeliness of our work, as well as its potential interest to the NeurIPS community.
In this rebuttal, we would lik... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fast Channel Simulation via Error-Correcting Codes | Accept (poster) | Summary: Inspired by the duality between source and channel coding, the authors use polar codes to develop a channel simulation algorithm for binary output channels. Notably, the authors' scheme scales as $O(n \log n)$ where $n$ is the channel dimension, providing an example of a channel simulation algorithm whose runt... | Rebuttal 1:
Rebuttal: Thank you for your encouraging feedback, and for suggesting the improvement to our comparison plot.
> ... description of the experimental setup ...
We will describe the experimental setup for Figure 3 here, which we will also include in the camera-ready version of the paper. This also takes into... | Summary: The manuscript considers the design of algorithms for channel simulation. This topic has been extensively explored in the information theory literature, under various names including 'Reverse Shannon Theorems', 'Channel Synthesis', and 'Channel Simulation'.
The primary concern is that the problem formulation,... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We address your comments and questions below:
> ... This topic has been extensively explored in the information theory literature ...
We disagree with this description. The information theory literature on this problem has predominantly focused on fundamental limits... | Summary: This paper considers the scalability problem in the channel simulation. Channel coding, specifically polar coding, is introduced to improve the performance of channel simulation. The topic is interesting and the work is valuable.
Strengths: This paper uses error correction codes to improve the channel simulat... | Rebuttal 1:
Rebuttal: Thank you for your feedback and comments. We have addressed specific comments and questions below.
> The paper stresses ``fast'' in the title, but the corresponding justification or even a statement is missing in the main body of the paper. Some notations are not well defined. For example, what ... | Summary: The paper studies the channel simulation problem, whose goal is to minimize the number of transmitted bits so that the decoder can generate an output according to a target distribution given the encoder’s input. The paper proposes a channel simulation method, called PolarSim, based on polar codes. The rate of ... | Rebuttal 1:
Rebuttal: Thank you for your encouraging assessment of our work and for the comments and feedback you have provided. We have addressed specific comments and questions below.
> The smallest used in the experiments in the paper is 1024. How would $\mathtt{PolarSim}$ perform for smaller $n$? Could the authors... | Rebuttal 1:
Rebuttal: We thank all the reviewers for taking the time to review our paper. Their feedback and suggestions will be valuable in helping us refine our work.
Based on their comments, these emerged as the main areas for improvement for our paper:
* Studying the performance of $\mathtt{PolarSim}$ for small $n... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Efficient Adversarial Training in LLMs with Continuous Attacks | Accept (spotlight) | Summary: Large language models (LLMs) are vulnerable to adversarial attacks that can bypass their safety guardrails. However, current adversarial training methods for LLMs are hindered by the high computational costs required to perform discrete adversarial attacks at each training iteration. To solve this problem, the... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments and address each point/question in turn:
**W1: The authors should conduct a controlled experiment to measure runtime differences between the approaches:**
**A1:** This is a valid concern and we address it by measuring the wall time it takes to... | Summary: This paper introduces adversarial attacks in continuous space in the context of LLMs. In addition, it utilizes continuous attacks for adversarial training to robustify LLMs and demonstrate that this efficient training algorithm can indeed protect LLMs against various attacks, including discrete ones, while mai... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments and address each point/question in turn:
**W1: R2D2 should be evaluated on more models.**
**A1:** Unfortunately, Zephyr R2D2 is the only model available trained with R2D2 and training more base models with R2D2 is prohibitively expensive (see T... | Summary: This paper proposes adversarial training for LLMs, in which perturbations are created in the continuous embedding space rather than finding discrete suffixes. The proposed fast adversarial training algorithm (AdvUL) consists of two losses: the first strengthens the model against continuous embedding attacks co... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments and address each point/question in turn:
**W1: Is adversarial training suitable to robustify LLMs in the context of diverse threats?**
**A1**: Thanks for initiating this discussion. We agree that the perturbations set in LLMs are much less con... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their efforts and suggestions. We included a pdf, which provides an overview of the new results. The following experiments have been added to the paper:
1. Training and evaluation of Llama2-C-AdvUL, which results in considerable robustness improvements.
2. Adaptive atta... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
BricksRL: A Platform for Democratizing Robotics and Reinforcement Learning Research and Education with LEGO | Accept (spotlight) | Summary: The paper presents a framework for training reinforcement learning policies on LEGO robots in the real world.
BricksRL integrates the LEGO robotics hub PyBricks and a reinforcement learning library TorchRL, providing an easy interface to implement and deploy RL algorithms. A robust infrastrure for robot-enviro... | Rebuttal 1:
Rebuttal: Response Weaknesses:
Thank you for your assessment of the weaknesses, we are happy to address these points to provide more clarity.
We recognize the potential benefits of increased communication speed and are willing to improve this. We've contacted PyBricks directly to collaborate on enhancing ... | Summary: This paper presents BrickRL which is a system for using reinforcement learning within the context of lego robotics. The paper provides an overview of their setup, how they used TorchRL and PyBricks to interface with the the lego robots. They also provide results that show the feasibility of this system to use ... | Rebuttal 1:
Rebuttal: Response Weaknesses:
We greatly appreciate your valuable input and suggestions for improving the clarity of our paper. We are pleased to incorporate the proposals as far as possible in the final version of our paper.
Response Questions:
We're happy to provide more details about our LEGO robot... | Summary: This work proposes a flexible and cost-effective platform named BricksRL with LEGO builds aiming to lower the cost of RL research and education. Experiments also demonstrated that LEGO robots can be trained within 120 minutes on normal computers to achieve simple tasks such as moving, walking, and grasping. An... | Rebuttal 1:
Rebuttal: Response Weaknesses:
Thank you for your thoughtful feedback, we appreciate your recognition of its practical utility and educational value. However, we respectfully disagree with the assessment that the scientific contribution is limited.
We would like to emphasize several key points that demon... | Summary: This paper demonstrates that cheap LEGO robots can be trained in under 120 mins on a laptop to perform simple tasks using sim2real approaches. The paper is motivated by making robot learning research more accessible for educational settings. A software framework called BricksRL is released in open source that ... | Rebuttal 1:
Rebuttal: Response Weaknesses:
- Our primary contribution lies in providing accessible, low-cost hardware integration for real-world robotics experimentation. Our goal is to lower the barrier to entry for robotics research and education, enabling a wider audience to engage with physical robots beyond simul... | Rebuttal 1:
Rebuttal: We have added this subsection (PDF) to the paper.
Pdf: /pdf/1b89065b0855f7f68d433abcc5f55ea1967373e8.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
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