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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On scalable oversight with weak LLMs judging strong LLMs | Accept (poster) | Summary: The paper provides a comprehensive study of scalable oversight across 3 access: (1) task, (2) scalable oversight protocol and (3) judge capacity / strength. The authors focus on inference-time scalable oversight, i.e. the debater models are not trained to do debate with a given judge. The authors consider seve... | Rebuttal 1:
Rebuttal: Thank you for your thorough review, and interesting questions and comments. We are glad you found the study to be “carefully designed” and “comprehensive”, and appreciated the use of weak models as a complement to information asymmetry.
Weaknesses:
1. **Training vs prompting:** we agree train... | Summary: This paper focuses on scalable oversight protocols using debates between AI agents to align superhuman AI with human supervision. By studying debates judged by less capable LLMs across various tasks, the research finds that Debates, especially without artificial limitations on judges, more effectively bridge c... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments and agree that further discussions and explanations will help the reader better understand our results. We’d be happy to include these in a revision. Please see below for a more detailed response to your questions:
i) **QA without article vs deb... | Summary: This paper is concerned with the study of scalable oversight methods, i.e, how can one devise methods that will allow humans to supervise and align superintelligent models (ASI) whose capacities (which include reasoning, strategic thinking, and deception) vastly exceed the ones of humans. Inspired both by rece... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments and are heartened to see that the reviewer appreciates the importance of the setting, likes the clarity of our exposition, and notes that our paper is a scientific study of protocols rather than promoting debate in particular.
Weaknesses:
* **S... | Summary: This paper primarily investigates scalable oversight by analyzing whether a weaker LLM can supervise a stronger LLM through various prompting pipelines.
Specifically, the paper compares the accuracy of responses from a weaker judge model under different interacting protocals with a stronger model, such as deba... | Rebuttal 1:
Rebuttal: We thank the reviewer for their assessment. We emphasise that the key contribution of our paper is a rigorous, carefully-controlled scientific study of various scalable oversight protocols, rather than showing a particular protocol is better than others, see our top-level comment. We encourage the... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful comments. We are glad to hear that reviewers identified our comprehensive experimentation and ablation, the importance of using weaker judge models to supervise stronger debaters, and the clarity of our writing and presentation as strengths of this work.... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration | Accept (poster) | Summary: This paper proposes a multi-agent architecture for mobile device operation, Mobile-Agent-v2. Mobile-Agent-v2 includes three agents : planning agent, decision agent and reflection agent. To retain focus content, this paper designs a memory unit to record task-related focus content. The planning agent generates ... | Rebuttal 1:
Rebuttal: ## **Question 1: Can Mobile-Agent-v2 be transferred to iOS or other platform?**
## **Response:**
* Mobile-Agent adheres to a purely visual solution, making it universally applicable across platforms since Mobile-Agent-v1. Mobile-Agent-v2 continues this approach, allowing it to be transferred to a... | Summary: This paper proposes mobile-agent-v2, a multi-agent framework for mobile device operation. The framework includes a planning agent, decision agent, reflection agent and memory unit. The multi-agent framework exhibits advantages in reducing errors in long-horizon tasks and achieves better results than single-age... | Rebuttal 1:
Rebuttal: ## **Question 1: Lack of other baselines such as CogAgent and AppAgent.**
## **Response:**
**CogAgent**
CogAgent is a QA model that does not possess the capability to perform concrete operations on real devices through tool invocation. Therefore, our dynamic evaluation framework is not applicabl... | Summary: This paper introduces an agentic framework, Mobile-Agent-v2, designed to address the challenges of planning and sequential function/tool invocation for Large Language Models (LLMs) in mobile operation scenarios. Mobile-Agent-v2 comprises three agents: a planner, an actioner, and a reflector, which jointly enha... | Rebuttal 1:
Rebuttal: ## **Question 1: Similar multi-agent architectures have been applied in other scenarios.**
## **Response:**
Mobile-Agent-v2 is the first work to use a multi-agent architecture in the mobile domain. The required capabilities of each agent, the tasks they perform, the interaction between agents, an... | Summary: In this paper, the authors present Moble-Agent-v2, a multi-agent architectruure deisgned to assist mobile device operation. Mobile-Agent-v2 comprises three agents: planning agent, decision agent, and reflection agent. Frist, planning agent summarizes the task progress based on the operation hsistories. Then, ... | Rebuttal 1:
Rebuttal: ## **Question 1: Lack of evaluation in special scenarios, such as unachievable or ambiguous tasks.**
## **Response:**
* For tasks that cannot be directly completed through the given instructions, Mobile-Agent-v2 can still attempt to complete them. Due to the high flexibility of Mobile-Agent-v2's ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers for their valuable and constructive feedback, which will be pivotal in enhancing the quality of our work.
We are encouraged by the following reviewers' perceptions:
* Innovative and interesting multi-agent architecture in the mobile domain (c2St, kbfL, cAKE)... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper titled "Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration" presents a multi-agent architecture designed to address the challenges of mobile device operation tasks, specifically focusing on task progress navigation and focus content navigation. ... | Rebuttal 1:
Rebuttal: ## **Question 1: Overhead of multi-agent.**
## **Response:**
* For time overhead, although multiple agents work serialize, there are still phases to parallel. The tables below compare the operation times for the Mobile-Agent single-agent framework and the Mobile-Agent-v2 multi-agent framework. "Sc... | null | null | null | null | null | null |
How Diffusion Models Learn to Factorize and Compose | Accept (poster) | Summary: This paper investigates the capabilities of diffusion models, particularly Denoising Diffusion Probabilistic Models (DDPMs), in learning factorized representations and achieving compositional generalization. The authors aim to quantify this by analyzing mechanisms to train the model, supporting the hypothesis ... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to provide us with constructive feedback. Please find our responses to the specific concerns and questions below.
Weaknesses
1. We thank the reviewer for the very positive feedback. As noted in the global response section “Regarding the toy setting,” usi... | Summary: This paper investigates how and when diffusion models learn factorized representations of composable features. To this end, the authors construct controllable synthetic datasets by compositionally combining 1D and 2D Gaussian data and examine the factorized representation and the compositional generalization c... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to provide us with constructive feedback. Please find our responses to the specific concerns and questions below.
Weaknesses
1. We appreciate the reviewer’s feedback. In our paper, we explored both additive and multiplicative composition with the 2D Gaus... | Summary: This paper investigates, on a very simple toy dataset, how conditional diffusion models learn factorized representations of the data, and the extent to which they can compositionally generalize out of distribution. Additionally, the authors make a connection to percolation theory in physics.
Strengths: The mo... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to provide us with constructive feedback. Please find our responses to the specific concerns and questions below.
Weaknesses
Expanding on high-level
1. We kindly refer the reviewer to the first two sections of the global response.
2. We appreciate the r... | Summary: In this work, the authors investigate how diffusion models achieve compositional generalization. Through controlled experiments on conditional DDPMs with 2D Gaussian data, the authors find that these models learn semantically meaningful, factorized manifold representations of composable features. These represe... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to provide us with constructive feedback. Please find our responses to the specific concerns and questions below.
Weaknesses
1. We thank the reviewer for the feedback, we kindly refer the reviewer to the global response Section “Regarding the toy setting... | Rebuttal 1:
Rebuttal: We thank all the reviewers for the constructive feedback and suggestions. Below are some of the recurring concerns/questions that we would like to address to all reviewers.
Regarding the toy setting
It is a tradeoff between a simple setting, which allows for controlled studies to isolate all p... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
UrbanKGent: A Unified Large Language Model Agent Framework for Urban Knowledge Graph Construction | Accept (poster) | Summary: This paper presents a framework denoted UrbanKGent, for finetuning an LLM to assist the construction of knowledge graph construction (specifically triplet extraction relation prediction). The gist of the study is to construct an ad-hoc corpus for finetuning. In this process, a method of trajectory refinement i... | Rebuttal 1:
Rebuttal: > [W1] It seems that the generation of the corpus, which is a key ingredient of the framework, lacks a systematic and scientific methodology for its generation.
Sorry for the confusion. As mentioned by the reviewer, the corpus is very important for the UrbanKGent framework. Therefore, we construc... | Summary: The paper introduces UrbanKGent, a comprehensive framework utilizing a large language model (LLM) for constructing urban knowledge graphs (UrbanKGs). The key components of UrbanKGent involve creating an instruction set for tasks like relational triplet extraction and knowledge graph completion, which are tailo... | Rebuttal 1:
Rebuttal: > [W1] The computational efficiency of the proposed algorithms and their scalability with the size of the dataset is not thoroughly discussed.
We sincerely appreciate the reviewer's valuable comment, which helps us improve the quality of our paper. As suggested, we provide detailed inference late... | Summary: The paper presents UrbanKGent, a unified large language model agent framework for urban knowledge graph construction. The framework consists of knowledgeable instruction generation, tool-augmented iterative trajectory refinement, and hybrid instruction fine-tuning. The authors evaluate the framework on two rea... | Rebuttal 1:
Rebuttal: > W1&Q1
Due to the tables related to weakness 1 and question 1 are included in the Supplementary PDF of the **Author Rebuttal**, we have moved the corresponding responses to the **Author Rebuttal** for better understanding. We apologize for any inconvenience this may cause.
> W2,W3&Q2
We choose... | Summary: This paper proposes a unified large-scale language model agent framework called UrbanKGent, specifically for the Construction of Urban Knowledge Graph Construction (UrbanKGC). UrbanKGent utilizes instruction generation of heterogeneous and geospatial information, as well as an iterative trajectory optimization... | Rebuttal 1:
Rebuttal: > [W1] As the size of the dataset grows, the algorithmic efficiency and scalability of UrbanKGent may become an issue. Although not mentioned in the paper, in practical applications, the processing of large-scale data sets may require more computational resources and time. Although the paper notes... | Rebuttal 1:
Rebuttal: **Dear Reviewer GYCX**:
We thank you for the precious review time and valuable comments. To ease understanding, we have moved weakness 1 and question 1 here, which are related to UrbanKG entity and relation ontology statistics. Please refer to Table 13, Table 14, and Table 15 in the **PDF** for d... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Reinforcement Learning with Euclidean Data Augmentation for State-Based Continuous Control | Accept (poster) | Summary: This paper introduces a novel data augmentation method for reinforcement learning in continuous control tasks. The key innovation is leveraging Euclidean symmetries inherent in these tasks by applying rotational transformations to the original states. The authors propose using a limb-based state representation... | Rebuttal 1:
Rebuttal: Thank you for your insightful review! We are glad to provide a response to address your concerns and look forward to follow-up discussions.
**On Weaknesses 1**
Our method (Euclidean rotations) can be straightforwardly applied to constraints like obstacles: one just needs to include constraints... | Summary: This paper introduces a novel data augmentation strategy tailored for reinforcement learning (RL) agents operating in state-based continuous control environments. The method leverages limb-based state features rather than joint-based configurations, allowing for more effective augmentation. Experiments conduct... | Rebuttal 1:
Rebuttal: Thank you for your insightful review! We are glad to provide a response and look forward to follow-up discussions.
**On Weaknesses - theoretical support**
The paper does not state a Theorem 1. Can you further clarify your concern? Thanks.
**On Weaknesses - computational overhead**
Our met... | Summary: This paper proposes a data augmentation technique that leverages Euclidean symmetries (e.g. rotational symmetry) in a task's dynamics to generate augmented data. When a task's state features do not have such symmetries, the paper also discusses how to define a new state representation with these symmetries (so... | Rebuttal 1:
Rebuttal: Thank you for your insightful review! We are happy to provide a response. We hope it can trigger your re-evaluation and look forward to follow-up discussions.
**On Weakness 1**
Thank you for suggesting the papers as they are indeed related to our work.
As some of the papers are also mentioned ... | Summary: This work proposes a novel data augmentation approach that leverages Euclidean symmetry for continuous control to improve the efficiency and performance of RL algorithms. The authors integrated their approach with DDPG and performed a series of comparison against the vanilla SAC algorithm, other augmentation t... | Rebuttal 1:
Rebuttal: Thank you for your insightful review! We are glad to provide a response and look forward to follow-up discussions.
**On Weakness 1**
Our work focuses on robot locomotion tasks as instances of continuous control, which clearly exhibit Euclidean symmetries. Other robotics tasks (e.g., navigation)... | Rebuttal 1:
Rebuttal: We attach here a pdf that contains additional results and illustrations, which we refer to in our response to individual reviews.
We below compare our work with papers [1-6] suggested by reviwer X7ZX to clarifies our different and orthogonal contribution.
Some of these papers are also suggested b... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Streaming Bayes GFlowNets | Accept (poster) | Summary: Generative flow nets are extended to streaming inference by using the prediction of the previous network as the prior for the current one. Two training methods are proposed: streaming balance which uses squared difference of target and learned log posteriors, and VI which uses KL divergence. Analytic results f... | Rebuttal 1:
Rebuttal: Thank you for carefully reading and appreciating our work. Below, we address each of your comments.
> Both $𝑝\_{𝐵}$ terms in the streaming balance condition (3) appear to be unnecessary.
Indeed, when $p_{B}^{t}(\cdot | s)$ is fixed, it does not depend on $t$ and the terms corresponding to th... | Summary: The authors provide a framework that allows training of GFlowNet models with streaming data by checkpointing a previously trained GFlowNet model and proposing the streaming balance condition. The problem as well as the approach is well motivated and theoretically sound and this work would be a valuable contrib... | Rebuttal 1:
Rebuttal: Thank you for the suggestion of additional experiments and references, which we will include in the revised manuscript. We hope our additional experiments and clarifications enhance your perception of our work. Please let us know if we have left blank spots or if further clarifications are needed.... | Summary: This paper introduces Streaming Bayes GFlowNets (SB-GFlowNets), which enables streaming Bayesian inference over discrete parameter spaces, relying on the expressive power of GFlowNets as amortized samplers over discrete compositional objects. The process is akin to Bayesian streaming, where the posterior updat... | Rebuttal 1:
Rebuttal: Thank you for carefully reviewing our work. We have corrected all the typos you pointed out.
We hope our clarifications address your concerns and enhance your view of our work.
> I'm not sure where equation (9) comes from ...
Thank you for catching this typo, some of the indices were written i... | Summary: This paper introduces Streaming Bayes GFlowNets, a method for performing approximate Bayesian inference over discrete parameter spaces in streaming data settings.
SB-GFlowNets allow efficient updating of posterior approximations as new data arrives. The method reduces training time compared to retraining GFlo... | Rebuttal 1:
Rebuttal: Thank you for thoughtful feedback. We hope our clarifications solve your concerns and elevate your appraisal of our work. Otherwise, we will gladly engage further during the discussion period.
> Do you think the accumulative error issue can be solved by cache previous gradient (borrowing tricks f... | Rebuttal 1:
Rebuttal: Dear reviewers and AC,
We are glad to know reviewers found our work to be valuable contribution to the research community (c2ea), our method to be elegant (bMoM) and our experiment results to be strong (bMoM), diverse (ZbQa), and convincing (xCCn) — opening a path for new applications of Bayesia... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper introduces a method for Bayesian inference in streaming data scenarios, particularly for discrete parameter spaces, called Streaming Bayes GFlowNets (SB-GFlowNets). The main contributions are:
* Bayesian Streaming Inference: SB-GFlowNets enable the continuous updating of posterior distributions as n... | Rebuttal 1:
Rebuttal: Thank you for your suggestions to improve our manuscript. We hope our answers elevate your appraisal of our work. Should you feel that any points require additional clarification, we are more than willing to engage further.
> A weakness of the paper is that it may not sufficiently highlight the s... | null | null | null | null | null | null |
Breaking Semantic Artifacts for Generalized AI-generated Image Detection | Accept (poster) | Summary: This paper addresses the task of AI-generated image detection and specifically the challenge that arises by shifts in the semantics between training and test samples. It is demonstrated through experiments that different artifacts are produced by different generators, leading to performance drops in the cross-... | Rebuttal 1:
Rebuttal: Thank you for taking the time to read our manuscript and for providing detailed comments! Below are our responses to the comments provided:
> Q1: Comparison to (Dogoulis et al. 2023), which has discussed the problem of performance drop in cross-scene settings.
First, we acknowledge that the cr... | Summary: This paper identifies a significant drop in accuracy for existing detectors when generalizing across different image scenes, attributing failures to "semantic artifacts." To address this, a new approach is proposed that involves image patch shuffling and training a patch-based classifier, enhancing generalizat... | Rebuttal 1:
Rebuttal: Thank you for your comments! Below are our responses to the comments:
> Q1: Idea-level comparison to PatchFor [6].
The key idea-level difference between PatchFor and our method is the use of patch shuffle. This is the core of our method, which ensures that the model completely removes the sema... | Summary: The paper looks into the problem of AI generated image detection. Given the onset of diffusion models they highlight how previous approaches which claim to generalize fail in setting with new datasets and models. The authors also motivate by visualizing frequency spectrum images. The paper then proposes a patc... | Rebuttal 1:
Rebuttal: Thank you for the feedback and comments! We are glad to hear that you believe this is important work and that it meets the standard for publication. Below are our responses to the comments provided:
> Q1: Missing frequency visualizations on patch-shuffled images as well as image patches.
Thank... | Summary: The paper investigates the robustness and generalization of AI-generated image detectors, with a particular focus on the influence of semantic artifacts on detector performance. The authors highlight that semantic artifacts can significantly impact the effectiveness of AI detectors.
To explore this phenomenon,... | Rebuttal 1:
Rebuttal: Thank you for taking the time to read our manuscript and for providing detailed comments! Below are our responses to your comments:
> Q1: Missing ablations of patch size on the performance.
We have already reported the ablation results of patch size (and model depth) in Appendix A.1.1, and we ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their insightful assessment of our work and for providing useful feedback and actionable suggestions. They found that our research makes novel and insightful contributions (reviewer a5zv), with a clear and sound motivation (reviewers a5zv, Zwma, and 4wrZ), an effective/g... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Seeing the Image: Prioritizing Visual Correlation by Contrastive Alignment | Accept (poster) | Summary: The paper proposes a new re-weighting strategy for training VLMs to improve visual-language alignment. The motivation is to assign higher weight to visually relevant tokens and lower weight to visually irrelevant and visually contradictory tokens. The re-weighting factor relies on the logit difference with and... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their constructive feedback and for recognizing the strengths of our work. We appreciate the detailed insights and suggestions provided. Below, we address the key points raised in your review.
> **W1: Lack of Clarification in Presentation**
We acknowledge... | Summary: This paper introduces Contrastive Alignment (CAL), a straightforward yet effective re-weighting strategy designed to improve multimodal alignment. Specifically, the authors propose contrasting image inputs and calculating the differences in prediction logits for each text token to determine its training weight... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their constructive feedback and for recognizing the strengths of our paper. We appreciate the detailed insights and suggestions provided. Below, we address the key points raised in your review.
> **W1: Robustness in Complex Task Settings Like VQA**
Actual... | Summary: This paper points out that parts of samples in the broadly used datasets contain visually contradictory text tokens. To mitigate the sub-optimal cross-modal alignment in VLMs, this proposed method is to assign distinct contributions for each text token based on its visual correlation.
Strengths: 1. The propos... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their positive feedback and for highlighting the strengths of our work. We appreciate the detailed insights and suggestions provided. Below, we address the key points raised in your review.
> **Q1: How to Use on CLIP**
Thank you for your question regardin... | Summary: This study proposes a reweighting strategy, namely contrastive alignment, to enhance model learning on visually correlated tokens. Specifically, the authors divided the tokens into three sub-groups, virtually related, non-related, and contradictory, and assigned different weights to each group. The weight is c... | Rebuttal 1:
Rebuttal: We would like to thank you for your constructive feedback and highlighting the strengths of our work. We appreciate the detailed insights and suggestions provided. Below, we address the key points raised in your review.
> **W1: Lack of Baseline Comparisons**
We are happy to include these papers ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Motion Consistency Model: Accelerating Video Diffusion with Disentangled Motion-Appearance Distillation | Accept (poster) | Summary: This work presented a new consistency based framework for video diffusion model distillation.
Specifically, the adversarial loss is leveraged to enhance the video quality and consistency distillation loss is performed in the motion embedding space to learn the video motion patterns effectively. In addition, t... | Rebuttal 1:
Rebuttal: We are grateful for your recognition of our novel contributions and promising results. We follow your advice to conduct the following set of experiments.
**Weakness 1: Perceptual loss instead of adversarial loss.** Following your suggestion, we conducted experiments comparing perceptual loss with... | Summary: This paper proposed a single-stage video diffusion distillation method that can disentangle motion and appearance learning, thus improving frame appearance using various high-quality image data. The proposed mixed trajectory distillation mitigates the training-inference differences in terms of video quality. T... | Rebuttal 1:
Rebuttal: Thank you for recognizing our MCM’s contributions and providing constructive feedback. We respond to your concerns below.
**Weakness 1: Motion jittering.** Thank you for the feedback! The motion jittering is largely inherited from the teacher model (the same phenomenon was also reported in Animat... | Summary: This paper proposes a video diffusion distillation method that disentangles motion and appearance learning. Basically, it proposes to enhance the appearance generation with high-quality image data and distill motion knowledge from the video teacher model.
Strengths: 1. The proposed method can distill motion k... | Rebuttal 1:
Rebuttal: Thank you for identifying our key strengths and providing insightful comments. We address your concerns point by point.
**Weakness 1: Gaps in training and inference distillation inputs.** Thank you for raising the concern! There exist two major gaps in the mixed trajectory distillation.
- Distrib... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers for identifying our novel contribution in video diffusion distillation, promising qualitative and quantitative results, and well-written paper. We address all concerns in the individual responses below. Please find the attached PDF file for our additional qualitative results... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
GaussianCut: Interactive segmentation via graph cut for 3D Gaussian Splatting | Accept (poster) | Summary: The paper proposes a method for interactive multiview segmentation of scenes represented as a set of 3D gaussians (3D gaussian splatting). The method takes as an input multiple views of a scene, constructs 3D Gaussian splatting representation of the scene, takes interaction from the user (scribbles, points, or... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback and kind words about our work.
> Computational cost is not real time.
The reviewer is right in noting that the graph cut algorithm does not run in real-time. However, once segmented into foreground and background Gausisans, it can be rendered ... | Summary: The paper proposes GaussianCut for interactive multi-view scene segmentation using 3D Gaussian Splatting. It first accepts user input (clicks, scribbles or text, similar to SAM) on single images, and them aim to segment the corresponding 3D Gaussians. The method constructs a graph based on scene Gaussian, and ... | Rebuttal 1:
Rebuttal: We thank the reviewer for accessing our work and providing valuable feedback. We provide clarifications to the concerns and questions they raise.
> Justify the tech novelty. Graph cut from 2d pixels to 3D Gaussians seems very straightforward.
Extending graph cut from 2D pixels to 3D Gaussians in... | Summary: This paper presents GaussianCut, a new method for foreground/background segmentation of 3D Gaussian scenes. GaussianCut relies on 2D image/video segmentation masks, which are generated for a subset of the training images. GaussianCut propagates these masks to the 3D Gaussians by projecting each Gaussian onto e... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and constructive feedback. We have provided clarifications throughout and added additional experiments to address the concerns they raised. We appreciate the reviewers’ attention to detail and will incorporate all the suggestions made by them.
> Runtime... | Summary: This paper proposes GaussianCut for interactive 3D segmentation. The GaussianCut takes a trained 3DGS model and the user prompt as inputs. The SAM model first transfers the user prompt into an initial mask. Then the 3DGS model renders multiple view images and an existing video-tracking model is used for segmen... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive review. We would like to respond to the questions and concerns that they have posed.
> Key contributions clarification
We kindly refer the reviewer to the global response which provides a detailed explanation of our key contributions.
> Details of as... | Rebuttal 1:
Rebuttal: We thank the reviewers for their helpful and valuable comments and appreciate that they found our paper well-written, easy to follow (DeYw, 24kC, BR3k), and well-motivated (MadC, DeYw). We are delighted to see that the reviewers recognize the performance improvement of our training-free approach o... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper presents a method for segmentation in 3D Gaussian Splatting. Given a prompt in 2D, the proposed approach is capable of segmenting objects of interest from the 3D Gaussians. Specifically, the method first performs 2D segmentation in all training views, which are then propagated into 3D using a techniq... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for their feedback on our paper. We thank the reviewer’s suggestion for helping to improve the clarity of our work
> Clearer motivation for the introduction
Our work is motivated by leveraging the explicit nature of 3DGS representation. With user inputs and the under... | null | null | null | null | null | null |
ANAH-v2: Scaling Analytical Hallucination Annotation of Large Language Models | Accept (poster) | Summary: The paper proposes to augment hallucination annotation dataset and improve the performance of the hallucination annotator simultaneously in an iterative self-training framework. During each iteration, they use a Expectation-Maximization Algorithm for data annotation and hallucination annotator training. The tr... | Rebuttal 1:
Rebuttal: Thank you for your constructive comment. Following are our responses to each individual comment (which are highlighted in italics).
### **Response to Weakness1 about stability of the annotation steps:**
> *My biggest concern for this paper is that the model essentially does three things: Factual... | Summary: This paper introduces an iterative self-training framework to address hallucinations in large language models (LLMs), enhancing the accuracy of annotators and scaling up hallucination detection datasets. The framework utilizes the Expectation Maximization algorithm to progressively improve the hallucination an... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful comments. Following are our responses to each individual comment (which are highlighted in italics).
### **Response to Weakness1 about contributions:**
> *The description of the methodological contributions in the paper is not very clear. The EM algorithm and the self-... | Summary: The authors propose an innovative approach to tackle the persistent issue of hallucinations in large language models (LLMs) during long-form question-answering tasks. Current methods for detecting and mitigating these hallucinations are constrained by limited data and high labor costs. To address this, the pap... | Rebuttal 1:
Rebuttal: Thank you for your constructive comment. Following are our responses to each individual comment (which are highlighted in italics).
### **Response to Weakness1 about initial condition:**
> *One of the big drawbacks of the EM algorithm is its sensitivity to initial conditions. If you don't start ... | Summary: This paper proposes an iterative self-training framework that simultaneously and progressively scales up the hallucination annotation dataset and improves the accuracy of the hallucination annotator. The framework is based on the expectation maximization algorithm, alternately annotating a scaled dataset and t... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and for recognizing our efforts! Here are the answers to your question (which are highlighted in italics) regarding the metrics used to evaluate generated texts.
### **Response to Weakness and Question:**
> *The RougeL and BertScore may not be the most capabl... | Rebuttal 1:
Rebuttal: We sincerely appreciate the valuable feedback from the reviewers!
We are honored that our work can be reviewed as:
- The paper is "well motivated" and addresses the critical issue (R-5wgD).
- It provides a novel, effective and feasible solution (R-nXFU & 4rBJ & i3ot), and "obtains strong and ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Occupancy-based Policy Gradient: Estimation, Convergence, and Optimality | Accept (poster) | Summary: With a focus on "occupancy-based" methods for RL, the authors propose a
model-free, policy gradient method for policy optimization in finite-horizon
MDPs via occupancy estimation. Guided by the representation of return in terms
of the state-visitation probability and reward, they express the gradient of
th... | Rebuttal 1:
Rebuttal: Thank you very much for your feedback.
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**re: "Introduce new assumptions in the appendix which appear to influence their main results"**
The omission of Asm. 7 and 8 from the preconditions of Theorem 16 was an oversight, rather than a deliberate choice to obscure dependencies. We will inclu... | Summary: This paper introduces policy gradient algorithms that focus on
estimating the gradient of the *occupancy measure* with respect to the
policy parameters. In this framework, one can easily extend convergence
analysis beyond the standard RL objective (e.g., maximization of
expected return) to a much broader class... | Rebuttal 1:
Rebuttal: Thank you for your detailed and insightful feedback, and for your appreciation of our work. We will refine our presentation per your comments, and include additional discussions according to the points below.
---
**Motivation**
Yes, these are the main contributions of the online and offline al... | Summary: This paper investigates the convergence of model-free policy gradient (PG) methods that compute the gradient through occupancy.
Strengths: The convergence analysis and the theorem appear to be sound.
Weaknesses: In my opinion, the paper makes overclaims. The convergence depends on a coverage coefficient, whi... | Rebuttal 1:
Rebuttal: Thank you very much for your comments. However, we respectfully disagree that our paper over-claims its results. The reviewer’s comment that “the coverage coefficient… removes the need of exploration” likely results from confusion over the term “exploration”, whose meaning in the PG literature is ... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
No-Regret M${}^{\natural}$-Concave Function Maximization: Stochastic Bandit Algorithms and NP-Hardness of Adversarial Full-Information Setting | Accept (poster) | Summary: This paper considers online learning variants of ${\rm M}^\natural$-concave function maximization. These types of function generalize maximum flows on graphs (where the variable is the vector of source values on each node), gross substitute valuations in economics, and have applications in resource allocation... | Rebuttal 1:
Rebuttal: We are truly grateful to the reviewer for the thoughtful comments and positive evaluation. First, we would like to address the following comment regarding weakness.
> Weaknesses:
> - For the upper bound, the techniques are fairly straightforward once we have Theorem 3.1. I am wondering if more ... | Summary: The authors consider online $M^\sharp$-concave optimization, similar to problems like online convex optimization and online DR-submodular optimization. $M^\sharp$-concave function classes include resource allocation, valuation, and flow problems, and unlike DR-submodular functions can be exactly optimized (at... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer's dedication to reviewing our paper and providing many insightful comments.
First, we would like to respond to some comments regarding weaknesses.
> Weaknesses:
>
> 1. The stochastic setting algorithms and analysis adapting a greedy algorithm from the offline ... | Summary: This paper studies the online version of M-concave function maximization. With the help of a new lemma that bounds local errors for greedy algorithms, the authors derive an efficient $T^{-1/2}$ simple regret algorithm together with a $T^{2/3}$ regret algorithm for stochastic settings. Moreover, the authors sho... | Rebuttal 1:
Rebuttal: We really appreciate the reviewer's effort in reviewing our paper and providing thoughtful comments. Below is our answer to the question.
> Questions:
>
> 1. What is the main technical challenge in getting $\sqrt{K}$ regret for stochastic settings? Specifically, what is the difficulty when impl... | Summary: This paper studies the online optimization problem with $M^\natural$-concave function, which has many real-world implications such as maximum-flow on bipartite graphs, gross substitute valuation, resource allocation and bandit problem. $M^\natural$-concave function forms the fundamental basis of discrete conca... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's insightful comments and positive evaluation. We address the questions below.
> 1. In theorem 4.2, does $sReg_T$ is equivalent to the error in the Best-arm identification problem?
Our ${\rm sReg}_T$ is essentially the same as the error measure in the best-arm ... | Rebuttal 1:
Rebuttal: ## **Global response: a discussion on the tightness of the $O(KN^{1/3}T^{2/3})$ regret bound in Theorem 4.3**
We sincerely thank all reviewers for their efforts in reviewing our paper and providing invaluable feedback. We are pleased to see that all reviewers have positively evaluated our work.
U... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Multi-Instance Partial-Label Learning with Margin Adjustment | Accept (poster) | Summary: The paper proposes an approach to tackle the multi-instance partial-label learning (MIPL) problem. In MIPL, each training sample is represented as a bag of instances associated with a set of candidate labels. The existing MIPL algorithms may gives the high prediction probability on non-candidate label, which c... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and positive feedback on our paper. We are pleased that you found the paper well-organized, comprehensive, and theoretically supported. Below, we have summarized your comments and provided our responses accordingly.
> The details of how the gap of attention sco... | Summary: In this paper, the authors observe the presence of ‘margin-violations’ in the MIPL problem, which manifest in two aspects: between positive and negative instances, and between candidate and non-candidate labels. Therefore, the paper proposes modifications to the attention mechanism and enhancement of the loss ... | Rebuttal 1:
Rebuttal: Thanks for your detailed and positive feedback on our paper. We are glad you found our approach to addressing ‘margin violations’ in the MIPL problem intriguing. Below, we address your comments and questions.
> In Figure 1, a comparison with ELIMIPL and MIPLGP would be more beneficial.
MIPLGP fo... | Summary: This paper deals with an emerging learning framework, i.e., multi-instance partial-label learning framework, which can be regarded as an extension and combination of multi-instance learning and partial-label learning. Overall, such dual inexact supervision makes the MIPLL difficult to resolve. This paper intro... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and constructive feedback. In the following, we address your comments and concerns.
> Definition of negative instances.
In line with previous MIPL work such as MIPLGP, DEMIPL, and ELIMIPL, we do not associate negative instances with the label space. For the MNI... | Summary: In this paper, the learning scenario of multi-instance partial learning (MIPL) is studied. The paper proposes a new approach for MIPL, whose key technical idea is margin regularization. The paper points out that an important issue for previous MIPL approaches is the ignorance of margin information in both the ... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and positive feedback on our paper. We are glad you found the idea of margin regularization and our results valuable. Below, we answer your questions.
> More discussion on setting the parameter $\lambda$. The parameter could significantly affect learning per... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their time and valuable feedback on our submission.
As we cannot resubmit the paper during the rebuttal period, we have attached a one-page PDF with additional experimental results for the new PLL algorithm POP on both benchmark and real-world datasets, result... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes MIPLMA, a new Multi-Instance Partial Label algorithm that focuses on margin adjustments in both instance and label space. While computing the bag level representations, it introduces a temperature parameter in the margin-aware attention mechanism to widen the gap between attention scores for... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and valuable comments aimed at improving our paper. Below, we provide a summary of your comments along with our corresponding responses.
> Concerns regarding generalization due to low class numbers or false positives.
We conducted additional experiments on the ... | null | null | null | null | null | null |
Safe and Sparse Newton Method for Entropic-Regularized Optimal Transport | Accept (poster) | Summary: This paper proposed a new Newton-type algorithm for entropic-regularized optimal transport (OT) which utlizes sparsification and safeguard techniques and achieves global convergence and local quadratic convergence for the entropic-OT problem. Numerical experiments are provided to verify the effectiveness of th... | Rebuttal 1:
Rebuttal: ### **Weaknesses**
Thanks for the comments. First of all, we shall clarify that SSNS is directly inspired by the regularized Newton method [R6] and the Levenberg–Marquardt algorithm [R7,R8], which have close relations to the trust-region methods but with some subtle differences. Specifically, sta... | Summary: This paper proposes to solve the entropy-regularized OT problem by proposing a customized Newton method. More specifically, the authors propose a new way to sparsify the Hessian matrix as well as adaptive mechanisms to adjust the hyper-parameters in each iteration. Experimental results are also encouraging.
S... | Rebuttal 1:
Rebuttal: Thanks for the comments. Below are our point-by-point responses.
### **Weaknesses**
1. The dimension of the features does not impact the scale the problem. The extracted features are only used to compute the cost matrix, which has a size of $n\times m$. We do not make the feature dimension $d$ t... | Summary: This paper proposes a Newton-based algorithm to solve the entropic optimal transport problem on the basis of samples. The approach hinges on a "sparsification" scheme for the Hessian (which is explained in Algorithm 1) that retains many favorable properties such as an approximation error due to the sparsificat... | Rebuttal 1:
Rebuttal: ### **Weaknesses**
Thanks for the comments. As we have explained in the global rebuttal and other threads, the experiments are intentionally designed to reflect two typical uses of OT, one for image morphing and interpolation, and the other for computing statistical distances. In our local revisi... | Summary: The paper proposes a Newton method to solve the entropic-regularized optimal transform (OT) problem. The method includes a novel strategy for the sparse approximation of the Hessian to reduce computational complexity compared to the classical Newton method and applying a diagonal shift on the sparse Hessian t... | Rebuttal 1:
Rebuttal: Thank you for the comments. Below are our point-by-point responses for the questions.
### **Weaknesses**
1. To enhance the reproducibility, we do not pick the pairs of images. Instead, the randomly selected image IDs are taken from the prior literature [R1] that studies quadratically regularized... | Rebuttal 1:
Rebuttal: # To All Reviewers
Thank you all reviewers for the encouraging and insightful comments. We appreciate the time and effort the reviewers have dedicated to providing valuable feedback on our manuscript. In this round, we have made every effort to address the comments of the reviewers. **The point-b... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
When does perceptual alignment benefit vision representations? | Accept (poster) | Summary: The paper investigates the benefits of aligning vision model representations with human perceptual judgments to improve their performance across various computer vision tasks. The study fine-tunes state-of-the-art models using a dataset of human similarity judgments and demonstrates that this alignment enhance... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful comments. We are glad that the reviewer found our evaluation to be comprehensive, the paper idea innovative, and the analysis insightful. We address questions and concerns below.
**Writing comments**
We thank the reviewer for their helpful notes and will f... | Summary: This paper investigates the effects of aligning pretrained vision models to human judgments. Image-level and patch-level learning objectives are proposed for fine-tuning the pretrained models. The experimental results demonstrate that fine-tuning pretrained models (such as CLIP, DINO, and DINOv2) on the NIGHTS... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful comments. We address questions and concerns below.
**How does perceptual alignment affect model features?**
We emphasize that this paper aims to answer this question in terms of the competency of representations. There is a rich precedence of understanding... | Summary: This paper aligns representations with human perception on mid-level semantics to improve performance on various downstream tasks. Specifically, it does this by pre-training models on additional synthetic image triplets, where the visual similarity within each triplet is annotated by human subjects.
Strengths... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful comments. We address questions and concerns below.
**Which levels of alignment benefit or impair different tasks?**
To strengthen our empirical results, we extend our comparison of different “levels” of alignment (i.e. fine-tuning on THINGS, BAPPS, ImageNe... | Summary: As the title clearly indicates, this article investigates how aligning vision model representations to human perceptual judgments impacts the performance of models relying on these representations for downstream tasks such as dense prediction (semantic segmentation and depth estimation), retrieval-augmented ge... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful comments. We are glad that the reviewer finds the method convincing, the paper well-written, and the experiments comprehensive. We address specific concerns and questions below.
**Global v. local representations**
We provide details regarding training patc... | Rebuttal 1:
Rebuttal: We thank all reviewers for their insightful questions and feedback. We are glad they found:
* The paper is clear, well-written, and easy to follow. [kSUr, 9gB3]
* The experiments are comprehensive. [kSUr, 9gB3, VQay, rQrx]
* The analysis is insightful and interesting. [9gB3, VQay]
* The paper stud... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper studies the question of when perceptual alignment supervision makes better vision representations. They fine-tune existing pre-trained vision backbones (eg, CLIP and DINO) with LoRA on NIGHTS, a synthetic triplet-based dataset of human similarity judgments. Models fine-tuned in this way generally pe... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful comments and appreciate that they found the evaluation comprehensive, the paper clear, and the topic interesting. We address questions/concerns below.
**“It is expected that human preference make generally better representations.”**
We broadly agree with t... | null | null | null | null | null | null |
Just Add $100 More: Augmenting Pseudo-LiDAR Point Cloud for Resolving Class-imbalance Problem | Accept (poster) | Summary: This paper proposes a new method to augment pseudo-LiDAR point cloud to resolve the class-imbalance problem. It consists of generated 3D models of minority classes from video or miniaturized models with 3D reconstruction NERFs. Such models are then sampled from the target LiDAR and added to a Real LiDAR point ... | Rebuttal 1:
Rebuttal: Thank you for your time and comments! Please see our response below.
---
**[S1] Additional Paragraph for Related Work**
This is a great suggestion. We will add more paragraphs to the Related Work Section, summarizing the literature on data augmentation techniques.
---
**[W1] Additional Compariso... | Summary: The paper presents a novel pipeline for LiDAR-based object detectors to solve the class imbalance problem by generating pseudo-LiDAR samples (from multi-view images of miniatures and public videos of an object) and augmenting them during training to balance the performance gap across classes. The augmentation ... | Rebuttal 1:
Rebuttal: Thank you for your time and comments! Please see our response below.
---
**[W1, Q1] Comparison between other methods that aim for class imbalance problems**
As the reviewer pointed out, there are other comparative methods dealing with class imbalance using loss-based methods [D, E] without adding... | Summary: This paper introduces Pseudo Ground Truth Augmentation (PGT-Aug), a novel data augmentation technique for LiDAR-based 3D object detection. The goal of PGT-Aug is to address class imbalance in training datasets by generating diverse point clouds for minority-class objects.
- **PGT-Aug:** a novel cost-effective... | Rebuttal 1:
Rebuttal: Thank you for your time and comments! Please see our response below.
---
**[W1] Code Documentations**
Our apologies. We will revise the current documentation and make them easy-to-follow for researchers to use and understand our code easily. Plus, we will add a Jupyter Notebook-based tutorial to ... | Summary: This paper introduces Pseudo Ground Truth augmentation (PGT-Aug) to address class imbalance in LiDAR-based 3D object detection. PGT-Aug generates diverse pseudo LiDAR point clouds from low-cost miniatures or real-world videos and involves three steps: 3D instance reconstruction, object-level domain alignment, ... | Rebuttal 1:
Rebuttal: Thank you for your time and comments! Please see our response below.
---
**[W1] Data Size**
Our dataset is created to provide pseudo-LiDAR point clouds of minority-class objects, which can be augmented into typical driving datasets (e.g., nuScenes, KITTI, and Lyft) to compensate for the class imb... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their time and their thoughtful comments and questions. We are encouraged that the reviewers find that: “very well-written and easy to follow” (R-iAKq, R-eXGm, R-kswe, R-jNRg); our method being described as “novel, cost-effective pipeline” (R-iAKq), “superi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
No Free Lunch Theorem and Black-Box Complexity Analysis for Adversarial Optimisation | Accept (poster) | Summary: This paper considers the complexity of finding (Pure Strategy) Nash Equilibrium in black-box optimization for adversarial optimization.
By denoting the loss of a learned solution $x\in X$ on a possible test case $y\in Y$ as $g(x,y)$, the authors show that no algorithm is on average better than any other in fi... | Rebuttal 1:
Rebuttal: **RwGpW2. While the main text looks pretty technically rigorous, it is a little hard to read ...**
A: We agree with the reviewer that the role of $b_1$ and $b_2$ was not fully explained.
We will improve this in the final version. In particular, Definition 5 can be improved as follows:
Let $O\su... | Summary: In this paper, the authors theoretically analyze the query complexity for general black-box Adversarial Optimisation under the "closed under permutation" assumption. A no-free-lunch theorem is proved for all algorithms in achieving the same average performance of all possible problem instances (or problem ins... | Rebuttal 1:
Rebuttal: **7LPoW1+Q1.**
- **The technical challenge of the analysis for black-box adversarial optimization
vs the standard adversarial optimization analysis**
- **The key technique employed for the black-box cases for two-player zero-sum games**
- **Black-box adversarial optimisation vs white-box adversa... | Summary: Black-box optimization is a critical area in the field of optimization. The original No Free Lunch (NFL) theorem highlights the inherent limitations of traditional black-box optimization and learning algorithms, establishing a theoretical basis for these methods. The paper addresses a long-standing problem in ... | Rebuttal 1:
Rebuttal: **PuqfW1. The notations used in the paper are not clear in some places.**
A: We will improve the accessibility of this paper in the updated version.
There is a typo here, $v \in \mathbb{R}^n$ rather than $v \in \mathbb{R}$ and $v_i$ is the $i$-th opponent of $v$.
We refer to $g$ as the e... | Summary: The paper mainly focuses on the analysis of black-box adversarial optimization algorithms with an emphasis on Nash Equilibrium (NE) as the solution concept. It introduces the concept of No Free Lunch (NFL) Theorem for general black-box adversarial optimization, showcasing the equal average performance of all a... | Rebuttal 1:
Rebuttal: **RXe7yQ2 I am curious about more difficult games than DIAGONAL and PLATEAU that can
be solved by Algorithm 1. Can you provide other examples?**
A: Before answering the question, we would like to clarify two confusions in the question:
(1) As mentioned in the paper **(lines 223-230)**, Algorithm... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their useful and detailed comments.
Due to the space limit, we only keep the reference list in the global response. All the responses will use the **same** reference list.
**Q1 Two-player zero-sum game with a unique NE or PSNE. PuqfW3; RwGpW1**
A: Our current theo... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Test-Time Dynamic Image Fusion | Accept (poster) | Summary: This paper introduces a method for dynamically adjusting fusion weights for image pixels based on their relative dominability, calculated using pixel-wise reconstruction losses. This approach aims to minimize generalization error by considering the correlation between fusion weights and reconstruction losses.
... | Rebuttal 1:
Rebuttal: We’d like to thank the reviewer for the valuable comments, the acknowledgment of our **good results**, and the **simple but clear presentation**. We provide detailed responses to the constructive comments.
* Weakness 1: More explanations for RD.
Thanks for the valuable comments. We have added exp... | Summary: This paper proposes a theoretical justification of image fusion from a generalization perspective and reduces the upper bound of generalization error by decomposing the fused image into multiple components corresponding to its source data. A new test-time dynamic image fusion paradigm TTD is further proposed w... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for your valuable comments and appreciate your recognition of the **theoretical justification**, **meaningful and sufficient experiments** as well as the **superiority of our work**. We believe the constructive feedback will improve the paper and increase its potent... | Summary: This paper proposes a theoretically guaranteed new paradigm for test-time dynamic image fusion, which exploits the negative correlation between the fusion weights and the single-source reconstruction loss to reduce the upper bound of the generalization error. Extensive experiments demonstrate its effectiveness... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the thoughtful and thorough comments on our paper as well as for recognizing our **theoretical guarantee**, the **simple but effective framework** of our TTD, the **detailed and rich experiments**, and the **competitive effect compared to SoTAs**. We will al... | Summary: - This paper tries to solve the image fusion task, where multi-source images are provided and one needs to extract and integrate effective information from them.
- The paper demonstrates its effectiveness on four different tasks: VIF, MIF, MEF, and MFF.
- The paper proposes a test-time dynamic image fusion met... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our **effectiveness on multiple tasks**, **theoretical guarantee**, and **well presentation**. We appreciate your support and constructive suggestions and address your concerns as follows.
* Weakness 1: The adaptation method heavily relies on the performance o... | Rebuttal 1:
Rebuttal: Dear PCs, SACs, ACs, and Reviewers,
We would like to thank you for your valuable feedback and insightful reviews, which have greatly contributed to improving the paper. This is a **clear and well-written** (Reviewer gBb1, Reviewer EtNz) manuscript with a **theoretical guarantee** (Reviewer gBb1, ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Paralinguistics-Aware Speech-Empowered Large Language Models for Natural Conversation | Accept (poster) | Summary: The authors present a new large language model (LLM) framework called the Unified Spoken Dialog Model (USDM) that can directly understand and generate spoken dialog responses with natural prosody. This is achieved by incorporating prosodic information into the speech tokens and using a multi-step spoken dialog... | Rebuttal 1:
Rebuttal: First of all, thank you for your thoughtful comments, feedback, and questions. We provide explanations and answers to several questions below.
**[W1] Comparative Analysis: Extend the comparative analysis to include a wider range of previous methods in addition to SpeechGPT.**
Thank you for your ... | Summary: This paper introduces a method for modeling spoken dialog which relies on a speech-text LLM, pretrained with a combination or text and discrete speech tokens which capture semantics as well as prosody. The pretraining regime attempts to get the LLM to capture two types of relations between text and speech toke... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback. We provide our point-to-point response below.
**[W1,2, L1] The main weakness that the method relies on a massive amount (~10 years) of transcribed English speech. This makes it limited in its applicability to just a handful of very resource rich languages,... | Summary: This paper introduces an extensive speech-text LLM framework, the Unified Spoken Dialog Model (USDM), designed to generate coherent spoken responses with naturally occurring prosodic features relevant to the given input speech without relying on explicit automatic speech recognition (ASR) or text-to-speech (TT... | Rebuttal 1:
Rebuttal: We appreciate your insightful comments and constructive questions. We address your concerns below.
**[Q3] Do authors have open-source plan for pre-trained model?**
Sure. We plan to release our code and pretrained models. Our model consists of a speech-text pretrained model, fine-tuned spoken dia... | null | null | Rebuttal 1:
Rebuttal: We would like to extend our sincere gratitude to all the reviewers for their insightful comments. Before addressing each reviewer's concerns, we want to clarify a point that may have caused some confusion caused by our paper's title, "Integrating Paralinguistics in Speech-Empowered Large Language ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Provably Efficient Interactive-Grounded Learning with Personalized Reward | Accept (poster) | Summary: This paper considers Interactive-Grounded Learning (IGL) with personalized reward, where the feedback can be context-dependent. The authors propose two algorithms, which are provably efficient by utilizing the novel Lipschitz reward estimators. Empirical results on the image classification dataset and the conv... | Rebuttal 1:
Rebuttal: > **“The paper does not compare the proposed algorithms to the IGL-P algorithm proposed in (Maghakian et al., 2022).”**
Reply: We followed the reviewer’s suggestion and tested the algorithm of Maghakian et al. on the MNIST dataset. We found that it achieves less than 0.2 average progressive rewar... | Summary: This paper studies the problem of personalized rewards in the context of Interactive-Grounded Learning (IGL), where the goal is to maximize the unobservable latent rewards from the observed reward-dependent feedback on actions being taken. Specially, authors introduce provably efficient algorithms with subline... | Rebuttal 1:
Rebuttal: > **Comparison with contextual bandits and POMDP**
As the reviewer already pointed out, in IGL one only observes indirect feedback about the reward, while in standard contextual bandits one observes the reward directly. This clearly makes IGL more challenging than contextual bandits — in fact, wi... | Summary: In this work, the authors provide the first provably efficient algorithms with sublinear regret guarantees for Interactive-Grounded Learning (IGL) with personalized rewards under realizability. Based on a novel Lipschitz reward estimator, the authors propose two algorithms: one based on explore-then-exploit an... | Rebuttal 1:
Rebuttal: > **“The theoretical contributions are limited by the realizability and identifiability assumptions. It would be helpful to provide practical examples where all these assumptions are satisfied.”**
Reply: Realizability is a well-established assumption in the contextual bandit literature [Foster et... | Summary: The authors provide sublinear regret algorithms for Interaction Grounded Learning (IGL) setting, a modification of the standard contextual bandit setting where instead of getting the reward signal, the learner receives some alternative signal from an arbitrary space. The game proceeds for T rounds where at eve... | Rebuttal 1:
Rebuttal: > **“Can the authors provide a more detailed discussion of how IGL is different from the partial monitoring setting. From what I understand, the primary difference is the assumption that the learner has access to a class $\Phi$ which contains a decoder $\phi$ that maps the context and observation ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their detailed and valuable comments. To further illustrate that Algorithm 2 outperforms Algorithm 1, as suggested by Reviewer LmpU, we include the average progressive reward plot in the rebuttal PDF, which indeed demonstrates that Algorithm 2 consistently outperform... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Nearest Neighbor Speculative Decoding for LLM Generation and Attribution | Accept (poster) | Summary: The paper presents a new semi-parametric language modeling approach that can incorporate text spans from a datastore into LLM-based generation improving both quality and attribution of generated texts. They propose a two-step approach that requires constructing an on-the-fly token-level datastore based on a sm... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the novelty and effectiveness of our work. The chunk retrieval and the neighbour selection process in the suggested paper are indeed related to NEST. For example, it is possible to improve the span selection process of NEST with the batch-beam-level neighbours... | Summary: This paper:
* Introduces NEST, a semi-parametric language modeling approach that integrates real-world text spans into language model generations.
* Enhances generation quality and reduces latency by using token-level retrieval and speculative decoding.
* Outperforms conventional kNN-LM and competes well with... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the novelty and effectiveness of NEST. Below, we address each weakness point raised:
- *“Performance heavily relies on the accuracy of the first-stage passage retrieval”*: We acknowledge the performance of NEST is impacted by the first-stage passage retrieval... | Summary: This work presents Nearest Neighbor Speculative Decoding (NEST), a technique to better inject real-world text spans into the output of existing language models. NEST is a kNN-LM approach adding an initial passage retrieval step. During inference, NEST uses Relative Retrieval Confidence (RRC) for confidence-bas... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's feedback and would like to clarify the main contributions of NEST:
- **Source Attribution**: By injecting retrieved segments into the generation of LLM, NEST provides direct attribution for the generation at a span level, enabling users to verify the reliability of the... | Summary: The paper introduces NEST, a novel semi-parametric language modeling approach that enhances the generation quality and attribution of Large Language Models (LLMs) by incorporating real-world text spans. NEST employs a two-stage k-NN search and speculative decoding, achieving improved performance and reduced in... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the contributions of NEST. Below, we address each weakness point raised:
- **Comparison with SOTA models**: We appreciate the reviewer's comment on comparing NEST with state-of-the-art (SOTA) models. To clarify, NEST is an algorithm that can be used to combine... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Local Method for Satisfying Interventional Fairness with Partially Known Causal Graphs | Accept (poster) | Summary: This paper develops a method for satisfying interventional fairness when the causal graph is partially known. The paper first develops a method for checking possible parental sets as well as a method for estimating propensities given a possible parental set. Then, the interventional distribution is computed us... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s great efforts and insightful comments to improve our manuscript. We are also encouraged for the reviewer to recognize the **Originality**, **Quality**, **Clarity**, and **Significance** of our work. In below, we would like to address the reviewer's concerns r... | Summary: In this paper, the author(s) talk about a better way to estimate interventional fairness in the case where we have partially known Directed Acyclic Graphs (DAG). It lists out how most previous works have only done this for fully known DAG's and recent attempts to address the case of unknown graphs have utilize... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s great efforts and insightful comments to improve our manuscript. We are also encouraged by the very positive comments on our paper.
> The paper's approach relies heavily on the accuracy of the CPDAG and on the generated propensity scores. The paper could add... | Summary: This work investigates interventional fairness given partially known causal graphs. Compared to existing methods, it employs all variables and does not need to rely on additional strong assumptions for identification. Specifically, it offers a min-max optimization framework which produces counterfactual fairne... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s great efforts and insightful comments to improve our manuscript. In below, we address these concerns point by point and try our best to update the manuscript accordingly.
> **The statement “One may notice that a function of X1 − X2 can be used to predict Y” ... | Summary: This paper aims for interventional fairness with sufficient prediction accuracy by employing a min-max optimization framework. The proposed approach aims for partially directed acyclic graphs (PDAGs) and extends itself to maximally oriented PDAGs (MPDAGs). Finally, the approach is evaluated on synthetic and re... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s great efforts and insightful comments to improve our manuscript. In below, we address these concerns point by point and try our best to update the manuscript accordingly.
## Weaknesses
> **Above line 188 : It is unclear what $S^{(i)}(A) ; 1<=i<=M$ refers to... | Rebuttal 1:
Rebuttal: Dear all reviewers and AC,
Please kindly find the attachment as our added one page experimental results.
Thanks,
Authors from Submission #18019
Pdf: /pdf/8dbd1a199447d61e54d83738661baa832ad338de.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond | Accept (poster) | Summary: The paper proposes a new approach to understanding neural networks by examining a model consisting of a sequence of first-order approximations telescoping into an empirically operational tool. This model aims to isolate components of the training process and offers a lens to analyze the effects of design choic... | Rebuttal 1:
Rebuttal: We thank this reviewer for their in-depth review and their positive assessment of our work! We were delighted by their appreciation of the utility of the telescoping model itself, the wide range of phenomena we are able to consider using our model and the insights we provide. We respond to major p... | Summary: This paper tries to understand the design of optimizer, model architectures and some deep learning phenomenon empirically. Additionally, the authors find a proxy model of neural networks other than the simply linearized model.
Strengths: 1. The topic is interesting, especially the proxy model of neural networ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. We are delighted that the reviewer finds our proxy model for a neural network interesting! Before we move to address the specific points raised in the review below, we would like to make two high-level comments. First, while we similarly believe that intere... | Summary: The paper replaces the traditional "lazy learning" regime approximation with an approximation using a telescoping sum. This splits up the usual interval of approximation $\theta_T - \theta_0$ into many smaller pieces. The authors then show why this approximation is useful for explaining various interesting asp... | Rebuttal 1:
Rebuttal: Thank you for the very detailed and constructive review! Limited by space constraints, we respond to major comments below.
**(1) Clarity.** We appreciate that our writing was somewhat densely packed in the mentioned sections, and will utilize the camera-ready extra page to decompress for improve... | Summary: This work proposes a new analytical model of neural networks (NNs) extending popular 'linearized' approximations over the initial parameters built from the gradient vectors. In particular, they consider approximating the full learning trajectory of NNs as a sequence of first-order approximations rather than a ... | Rebuttal 1:
Rebuttal: We thank this reviewer for their very constructive review and the positive assessment of our work! We were especially delighted to read that the reviewer found our uncovered connection between the telescoping model and gradient boosting to be particularly instructive. We respond to the comments ra... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for the time and effort put into the review process! We are grateful for the constructive nature of the reviews and were delighted by the largely positive assessment of our work. We were especially excited by the recurring appreciation of the new insights on (i... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Single-Loop Stochastic Algorithms for Difference of Max-Structured Weakly Convex Functions | Accept (poster) | Summary: This paper introduces a novel optimization problem termed Difference of Max-Structured Weakly Convex Functions (DMax). The DMax problem extends traditional frameworks such as difference-of-weakly-convex (DWC) optimization and weakly-convex-strongly-concave (WCSC) min-max optimization, which are widely utilized... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments and feedback on our paper.
**Q1:** Clarification of theoretical contribution within a novel setting.
**A.** We proposed a unified framework of analysis for non-smooth DWC and WCSC min-max problems, which leads to single-loop methods that achieves ... | Summary: This paper proposes a stochastic Moreau envelope approximate gradient method dubbed SMAG, the first single-loop algorithm for solving these problems, and provides a state-of-the-art non-asymptotic convergence rate.
This paper achieves the best complexity of order $O(\epsilon^{-4})$.
Futhermore, the agorithm of... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments and feedback on our paper.
**Q:** The special structure of the difference of convex functions is not used.
**A:** Thank you for your good question. (1) In this paper the problem considered is the Difference of Max-Structured Weakly Convex Function... | Summary: The paper considers the problem of minimizing a difference of maximum of two functions, under various regularity assumptions. Two important settings are difference of weakly convex functions and weakly convex strongly concave min-max problems. The authors propose a single loop algorithm with a convergence rate... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments and feedback on our paper.
**Q1.** The authors should distinguish if the difference/improvement from previous work are "double loop vs single loop", and/or faster rates and/or weaker assumptions. For the DWC setting, it seems it is "double loop vs s... | Summary: The paper studies a class of optimization problem named DMax, i.e., to minimize a loss that is a difference of two max functions. This problem covers both difference of weakly-convex optimization and weakly-convex strongly-concave minimax optimization. Existing algorithms require double-loop structure to solve... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments and feedback on our paper.
**Q1.** There are some typos that may affect readability
**A:** Thank you for pointing out these typos. We will fix them in our revision.
**Q2.** Are there examples that DMax gives more applications beyond DWC and min-m... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable feedback. As Reviewer gunK noted, the training curve of SMAG for the FER2013 dataset in Figure 1 exhibits unusually high variance. After careful checking, we identified a bug in the code, which loads a wrong file of one trial result for plotting. We have i... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Understanding Visual Feature Reliance through the Lens of Complexity | Accept (poster) | Summary: This paper proposes a method to measure the complexity of features extracted by deep learning models. This method is based on $\mathcal{V}$-information, an extension of Shannon's mutual information that takes the computational capabilities of a decoder into account. The proposed measure of feature complexity i... | Rebuttal 1:
Rebuttal: > "The proposed measure is closely related to the accuracy with which a feature can be linearly decoded from the layers of a network. I believe the paper would benefit from an explicit discussion of what, exactly, is the additional information provided by the proposed method which cannot be gained... | Summary: The current work proposes a method to assess the complexity of features in a representation space in terms of usable information. The measure of complexity is related to how far back in the layers of a trained network one can find information about the feature that is recoverable by a linear decoder. Equippe... | Rebuttal 1:
Rebuttal: We greatly appreciate the time and effort you invested in reviewing our work; thank you.
> "Regarding the qualitative exploration of dictionary vectors, and their grouping into clusters (for “what”): is there a good reason to think relative positions mean anything in feature space, and therefore... | Summary: The paper introduces a metric based on V-information to measure feature complexity in deep learning models. Using ResNet50 trained on ImageNet, it explores feature spectrum, training dynamics, network flow, and decision impact.
The study also highlights the role of simplicity bias and the evolution of featur... | Rebuttal 1:
Rebuttal: > "Not Easy To Follow On Math: The paper's mathematical sections are difficult to follow. Clearer and intuitive explanations would improve accessibility."
We thank the reviewer for this feedback. We strove to keep the math portions of the paper as accessible as possible, but welcome feedback ... | Summary: The paper introduces a novel metric for quantifying feature complexity in deep learning models, specifically focusing on an ImageNet-trained ResNet50 model. This V-information-based metric captures whether a feature requires complex computational transformations for extraction. The study addresses four key qu... | Rebuttal 1:
Rebuttal: > "While the findings are insightful, they are based on a single architecture (ResNet50) and dataset (ImageNet). The results might not generalize to other models or tasks without further validation." "The assumption that each layer optimally represents features for downstream linear probes may not... | Rebuttal 1:
Rebuttal: ### General comments
Thank you to the reviewers for taking the time to read and review our paper. Your critiques are sharp and insightful. You found the paper "extremely well-written" and "well presented and easy to follow." Regarding the results, you noted that it "adds significant depth to exis... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes a new measure of feature complexity based on an information-theoretic metric, the $\nu$-information metric. Utilizing this complexity measure, the paper shows (1) visualization of features of different complexities, (2) simple features are propagated through the residual connections to reac... | Rebuttal 1:
Rebuttal: > "The paper lacks coherence because multiple components (e.g., metrics, algorithms) introduced in this paper do not come from the same theoretical framework. [...] These metrics/algorithms are borrowed from different theoretical frameworks and contexts, so it is in doubt whether they can be used ... | null | null | null | null | null | null |
SyntheOcc: Synthesize Geometric-Controlled Street View Images through 3D Semantic MPIs | Reject | Summary: The paper introduces SyntheOcc, a framework utilizing diffusion models to synthesize photorealistic images for autonomous driving simulations. The proposed method addresses limitations in the existing 2D diffusion model to generate multi-view driving videos by integrating detailed 3D geometric data.
The author... | Rebuttal 1:
Rebuttal: Thank you for the detailed feedback and insightful comments. We appreciate your time and great efforts in reviewing. We now reply to each comment below.
**(1) Reweighing strategy**
We agree with the reviewer that the reweighing strategy represents a minor level of innovation. As a result, we do ... | Summary: This paper introduces SytheOcc, a method that employs a diffusion model with 3D occupancy as conditions to generate street view images.
Strengths: 1. Unlike previous methods that use box conditions, this paper proposes the use of 3D occupancy, resulting in finer geometric control ability.
2. The paper further... | Rebuttal 1:
Rebuttal: Thanks for your positive comments and constructive suggestions. In the following, we reply to individual questions and comments raised by the reviewer:
**(1.1) Inconsistency between views and frames**.
In our paper, while contributing to 3D controllable image generation, we acknowledge its limit... | Summary: The paper propose a new 3D semantic multi-plane images (MPIs) based image generation pipeline, which enables finer geometric control for 3D editing, dataset generation, and long-tailed scene generation. Through extensive experiments, the work demonstrates substantial advancement in generation quality and bette... | Rebuttal 1:
Rebuttal: We greatly appreciate the careful proofreading of our paper. We would like to thank the reviewer for the valuable feedback and for appreciating our clear writing. In the following, we reply to individual questions and comments raised by the reviewer:
**(1) W1. Geometry Constraint**
In our unders... | Summary: In this paper, the authors propose a new controllable diffusion-based image generation method named SyntheOcc, which takes an occupancy map as input and generates camera images. SyntheOcc enables the application of scene editing and long-tail corner case generation and shows a strong capability of data augment... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the positive and detailed feedback. Below, we reply to individual comments and questions raised by the reviewer:
**(1) W1. The reliance on occupancy introduces a minor burden but with enhanced controllability**
We agree with the reviewer regarding the reli... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' constructive feedback and acknowledge their consensus on the merits of our work. We list the consensus among the reviewers below.
**(1) Occupancy as condition: an innovative approach**
Our work introduces 3D semantic Multi-Plane Images (MPIs) to capture both geometri... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Matrix Denoising with Doubly Heteroscedastic Noise: Fundamental Limits and Optimal Spectral Methods | Accept (poster) | Summary: This paper studies the following "matrix denoising"/low-rank estimation problem. Given a rectangular matrix $Y = uv^\top + W$, the goal is to recover the rank-one factors $u,v$, when $W$ is random, with as little $\ell_2$ error as possible.
A classic and well-studied setting takes $W$ to have iid entries; oft... | Rebuttal 1:
Rebuttal: We thank Reviewer Fvn7 for the insightful comments and positive evaluation. Below we address the comment concerning our various assumptions.
We agree with the reviewer that the Gaussian-ness assumptions, the assumption that
$n,d$ are of comparable order, and the assumption that the empirical sp... | Summary: The authors study how to recover a rank one spike corrupted by doubly heteroscedastic gaussian noise in the high dimensional regime. We are given a condition on the signal to noise ratio to indicate whether it's information-theoretically possible to have a non-trivial recovery of the spike. If this is satisfie... | Rebuttal 1:
Rebuttal: We thank Reviewer hDgq for carefully reading the manuscript and for the insightful comments and suggestions. Below we address each point raised in the review separately.
**I believe one issue in the writing is its lack of clarity in stating which results are completely rigorous and which aren't**... | Summary: This paper considers the problem of matrix denoising. Given an observation X = A + W where W is noise, our goal is to estimate A, which is typically low-rank. Unlike previous works, this paper treats the case where W is doubly heteroscedastic. The authors identify a condition for non-trivial estimation of the ... | Rebuttal 1:
Rebuttal: We thank Reviewer S4fP for the positive evaluation. Below we address the comments and questions.
**Strictly speaking, you do not show that whitening fails, only that the whitened matrix does not match the proposed AMP approach (lines 262-265).**
We will make the following 2 clarifications regar... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their reviews and we have responded to their comments separately below. In this common response, we attach a plot that shows the presence of spectral outliers in $A^*$, as well as their absence in $A$. We will add this plot to the revision.
Pdf: /pdf/e493c185cebd5bd8543c... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Non-asymptotic Convergence of Training Transformers for Next-token Prediction | Accept (poster) | Summary: This manuscript focuses on the next-token prediction (NTP) task, and provides a fine-grained non-asymptotic analysis of the training dynamics of a one-layer transformer. Specifically, the authors first characterize the essential structural properties of training datasets for NTP using a mathematical framework ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful comments, and would like to provide the following responses.
**Q1:** How is the loss function between lines 227 and 228 obtained? Why is its form different from Eq. 1?
**A1:** The loss in Eq.\ (1) is for the general-length case. The loss between lines 227 a... | Summary: In this work, the authors mathematically examine the learning dynamics of simple transformers for next token prediction. To allow a mathematical analysis, they consider a highly simplified setting: the transformer consists of a single attention layer followed by a single feed-forward layer, the layers are tra... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful comments, and would like to provide the following responses.
**Q1:** How would you change the manuscript to make it more easy to understand your definitions and motivation behind the mathematics? For example, the example dataset provides some additional insi... | Summary: The paper presents a non-asymptotic analysis of training dynamics for a single-layer transformer used in next-token prediction tasks. It introduces a two-stage training algorithm leveraging structural properties of the training dataset, defined via collocations and query-dependent partial orders. The findings ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful comments, and would like to provide the following responses.
**Q1:** Some of the mathematical concepts, particularly the query-dependent partial orders, are complex and could be better explained.
**A1:** We appreciate the feedback on the complexity of the ... | Summary: This paper conducts a non-asymptotic analysis of training dynamics for a one-layer transformer, focusing on next-token prediction tasks.
It provides a mathematical framework based on partial order to formally characterize a realizable training dataset for next-token prediction.
It also introduces a two-stage ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful comments.
**Q1:** Deterministic ground truth model seems uncommon. Besides assuming n, where is the existence of $p_L^*$ theoretically necessary?
**A1:** In recent line of theoretical research on transformers, deterministic models are often adopted such as... | Rebuttal 1:
Rebuttal: We thank all reviewers for their feedback, which will greatly improve our paper.
In the attached PDF file, we provide a figure for an additional experiment verifying the generalization ability described in Theorem 3, as a response to the second question Q2 of Reviewer jsSk. In the experiment, we... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents | Accept (poster) | Summary: This paper focuses on backdoor attacks on LLM-based agents via poisoning of the fine-tuning data. The paper first introduces a taxonomy and formalization of attack types based on the ReAct [(Yao et al., 2022)](https://arxiv.org/abs/2210.03629) paradigm. In this paradigm, an agent produces a "trace" of repeated... | Rebuttal 1:
Rebuttal: We sincerely thank you for your careful reviewing. We make the following response to all your questions.
**Q1:** Regarding the poisoning ratios used in Thought-Attack.
**A1:** The detailed response to this question is in the global Author Rebuttal. In summary, (1) we clarify there is a differen... | Summary: This paper proposes a backdoor attack method against LLM-based agents. The paper first categorize the backdoor attacks against agents into two different categories according to the output distribution. Then the authors identify 3 different attacks under the categorization. Experiments show that the attack is e... | Rebuttal 1:
Rebuttal: We sincerely thank you for your great efforts on reviewing our paper. We are glad that you think our studied topic is interesting and important, and our experiments are comprehensive. We make the following response to address your remaining concerns.
**Q1:** Regarding the comparison with the bac... | Summary: This paper investigates the practical safety risks of LLM-based agents against backdoor attacks. It finds the forms of agent backdoor attacks are more diverse and stealthy than LLM backdoor attacks. First the backdoor trigger can be inserted into the observation of the environment and does not have to occur in... | Rebuttal 1:
Rebuttal: We sincerely thank you for your positive review. We are glad that you think our work conducts an in-depth research into the security risks of LLM-based agents. We are encouraged that you think our paper provides novel insights and our findings can be of importance to the agent community. To addres... | Summary: This paper studies the backdoor vulnerability of LLM-based agents. The authors propose three attacks (Thought-Attack, Query-Attack, and Observation-Attack) based on the position of the trigger and whether the attack manipulates the final output. The authors conduct experiments on six real-world agent tasks and... | Rebuttal 1:
Rebuttal: We sincerely thank you for your positive review. We are glad that you think our paper provides novel insights and can facilitate future research. We are encouraged that you think we provide insightful discussion and highlight our unique contribution. We make the following response to address your ... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their time and efforts on reviewing our paper. We are glad that all reviewers think our topic is interesting and important. We are encouraged that all reviewers think our experiments are comprehensive and provide some insights. Here, we make the general res... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Taming Generative Diffusion Prior for Universal Blind Image Restoration | Accept (poster) | Summary: This paper proposes a blind image restoration method by using a pre-trained diffusion model without additional prior knowledge. The proposed adaptive guidance scale is fancy which uses the loss function to judge its value while the degradation function design is confusing. The results on real-world benchmarks ... | Rebuttal 1:
Rebuttal: We appreciate the valuable questions and suggestions raised by Reviewer rhDr regarding this article.
> **Q1:** "(i)What is the “∑” in Line 112 and Line 421? (ii)The design of the degradation function D does not make sense, why does adding the M term estimate the noises and what does the noise mea... | Summary: This research introduces BIR-D, a novel approach to the universal challenge of blind image restoration. It leverages an adaptable convolutional kernel designed to emulate the degradation model, with the capability to refine its parameters progressively during the diffusion process. Furthermore, the work presen... | Rebuttal 1:
Rebuttal: We greatly appreciate the valuable comments and suggestions provided by Reviewer CUUT on this article.
> **Q1:** Detailed comparison with GDP.
**A:** We greatly appreciate your suggestions. BIR-D has the following differences compared to GDP.
1. Differences in the setting of degradation functio... | Summary: The paper introduces BIR-D, a novel approach utilizing generative diffusion models for blind image restoration without requiring predefined degradation types. Traditional methods assume degradation models and optimize their parameters, limiting their applicability. BIR-D overcomes this by employing an optimiza... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer xbfe for devoting time to this review and providing valuable comments.
> **Q1:** (i)"The reviewer appreciates the innovative use of an optimizable convolutional kernel to dynamically adapt the degradation model during the time steps. This is considered the most signif... | null | null | Rebuttal 1:
Rebuttal: We are very grateful to all the reviewers for their valuable comments and suggestions on this article.
___
We are glad to see the reviewers' recognition of our work.
* "The method stands out by integrating an optimizable convolutional kernel to dynamically adapt the degradation model during the ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
AID: Attention Interpolation of Text-to-Image Diffusion | Accept (poster) | Summary: This paper proposes a training-free method for generation interpolation of diffusion models with attention manipulation. Targeting on the layout transition inconsistency and nonuniform step-wise transition, the paper proposes to extend the attention interpolation from previous cross-attention to self-attention... | Rebuttal 1:
Rebuttal: We thank Reviewer frah for the feedback. Here, we aim to clarify our contributions and the application of the proposed methods.
> W1: The application of the generation interpolation seems somewhat restricted, and the practical meaningness of the task is doubted, is the interpolation could benefit... | Summary: Summary and Contributions: The authors introduce a novel task called conditional interpolation, which is to generate interpolation images with various conditions like text or pose. They propose an attention-base (and prompt-guided) method to achieve conditional interpolation, and three evaluation metrics to as... | Rebuttal 1:
Rebuttal: We thank Reviewer THPg for the constructive feedback. Here is the response to reviewer's concerns.
> W1:The paper lack of a clear and detail definition of the conditional interpolation. The authors claims that they formulate a new task call conditional interpolation, which is doing interpolation ... | Summary: In this work, the authors propose Attention Interpolation via Diffusion (AID), a novel, training-free technique for improving image interpolation under specific conditions like text or pose. Traditional methods using linear interpolation often produce inconsistent, low-fidelity images. AID enhances image consi... | Rebuttal 1:
Rebuttal: We thank reviewer oFS1 for the feedback. We provide our response to the reviewer's concerns in the following.
> W1 (Q1): For the fidelity metric in Eq. 7, one should expect the metric is maximized when the interpolation starts the same as image A, has an abrupt change from image A to B, and stays... | Summary: In this paper the authors explore the task of interpolation between images in conditional diffusion modal. First the authors list out the three desirable properties of successful interpolation: perceptual consistency, smoothness, and image quality. The authors first introduce a method AID that incorporates thr... | Rebuttal 1:
Rebuttal: We thank reviewer oKyj for the candid feedback and helpful suggestions. We provide our response to the reviewer's concerns in the following.
> W1: One aspect of diffusion models that the authors have not considered here is the classifier free guidance and the use of negative prompts, which is sta... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Diffusion for World Modeling: Visual Details Matter in Atari | Accept (spotlight) | Summary: The paper proposes a visual diffusion-based world model for reinforcement learning. The authors argue that current world models using discrete latent variables may lose important visual details, which are critical for reinforcement learning tasks. DIAMOND addresses this by generating images in the original pix... | Rebuttal 1:
Rebuttal: Thank you for your clear and concise review. We’re pleased you appreciate our carefully written paper, strong results, ablations and open-source code.
To address your concerns:
> It would be useful to include a comparison of training time for DIAMOND compared to prior baselines, e.g. what is the... | Summary: This paper introduces DIAMOND (DIffusion As a Model Of eNvironment Dreams), a novel RL agent trained within a diffusion-based world model. DIAMOND's world model is a diffusion model that, conditioned on past observations and actions, generates an observation at the next time step. This approach diverges from p... | Rebuttal 1:
Rebuttal: Thank you for your insightful review. We are pleased that you appreciate the potential of our diffusion-based approach, our thorough analysis of design choices and the clarity of our paper.
We can understand your minor concern with a lack of more quantitative analysis of our design choices. To ad... | Summary: This paper introduces a new world model for learning behaviors in imagination using reinforcement learning.
In particular, a diffusion model is used to generate the next frame, conditioned on previous frames and actions.
At each environment step, multiple denoising steps are performed to convert a noise image ... | Rebuttal 1:
Rebuttal: Thank you for your clear and concise review. We are pleased that you appreciate our idea and strong results.
Regarding your concern with our analysis being mainly qualitative (W1), we agree that a more quantitative measure of the compounding error of different methods for long trajectories would ... | Summary: This paper proposes an approach for learning world models with diffusion based approaches, compared to the recently proposed ones using transformers or the ones dependent on discrete latent variables in general. The core idea is that given success of image generation using diffusion models, the visual details ... | Rebuttal 1:
Rebuttal: Thank you for your review. We are pleased you believe our work does a good job in demonstrating that using diffusion for world modeling can work well in Atari and recognize our experimental gains on this competitive benchmark.
Your main concern seems to be the focus of our evaluation on the Atari... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for taking the time to review our paper, and for their positive and constructive feedback.
We are pleased to see a general consensus regarding the motivation of our work, the clarity of our paper, and the strong results achieved by our method.
The main sugges... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards a theory of how the structure of language is acquired by deep neural networks | Accept (poster) | Summary: This paper proposes a conceptual characterization of how much data is required to learn the latent hierarchical structure of input languages. The theory is based on token correlations, which themselves are qualified based on token distances; these correlations are further traced back to the hierarchical struct... | Rebuttal 1:
Rebuttal: **Weakness 1,a (context-freeness).** We agree with the reviewer's remark on the context-freeness of natural languages and we will modify the text accordingly, stating specifically in both the abstract and the limitations section that the context-free description is approximate and captures many (b... | Summary: This paper investigates the sample complexity of learning languages generated by a kind of PCFG. The paper uses a model called the Random Hierarchy Model (RHM; a PCFG with a fixed tree structure) and identifies a relationship between the size of the training set and the "effective context window" that can be l... | Rebuttal 1:
Rebuttal: **Weakness 1.** While the assumption of fixed tree geometry is unlikely to be satisfied in natural language data, we do not believe this assumption to be necessary for the power-law decay of correlations. [This paper](https://arxiv.org/abs/1606.06737), for instance, shows other examples of text da... | Summary: This work studies the relation between the amount of training data required to learn the structure of language via the next token prediction objective & neural nets (CNNs, Transformers). The underlying training data is systematically varied using PCFGs and the authors find that that the size of the training da... | Rebuttal 1:
Rebuttal: **Weakness 1/Question 1.** Please see the comment above (author rebuttal).
**Weakness 2.** We agree with the reviewer that this key point needs to be emphasised much more. In particular for deep generative models of data (large $L$), we indeed find that for large $P$, the learning curve can be de... | Summary: 1) This paper looks at the relationship between training dataset size in language model settings and the token x token correlations learned by the model.
2) The authors first use a synthetic setting to study this relationship and derive the results followed by testing it on a real dataset of lines from a Sha... | Rebuttal 1:
Rebuttal: **Weakness 1/Question 1.** Please see the comment above (author rebuttal).
**Weakness 2**. Although the generation process is cheaper, the costs of training limit the available range of $P$ to that considered in the paper (up to a few million).
**Question 2.** Since this paper focuses on sample ... | Rebuttal 1:
Rebuttal: We thank all the referees for the detailed comments, and for finding our work interesting and supporting publication. They all pointed out that the paper will be improved by adding an additional set of experiments, perhaps involving a larger dataset made of contemporary text. We agree with the rev... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MeLLoC: Lossless Compression with High-order Mechanism Learning | Accept (poster) | Summary: This paper introduces a novel approach combining high-order mechanism learning with classical encoding techniques to enhance lossless compression for large-scale scientific floating-point data. The core innovation lies in treating data as discrete samples from a physical field governed by differential equation... | Rebuttal 1:
Rebuttal: We thank the reviewer for the overall positive feedback and the valuable comments. We have revised our manuscript taking your concerns and suggestions into consideration.
To answer your questions/comments:
> Q1: Clarification on Differential Equation Models: Can you provide more details on how yo... | Summary: This paper introduces MeLLoC (Mechanism Learning for Lossless Compression), an approach that combines high-order mechanism learning with classical encoding to enhance lossless compression for scientific data. The core concept is to interpret the data as discrete samples derived from an underlying physical fiel... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the thoughtful comments and valuable suggestions to make the paper clearer. We have revised our manuscript taking all comments and suggestions into consideration.
> Q1: Is [1] a lossless compression method? It is recommended to clarify the differences betwee... | Summary: This paper propose a near lossless compression method named MeLLoC to compress the scientific data by learning the inherent mechanisms. By solving the inverse problem of Partial Differential Equations (PDEs), MeLLoC transforms the scientific data from original data domain into discretized source domain, which ... | Rebuttal 1:
Rebuttal: We greatly appreciate the careful review of the manuscript. We sincerely hope that the following answers will better illustrate our work. We also recommend that the reviewer read the “Author Rebuttal.” We hope the reviewer finds the efforts and improvements we made in both the theoretical and expe... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful comments and suggestions. Accordingly, we try our best to make substantial revisions. The revised article now contains the extensions of the proposed lossless compression framework, and the additional experiments on comparison studies. We have addres... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Why Warmup the Learning Rate? Underlying Mechanisms and Improvements | Accept (poster) | Summary: The paper analyses different aspects of warmup in the gradient based training focusing on SGD, ADAM and their variants under two types of parameterization. It shows through mostly empirical analysis that warmup facilitates training at higher learning rates and stabilizes the training dynamics by keeping it awa... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our paper and for their encouraging comments. Below are our responses for the questions and comments.
> The figures are not quite clear...
We noticed some errors in the Figure 3 caption in the submission and have revised it as follows:
''Test a... | Summary: This paper studies the mechanisms of the warmup technique. The authors experimentally demonstrate that the primary benefit of warmup is its ability to enable the network to handle larger learning rates.
Strengths: Warmup is an essential trick for training modern deep neural networks, and understanding its rol... | Rebuttal 1:
Rebuttal: We thank the reviewer for reviewing our paper and they encouraging feedback.
> The experiments are primarily ....
We have extended our experiments to include Transformers trained on language modeling tasks and found that our results extend to these models. The results are detailed in the global ... | Summary: The authors explain the mechanisms of the warmup technique showing that with warm up the loss of NN will go to a flatter space than direct optimization. Further, based on analysis, the authors propose a new optimization algorithm called GI-Adam.
Strengths: 1. The authors explain why the warm-up technique can ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and effort for reviewing our paper and providing comments.
> The conclusions are from FCNs and WideResnet, which can be trained well without warm-up. Does the conclusion still hold for some "hard" models and datasets (e.g., Transformer)?
We have extended our ... | Summary: The paper examines the learning rate warmup technique from the perspective of its influence on the evolution of loss sharpness for different optimizers (GD, SGD(-M), Adam) and network parametrizations (Maximal Update Parameterization – $\mu$P and Standard Parametrization – SP). The authors demonstrate that the... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and effort in reviewing our paper and providing comments.
> 1. Could you summarize the main novel insights ...
While Gilmer et. al. 2021 is a key reference that our paper builds on, there are several novel insights in the empirical analysis part of our paper.... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and effort in reviewing our paper. Based on reviewers feedback, we have added the following results:
1. **Language Modeling Experiments:** We have extended our experiments to include Transformer models trained on language modeling tasks using SGD and Adam. I... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper finds that the learning rate warmup allow the network to tolerate larger learning rates. It gradually reduces the sharpness and forces the model to leave poorly conditioned areas of the lossscape and move toward flatter regions which can tolerate larger learning rates.
Strengths: 1. This paper anal... | Rebuttal 1:
Rebuttal: > Though the author claims to find that warmup allows for larger learning rates, it has been found in existing work such as Gilmer 2022. Further elaboration on the difference and novelty will make the paper more convincing....I suggest further summarize the contribution part.
Whille Gilmer 2022 i... | null | null | null | null | null | null |
Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization | Accept (poster) | Summary: This paper introduces adaptive, line search-free second-order methods aimed at solving convex-concave min-max problems. The proposed algorithms use an adaptive step size, simplifying the update rule to require solving only one linear system per iteration. The paper presents two main contributions: an adaptive ... | Rebuttal 1:
Rebuttal: **W1 While the parameter-free method is innovative, it may still require careful tuning of initial parameters in practice.**
**R1** This is an important observation, and thanks for bringing this point up. We agree that removing the necessary parameters will not come for free and we need to initia... | Summary: This paper proposes adaptive, line search-free second-order methods with an optimal rate of convergence for solving convex-concave min-max problems. By defining the step size recursively as a function of the gradient norm and the prediction error, they eliminate the need for line search or backtracking mechani... | Rebuttal 1:
Rebuttal: **Q1 The limitation of using line search is not clearly demonstrated. For example, it is unclear if using line search would incur higher computational costs and take more time. This limitation should be illustrated with experimental results.**
**A1** We believe our reviewer would agree with us th... | Summary: This paper consider solving convex-concave minmax problems using second order optimization method. A modified adaptive Optimistic algorithm by approximating the proximal point method using second order information is proposed. It is shown that this method can achieve the optimal convergence rate. Moreover, com... | Rebuttal 1:
Rebuttal: **Q1 Can the authors provide more explanation on the advantages of Adaptive SOM than optimal SOM?**
**A1** In terms of convergence rate (# of iterations needed to reach an accuracy), one cannot expect our method to beat the optimal SOM. This is because the optimal SOM leverages the line search sc... | Summary: The authors present a new second-order method for convex-concave min-max optimization based on the optimistic gradient method but modified to work with second-order information. The authors first propose a variant that requires the value of Jacobian Lipschitzness $L_2$ and then introduce an additional paramete... | Rebuttal 1:
Rebuttal: **W1 This work requires Lipschitz gradients thus lower bounds are not applicable.**
**R1** First, we would like to highlight that our first method (Option I) does not require Lipschitz gradients and achieves the optimal rate for its setting. However, the parameter-free version (Option II) does re... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful feedback. Following your suggestions, we have performed new experiments, and the plots are included in the shared PDF file.
- **Practical advantage compared to optimal SOM**. To demonstrate the computational efficiency of our proposed line-search-free... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Neural Pose Representation Learning for Generating and Transferring Non-Rigid Object Poses | Accept (poster) | Summary: The authors propose a skeleton-free pipeline for implicit 3D pose representation and transfer. They introduce the prediction of Jacobian fields to achieve shape-preserving representation, which facilitates the application of transferred poses. To enhance accuracy, a per-identity refinement step is included, ut... | Rebuttal 1:
Rebuttal: Dear reviewer RKb2,
Thank you for your positive comments. We are especially grateful for your recognition of the use of Jacobian fields as "innovative" and for acknowledging that our approach is well "justified". We provide our responses to your queries below.
**Impact of the Sampling Method and... | Summary: This paper presents a novel representation learning framework for pose estimation which is disentangled from the identity of the object. This implicit representation is used for generating and transferring poses using a cascade diffusion model. A keypoint-based hybrid pose representation with a sparse mesh is ... | Rebuttal 1:
Rebuttal: Dear reviewer xBSu,
We highly appreciate your positive comments on our work, particularly regarding the writing and experiment procedures. Our response to your question can be found below.
**Extension to Images and Videos.** We strongly agree that our work can be extended to other modalities, su... | Summary: This paper introduces a novel 3D generative model for pose-identity disentangled representation of 3D shapes. It proposes to use a set of keypoints with features to represent the pose of a 3D shape and learn a pose-extractor and pose-applier to accomplish pose transfer between instances. Experiments are conduc... | Rebuttal 1:
Rebuttal: Dear reviewer skND,
Thank you for recognizing our work as a study of a "novel" and "important" task, and for appreciating our "sound" and "well-motivated" approach. We have carefully reviewed your queries and provide our responses below.
**Temporal Consistency.**
Thank you for providing the cons... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers for taking the time to review our submission and for providing constructive and insightful comments and feedback. We have compiled the qualitative results discussed in our rebuttal in the attached PDF file.
Pdf: /pdf/321415053c54f359a1c6d9dde9057a4f1ec61cb2.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
WildGaussians: 3D Gaussian Splatting In the Wild | Accept (poster) | Summary: This paper achieves in-the-wild reconstruction of 3D Gaussian splatting by introducing appearance embedding and DINO uncertainty mask. The appearance embedding is divided into a per-photo global embedding and a per-Gaussian local embedding. The uncertainty mask is obtained by comparing the rendered image with ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback. We appreciate that you find that “the performance in removing occluders is impressive”, and would like to raise the score if we can justify half-image test-time optimization. We will address all comments below and will adjust the paper accordin... | Summary: The authors present WildGaussians, an innovative method designed to address occlusions and appearance changes using 3DGS. By utilizing robust DINO features and incorporating an appearance modeling module into 3DGS, their approach achieves state-of-the-art performance. WildGaussians not only matches the real-ti... | Rebuttal 1:
Rebuttal: Thank you very much for the positive and constructive feedback! We highly appreciate that you find our approach “commendable, with detailed methods and experiments” and that the paper “demonstrate[s] that WildGaussians can handle complex scenarios involving dynamic objects and varying appearances,... | Summary: The authors are proposing WildGaussians, an approach based on 3D Gaussian Splatting (3DGS), that tries to address its robustness issues, specifically to significant appearance changes due to varying illumination or occlusions and dynamic objects.
First, explicit appearance modeling is introduced into 3DGS by ... | Rebuttal 1:
Rebuttal: Thank you very much for the positive and constructive feedback! We really appreciate that you consider our work “highly-relevant to the research community”, and that our “contributions are clear and simple” while our method “significantly outperforms baselines”. We will address all concerns below,... | Summary: The author proposes an improvement strategy for reconstructing 3D scenes from in-the-wild data based on the latest 3DGS method, primarily addressing occlusion and appearance changes. The main improvements are as follows:
1.Appearance Encoding with MLP: Introduce a Multi-Layer Perceptron (MLP) to encode the app... | Rebuttal 1:
Rebuttal: Thank you very much for the positive and constructive feedback! Below, we address the concerns raised in the review. We will adjust the paper accordingly and fix the typos.
**W1: The ideas in the paper are not uncommon, similar to SWAG, GS-W, and Scaffold-GS**
To our understanding, Scaffold-GS ... | Rebuttal 1:
Rebuttal: **Global Response**
We thank all reviewers for their constructive comments. Please check the attached one-page PDF for more numerical and visual results on:
* **Figure 1**: Multiview consistency for single app. embedding
* **Table 1**: SWAG and GS-W comparison on Photo Tourism
* **Table 2**: Add... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Amnesia as a Catalyst for Enhancing Black Box Pixel Attacks in Image Classification and Object Detection | Accept (poster) | Summary: The authors present a few-pixel querying blackbox attack for image classification and object detection models, called Remember and Forget Pixel Attack using Reinforcement Learning (RFPAR). As this is a blackbox attack, the setting assumes the attacker does not have access to the target model weights, though as... | Rebuttal 1:
Rebuttal: Dear Reviewer R24Y,
Thank you for reading our paper and providing comments to help improve it. Below, we address your concerns.
---
**C1: "Details of the Ablations could be presented more clearly. "**
1) **M ablation:**
M stands for Memory in the Remember process, and I represents Initializatio... | Summary: This paper proposes a new adversarial attack method called Remember and Forget Pixel Attack using Reinforcement Learning (RFPAR) for image classification and object detection models. The key contributions are:
- A novel pixel-based black-box attack using reinforcement learning with "Remember" and "Forget" proc... | Rebuttal 1:
Rebuttal: Dear Reviewer RX8p,
Thank you for reading our paper and providing comments to help improve it. Below, we address your concerns.
---
**C1) "Can you provide more theoretical insight into why the Remember and Forget processes lead to improved performance?"**
We initially used a multi-step REINFOR... | Summary: The paper proposes a pixel-based black box attack called RFPAR that uses RL to perturb pixels. The paper has a remember and forgetting mechanism. In the remember step, they memorize the perturb images to reduce the number of queries and in forgetting, they try to remove them from the memory. Although their mai... | Rebuttal 1:
Rebuttal: Dear Reviewer p4SQ,
Thank you for reading our paper and providing comments to help improve it. Below, we address your concerns.
---
**C1:“The only transformer model used is the VIT which paper didn't specified which variant. Given the numbers, assuming the authors used VIT-Based, I'd like to k... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers,
Thank you once again for reading our paper and providing insightful comments. We have made every effort to address your concerns. If our responses adequately address the reviewers’ concerns, we kindly request that the reviewers consider increasing your scores.
Due to space constr... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Meta-Diffu$B$: A Contextualized Sequence-to-Sequence Text Diffusion Model with Meta-Exploration | Accept (poster) | Summary: This paper presents a novel approach to improving sequence-to-sequence (Seq2Seq) text generation models using diffusion models. The authors identify limitations in existing Seq2Seq-Diffusion models, which typically rely on fixed or hand-crafted noise scheduling rules that do not account for the specific charac... | Rebuttal 1:
Title: Thank you for your valuable suggestions. We have included additional baseline model experiments and addressed each of your questions to clarify any doubts. We assure you that these experiments will be included in the final version.
Comment: >Q1: My first concern is that this paper mainly compares the... | Summary: Comprehensive Evaluation of the new S2S Diffusion framework Meta-Diffu$\beta$.
Strengths: - Clear and well-written presentation of the method
- It provides a new framework that uses an additional scheduler based on Meta-Exploration to schedule contextualized noise, which performs well in the four given benchm... | Rebuttal 1:
Title: Thank you for your valuable suggestions. We have included additional baseline model experiments, machine translation experiments, and a variety of different baselines. We have addressed each of your questions to clarify any doubts. We assure you that these experiments will be included in the final ve... | Summary: The paper introduces the Meta-DiffuB, a scheduler-exploiter diffusion framework that focuses on sequence-to-sequence (Seq2Seq) setting. Its novel trainable contextualized noise scheduler, inspired by Meta-exploration, is also flexible and plug-and-play with other models like DiffuSeq without re-training. This ... | Rebuttal 1:
Title: Thank you for your valuable suggestions. We have included the inference times for the models and added machine translation experiments. In the final version, we will also address and revise the format and references you mentioned.
Comment: >Q1: L120, 123: the quote for 'skipping' is wrongly formatted... | Summary: This paper proposes a meta-learning based noise scheduler to incoporate contextualisation in texts. The scheduler is a plug-and-play module which could be applied to other similar sequential setting. The experimental results demonstrate its superiority comparing to the baselines, in terms of generating quality... | Rebuttal 1:
Title: Thank you for your valuable suggestions. We have included the training curves and addressed your comments as below.
Comment: >Q1: For example, Figure 3, the noise level is quite stable for the proposed method but in the text below, it claims 'Meta-Diffu$B$ applies adaptive noise at varying training e... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Dual-Diffusion for Binocular 3D Human Pose Estimation | Accept (poster) | Summary: The paper proposes a dual-diffusion model for Binocular 3D Human Pose Estimation, which models the uncertainties integrated in the binocular configuration. Since constructing the depth distribution of 3D poses is difficult, the authors use 2D poses with triangulation to initialize the 3D poses during the diffu... | Rebuttal 1:
Rebuttal: Dear Reviewer 9xXX,
We greatly appreciate your thoughtful review and the time you have taken to provide insights and feedback on our submission. We are encouraged by the positive aspects you've highlighted and grateful for the critical points you've raised. In response, we have addressed the weak... | Summary: This paper presents a new method for binocular human pose estimation. The key idea is to employ diffusion model into this problem. The authors propose a novel dual-diffusion process: jointly diffuse 2D pose uncertainty and 3D pose uncertainty. Because 3D uncertainty relates to both 2D noise (which directly re... | Rebuttal 1:
Rebuttal: Dear Reviewer o1bT,
First and foremost, we'd like to express our gratitude for your comprehensive review and insightful comments. In response, we have supplemented the evaluation and provided clarifications to enhance the clarity of our experiments.
**1. About the comparison with RSB-Pose.**
**... | Summary: The authors suggest a 3D human pose estimation (HPE) in binocular (two-view) settings. In order to reduce the uncertainty of triangulation method, the authors leverage the diffusion model. They propose a method called Dual-Diffusion method, which simultaneously denoises uncertainties in both 2D and 3D to produ... | Rebuttal 1:
Rebuttal: Dear Reviewer cXv4,
We appreciate the detailed feedback and hope our clarifications address your concerns. We apologize if there was any misunderstanding or misinterpretation of our paper. We're eager to highlight the significance and potential of our work.
**1. The difference from DiffPose.**
... | Summary: This paper presents a method for 3D human body keypoint estimation from binocular images. Different from traditional multi-view settings, such methods only take two views as input, which suffer from larger uncertainty at detph wise. To alleviate the depth ambiguity, the paper describes a diffusion-based framew... | Rebuttal 1:
Rebuttal: Dear Reviewer cBDy,
First and foremost, we extend our deepest gratitude for your thorough review and insightful feedback. Your recognition of our insights, experiments, and writing is appreciated. The suggestions to add more experiments and discuss 3D uncertainty modeling are particularly valuabl... | Rebuttal 1:
Rebuttal: Dear ACs and Reviewers,
We are very grateful for your time and effort in reviewing this manuscript. We value every helpful suggestion and comment. **The two most frequently concerned questions are: 1) comparative evaluation with more 2D pose detectors, and 2) comparative evaluation with Diffpose.... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Harmonizing Stochasticity and Determinism: Scene-responsive Diverse Human Motion Prediction | Accept (poster) | Summary: The paper present a method for human motion prediction that take into account the environment (3d point cloud) in which the person moves. The method work with several steps: first from the past motion an area of interest is estimated and several object of interest (bed, chair...) with which the person might in... | Rebuttal 1:
Rebuttal: **Q1**: Synthesis methods move to the main paper.
**A1**: We will incorporate the discussion of the synthesis method into the main body of the paper in the next revision. Specifically, Section B.1 will be moved to Section 2.3, B.2 will be positioned in a new subsection between Sections 4.2 and 4.... | Summary: This work studies human motion prediction in 3D scenes. The proposed DiMoP3D leverages context-aware intermodal interpreter, behaviorally-consistent stochastic planner, and self-prompted motion generator to solve the task. The authors conduct experiments on GIMO and CIRCLE to demonstrate a superior performance... | Rebuttal 1:
Rebuttal: **Q1**: The novelty of this work.
**A1**: The task of predicting stochastic human motions with real-world scene awareness is crucial for embodied applications like robotics and autonomous vehicles, enhancing their navigational systems to effectively avoid collisions by considering dynamic real-wo... | Summary: The paper introduces a novel task that incorporates real-world 3D scene information into the existing Human Motion Prediction task. The authors propose a model (DiMoP3D) that, starting from the observed motion sequence and the 3D scene, stochastically predicts the future poses and the interactions with the con... | Rebuttal 1:
Rebuttal: **Q1**: How it differs from scene-aware 3D human motion forecasting or synthesis, and comparisons with similar tasks.
**A1**: **For scene-aware motion forecasting**: Traditional scene-aware 3D human motion forecasting [5,6,7] typically predicts a single sequence based on observation. In contrast,... | Summary: This paper introduces a task that is scene-aware diverse human motion prediction. To be specific, given a 3D scene and history motion, this task aims to predict diverse future human motions that are consistent with the scene and history motion. This paper also proposed a model, DiMoP3D, to tackle this task. Th... | Rebuttal 1:
Rebuttal: **Q1**: How to generate diverse motions training on such datasets?
**A1**: Similar to [1,2] utilizing single\_history-to-single\_future data, DiMoP3D is designed to model the posterior distribution of potential motions $P(\hat{X}_{L:L+\Delta L} | X\_{1:L}, S)$, rather than a deterministic mapping... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Robustly overfitting latents for flexible neural image compression | Accept (poster) | Summary: The authors build on previous autoencoder-based neural image compression methods. Earlier works have shown that the quantized latent representations output by an end-to-end trained encoder for any given image are suboptimal for a decoder. Concretely, for a fixed set of decoder weights, one can usually find lat... | Rebuttal 1:
Rebuttal: Thank you reviewer Vgvs for your comments.
We think there is a misunderstanding, the reviewer believes that the probability function used in [1] only contains infinite gradients at 1. However, the reviewer may have overlooked the **normalization** from the Gumbel softmax that causes infinite gra... | Summary: This paper proposes a technique which takes a neural compression method based on a VAE and runs an optimization using the same loss function as the original method was trained with, but instead of back-propagating into the weights of the network, the gradients accumulate into the quantized latents. Due to the ... | Rebuttal 1:
Rebuttal: Thank you reviewer 5M8z for your comments and questions. The reviewer thinks the method is straightforward and easy to implement.
_To answer the weaknesses:_
Concerning point 1: the motivation behind the 3-class rounding. Besides our scientific curiosity, we did find that 3-class rounding impro... | Summary: In this paper, the authors proposed the method to improve the compression performance of the pre-trained end to end neural image compression methods by fine-tuning the latents of each image at the test time with the rate-distortion loss. They proposed three class rounding method, named SGA+ which is an extensi... | Rebuttal 1:
Rebuttal: Thank you reviewer N6DE for your comments and questions.
_To answer the weaknesses:_
Regarding 1, whether the method might work better for higher BPP points in the R-D curve: For certain experiments this seems to be the case (e.g. on Kodak), for other experiments not. Due to the complexity of op... | null | null | Rebuttal 1:
Rebuttal: Contains an additional experiment requested by reviewer N6DE.
Pdf: /pdf/38981bc9ca8d21ad7aab210b2d0af4db9a360ee3.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Adaptive Depth Networks with Skippable Sub-Paths | Accept (poster) | Summary: The authors propose an easy-to-train supernet, in which you can then adaptively change the network depth by disabling some blocks (skippable sub-paths) and using skip connections instead, while always keeping mandatory sub-paths. They found that the length of mandatory and the skippable sub-paths should be the... | Rebuttal 1:
Rebuttal: * **Weaknesses a)**
*In Figure 4 (b), SpViT-Swin-Ti is more accurate and has fewer flops than the proposed approach Swin-T-ADN.*
Thank you for your positive feedback and valuable suggestion.
While our work outperforms many state-of-the-art approaches, some efficient networks, notably SpViT-Swin... | Summary: The submission presents an approach to adaptive depth networks, where each hierarchical residual stage is divided into two sub-paths and they are trained to acquire different properties: the first sub-path is essential for hierarchical feature learning, the second one is trained to refine the learned features... | Rebuttal 1:
Rebuttal: **Weaknesses)** *The overall contribution of the paper is solid. The only thing I would suggest is to expend the experiments to 1 more low-level vision task, for example Single-Image Super Resolution, image denoising and so on. Since usually the decoder part of such low-level vision task is comput... | Summary: The paper presents adaptive depth networks. During training, the network is trained like a “super-net” that contains all the paths. During inference, the skippable networks can be skipped in devices that have limited resources. The whole framework is useful in real-world applications that require various accur... | Rebuttal 1:
Rebuttal: * **Weakness a)** *There seem to be several solutions with ideas similar to those in this paper, such as [13]. ... it would be better to include a comprehensive analysis of the differences and performance. If the proposed method could stand out in performance among the methods, it would make the p... | Summary: The paper provides a training methodology to develop small deployable subnetworks (also high performing) when training a single large network. The key claim of this paper is that due to their innovative training techniques the smaller subnetworks learns better feature and proves the point with reasonable basel... | Rebuttal 1:
Rebuttal: * **Weakness a)** *The claim ... is a bit of an overclaim.*
Thank you for your positive feedback and valuable suggestion.
We appreciate the opportunity to clarify our claims. We acknowledge that there have been prior works in the CNN literature that perform sub-network distillation. Our intent... | Rebuttal 1:
Rebuttal: Dear reviewers,
We sincerely thank all reviewers for their positive feedback and constructive suggestions.
We have made every effort to address each question and suggestion in detail.
Specifically, we conducted three additional experiments to respond to the reviewers’ questions and suggestions.
... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Decompose, Analyze and Rethink: Solving Intricate Problems with Human-like Reasoning Cycle | Accept (oral) | Summary: The paper introduces a reasoning framework called Decompose-Analyze-Rethink (DeAR) for enhancing the reasoning capabilities of large language models (LLMs). DeAR mimics human cognitive reasoning by decomposing complex problems into simpler sub-problems using a Reasoning Tree structure, analyzing these sub-prob... | Rebuttal 1:
Rebuttal: We appreciate your affirmation of the contribution of our paper. For your concerns:
**Q1**: The process for obtaining decomposition demonstrations in the logic heuristics lacks a detailed explanation.
**A1**: Thank you for your comments. We have provided a detailed explanation for the obtaining ... | Summary: The paper proposes DeAR-prompting (decompose, analyze, rethink) as a new prompting paradigm. The framework basically consists of decomposing the original question into subquestions, answering the subquestions and analyzing the answers and potentially rethinking answers to earlier questions based on the new ans... | Rebuttal 1:
Rebuttal: We appreciate your affirmation of the motivation of our idea, and the implementation of our approach DeAR. As for your concerns:
**Q1**: Question decomposition examples.
**A1**: Thanks for your insightful comments. In our approach, the logic heuristics provided in the problem decomposition promp... | Summary: This paper proposes a recursive method for LLMs to solve complex reasoning tasks. The approach formulates problem-solving as a hierarchical tree structure, where each problem is broken down into a tree of sub-problems. Each sub-problem is then analyzed and updated. This method has been evaluated on datasets su... | Rebuttal 1:
Rebuttal: We appreciate your acknowledgement of on our study motivation, model design, experimental results, and presentation. Your suggestions are insightful for us.
**Q1**: The effectiveness of the “self-check” method in “Analyze Stage” may need further validation.
**A1**: Thank you for your insightfu... | Summary: The paper presents **DeAR**, a new reasoning framework for large language models to perform intricate reasoning tasks. Inspired by human cognition, it decomposes problems into sub-questions within a Reasoning Tree, refining solutions through iterative Decompose-Analyze-Rethink cycles. Compared to existing stat... | Rebuttal 1:
Rebuttal: We appreciate your positive comments on the novelty and efficiency of our DeAR and the affirmation of its superior performance over SOTA methods.
**Q1**: Lack of ablation studies to analyze the contribution of each individual step.
**A1**: Thanks for your insightful comments. The construction pr... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers’ efforts in reviewing our paper. We would like to thank all of them for providing constructive and valuable feedback, which we will leverage to improve this work. We are encouraged by the positive comments from reviewers, including:
- **Motivation**: “offering a f... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper presents a novel reasoning framework DeAR (Decompose-Analyze-Rethink), which aims to advance the capabilities of large language models (LLMs) in handling complex reasoning tasks. DeAR introduces a Decompose-Analyze-Rethink cycle that involves breaking down intricate problems into simpler sub-question... | Rebuttal 1:
Rebuttal: We appreciate your affirmation of the motivation of our paper, the significance of our experimental results and the novelty of DeAR.
**Q1**: The iterative nature of the cycle may lead to increased computational demands.
**A1**: Thank you for your comments. In section 5.7, we compare the efficien... | null | null | null | null | null | null |
Smoothed Online Classification can be Harder than Batch Classification | Accept (poster) | Summary: The paper studied online classification under smoothed adversaries. They constructed a hypothesis that is learnable under batch learning (PAC learning) but not learnable under smoothed online learning. They also showed that a sufficient condition that a hypothesis class learnable under the PAC learning is also... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments.
We are unsure what the reviewer meant by "They didn’t show a necessary and sufficient condition that a hypothesis class learnable under the PAC learning is also learnable under the smoothed online learning." We would like to point out that the main resul... | Summary: This paper studies online learning under the smoothed analysis framework. Under the smoothed analysis framework, the example that is presented to the learner in each round is not chosen adversarially but instead is drawn from some distribution that is close to a known based distribution. Previous works have sh... | Rebuttal 1:
Rebuttal: We thank the reviewer for noting that our results "enhance our understanding of the smoothed online learning problem." We address their concerns below.
- Our sufficiency condition strictly improves the sufficiency condition presented in Haghtalab's thesis, as noted in lines 312-316. Specifically... | Summary: This paper studies the problem of distinguishing batch learning from smoothed online learning when the label set size is unbounded. It shows that there exists a class that can be PAC-learned but does not admit sublinear regret, even with features generated i.i.d. The paper then provides a sufficient condition ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their comments and address their concerns below.
**Weaknesses**
1. In online classification, smoothness is only an assumption on the instances $x_1, ..., x_T$, and the labels can still be adversarial. This is the standard smooth model considered in
[1,2,3,4]. If by 'a... | Summary: They consider the problem of smoothed online classification under oblivious adversaries. From earlier work it is known that this problem is as easy as batch classification when the label space is bounded. However, when the label space is unbounded they provide a lower bound and show that this problem can be ha... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding our paper well-written. We will make sure to define $\Sigma$ in the camera-ready version.
---
Rebuttal Comment 1.1:
Comment: I went through the other reviews and responses, and I agree that a more theoretically oriented venue like ALT/COLT might be a better fit.... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ActFusion: a Unified Diffusion Model for Action Segmentation and Anticipation | Accept (poster) | Summary: This paper extends DiffAct to perform both action segmentation and action anticipation. An anticipative masking with a learnable mask token is proposed. Experiments are conducted on the three common benchmark datasets.
Strengths: 1. The motivation for unifying action segmentation and action anticipation is re... | Rebuttal 1:
Rebuttal: ### **[Inference setup in LTA]**
We would like to clarify that our model can predict future actions with an arbitrary length by adjusting the number of mask tokens needed for prediction; our model itself does not require a ground-truth length of anticipation, and the ground-truth length is used to... | Summary: This paper proposes a new unified diffusion model called ActFusion, which solves the tasks of Time Action Segmentation (TAS) and Long Term Action Prediction (LTA) in a joint learning framework. To unify the two tasks, the model effectively handles the visible and invisible parts of the sequence during the trai... | Rebuttal 1:
Rebuttal: ### **[Comparison with DiffAct]**
Please refer to the general response to the comparison with DiffAct.
### **[Loss ablation studies]**
We conduct ablation studies on the loss functions: boundary loss, smoothing loss, and cross-entropy loss. Table R4 presents the results, demonstrating that the co... | Summary: The author introduce a unified diffusion model for temporal action segmentation (TAS) and long-term action anticipation (LTA), dubbed ActFusion, where a single model is jointly trained to address these two problems effectively. A new anticipative masking is presented for the effective unification of two tasks,... | Rebuttal 1:
Rebuttal: ### **[Novelty]**
Please refer to the general response for our novelty.
### **[Marginal performance]**
We would like to clarify that the performance improvements achieved by our model are significant. Figure R1 in the pdf file illustrates the performance of the Top 10 TAS models for each dataset ... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful comments and suggestions. We are happy to see that the reviewers have given our work a positive evaluation, noting that “this is the first time these two problems have been investigated together in a single framework, highlighting the originality (FW... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ManiBCI: Manipulating EEG BCI with Invisible and Robust Backdoor Attack via Frequency Transform | Reject | Summary: The paper presents ManiBCI, a novel backdoor attack method targeting EEG-based brain-computer interface (BCI) systems. ManiBCI leverages a three-stage clean label poisoning approach without needing access to the training phase of the target deep learning models. This method optimally selects EEG electrodes and... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments, and we'd like to express our appreciation that the novelty and strong experimental evidences of our work are well recognized. Below we have addressed your questions and concerns point-by-point.
> Standard baselines (fast gradient sign method and universal advers... | Summary: This paper proposes a backdoor attack strategy for EEG, addressing three inherent issues: low quality, task variances, and morphology variances. The authors introduced a three-stage clean label poisoning attack. The proposed algorithm has been evaluated on three EEG datasets, demonstrating its effectiveness an... | Rebuttal 1:
Rebuttal: We truly thank you for your appreciation of our work and the positive comments of "a very interesting work". Our point-by-point responses are as follows.
> In terms of general EEG analsyis, one of my main concern is the experiment settings. In normal EEG analysis domain, we usually set inter-subej... | Summary: Unfortunately, the authors begin the manuscript by demonstrating a lack of knowledge about the topic. They claim that deep learning (DL) has been highly successful in the field of brain-computer interfaces (BCI) based on electroencephalogram (EEG) data. However, in reality, the application of deep learning in ... | Rebuttal 1:
Rebuttal: We thank the reviewer for your time and effort in reviewing our work. We are learning from all the feedbacks from the reviewers and feel that this is a great opportunity to exchange ideas deeply. Therefore, we're making the following statements to ignite more discussion since we value more on the ... | Summary: This paper presents an EEG backdoor for manipulating EEG BCI, called ManiBCI, where the adversary can arbitrarily control the output for any input samples. Experiments conducted on three EEG datasets demonstrate the effectiveness of ManiBCI; which easily bypass existing backdoor defenses.
Strengths: - A backd... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. Below we have addressed your questions and concerns point-by-point.
**Weaknesses**
> The proposed methodology is not well described. It mainly based on the application of Fourier transform (FFT) and reinforcement learning (RL).
Thanks for your advise, there is ... | Rebuttal 1:
Rebuttal: We are grateful to all four reviewers and AC/SACs for their valuable time, insightful comments, and useful suggestions. We will carefully revise our paper according to the comments. Our point-by-point response to the reviewers’ comments has been added to the individual chat box for each reviewer. ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Understanding the Role of Equivariance in Self-supervised Learning | Accept (poster) | Summary: This paper provides a theoretical understanding of equivariant self-supervised learning (E-SSL) methods and their effectiveness. The authors propose an information-theoretic analysis that explains the synergy effect between class information and equivariant transformations, leading to improved downstream perfo... | Rebuttal 1:
Rebuttal: ##
We thank Reviewer PgKg for appreciating our writing and theoretical contributions. Below we address each of your concerns.
---
**Q1.** The experiments are conducted on few datasets which is not very convincing.
**A1**. Following your suggestion, we further validate our findings on ResNet... | Summary: This paper aims to reduce the gap between theory and practice for equivariant SSL, which refers here to the sub-family of discriminative SSL methods that do not enforce the invariance of representations to augmentations. The authors propose an explanation based on the "explain-away" (_“Explaining away” is a co... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer TYZN for a critical reading of our work. We carefully examine the intuition you propose, and find that it actually fits well into our theory.
---
**Q1.** Discussion on the intuitive understanding of why E-SSL works and how it fits into our theory.
**A1**. We resonate... | Summary: This study contributes theoretical insights that enhance our understanding of conventional practices in SSL training. Building on these theoretical foundations and supported by experimental evidence, the study puts forward several principles for the practical implementation of equivariant self-supervised learn... | Rebuttal 1:
Rebuttal: We thank Reviewer yP9X for acknowledging our contributions to theoretical understandings. Below, we further address your concerns on the empirical side.
---
**Q1.** Experiments are conducted using smaller-scale models.
**A1.** Thank for your advice! The experiments are mainly designed to valida... | Summary: This paper proposes a theoretical and empirical study of the role of invariant and equivariant representations in self-supervised learning. While a number of works have been focused on learning equivariant representations, it remains unclear whether or not equivariance is beneficial in specific tasks. By study... | Rebuttal 1:
Rebuttal: We thank Reviewer a2cS for acknowledging our theoretical contributions. Below we address your main concerns.
---
**Q1**. Comparing augmentation aware methods (e.g. RotNet) and truly equivariant methods (eg CARE).
> While this distinction may not affect the general ideas and theoretical argument... | Rebuttal 1:
Rebuttal: We thank all reviewers for their positive comments and constructive critics on our work. Taking these valuable insights into consideration, we address these problems carefully in each response. We will also do the following:
- **A series of validation experiments on more datasets and models.** We... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
PTQ4DiT: Post-training Quantization for Diffusion Transformers | Accept (poster) | Summary: This paper presents PTQ4DiT, a quantization method designed for diffusion transformers. The method focuses on addressing quantization challenges due to extreme magnitudes in salient channels and the temporal variability of activations across multiple timesteps. To combat these issues, it incorporates technique... | Rebuttal 1:
Rebuttal: ## **Response to Weakness 1: Classifier-free guidance scales**
Thank you for pointing out this important concern. We set classifier-free guidance scales as 1.5 in all the experiments in our paper, following the origin DiT work [1].
[1] Scalable diffusion models with transformers. In CVPR, 2023.
... | Summary: - The paper introduces PTQ4DiT, a post-training quantization method for Diffusion Transformers. This approach can facilitate the widespread deployment of DiTs. By investigating the distribution of activation and weight of DiTs, PTQ4DiT designs the Channel-wise Balancing and timestep-aware Salience Calibration.... | Rebuttal 1:
Rebuttal: ## **Response to Weakness 1: Effectiveness of SSC**
We clarify the innovation of CSB and provide additional evidence of the efficacy of SSC.
**a. Innovation of CSB**
While CSB shares the general concept of distribution re-scaling and re-parameterization with Smoothquant, it has unique innovation... | Summary: This paper proposes the first post-training quantization (PTQ) method for Diffusion Transformer (DiT). It addresses the presence of salient channels with extreme magnitudes and the temporal variability in the distributions of salient activations over multiple timesteps. Experimental results demonstrate compara... | Rebuttal 1:
Rebuttal: Thank you very much for your acknowledgement of our work and your constructive feedbacks and suggestions.
## **Response to Weakness 1: Statistical experiments for the important property**
We validate the important complementarity property by additional statistical experiments on the well-establish... | Summary: This paper proposes a new PTQ method for DiTs. It develops a Channel-wise Salient Balancing method to suppress the outliers of linear layers in transformer blocks when applying activation quantization. Besides, it designs the Spearmen’s ρ-guided Salience Calibration to tackle the timestep dimension’s variety. ... | Rebuttal 1:
Rebuttal: ## **Weakness 1: More experiments**
**a. W8A8 ImageNet 512x512**
We evaluate PTQ4DiT on ImageNet 512x512 with W8A8, compared against strong Diffusion PTQ methods, including Q-Diffusion and PTQD:
|Timesteps|Method|FID↓|sFID↓|IS↑|Precision↑|
|---|---|---|---|---|---|
|250|FP|8.39|36.25|257.06|0.842... | Rebuttal 1:
Rebuttal: # **General Response by Authors**
We express our gratitude to all the reviewers for dedicating their time and providing valuable comments. They acknowledged that our work is novel (N2VF, USPH), effective for DiT quantization (USPH, jrKh, a8C3) and well-written (USPH, jrKh). However, the reviewers ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SaulLM-54B & SaulLM-141B: Scaling Up Domain Adaptation for the Legal Domain | Accept (poster) | Summary: The paper reports on two new legal-specific LLMs based on Mixtral. The primary contribution is to train larger law LLMs than previously reported, using (1) an extensive dataset of legal materials, and (2) a variety of best practices in pre-training. The results indicate incremental but significant improvements... | Rebuttal 1:
Comment: **Thank you for your detailed and thoughtful feedback on our paper. We are pleased that you found our contributions significant and our paper technically strong.**
We understand your concern regarding the limited generalizable knowledge for understanding legal tasks. **This limitation is partly du... | Summary: The paper introduces two large language models specialized in the legal domain with instruction-following capabilities. These models are an extension of previous work, particularly Colombo et al.'s “SaulLM-7B: A Pioneering Large Language Model for Law,” by scaling up the corpus size and the number of trainable... | Rebuttal 1:
Comment: We thank the reviewer for their review. We are glad they found our paper is well written and our contribution extremely valuable.
Below is the response to the concerns/questions:
**About the additional ablations**, they are already reported in the paper. See general comments.
**Adding compariso... | Summary: - The paper introduce two LLMs (at different sizes) specialized for law. These models have been adapted for law through continued pretraining, specialized legal instruction following, and a “legal alignment” process
- The paper studies the tradeoffs of domain adaptation at this scale and presents results for t... | Rebuttal 1:
Comment: **We thank the reviewer for their review we are glad they acknowledged that our paper is well written and find that our contribution is extremely valuable.**
Below is the response to the concerns/questions:
**About the generation of the post-training datasets and examples.** See the general comme... | Summary: The paper introduces SaulLM-54B and SaulLM-141B, two large language models specifically designed for the legal sector. These models utilize the Mixtral architecture and are developed through extensive domain adaptation strategies, including continued pretraining on a large legal corpus, instruction fine-tuning... | Rebuttal 1:
Comment: We thank the reviewer for their review we are glad they acknowledged the usefulness of our paper and our contribution to the community with these models.
Below are the answers to the reviewer's comments:
**There are no issues with the licenses:** The licenses permit commercial use for both the pre... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Parameter-Inverted Image Pyramid Networks | Accept (spotlight) | Summary: This paper presents a novel method named parameter-inverted image pyramid to handle the issues of high computation overhead of image pyramids. It uses different model sizes to process different resolutions. The method achieves significant results on tasks of object detection, segmentation, and classification b... | Rebuttal 1:
Rebuttal: We thank the reviewer for detailed comments and suggestions and provide our response below.
### **W1: Whether the #FLOPs and #Param in Tables 1,3 include a branch merging module.**
To clarify, the #FLOPs and #Param in Tables 1 and 3 contain the branch merging module, which constitutes only a small... | Summary: This paper proposes two techniques: 1) Models with different parameter sizes to process different resolution levels of the image pyramid. 2) A feature interaction mechanism to integrate information from different spatial scales. Extensive experiment results are used to support the claims.
Strengths: Both tech... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's comments and provide additional experimental results to address these concerns.
### **Q1: Baseline with higher resolution is needed.**
To evaluate the impact of using higher resolutions, we add another baseline, ViTDet-L with 1792 resolution (matching the largest resol... | Summary: The authors propose a novel vision architecture, Parameter-Inverted Image Pyramid Networks (PIIP), which can be applied to different tasks, including classification, object detection, and instance or semantic segmentation. The authors aim to take advantage of the multi-scale information of image pyramids, with... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for providing detailed comments and highlighting our strengths. We hope our response will address the reviewer's concerns.
### **W1: Actual timing not reported; Gains in FLOPs don’t always translate to benefits.**
We acknowledge that the reduction of FLOPs does not guar... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers for their time and efforts. Please refer to rebuttals under each review of detailed responses.
Pdf: /pdf/6194ae9d5bebbd07a1e2403f1b6401065aea0134.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Time-Constrained Robust MDPs | Accept (poster) | Summary: This paper proposed a novel concept, time-constrained robust MDP, to address the conservativeness issue of the rectangular assumption. They assume the transition depends on an underlying parameter, and the parameter can be adversarially chosen from an uncertainty set. Several algorithms are proposed to solve t... | Rebuttal 1:
Rebuttal: Thank you for your review and the questions you raised regarding our paper. We appreciate your feedback and would like to address your concerns as follows:
> My concern is that the methods proposed in this paper might be too heuristic, given the non-stationarity and possibly non-existence of the ... | Summary: This paper aims to develop a new framework of robust RL addressing the over-conservability issue of rectangularity and dynamic uncertainty set assumption. This framework allows for time-dependent, correlated and multifactorial disturbance to the dynamics. Three distinct algorithms are developed depending on th... | Rebuttal 1:
Rebuttal: We thank the reviewer for this feedback.
We are quite surprised by the mismatch between the expressed comments and the overall grade attributed to the paper. To us, the three comments somehow discard important parts of the paper and we warmly welcome further discussion to address them if necessary... | Summary: The paper introduces Time-Constrained Robust MDPs (TC-RMDPs) as a novel formulation to address the overly conservative nature of traditional robust RL under sa-rectangularity assumptions. The authors propose three algorithms to handle time-dependent and correlated disturbances in different situations. Extensiv... | Rebuttal 1:
Rebuttal: We thank the reviewer for these insightful comments.
We believe the major concerns raised are actually minor, in the sense that they can all be answered by elements already in the paper, and better explanations and phrasing. We explain why below and welcome further discussion with the reviewer.
... | Summary: The paper defines a robust training method for MDPs under uncertainty in the environment dynamics. Current methods assume sa-rectangularity, where the transition dynamics from consecutive states are independent of one another. The authors argue that this assumption is unrealistic and leads to overly conservati... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback, their careful analysis of our contributions, and positive assessment of them.
> Reporting that the worst-case TC variants outperform vanilla adversarial models as M2TD3 or RARL is expected. As the decisions of the adversary are rather limited when compare... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
VideoLLM-MoD: Efficient Video-Language Streaming with Mixture-of-Depths Vision Computation | Accept (poster) | Summary: This paper aims to comprehend long video streams with multi-modal large language models. The existing works use too few visual tokens to represent the video stream to guarantee efficiency but may sacrifice visual perception performance. The authors propose to keep more visual tokens to represent each video fra... | Rebuttal 1:
Rebuttal: Many thanks for your insightful feedback and valuable suggestions.
**W1: The problem of the indicator.**
Thanks for pointing this out. We follow VideoLLM-online and it makes an error. The revised equation is as follows:
$$
L = \frac{1}{N}\sum^N_{j=1}\left(-l_{j+1}\log P_j^{\texttt{[Txt$_{j+1}$]... | Summary: The document presents a novel approach called VideoLLM-MoD, which aims to efficiently scale up the vision resolution for online video large language models (VideoLLMs) without incurring high computational costs. The approach is inspired by the "mixture-of-depths" approach and learns to skip the computation for... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and efforts in reviewing our paper. We will address each of your concerns point by point.
**W1/Q1: This novelty and contributions compared to Mixture-of-Depths.**
We summarize the differences between our proposed framework and Mixture-of-Depths (MoD) as follows... | Summary: This paper proposes a novel layer skipping approach to reduce the computation and memory consumption in modern vision-language models. The overall performance is good on several egocentric video understanding benchmarks.
Strengths: 1. The proposed layer skipping strategy enables efficiient attention computati... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful feedback and valuable suggestions.
**W1: The technical contribution of this method is relatively limited.**
Our technical contributions are summarized as follows:
1. **Efficient Vision Modeling in Context:** It is non-trivial to reduce computation in con... | Summary: The core idea of this paper is to scaling up vision resolution for online video large language models. Instead of distributing FLOPs uniformly across all vision tokens in every decoder layer, they utilize a learnable module LayerExpert to allocate compute to critical vision tokens within the frame dynamically.... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive comments on our work and will address each of the issues you mentioned below.
**W1: Performing experiments to show how does this MoD approach for making a sparse vision encoder work on standard benchmarks.**
To validate the effectiveness and generalization ... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their constructive comments.
We appreciate their recognition of our **motivation** (HwD3, x5JZ); the **novelty** of the approach (qExK, 2CSB); the **efficiency** (HwD3, qExK, 2CSB, x5JZ); and **sufficient experiments** (HwD3, qExK, 2CSB).
In the uploaded... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Would I Lie To You? Inference Time Alignment of Language Models using Direct Preference Heads | Accept (poster) | Summary: The paper introduces DPH, a new method for pre-trained language models that addresses the limitations of RLHF. Unlike traditional RLHF, which can compromise a model's reasoning abilities and cause hallucinations, DPH employs an auxiliary reward head to learn human preference signals without altering the LM's o... | Rebuttal 1:
Rebuttal: **Addressing Weakness 1:**
Although we do jointly train both the reward head and the preference distribution for completions this is realised through cDPO with a large beta penalty (which limits divergence of the output distribution) and a large epsilon (which limits the preferred vs dispreferred ... | Summary: This paper introduces Direct Preference Heads (DPH), a novel method for aligning language models (LLMs) with human preferences at inference time. DPH works by adding an auxiliary reward head to the LLM that learns to predict human preference scores for generated outputs. This allows the model to self-evaluate ... | Rebuttal 1:
Rebuttal: **Addressing Weakness 1:**
The paper does not include statistical significance measures as this would require performing each stage of the training pipeline multiple times which is computationally costly and we opted to use our compute budget to perform ablations with different hyperparameters and... | Summary: Paper proposes Direct Preference Heads, which learns a reward prediction head using a pretrained model, without affecting the model's output distribution.
Strengths: Comprehensive evaluation
- paper presented experimental results across a wide range of tasks (NLU, commonsense reasoning, reading comprehension,... | Rebuttal 1:
Rebuttal: **Addressing Weakness 1:**
The learned reward predictions are to be used to rerank candidate competitions at inference time, similar to rejection sampling. The number of samples to rank, however, is completely situational and can be determined by factors such as compute capability, memory availabi... | Summary: The authors propose an inference time method to align language models with human preferences without harming the model’s reasoning abilities. The method creates an auxiliary reward head that operates during inference to score potential outputs without changing the output distribution directly. The author valid... | Rebuttal 1:
Rebuttal: **Addressing Weakness 1:**
DPH does indeed have a higher inference-time overhead than other alignment methods which aim to produce optimal completions in a zero-shot manner. However we frame DPH as being a suitable method for smaller language models which, as cited in the paper, may be harmed by o... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Discrete Dictionary-based Decomposition Layer for Structured Representation Learning | Accept (poster) | Summary: The authors propose the Discrete Dictionary-based Decomposition method, a discrete representation learning for TPRs. It encodes the roles and unbinding queries (and potentially also the fillers) using a learned dictionary, which serves as a codebook. The roles and then unbindings share the codebook, encouragin... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for their constructive feedback and insightful suggestions. We will ensure that we reflect on our responses in our revised manuscript in the future.
> W1: Ablation study for the effect of the residual connection
**The *components* represent the vector representations o... | Summary: The paper presents a discrete dictionary-based decomposition (D3) layer for tensor product representation (TPR). The purpose is to enhance the decomposition capabilities of TPR so that it can perform downstream tasks more effectively. D3 uses the discrete, trainable key-value dictionaries to map input data to ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for their constructive feedback and insightful suggestions. We will ensure that we reflect on our responses in our revised manuscript in the future.
> W1: More explanation for some key concepts
We acknowledge that the initial description may have been challenging for r... | Summary: Drawing inspiration from discrete representation learning with dictionaries, the author introduced a novel Tensor Product Representation (TPR) framework, a Discrete Dictionary-based Decomposition (D3) layer designed to retain the learned symbolic features during training and apply them effectively to address d... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for their constructive feedback. We will ensure that we reflect on our responses in our revised manuscript in the future.
> W1: Technical novelty over AID and scalability issue
While the prior work introduced an iterative competitive attention-based decomposition modul... | Summary: This paper address the difficulty of decomposing input data into Tensor Product Representation (TPR) components, namely, roles, fillers, and unbinding operators. The proposal called D3 includes the use of learnable dictionaries for these components, and the mapping of input data into intermediate features (cod... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for their constructive feedback and insightful suggestions. We will ensure that we reflect on our responses in our revised manuscript in the future.
> W1: Comparison to AID
While our work and AID focus on enhancing the systematic generalization of TPR-based models by a... | Rebuttal 1:
Rebuttal: ## Global response
We thank all the reviewers for their constructive feedback and insightful suggestions. We believe that the additional ablation studies and comparisons they recommended provide a clearer understanding of D3's strengths and limitations. Our revised manuscript will thoroughly refl... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Q-Distribution guided Q-learning for offline reinforcement learning: Uncertainty penalized Q-value via consistency model | Accept (poster) | Summary: This paper addresses the well-known issue of OOD (Out-of-Distribution) actions in offline reinforcement learning by proposing the QDQ method, which penalizes the Q-values in regions with high uncertainty. To better estimate uncertainty, QDQ first constructs a truncated Q-value dataset for the behavior policy a... | Rebuttal 1:
Rebuttal: For weakness:
1. We apologize for any ambiguity caused by our description of the consistency model. Consistency is a feature of the consistency model, not a requirement for the Q-value. The consistency model ensures a consistent relationship between the prior sample and the target sample duri... | Summary: This paper proposes a new offline RL method, Q-Distribution guided Q-learning (QDQ), which uses a consistency model to model the distribution of Q-value for uncertainty estimation and then introduces an uncertainty-aware optimization objective for pessimistic Q-learning. This method has theoretical guarantees ... | Rebuttal 1:
Rebuttal: For weakness:
1. We apologize for the ambiguity. Please see 1. of the “author rebuttal” at the top for the rationale behind using Q-value data from the behavior policy. Although estimated uncertainty penalizes the Q target of the learning policy, it only pessimistic the Q-value in the OOD region ... | Summary: The paper proposes a method for estimating Q values in the offline/batch setting by leveraging consistency models. Via these, the uncertainty over the Q function can be estimated and used as a robust penalty to prevent distribution shift in offline RL. The authors provide both experimental validation on the st... | Rebuttal 1:
Rebuttal: For weakness:
Firstly, we apologize for any ambiguities. In offline RL, which aims to train policy without interacting with the environment, "distribution shift" is the main obstacle. A learning policy may take out-of-distribution (OOD) actions, leading to overestimated Q-values. One suggested wa... | null | null | Rebuttal 1:
Rebuttal: We appreciate the valuable comments from our three reviewers, which have helped us improve our manuscript. We have provided detailed answers to each question and included additional experiments in the attached PDF. To address any remaining confusion, we would like to introduce the motivation behin... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Enhancing LLM Reasoning via Vision-Augmented Prompting | Accept (spotlight) | Summary: Traditional large language models (LLMs) struggle with tasks requiring visual and spatial interpretation based solely on text. This study introduces a visual-augmented prompting (VAP) strategy, using an external image generation tool to iteratively create intermediate visual representations that aid reasoning.... | Rebuttal 1:
Rebuttal: We sincerely appreciate the constructive comments on our work, which have helped us to enhance the paper. The following are our detailed responses to each comments.
### W1.1:Efficiency of iterative reasoning
This is a good point. We added an efficiency experiment for VAP in our revision. The res... | Summary: This paper targeting an intresting topi in VL research: Can VLM understand the organized prompts as LLM? The authors proposed a method called VAP(visual augmented prompt) to improve the prompting learning methods for VLM. The authors argue that human have two specialized subsystems that process verbal inforati... | Rebuttal 1:
Rebuttal: We sincerely appreciate the constructive comments from the reviewer! We provide detailed responses to each concern in the following.
### W1: "I wonder, is generating an explicit image the best practice for this process. As the generation of images could result in unwanted features, we could also ... | Summary: This paper proposes a new prompting technique, vision-augmented prompting (VAP), to improve the reasoning capabilities of large language models (LLMs). Different from the mainstream chain-of-thought (CoT) frameworks that only involve textual reasoning steps, the proposed VAP framework automatically generates i... | Rebuttal 1:
Rebuttal: We thank the reviewer for the in-depth review! Below, we respond to the weaknesses raised in the review.
### W1.1:"For matplotlib and turtle, how can the framework guarantee the syntax correctness of generated code?"
We understand the reviewer's concern. We add an experiment for code generation as... | Summary: This paper proposes visual-augmented prompting (VAP) for large language models (LLMs) in reasoning tasks. Specifically, VAP translates textual questions into a sequence of self-synthesized images using API calls (Python Turtle, Matplotlib, DALL-E3). These images are then fed back to Vision-LLM (GPT-4o) in a st... | Rebuttal 1:
Rebuttal: We sincerely appreciate the constructive comments from the reviewer. We provide detailed responses to each concern in the following.
### W1&Q1: "When the LLM generates Python API calls, there is a chance that the generated code may not run successfully (bugs in the code). What is the probability ... | Rebuttal 1:
Rebuttal: We thank all detailed feedbacks provided by the reviewers! We address a few points in this response. All other questions are addressed in reviewer specific responses.
### Trade-off between number of iterations and performance
We add an experiment of controlling the number of iterations in Sudo... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
UniAudio 1.5: Large Language Model-Driven Audio Codec is A Few-Shot Audio Task Learner | Accept (poster) | Summary: The paper proposes a LLM-codec module that can plug into an existing LLM, i.e., LLAMA-2, to perform few-shot in-context learning for tasks including classification (emotion & sound event), and text-to-speech synthesis. The proposed module takes the raw audio waveform as an input, and encodes it into latent spa... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our contributions. We appreciate the constructive comments the reviewer provided to us to improve our paper further. We are delighted to have the following discussion with the reviewer.
**Q1:** The task seems to be really simple, I am wondering how could th... | Summary: The paper introduces LLM-Codec, which enables frozen LLMs to perform various audio tasks in a few-shot manner without fine-tuning LLMs. LLM-Codec operates in a RVQ-manner, hierarchically converting audio tokens into words or sub-words in the LLM vocabulary to compress the audio modality into the text space.
S... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's time and patience with our paper. We do find these suggestions constructive and helpful.
**Q1:** It’s hard to understand the meaning of the model setup ... What is the motivation for building a system in this pipeline?
**A:** We apologize that our present... | Summary: The paper introduces LLM-Codec, a novel audio codec model that leverages Large Language Models (LLMs) to perform various audio tasks with minimal training examples.
By translating audio signals into the token space of LLMs, it enables these models to understand and generate audio content.
The model uses a ... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our contributions. We appreciate the constructive comments the reviewer provided to us to improve our paper. We are delighted to have the following discussion with the reviewer.
**Q1:** Considering the paper proposes an encodec model, the most important re... | Summary: The authors introduce a three-step audio-to-discrete codec to encode continuous acoustic information into a form suitable for large language model-based audio and speech understanding.
Overall, this method is novel and represents an important step in audio modeling. The architecture targets different levels o... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our contributions. We appreciate the constructive comments the reviewer provided to us to improve our paper further. We are delighted to have the following discussion with the reviewer.
**Q1:** A few grammatical and formatting issues need to be very fixe... | Rebuttal 1:
Rebuttal: We thank the meta-reviewer for organizing this helpful peer review stage. We thank all reviewers for their time, patience, and constructive comments to help us improve our paper.
We specifically address the concerns raised by reviewers **4YYB** regarding the motivation of our paper. We are eager... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Integrating Suboptimal Human Knowledge with Hierarchical Reinforcement Learning for Large-Scale Multiagent Systems | Accept (poster) | Summary: The authors proposed a new framework (hhk-MARL) that integrates human abstract knowledge with hierarchical reinforcement learning to address the learning challenges in large-scale multi-agent systems. The framework employs fuzzy logic to represent human knowledge and a graph-based group controller to enhance a... | Rebuttal 1:
Rebuttal: Thank you very much for your kind review. We are pleased that you thought we have provided a flexible and adaptive approach for multi-agent systems.
We have addressed your comments below. We hope that this clarifies any concerns that you had and strengthens your support for the paper.
>Question1... | Summary: This paper integrates an human in the loop to provide knowledge that improves learning in marl. This is done through a hierarchical structure, but ultimately it is up to the agents the final decision of accepting the human suggestions (hierarchy comes from human knowledge to agents). Overall, there is an integ... | Rebuttal 1:
Rebuttal: We would like to thank you for your review and your kind words about our paper. We are pleased that you found our idea of integrating human knowledge to be interesting.
Below we have addressed your questions. We hope that this strengthens your support for the paper.
>Weakness1: ...what $M$ is e... | Summary: In this paper, the authors propose a novel method to tackle the multi-agent reinforcement learning problem. They do so by combining human abstract knowledge with hierarchical reinforcement learning. Specifically, human knowledge in the form of fuzzy logic rules is combined, at the top level, with each individu... | Rebuttal 1:
Rebuttal: Thank you very much for your review. We are pleased that you found our method to be creative and novel and that you thought we had set a general approach for MARL algorithms.
We address your concerns below and hope this will strengthen your support for the paper.
> The empirical evaluation is tho... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their constructive comments. We are encouraged that all the reviewers think our paper is well organized and our approach to integrating human knowledge with multi-agent reinforcement learning is flexible and effective (Reviewer JxHm, kCt8, and 8Cur). It is a gr... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Understanding Bias in Large-Scale Visual Datasets | Accept (poster) | Summary: This paper explores dataset biases and introduces a framework to identify the unique visual attributes that differentiate various datasets. The method involves applying a range of transformations to extract semantic, structural, boundary, color, and frequency information from the datasets, evaluating how each ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful review and the constructive comments. We would like to address your concerns below.
>w1: Even though the paper identifies various factors that help in distinguishing datasets, it does not provide any information/ takeaways on how this information could be u... | Summary: This work theorizes and investigates various concrete forms of inter-dataset biases, namely those among YFCC, CC, and DataComp (YCD). The authors analyze such inter-dataset biases in pure visual attributes as well as in semantic attributes using LLM-generated descriptions.
Strengths: The paper is a fluent rea... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review and the insightful questions. We would like to address your concerns below.
>w1: Could it be possible that a combination of multiple visual/semantic attributes jointly contributes to the large overall bias? This needs to be verified.
Thank you for this sugges... | Summary: This paper studies the problem of dataset bias prevalent in current multimodal datasets. It revisits the dataset classification experiment from Torralba et al, recently studied again by Liu and He, and deconstructs their findings to understand what aspects of datasets (structural, semantic, color, object-level... | Rebuttal 1:
Rebuttal: We sincerely thank you for your constructive comment. We are encouraged that you find our paper provides helpful insights about bias in large-scale datasets. We address your concerns below:
>w1: How should one interpret these results under the argument that neural networks in general can learn noi... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers:
Thanks for all your constructive comments! We hope our response and additional results can address your concerns. Please let us know if you have further questions or comments and we would be more than happy to discuss.
For reviewer yVzd, please note the PDF file attached contains... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Neural Residual Diffusion Models for Deep Scalable Vision Generation | Accept (poster) | Summary: The paper describes the gradual change in the state z_t of a diffusion-based neural network as an ODE, and links the flow-architecture and UNet-architecture to the parameters of the ODE. Then, it describes a way to better train this network based on the dynamics of this ODE, and proposes an alternative loss fu... | Rebuttal 1:
Rebuttal: **Q1: Explanation of some details in Fig.1 & 2**\
**R1:** Thanks. We will explain these details below:
1) In Fig.2, (a), (b) and (c) respectively represent **Flow-shaped Networks** (e.g., Transformer), **U-shaped Networks** (e.g., U-Net) and **Unified Stacking Network (our Neural-RDM)**. As depic... | Summary: This work presents a framework for visual generative diffusion models, aiming at addressing the challenges associated with deep stacked networks in terms of numerical propagation errors and scalability.
Strengths: 1. Clear motivation
2. The authors provide a theoretical analysis for their approach, includin... | Rebuttal 1:
Rebuttal: **Q1:** **Complexity of the model after introducing gated residual parameters**\
**R1:** We understand the reviewer's concern, but that's actually unnecessary. Firstly, we want to highlight the introduction of these gated residual parameters is a _**simple yet meaningful**_ change to the common ar... | Summary: This paper addresses the problem of the numerical propagation errors of progressively deeper stacked neural networks for generative models. It proposes Neural Residual Diffusion Models (Neural-RDM), which introduced a series of learnable gated residual parameters to the common architectures of deep generative ... | Rebuttal 1:
Rebuttal: **Q1: Definitions and clarifications on scalability metrics** \
**R1:** Thanks for the helpful comment. We first want to clarify that the "Scalability" columns in Tab.1 & Tab.2 indicates the scaling capability (i.e., parameter scale and architecture stackability) of the evaluated models, meanwhil... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for sparing their time and efforts reading our paper and giving many insightful comments. We notice that all reviewers hold a positive view regarding the _**well motivation**_ and _**superior performance**_ of our model. The major questions are summarized below... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DiPEx: Dispersing Prompt Expansion for Class-Agnostic Object Detection | Accept (poster) | Summary: The paper proposed a prompt expansion method to produce a diverse set of text queries for class-agnostic object detection. Sequentially performing inference using each text query and collating the prediction results often achieves high recall but incurs significant computational cost. Merging all text queries ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comment and constructive feedback! Please find our detailed response below:
> **R4.1** Clarification on "semantic overlap".
We appreciate the reviewer’s detailed feedback and understand that the section on “semantic overlaps” may seem disjointed. We conduct... | Summary: This paper proposes a novel Dispersing Prompt Expansion (DiPEx) approach to enhance class-agnostic object detection (OD) using vision-language models (VLMs). The authors observe that manually crafted text queries often result in undetected objects due to semantic overlap, and address this by progressively lear... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments and suggestions, which we address below:
> **R3.1** Discussion on more related works
Thanks for bringing the literature to our attention!
[1] proposed POMNet, which leverages a transformer-based Keypoint Interaction Module (KIM) to capture in... | Summary: This work identifies that the "semantic overlaps" may contribute to the diminished class-agnostic object detection performance for previous works utilizing VLMs, which is evidenced by the pre-experiments on had-crafted text queries on the MS COCO dataset. Furthermore, the authors derive a self-supervised promp... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments and constructive feedback.
> **R2.1** I prefer to owe the detection degradation to the disturbed attention, rather than the so-called semantic overlap.
We appreciate the insightful feedback. To clarify our "semantic overlap" hypothesis, we refer to... | Summary: This study investigates the use of visual-language models to improve class-agnostic object detection through a self-supervised prompt learning strategy. Diverse Prompt Expansion (DipEx) is proposed to enhance downstream task performance by learning to expand a set of diverse, non-overlapping prompts that boost... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments and suggestions, which we address below:
> **R1.1** Novelty and comparisons with [1,2]
**Contributions.**
We appreciate your feedback and would like to **clarify** the major contributions of our work:
(1) **General Impact**: Our work present... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We would like to extend our sincere gratitude for your thoughtful and encouraging feedback.
We are pleased to see that our exploration into class-agnostic object detection was recognized as **practical and universal** in real-world scenarios, with performance improvements over ba... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Multivariate Stochastic Dominance via Optimal Transport and Applications to Models Benchmarking | Accept (poster) | Summary: This paper considers the task of estimating the degree of stochastic dominance between two multivariate distributions. In the univariate setting, stochastic dominance is a useful tool for tasks such as benchmarking LLMs, where a practitioner may have estimates of some quality metric for responses. An efficient... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments regarding the experiments. We will work to clarify the experimental setup and address the comments regarding comparison to ChatGPT. We believe that both of these are important to further improve the quality of our submission.
___
**The experiments section i... | Summary: Insipred by the uni-dimensional case of First order Stochastical Dominance (FSD), the authors indtroduce multidimensional FSD which does away making approximations using aggregations and reductions of multi dimensional metrics and reduce the orderings to unidimensional case. This is done vi Optimal Transport f... | Rebuttal 1:
Rebuttal: We thank the reviewer for their support of this work. We take your feedback regarding unclear writing very seriously and will further polish the paper to minimize any ambiguities. We have corrected all of the typos you have identified and reworked the sections that were identified as unclear.
__... | Summary: In this paper authors propose a testing framework for First order Stochastic Dominance (FSD) for multivariate random variables. To achieve this, the authors use ideas from optimal transport using Entropic Regularization to derive a hypothesis testing procedure for testing multivariate FSD. The proposed method... | Rebuttal 1:
Rebuttal: We thank the reviewer for their support of this paper and for highlighting some additional experiments which would further clarify the performance of the method. We have run the proposed experiments and will add them to the appendix. We have also addressed the points from your other comments in th... | Summary: The paper studies the testing of multivariate stochastic dominance, i.e., deciding an order between two multivariate random variables. The authors generalized the notion of index of almost stochastic dominance, which is for uni-variate rv. The new index is based on regularized value of optimla transport proble... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading of the paper and for identifying sections which could use further clarification. This feedback is invaluable to us in improving the quality of our submission. We have corrected the typos identified and will further polish the paper to improve its rea... | Rebuttal 1:
Rebuttal: We thank the reviewers for their careful reading of our submission. Their questions and comments have proved invaluable in improving the quality of the paper and helped us in identifying passages which were unclear and confusing. We briefly summarize the main comments brought up in the reviewers h... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Chinese Inertial GAN for Writing Signal Generation and Recognition | Reject | Summary: The paper presents a novel Chinese inertial generative adversarial network (CI-GAN) designed to generate high-quality training samples for Chinese writing recognition using inertial sensors. The CI-GAN integrates Chinese Glyph Encoding (CGE), Forced Optimal Transport (FOT), and Semantic Relevance Alignment (SR... | Rebuttal 1:
Rebuttal: **For weaknesses 1 and Question 1:**
The input consists of a 100-dimensional random noise vector and the devised Chinese Glyph Encoding representing the character class, concatenated together to form an input vector. This combined vector passes through a fully connected layer, producing an output ... | Summary: This paper proposes CI-GAN to acquire unlimited high-quality training samples, alleviating the data scarcity in the inertial signal recognition of Chinese characters. By utilizing these generated data, the performance of recognition models is highly improved.
Strengths: - This paper is easy to follow.
- The p... | Rebuttal 1:
Rebuttal: **For weakness 1:**
The novelty of the CI-GAN consists of three proposed modules: Chinese Glyph Encoding (CGE), Forced Optimal Transport (FOT), and Semantic Relevance Alignment (SRA). These modules are interdependent, with many structures serving multiple functions. For example, CGE provides seman... | Summary: The paper address an important probem in human computer interaction: making computers accessible to vision impaired people. The paper address this my collection paired data of text and imu signals. First, the paper address the issues of limited data by training a generative model, to resample/bootstrap more da... | Rebuttal 1:
Rebuttal: We fully agree with your point: "It is very unlikely that we get more than we give to the system." In fact, what we give to the system is sufficient, as our training data provides multiple writing signals for each Chinese character. In comparison, humans can usually recognize new categories after ... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers and the conference chair for their valuable feedback and thoughtful consideration of our paper. First, we want to clarify that collecting handwriting samples of Chinese characters is not easy. During data collection, volunteers wrote different Chinese characters co... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Power of Hard Attention Transformers on Data Sequences: A formal language theoretic perspective | Accept (poster) | Summary: This paper focuses on analyzing the expressive power of transformers through the lens of formal languages, inspired by Angluin's approach. Specifically, while traditional unique hard attention transformers (UHAT) on strings are associated with $AC^0$ and regular languages definable in first-order logic, the au... | Rebuttal 1:
Rebuttal: **Q: How much of $TC^0$ is covered?**
A: Firstly, Barcelo et al. [3] showed that there is an $AC^0$ language over the alphabet {0,1} that is not in UHAT. The same language is also a witness that UHAT over sequences of numbers (i.e. our setting) cannot even recognize some $AC^0$ language.
While ... | Summary: The paper studies the computational expressiveness of unique hard attention transformers (UHAT) on formal languages formed over an infinite alphabet. The work is motivated by the application of transformers to time series forecasting where input values can be unbounded. Specifically, the authors assume the lan... | Rebuttal 1:
Rebuttal: **Q: Is there a restriction to input lengths up to $n$?**
A: We do not restrict the language to sequences of length up to $n$. Instead, the definition of the circuit complexity class $TC^0$ is that there is a family of circuits: namely one circuit for each input length $n$ (with Boolean and majo... | Summary: This paper studies the expressive power of transformer encoders with leftmost-hard attention and strict past-masking, where the input is not a sequence of symbols from a finite alphabet, but a sequence of rational vectors. The three main results are:
1. Transformers (under the assumptions above, with or witho... | Rebuttal 1:
Rebuttal: **Q: What is the role of past masking in the paper?**
A: Our paper mostly does *not* use masking, but instead permits arbitrary position encodings. This follows the classic model of UHAT formalized by Hao et al. [18], and has been used in various papers (e.g. see [3]). We used masking to obtain a... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their in-depth reviews and useful feedback. There are some common questions with regards to practical implications of the results, which we will address here. Other questions are addressed directly to the reviewers.
**Q: What are the practical implications?**
A: We bel... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Scaling laws for learning with real and surrogate data | Accept (poster) | Summary: This work addresses the challenge of augmenting limited data with more accessible surrogate data to improve generalization. The authors proposed a weighted ERM approach for integrating surrogate data into training and analyzed its performance under various statistical models. It was shown that incorporating su... | Rebuttal 1:
Rebuttal: Essentially all weaknesses pointed by the referee concern literature review.
We will expand the comparison with literature.
Regarding the specific suggestions provided:
1. Data augmentation. We feel this is a significantly different setting. In data augmentation, the new data is typically obtain... | Summary: This work investigates the effects of augmenting training datasets with lower quality data, under a weighted ERM scheme. It is shown that introducing this surrogate data to optimally weighted training can improve predictive performance on the original data distribution, as measured by test error, even when the... | Rebuttal 1:
Rebuttal: We agree that it would be important to generalize our theory to classification loss. At the same time, a substantial number of results suggest that the two settings are not as different in high dimension. Among others
1. Vidya Muthukumar, Adhyyan Narang, Vignesh Subramanian, Mikhail Belkin, Daniel... | Summary: This study addresses the challenge of computational scenarios where true data are scarce, and surrogate data are available to assist in building statistical models. The authors provide novel theoretical and empirical insights demonstrating that training models with both true and surrogate data, with appropriat... | Rebuttal 1:
Rebuttal: 1. Thanks for the positive feedback!
2. We agree that studying classification and other losses would be an important next step.
3. Thanks for pointing out the typos. We will correct them in the final version. We will also add the definitions of the symbols/concepts pointed out by the reviewer.
... | Summary: This is a technical report on predicting training loss for a model trained on a weighted combination of real (in-distribution) and surrogate (out-of-distribution) data. The report proposes a parametric function for predicting how the training loss scales with the amount of real and surrogate data. The report f... | Rebuttal 1:
Rebuttal: We agree: we will expand the related-work discussion.
At the same time, part of this criticism is unfair. The distinction between “technical report” and “research paper” depends on the subcommunity. Mathematical papers tend to be more technical and less focused on positioning.
Review: ‘The wo... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: In the context of linear regression, this paper proposes to complement training data with surrogate data (generated / synthesized, or from an unrelated task / domain). For an optimal combination of real and surrogate data, new scaling laws are derived which show that that surrogate data can can help reduce the... | Rebuttal 1:
Rebuttal: In our paper we provide two recommendations for selecting $\alpha_*$
1. Compute the error on a validation split from the original data for various values of $\alpha$, and optimize among those. (This can be improved using cross validation)
2. Use the scaling law expression, with free parameter fitt... | null | null | null | null | null | null |
TFG: Unified Training-Free Guidance for Diffusion Models | Accept (spotlight) | Summary: The paper introduces Training-Free Guidance (TFG), a novel framework designed to enhance the generation of samples with desired properties using diffusion models, without necessitating additional model training. TFG aims to resolve the shortcomings of existing training-free methods by offering a unified algori... | Rebuttal 1:
Rebuttal: We sincerely thank you for your insightful and constructive review, and we are so honored that you believe our work is novel, effective, comprehensive, and offers commendable contributions. We are delighted to address your concerns and questions below.
1. The existence of a broader spectrum of ... | Summary: The authors propose a framework (TFG) for training-free guidance of unconditional diffusion models, enabling their application to conditional generation tasks such as super-resolution, deblurring, etc. via the use of a predictor that evaluates the quality of a clean sample. TFG, as in related past methods, ai... | Rebuttal 1:
Rebuttal: We sincerely thank you for your insightful and constructive review, and we are honored that you think our work is non-trivial, novel, organized, comprehensive, substantive in experiments, and presents a valuable contribution. We are happy to address your concerns:
> The authors claim all existing... | Summary: This paper scrutinizes existing works on training-free guidance in diffusion models and proposes a unified framework that includes all existing methods as special cases. With this unified framework, this work presents a detailed and informed investigation of the design choices and hyperparameters within this f... | Rebuttal 1:
Rebuttal: We sincerely thanks for your insightful and constructive review, and we are more than honored that you think our work is interesting, novel, comprehensive, and will be helpful for the community. Regarding your concerns,
> While I appreciate the comprehensiveness of the experiments on the proposed... | Summary: This paper focuses on the unification of training-free guidance methods for diffusion models. It defines each method within a unified framework and finds that restricting the hyperparameter space is consistent with existing methods. This framework can be broadly categorized into mean guidance, variance guidanc... | Rebuttal 1:
Rebuttal: Thank you for the insightful and constructive review, and we are delighted that you think our work will greatly benefit the diffusion community. Regarding to the concerning questions:
> Algorithm 1 seems to be one of the most important parts of this paper, but it lacks a detailed explanation. An ... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their insightful and constructive reviews, and we are honored that all reviewers believe that our paper is novel, beneficial, comprehensive, and well-written. Reviewer aTDj thinks the work is “a valuable contribution”, reviewer Xm6V thinks it “reflects a high d... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Natural Counterfactuals With Necessary Backtracking | Accept (poster) | Summary: The authors propose a novel approach for generating causally valid and *natural* counterfactuals. These counterfactuals are *natural* in that they are close to the observed data manifold. This is achieved by allowing a certain amount of backtracking, which involves tracing back upstream causal effects on varia... | Rebuttal 1:
Rebuttal: ***Weaknesses***
**1. No standard errors reported in Tables, so difficult to assess how substantial the performance differences are after accounting for noise.**
Thank you for asking this. We have included the standard errors of all experiments in Tables 9-12 in Section A.4 of the Appendix. As y... | Summary: The paper presents a framework for generating "natural counterfactuals" that are more feasible within the support of the training data distribution. This approach includes controlled backtracking through an optimization method that uses a "naturalness" criterion as a constraint.
Strengths: Original combinat... | Rebuttal 1:
Rebuttal: ***Weaknesses***
**(1) My first concern is ... However, the experiments in this paper sample from the prior distributions of ... counterfactual distribution.**
Thanks for raising the concern. To clarify, we sample from the posterior distribution of $U$. First, due to the monotonicity assumption... | Summary: The paper takes the recently developed idea of backtracking counterfactuals and applies it to improve the realistic generation of counterfactuals from data, which is known to be a hard task as the standard, non-backtracking, counterfactuals lie out of the distribution and generative models perform badly on tho... | Rebuttal 1:
Rebuttal: We are very grateful for your previous feedback, which helped a lot to improve the paper. Thank you also for the new comments. We will address them and correct the typos you pointed out.
**1: Surely the main focus should be: which counterfactual (backtracking, non-backtracking, partial backtracki... | Summary: This paper addresses a key limitation of non-backtracking counterfactual reasoning in causal inference. The authors argue that Pearl's framework often generates unrealistic scenarios. To solve this, they propose "natural counterfactuals," which allow controlled backtracking to ensure scenarios remain realistic... | Rebuttal 1:
Rebuttal: ***Weaknesses***
**1. FIO involves several parameters and choices (naturalness criteria, distance measures), which may require careful tuning and consideration for different applications.**
Thanks for raising this point. We agree that different applications may warrant different choices, as stat... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their time dedicated to reviewing this paper and their valuable comments. We are encouraged by and grateful for the comments that say our paper was described as ``"well-written"`` (DQof) and ``"interesting"`` (fYWn), and was considered to``"make a strong ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On Divergence Measures for Training GFlowNets | Accept (poster) | Summary: This paper investigates alternative training methods for Generative Flow Networks (GFlowNets) by evaluating various divergence measures, including Renyi-α, Tsallis-α, reverse, and forward Kullback-Leibler (KL) divergences. Traditional methods focusing on minimizing log-squared differences are shown to lead to ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback and suggestions. Below, we address the specific weaknesses and questions. We hope our clarifications and additional experiments address your concerns and elevate your appraisal of our work.
.
> Because the Measurable pointed DAG is from previous ... | Summary: GFlowNets are a probabilistic framework for training amortized samplers for high-dimensional compositional spaces. The samplers are typically trained using local consistency objectives which are squared log losses. This paper examines alternatives to these obejctives in the form of general statistical f-diverg... | Rebuttal 1:
Rebuttal: Thank you for the suggestions and for appreciating our work. We did our best to address each of your concerns below. Please let us know if you have other questions or require further clarification.
> The paper considers the on-policy setting for training GFlowNets and the proposed learning object... | Summary: This paper investigates the potential of using a variety of divergence measures directly as training losses for GFlowNets, relying on many of the connections made between GFlowNets and variational inference. Training GFlowNets essentially consists in enforcing balance/flow-matching conditions between a proposa... | Rebuttal 1:
Rebuttal: Thank you for valuable suggestion and review. We did our best to address each of your questions, and extended our experiments following your suggestion. Please let us know if you have other questions or require further clarification.
> How is the diversity of samples impacted?
Considering that t... | Summary: This paper investigates divergence measures as learning objectives for Generative Flow Networks, which are amortized inference models designed for sampling from unnormalized distributions over composable objects. The authors review four divergence measures - Renyi-\alpha, Tsallis-\alpha, reverse and forward Ku... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We hope our answers address your concerns and elevate your appraisal of our work. Otherwise, we would be happy to engage further.
> … a more extensive comparison across a wider range of datasets and applications would strengthen the claims.
We have included SubTB [1]... | Rebuttal 1:
Rebuttal: Dear reviewers and AC,
We appreciate that reviewers evaluation of our work as both theoretically principled [Gn1e, HRNm] and empirically well-grounded [Gn1e, 3sBX, NozV], expanding the link between VI and GFlowNets [Gn1e, 3sBX, HRNm], with practical contribution of effective control variates fo... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive Reasoning | Accept (poster) | Summary: This paper introduces a new framework, AVATAR, that allows LLM agents to optimize the performance of Knowledge Retrieval. In the framework, a Comparator agent is adopted to extract insight from the positive and negative samples. The experiments on four retrieval datasets show the effectiveness of the method.
... | Rebuttal 1:
Rebuttal: We appreciate your comments! We carefully justify our novelty and clarify the misunderstandings. We will be grateful for your patience of reading our response:
---
## **Comment 1: Comparison with Expel [1] and Autoguide [2]**
We apologize for missing these important and relevant papers. Thank yo... | Summary: This paper introduces AVATAR, a novel framework for optimizing large language model (LLM) agents to effectively use provided tools and improve performance on complex multi-step tasks, with a focus on retrieval tasks. The key innovation is a comparator module that generates holistic instructions to improve the ... | Rebuttal 1:
Rebuttal: We are grateful for your positive feedback! We provide point-to-point responses:
---
## **Comment 1: Applying AvaTaR to more tool-use benchmarks**
Thanks! We've conducted initial experiments on ArxivQA and are currently running tests on ToolQA. Please stay tuned! For ArxivQA, we randomly sample... | Summary: This paper proposes a new framework AVATAR for LLM agent that operates in two stages:
-The first stage is optimization during the training process, which integrates the LLM comparator component into the AVATAR. The comparator summarizes holistic prompts from positive and negative queries and iteratively optimi... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and insightful comments! Here are our point-to-point responses:
---
## **Comment 1: Clarification about when to stop iteration and how to sample the queries**
Thanks! We apologize for the brief information there. We added the following details in our revision:
... | null | null | Rebuttal 1:
Rebuttal: # **General response**
We truly appreciate the reviewers' efforts and valuable suggestions in reviewing our paper. We are glad that all/most reviewers reached a positive consensus on our work's presentation, motivation, novelty, and experimental effectiveness. Here is a summary of the reviewers’ ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Understanding Scaling Laws with Statistical and Approximation Theory for Transformer Neural Networks on Intrinsically Low-dimensional Data | Accept (poster) | Summary: This work derived a generalization error bound of using transformer architecture to estimate beta-Holder continuous functions. The bounds depend on the intrinsic dimension of the data. The generalization error can be decomposed into an approximation error and a variance error. They further showed that TFs with... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough repsonse! We address some of your comments below.
**Strengths:**
1. The paper present novel approximation theory for transformers in terms of the intrinsic dimension. Empirical observations are well-aligned with the theory.
We are glad the reviewer found... | Summary: This document appears to be a research paper on predicting scaling laws for transformer neural networks, particularly when applied to data with low intrinsic dimensionality. Here are some key points:
1. The paper aims to establish mathematical theories to explain and predict scaling laws observed in transforme... | Rebuttal 1:
Rebuttal: **Strengths:**
1. The paper provides a rigorous mathematical framework for understanding transformer scaling laws, which has been a significant open question in the field. [...] The authors test their theoretical predictions on actual language models, providing evidence for the practical relevanc... | Summary: This paper makes a series of contributions:
- Transformer Generalization Error: Loosely speaking, assuming a transformer is trained to approximate a Holder function in a regression setting, and assuming the data lives on the low dimensional manifold, then the generalization error of the transformer is upper b... | Rebuttal 1:
Rebuttal: **Strengths:**
- Having minor familiarity with Sharma and Kaplan, I think this work is a great extension towards language modeling.
We thank the reviewer for recognizing this work as a great extension towards lanugage modeling! However, we want to emphasize the main contribution of our work is *... | Summary: This paper investigates the representational capabilities of transformers in regression tasks and their correlation with scaling laws. The authors present a novel analysis of the transformer's sample complexity on datasets with low intrinsic dimension $d$, or those residing on a $d$-dimensional manifold $\math... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough response! We address some of your concerns below.
**Strengths**:
- As far as I know, the theoretical result of the paper is novel and highly non-trivial. To a certain degree, it sheds light on the fundamental difference between a transformer and simpler mo... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Geometry of naturalistic object representations in recurrent neural network models of working memory | Accept (poster) | Summary: The paper presents a study of Working Memory (WM) in Recurrent Neural Network (RNN) models.
The main contribution is the study of the latent space dynamics of RNNs during WM-related tasks with respect to naturalistic stimuli, instead of abstract categorical stimuli that are commonly used. The paper analyses ga... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's positive assessment and their suggestions. We address their specific questions and weaknesses below:
* __Small dataset and limited N-back window__
We appreciate the reviewer highlighting the limited size of our stimuli and task sets. We would like to clarify that our s... | Summary: The manuscript uses various RNN architectures as "model animals" to study the representation and processing of naturalistic stimulus during several working memory (K-back) tasks. Unlike prior work, the current study considers various contexts for the cues, making the representation by the RNNs inherently highe... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful response. We also appreciate the acknowledgment of the potentially conflicting results our study highlights in relation to prior works. Below, we respond to individual questions the reviewer raised:
* __Presentation styles__
We significantly revised the t... | Summary: This paper examines the mechanisms of working memory in recurrent neural networks (RNNs) trained with naturalistic objects and N-back tasks, a classic task in cognitive and neuroscience. The aim is to study more complex, ecologically valid stimuli than the abstract categorical input that previous studies of wo... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the importance of our study. We have carefully considered the reviewer's comments and have provided the following response:
* __Typos.__
We sincerely thank the reviewer for carefully listing all of the typos in our original draft. We will fix them and more tho... | Summary: This work involves training RNNs on the n-back task with naturalistic images fed into the RNN through a CNN front-end. Through a large number of permutations comprising different task requirements, different combinations of tasks and different architectures, the authors study how task relevant and task irrelev... | Rebuttal 1:
Rebuttal: We thank the reviewer’s recognition of the strengths of our manuscript, particularly noting the principled and rigorous nature of the approach. We are glad that the extensive training across various model-task permutations robustly supported our conclusions. In response to the weaknesses and limit... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their insightful and positive feedback. We are encouraged by the reviewers’ acknowledgement of the rigorousness and thoroughness of our work (*reviewer VPbf and YsNL*), creativity and soundness of our experiments (*reviewer YsNL*), and novelty and impactfulness... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards Global Optimal Visual In-Context Learning Prompt Selection | Accept (poster) | Summary: The paper proposes a framework called Partial2Global for ranking in-context examples in visual language models. This method uses a transformer-based list-wise ranker and a consistency aware ranking aggregator to approximate the global optimal prompt selection. The framework is validated through experiments on ... | Rebuttal 1:
Rebuttal: **Q1: The workload is relatively low. I think this work should also explore the impact of models with a larger number of shots on ICL. Additionally, it should investigate the impact of data quality in the alternative dataset, and include the importance of different parts of the loss in the ranking... | Summary: This paper addresses the fundamental problem in Visual In-Context Learning (VICL), which is how to select the best prompt. Specifically, it focuses on the ranking problem. The paper proposes an algorithm called Partial2Global, which conducts an in-context example selection framework to find the global optimal ... | Rebuttal 1:
Rebuttal: **Q1: The method seems to incur additional costs due to the ranking process. Is there any further analysis on this?**
A1: Thank you. Please refer to the general response. Generally, we can summarize that the usage of list-wise ranker and the ranking aggregation will inevitably introduce additiona... | Summary: This paper addresses Visual In-Context Learning (VICL), which uses examples to help models learn new tasks. The main challenge is choosing the best prompt to improve learning and prediction. The authors introduce Partial2Global, a new method to find the best examples for each query. This method uses a transfor... | Rebuttal 1:
Rebuttal: **Q1: The paper does not include ablation studies on the critical parameters, such as δ and τ (NDCG). These studies are essential for understanding how variations in these parameters affect the model's performance. Without this information, it is difficult to assess the robustness and sensitivity ... | Summary: This paper studies the demonstration retrieval mechanism for visual in-context learning. The authors propose Partial2Global which uses a transformer-based ranker and a consistency-aware aggregator to find the optimal demonstration. Experiments show that Partial2Global outperforms existing methods in tasks like... | Rebuttal 1:
Rebuttal: **Q1: Since training exclusively on a single dataset is limiting, I would appreciate a demonstration of transfer learning performance to prove its adaptability and universality across a broad range of data.**
A1: Thank you for the suggestion. We would like to highlight that the current experiment... | Rebuttal 1:
Rebuttal: We thank the reviewers for all of your time to write valuable and constructive comments. Your feedback will definitely assist us in enhancing the quality of our paper, and thus we are committed to incorporating these suggestions in our revision process. Meanwhile, we feel encouraged that the revie... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Mirror and Preconditioned Gradient Descent in Wasserstein Space | Accept (spotlight) | Summary: The authors study the mirror descent method in Wasserstein-2 spaces. This is based on constructing the Bregman divergence functionals in Wasserstein-2 spaces. One can use it to define a mirror descent direction in the sense of pushforward mapping functions, which generalizes the classical Wasserstein gradient ... | Rebuttal 1:
Rebuttal: Thank you for your appraisal and positive comments on our paper.
**On page 8, the authors don't study the mirror descent of KL divergences for non-Gaussian target distributions. This could be a very intriguing example. The authors may think about how to implement this case. This could be compared... | Summary: In this paper, the authors endeavor to integrate the concepts of mirror descent (MD) and preconditioned gradient descent (PGD) within the framework of Wasserstein distances, from a convergence theory perspective. Initially, they provide a comprehensive background including, but not limited to, Wasserstein dist... | Rebuttal 1:
Rebuttal: Thank you for reading the paper and for your feedback. We answer your comments below. Please do not hesitate if you have other questions.
**The presentation of results could be enhanced by providing more detailed visualizations.**
Thank you for these suggestions, we will take it into account and... | Summary: This paper presented a unified view on the functional optimization on the Wasserstein space. Authors proposed a mirror descent algorithm and a preconditioned gradient descent algorithm, both are applied to minimize the objective functional over the Wasserstein space. Many existing algorithms in the Wasserstein... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and time. We have addressed your comments point-by-point below. Please don't hesitate to let us know if you have any further questions.
**What new tool does the MD formulation bring to the table?**
First, the Mirror Descent algorithm on the Wasserstein space ... | Summary: The paper generalises mirror descent and preconditioning approaches from optimisation over R^d to optimisation over Wasserstein space, which is applicable in some actual problems, and confirms by the corresponding numerical experiments the efficiency of these approaches.
Strengths: The paper completely corres... | Rebuttal 1:
Rebuttal: Thank you for your appraisal and positive comments on our paper.
**The preconditioning is considered in some particular yet practically important case, but the complete generalisation of preconditioning is still to be finished.**
We acknowledge that the complete generalization and study of the p... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their positive comments and common appraisal of the soundness of our approach to lift Euclidean optimization schemes to the Wasserstein space. Following the reviewers' comments, we will improve the clarity of the exposition for the revised version, taking advantage o... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper provides convergence analysis for Mirror descent (with and without additional preconditioning) in the Wasserstein space. The analysis is performed for relatively smooth and convex functionals. The use of the studied algorithms on computational biology problem is illustrated.
Strengths: The theory in... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and time. We have addressed your comments point-by-point below. Please don't hesitate to let us know if you have any further questions.
**The paper is very dense. I would advise the authors to revise the paper to make it more accessible.**
We understand your ... | null | null | null | null | null | null |
DiffCut: Catalyzing Zero-Shot Semantic Segmentation with Diffusion Features and Recursive Normalized Cut | Accept (poster) | Summary: This paper proposes a model for zero-shot semantic image segmentation based on clustering
feeatures from an off-the-shelf text-to-image diffusion model. Features are extracted
from the U-Net used in the diffusion model. The features are then clustered using
a recursive normalized cut algorithm. The clustering ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful comments and appreciate the positive feedback.
#### **Weaknesses**
#### **1. The main contribution explicitly states the improvements over the TokenCut and MaskCut models, but does not compare to these models in the experiments.**
Unlike TokenCut or MaskCut... | Summary: This work proposes a new strategy for the task of unsupervised zero-shot segmentation using diffusion model features. The semantic maps are extracted from the U-net features of a diffusion model by applying a recursive algorithm that allows various levels of granularities of the segmentation maps. The proposed... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful comments and appreciate the positive feedback.
#### **Weaknesses**
#### **1. [...] The proposed method is not applied with SD1.4, which I think would be mandatory. [...] to allow a direct comparison with the original DiffSeg method. [...].**
As mentioned in ... | Summary: This paper addresses harnessing semantic localization information in pretrained diffusion UNet models. With the proposed recursive normalized cut on the final self-attention features of the diffusion UNet encoder, this paper achieves better performance on unsupervised zero-shot segmentation comparing to previo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful comments and appreciate the positive feedback.
#### **Weaknesses**
#### **1. Writing is a bit too vague [...]. For example, can you demonstrate the "patch-level alignment" with visual examples?**
For vision tasks, we expect the vision encoder to be semantic... | Summary: This paper introduces an innovative unsupervised, zero-shot image segmentation method called DiffCut. This method leverages the encoder features of a pre-trained diffusion model within a recursive graph partitioning algorithm to create finely detailed segmentation maps without requiring labels from downstream ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful comments and appreciate the positive feedback.
#### **Weaknesses**
#### **1. [Unsupervised Instance or Panoptic Segmentation Performance]**
We have not yet evaluated DiffCut’s performance in instance or panoptic segmentation. The goal here is to demonstrate t... | Rebuttal 1:
Rebuttal: We thank the reviewers for their helpful comments and valuable suggestions. We would like to clarify here some key points raised by the reviewers and we then provide individual responses to each one.
### **Comparison with MaskCut**
Reviewers jdn9 and 2hwn mentioned that although we positioned ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MO-DDN: A Coarse-to-Fine Attribute-based Exploration Agent for Multi-Object Demand-driven Navigation | Accept (poster) | Summary: The paper introduces a new benchmark Multi-object Demand-driven Navigation and trains various models on it.
Strengths: 1. This paper introduces a new benchmark, which are historically undervalued
1. The ablation section is very clear and well written
1. There is adequate coverage of baselines to my knowledge,... | Rebuttal 1:
Rebuttal: We highly appreciate the time and effort you put into reviewing our paper! We are very grateful to you for appreciating our benchmarks and experiments! We hope the following clarification will ease your concerns, and hope to hear back from you if you have further questions!
>Q1: Some of the figu... | Summary: The paper presents Multi-object Demand-driven Navigation (MO-DDN) which extends the DDN task to work with multi-object search and personal preference. DDN task aims to find an object in a navigation setting based on a given demand instruction. The paper proposes a new attribute model for multi-object DDN where... | Rebuttal 1:
Rebuttal: We are very grateful for your time and effort in reviewing our paper! We appreciate your kind endorsement of our benchmark and method. We hope the following clarification will ease your concerns, and hope to hear back from you if you have further questions!
>Q1: In line 41, the authors claimed ... | Summary: The paper presents "MO-DDN," a novel benchmark and approach for Multi-object Demand-driven Navigation (MO-DDN), where an agent needs to find multiple objects to satisfy complex, user-specific demand instructions. The proposed approach leverages a coarse-to-fine attribute-based exploration strategy. The method ... | Rebuttal 1:
Rebuttal: We greatly appreciate your time and effort in reviewing our paper! We really value your recognizing the novelty of our benchmark and method and the comprehensive evaluation. We hope the following clarification will ease your concerns, and hope to hear back from you if you have further questions!
... | Summary: The paper introduces a new task called Multi-Object Demand Driven Navigation where an agent is tasked with searching for multiple-objects that satisfy a specified demand instruction by a user. The demand instruction is a natural language instruction specified by a user which might directly or indirectly ask fo... | Rebuttal 1:
Rebuttal: We are very grateful for your time and effort in reviewing our paper! We are also very appreciative that you have recognized the writing, the task setting, and the method proposed. We hope the following clarification will ease your concerns, and hope to hear back from you if you have further quest... | Rebuttal 1:
Rebuttal: # Common Response
We are very grateful to all the reviewers and AC for their time and effort. We highly thank the reviewers for their appreciation of our writing, benchmark, methods, and experiments. "The paper is well written and easy to follow.""The proposed task .... is a interesting task at t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
OptEx: Expediting First-Order Optimization with Approximately Parallelized Iterations | Accept (poster) | Summary: The paper proposes a general framework for accelerating first-order optimization methods. The framework leverages parallel computing by estimating the gradients using kernel methods and breaking iterative dependencies. The paper establishes theoretical guarantees that the method gives an acceleration rate of $... | Rebuttal 1:
Rebuttal: We are grateful to Reviewer YWBT for the positive and constructive feedback! We appreciate that the reviewer highly recognizes that our paper is **very well written**, its contribution is **novel**, and our paper forms a **complete story with both theoretical and empirical supports agreeing with ... | Summary: The paper "OptEx: Expediting First-Order Optimization with Approximately Parallelized Iterations" introduces a novel framework, OptEx, designed to improve the efficiency of first-order optimization (FOO) algorithms by approximately parallelizing their iterations.
Strengths: 1. The introduction of a general fr... | Rebuttal 1:
Rebuttal: We thank Reviewer 1m6n for recognizing that our OptEx framework is **novel**, it addresses a **significant inefficiency** in traditional optimization methods, and it has **robust theoretical guarantees** and **extensive empirical results** to support its **practical applicability** and **efficienc... | Summary: This paper presents OptEx, an approach to parallelize optimization methods by using gradients from previous iterations to predict gradient for subsequent iterations which in turn breaks the serial nature of standard stochastic optimization thereby enabling approximately parallel iterations. The gradient predic... | Rebuttal 1:
Rebuttal: We thank reviewer ieTR for taking the time to review our paper and appreciate the reviewer's feedback. We would like to provide the following response to address the concerns.
---
> The question of gradient estimation converging to the true gradient seems fairly far-fetched, and there is every r... | Summary: This paper introduces a new approach for parallelizing stochastic gradient descent for unconstrained, nonconvex, smooth stochastic optimization. The approach is based on building a Gaussian Process surrogate for the true gradient (based on a history of observed stochastic gradients), and uses this surrogate to... | Rebuttal 1:
Rebuttal: We thank Reviewer RYzW for recognizing the **interesting contribution, promise, and clarity** of our OptEx framework. We address your concerns below.
---
## Responses to Weaknesses
1. Thank you for pointing out this oversight. We appreciate you highlighting these relevant papers on data paralle... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
IQA-EVAL: Automatic Evaluation of Human-Model Interactive Question Answering | Accept (poster) | Summary: This paper proposes an automated evaluation framework for Interactive Question Answering tasks based on LLM-based agent. The authors utilize LLMs to simulate the interaction between human and IQA models and use them to evaluate the interactions automatically. Additionally, they assign predefined personas to LL... | Rebuttal 1:
Rebuttal: Thank you Reviewer RXxM for your reviews. Below we'd like to address your concerns.
# Weaknesses
----
> **This paper's weakness is its lack of technical contributions and novelty, and its experimental analysis is not strong enough. It only used prompts to implement its methods without any prompt ... | Summary: This paper introduces IQA-EVAL, a framework for automatically evaluating interactive question-answering (IQA) systems using large language models. The authors propose using LLM-based Evaluation Agents (LEAs) to simulate human behavior in both generating interactions with IQA models and evaluating those interac... | Rebuttal 1:
Rebuttal: Thank you for your helpful review. We would like to address the mentioned weakness and questions below.
----
# Weaknesses:
> **Limited novelty, as using LLMs as judges is quite common in evaluation tasks. This paper mainly focuses on a relatively new setting - the interactive QA setting.**
Pre... | Summary: The authors introduce a novel method to simulate a human conversation when evaluated on an interactive question answering (IQA) and evaluate the simulated interaction according to some predefined metrics.
Strengths: - The presentation of the paper is clear and easy to follow
- The paper is well-motivated. It ... | Rebuttal 1:
Rebuttal: Thank you for your helpful review. We would like to address the mentioned weakness and questions below.
----
> **When using LLMs to evaluate the outputs from LLMs, recent research has shown LLMs to be biased in preferring their own generations compared to generations from other models, even if... | Summary: This research addresses the evaluation methodology for multi-turn conversation using Large Language Models (LLMs), a topic of active research recently. The study proposes an evaluation framework called IQA-Eval, which consists of the target model (IQA model) and an agent (LEA model) that engages in conversatio... | Rebuttal 1:
Rebuttal: Thank you for your insightful review. We would like to address your concerns as follows:
# Weaknesses:
>1. The evaluation dataset consists entirely of multiple-choice questions, making it unsuitable for generating and evaluating multi-turn conversations. This is evident in Tables 3 and 5, where ... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their insightful reviews. Below we address common concerns by topics.
# Insufficient technical contribution, especially on the lack of prompt engineering.
Inspired by G-eval [4], we did conduct prompt engineering and designed our prompts by combining deta... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Quantifying the Gain in Weak-to-Strong Generalization | Accept (poster) | Summary: This paper studies the phenomena of weak-to-strong generalisation (WTSG) from a theoretical angle, aiming to explain why the phenomena occurs. The paper's main theoretical results show that, in a regression setting with a convex function class, the decrease in MSE from the weak to weak2strong model is bounded ... | Rebuttal 1:
Rebuttal: Thank you so much for reading our paper, and for your review and comments. We are really glad that you like our work!
With regards to the points you bring up:
> It's unclear why the authors chose the regression setting, when the original work and likely scenario of use is classification setting... | Summary: The paper provides a geometric perspective on weak-to-strong generalization [[Burns et al., 23](https://arxiv.org/abs/2312.09390)]. Specifically, the authors show that if the set of strong model-representable functions the following holds:
$$MSE(\phi^*, \phi^{ws}) \le MSE(\phi^*, \phi^w) - MSE(\phi^w, \phi^{ws... | Rebuttal 1:
Rebuttal: Thank you so much for reading our paper, and for your comments. We address your concerns ahead:
> W1..presentation very confusing..notation $d_P$ to denote the mean-squared distance..
The only reason we introduced the notation $d_P(f,g)$ was for ease of reading: it is convenient to have some nota... | Summary: This paper provides bounds for weak-to-strong generalization, where a strong student model is trained on the labels produced by a weaker teacher model. The authors prove that in a regression setup, under certain assumptions, the strong model gains over the weak model's accuracy by an amount equal to the *disag... | Rebuttal 1:
Rebuttal: Thank you so much for reading our paper, and for your review and comments. We are really glad that you like our work!
With regards to the points you bring up:
> The results of WSCM20 are not properly contextualized. Their analysis is not limited to a self-training scenario and applies for any st... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Boosting Weakly Supervised Referring Image Segmentation via Progressive Comprehension | Accept (poster) | Summary: Aiming for the WRIS task, the work proposes a novel framework to leverage target-related textual cues from the description for progressively localizing the target object. The authors use a Large Language Model (LLM) to decompose the input text description into short phrases which are taken as target-related cu... | Rebuttal 1:
Rebuttal: > **[W1]**: In table 1, only the results refined by SAM[16] are reported. Considering that FreeSOLO [40] is an unsupervised segmentation model, I am curious about the results refined by FreeSOLO.
**[Ans]**: Thanks for your advice. Here we present the **mIoU** after applying FreeSOLO refinement, a... | Summary: This paper proposes the Progressive Comprehension Network (PCNet) for the WRIS task. this model achieves visual localization by progressively incorporating target-related textual cues for visual-linguistic alignment. Although experimental results have demonstrated the effectiveness of this paper, several aspec... | Rebuttal 1:
Rebuttal: Sincerely thanks for useful comments. To the weaknesses, our response is as follows:
>**[W1]**: Conditional Referring Module (CRM) is implemented through multiple cross-attentions, which is a relatively common approach, lacking novelty.
Thanks for your comments. We would like to address the con... | Summary: Inspired by human's step-by-step cognitive process for localizing a target object in an image, the paper proposes Progressive Comprehension Network (PCNet) for the task of weakly-supervised referring image segmentation (WRIS) where text is the only supervision signal. PCNet first decomposes a long, complex tex... | Rebuttal 1:
Rebuttal: Sincerely thanks for your useful comments. To the weaknesses and questions, our response is as follows:
**[W1]**:
Thank you for you careful comment. We sincerely apologize for the less detailed presentation regarding the loss $\mathcal{L}_{\texttt{cls}}$ in **TRIS** [25] due to space restricti... | Summary: This paper proposes a Progressive Comprehension Network for weakly-supervised referring image segmentation, which mimics the human process of progressive understanding by breaking down sentences into segments and gradually narrowing down the target range. The main contributions include: A multi-stage Condition... | Rebuttal 1:
Rebuttal: Sincerely thanks for useful comments. To the weaknesses and questions, our response is as follows:
**[W1]**:
Thank you for your advice. Although both DGA [A] and our method adopt multi-stage refinement, there are significant differences:
- The motivation is different. DGA focuses on the fully-... | Rebuttal 1:
Rebuttal: **To Reviewers and AC:**
We extend our sincere gratitude to all the reviewers (**R1**-oLx4, **R2**-kTSb, **R3**-Tfcw, and **R4**-K7SG) for your time and insightful reviews, which help us emphasize the contributions of our work and revise the presentation. We are encouraged to hear that the revie... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Equivariant spatio-hemispherical networks for diffusion MRI deconvolution | Accept (poster) | Summary: The paper presents a convolutional neural network for spherical deconvolution of DWI data to estimate fiber orientation distribution. The main contributions over previous approaches include the introduction of Spatio-Hemispherical Equivariant Convolution, Dense Matrix Multiplication, and the use of Pre-compute... | Rebuttal 1:
Rebuttal: Thank you for the valuable feedback and for highlighting our improved efficiency and accuracy in comparison to current work!
We believe that there may have been some miscommunications on our part and hope to clarify them below:
# Unclear reasons for improvement
> _"Limited novelty as presentati... | Summary: The authors extend previous [work done](https://proceedings.mlr.press/v227/elaldi24a.html) in the diffusion MRI (dMRI) fibre orientation distribution function (fODF) domain with an efficient $\mathbf{E}(\mathbf{3}) \times \mathbf{SO}(\mathbf{3})$ equivariant network. The proposed model directly leverages the a... | Rebuttal 1:
Rebuttal: Thank you for the encouraging feedback! Broadly, we will incorporate all of the detailed suggestions and address the remaining high level questions/comments below:
> _”Whilst the authors here present a significant increase in computational efficiency, this study is an iterative improvement on pre... | Summary: This work introduces a novel framework for fODF estimation through equivariant spatio-hemispherical networks that achieve dMRI deconvolution. Experiments on simulated dMRI datasets with known ground truth, as well as on real in vivo dMRI data are conducted, showing promising results while improving over previo... | Rebuttal 1:
Rebuttal: Thank you for the highly detailed and valuable feedback! We will incorporate the suggestions and address the other concerns below:
# Weaknesses
> _"The main weakness of this manuscript is in the way the contributions section is written at the end of the Introduction. [...]"_
Thank you for the d... | Summary: This paper introduces a novel method for analyzing diffusion MRI data, leveraging convolutional network layers equivariant to the E(3)×SO(3) group, which respects the physical symmetries of dMRI data. The proposed spatio-hemispherical graph convolutions reduce computational complexity while maintaining high de... | Rebuttal 1:
Rebuttal: Thank you for the positive evaluation and for highlighting the importance and quality of our methodology, presentation, and experiments.
# Antipodal symmetry assumption
> _"It would be better to improve the flexibility of the model so that it could be applied to diverse scenarios."_
We agree t... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and their encouraging feedback. Their comments and suggestions have made the revision a stronger paper.
We were happy to find that they found the submission to be theoretically sufficient \[`JrFU`\], well presented and organized \[`JrFU, F5CU`\], extensively ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SlimGPT: Layer-wise Structured Pruning for Large Language Models | Accept (poster) | Summary: This paper presents a novel SlimGPT framework to conduct structured pruning for LLMs in a fast and low-cost way. Specifically, SlimGPT modifies the Optimal Brain Surgeon (OBS) framework, and proposes a Batched Greedy Pruning to enhance the performance of head-wise pruning through Cholesky decomposition. SlimG... | Rebuttal 1:
Rebuttal: We truly appreciate the reviewer for the constructive comments.
> W1: Further explanation about Incremental Pruning Ratio strategy.
In Section 5.3.2, we have discussed the impact of different pruning ratio strategies on performance, with detailed results presented in Table 5 (for convenience, Ta... | Summary: This paper presents SlimGPT, a method for structured pruning of LLMs to balance performance with efficiency. The method is based on the OBS framework and introduces Batched Greedy Pruning to enhance pruning accuracy and speed. The authors also propose the Incremental Pruning Ratio strategy to mitigate performa... | Rebuttal 1:
Rebuttal: We truly appreciate the reviewer for the constructive comments.
> W1: The impact of the calibration dataset on the pruning performance.
Thank you for the valuable comment. Table 9 displays the pruning results of SlimGPT **without fine-tuning** (for convenience, Table 9 is reproduced below). When... | Summary: The authors proposed a layer-wise pruning approach called SlimGPT that follows the Optimal Brain Surgeon framework but with a batched pruning procedure utilized to make it feasible on large models while remaining structured. The authors claim near-optimal pruning performance on commonsense reasoning datasets a... | Rebuttal 1:
Rebuttal: We truly appreciate the reviewer for the constructive comments. Due to text limitations, we try to answer your questions concisely and convincingly.
> W1: Unaligned evaluation.
We acknowledge your concerns. Achieving a fully aligned experimental setup is challenging since pruning tasks differ fr... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers,
We sincerely appreciate your valuable and insightful comments. Here I would like to address the concerns regarding inference speed or pruning efficiency raised by all reviewers.
> Inference speed and memory usage report.
As the inference speed is primarily influenced by the fin... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Why are Visually-Grounded Language Models Bad at Image Classification? | Accept (poster) | Summary: This article notes that current top-performing VLMs, like GPT-4V and LLaVA, are unable to perform image classification tasks like the CLIP model. Despite having a larger number of parameters and incorporating a vision encoder from a pre-trained CLIP model.
This author thinks the main reason for this underperf... | Rebuttal 1:
Rebuttal: We thank Reviewer CuMn for providing thoughtful feedback on our work. We address Reviewer CuMn’s questions below.
---
**Limited novelty**
> The novelty is limited. This article assesses how well current VLMs perform on classification datasets and fine-tunes them using the same datasets. These c... | Summary: This paper analyzes the issue of large vision language models (VLMs) that perform poorly in common image classification datasets such as ImageNet. The authors analyze different perspectives on the problem, including trying different inference and training methods. For inference, the authors tried using differe... | Rebuttal 1:
Rebuttal: We thank Reviewer 8fB5 for providing detailed and thoughtful feedback on our work. We address Reviewer 8fB5’s questions below.
---
**Multiple textual labels**
> There may be multiple textual labels for many ImageNet classes. For example, n01496331 can be electric ray, crampfish...
Thank you fo... | Summary: The paper presents an interesting observation of VLMs lagging in image classification performance as compared to the visual encoders lie CLIP used within them. Several hypothesis are explored to explain this observation including train-time (information loss, training objective used), inference-time (prompt va... | Rebuttal 1:
Rebuttal: We thank Reviewer SHnR for their positive comments and thoughtful feedback. We address Reviewer SHnR’s questions below.
---
**Main reason and solution**
> I'm not completely convinced with the paper's final conclusion of data being the reason why CLIP models are superior to VLMs… The data argum... | Summary: In this paper, the authors explored why Vision-Language Models (VLMs) significantly underperform as image classifiers. They compared several publicly available with proprietary VLMs on several classification benchmark datasets, including ImageNet, Flowers102, StanfordCars, Caltech101, and their newly collected... | Rebuttal 1:
Rebuttal: We thank Reviewer fPsb for their positive comments and thoughtful feedback. We address Reviewer fPsb’s questions below.
---
**Analysis of proprietary VLMs**
> Can the authors address the hypotheses related to inference, training objectives, and data for proprietary VLMs?
Thank you for your que... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful feedback on our manuscript. Below, we provide individual responses to each reviewer. Please let us know if you have any further questions or concerns!
**We have also attached a PDF for reviewers fPsb and SHnR, which includes visualizations to further ad... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DiffTORI: Differentiable Trajectory Optimization for Deep Reinforcement and Imitation Learning | Accept (spotlight) | Summary: This paper proposes DiffTOP as a model-based approach to reinforcement learning and behavior cloning. DiffTOP learns a cost function and a dynamics model through differentiable trajectory optimization, and then uses the learned model during inference for online optimization. For model-based RL tasks, DiffTOP i... | Rebuttal 1:
Rebuttal: We want to extend our heartfelt gratitude for taking the time to review our paper. Thank you for all valuable comments and suggestions on improving the quality of the paper. Below we respond to each of your comments in detail.
**Q: ... Given the abundance of similar ideas in the literature, the a... | Summary: The paper introduces DiffTOP, an approach leveraging differentiable trajectory optimization as the policy representation to enhance performance in deep reinforcement learning (RL) and imitation learning (IL). By utilizing the advancements in differentiable trajectory optimization, DiffTOP addresses the "object... | Rebuttal 1:
Rebuttal: We want to extend our heartfelt gratitude for taking the time to review our paper. Thank you for all valuable suggestions and comments on improving the quality of the paper. Below we respond to each of your comments in detail.
**Q: The work is not well-contextualized … It would be beneficial for... | Summary: The paper introduces DiffTOP, a novel policy class for reinforcement learning (RL) and imitation learning (IL) that utilizes differentiable trajectory optimization to generate policy actions. DiffTOP leverages recent advancements in differentiable trajectory optimization, allowing end-to-end learning of cost a... | Rebuttal 1:
Rebuttal: We want to extend our heartfelt gratitude for taking the time to review our paper. Thank you for all valuable comments and suggestions on improving the quality of the paper. Below we respond to each of your comments in detail.
**Q: The paper attempts to condense a large amount of information into... | Summary: The paper presents a method that uses Differentiable Trajectory Optimization as a policy representation. The proposed method extends the work of Temporal difference learning for model predictive control (TD-MPC) by incorporating a policy-gradient loss for which analytical backpropagation is possible thanks to... | Rebuttal 1:
Rebuttal: We want to extend our heartfelt gratitude for taking the time to review our paper. Thank you for all valuable comments and suggestions on improving the quality of the paper. Below we respond to each of your comments in detail.
**Q: [minor] The main weakness of the approach is the high computation... | Rebuttal 1:
Rebuttal: Dear reviewers,
We want to extend our heartfelt gratitude for taking the time to review our paper. Thank you for all valuable comments and suggestions on improving the quality of the paper. We respond to each of your comments in detail in the individual rebuttal.
Following the reviewer's suggest... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning | Accept (poster) | Summary: This paper introduces a strategy named FedEgoists designed to enhance collaboration in cross-silo federated learning (FL) scenarios, particularly in business sectors where participants (FL-PTs) are often competitive and self-interested. The proposed FedEgoists strategy presents a sophisticated and theoreticall... | Rebuttal 1:
Rebuttal: **Comments 1.** FedEgoists requires a central server to coordinate and enforce the coalition formation, which introduces a single point of control and potential failure. This centralization might conflict with the decentralized nature of federated learning and violate the fundamental motivation of... | Summary: This paper studies an interesting topic, which is about cooperation and competition in federated learning. It can be used to describe or simulate the real-world scenarios. The authors propose to use graph-related techniques to formulate the relation between the local clients. They test the algorithm on the ben... | Rebuttal 1:
Rebuttal: **Weaknesses 1 \& Limitations 2**
Overall, the original novelty of contribution includes multiple aspects: (1) identifying an interesting question to study, (2) proposing a desirable solution concept for this new problem, and (3) proposing an optimal solution.
Specifically, business sectors a... | Summary: The business sector is a main domain where cross-silo federated learning (FL) has many promising applications in various scenarios. The authors simultaneously consider the self-interest and competition features in the business sector. They develop a novel framework to both address the resulting free-riding pro... | Rebuttal 1:
Rebuttal: The authors would like to thank you sincerely for your overall positive comments on the manuscript, including your positive acknowledgement of the question under study, the theoretical soundness of the proposed framework, and the effective experimental validation of the proposed solution.
**Q1:*... | Summary: This paper focuses on client selection in cross-silo federated learning. The authors propose FedEgoists. In particular, FedEgoist participate clients into different clusters to avoid free riders and conflict of interests. Theoretical analysis is provided to validate the theoretical soundness of the proposed Fe... | Rebuttal 1:
Rebuttal: The authors would like to thank you sincerely for your overall positive comments, including the acknowledgement of the importance of the discussed topic in practical setting and the theoretical soundness of the proposed framework. Your comments are also constructive to help improve the manuscript.... | Rebuttal 1:
Rebuttal: More experiments have been conducted to verify the effectiveness of the proposed solution:
1. More experiments are conducted to verify the robustness of the proposed algorithm.
2. We define a new metric to better validate the performance of the proposed approach.
3. New datasets and baselines are ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Generating compositional scenes via Text-to-image RGBA Instance Generation | Accept (poster) | Summary: This study enhances text-based image generation diffusion models by introducing multi-layer noise blending and transparency-aware training procedures, enhancing control over the generated image content. This technique allows for finer control over the elements composing the image and improves the quality and c... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments that increase the quality of our work, and address main
concerns below.
**Novelty:** while we agree with the reviewer that layered-based representation are receiving increasing
attention, we argue that our proposed methodology has several unique components... | Summary: This paper introduces a novel method for generating complex images from textual descriptions with a high degree of control over object attributes and scene composition. They introduce a new training paradigm for adapting a diffusion model to generate isolated objects with transparency, using a disentangled rep... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments that improve the quality of our work. We are grateful
for the positive remarks (interesting work, fantastic generation ability, superior controllability), and
address the reviewer’s main concerns: comparison to LayerDiffusion and the impact of multi-layer
n... | Summary: This paper proposes to use a diffusion model to generate separate objects and then apply multi-layer noise blending to build a composite scene. A RGBA generator is finetuned from a latent diffusion model to generate alpha transparency for objects in addition to RGB. A transparency-aware training procedure is d... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments that increase the quality of our work and are encouraged
by the positive feedback. As discussed in our limitation section in supp. D, we agree that our
more expensive generation is a limitation of our work. However, we expect this cost to be heavily
amortis... | Summary: This paper describes a diffusion model based method for layered text-to-image generation with RGBA masks (transparency information). This is a useful approach when aiming to generate complex images with many objects. To that end, the base VAE as well as the latent diffusion model are adapted to handle another ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments that increase the quality of our work. We are encouraged
by the positive comments that our work is useful, our methodology is sound, and our visual results
are convincing. We now address the main limitations raised: 1) limited contribution and novelty,
and ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their detailed reviews, insights and comments that improve the quality of
our work. We are encouraged by the positive comments: the soundness of our method (RGbJP), the
quality of our results (RGbJP, RwSp1, RS8j5), the non triviality of our work (RwSp1), and detailed
con... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Concept-Based Explainability Framework for Large Multimodal Models | Accept (poster) | Summary: This paper introduces an explainability approach for interpreting the internal representations of large multimodal models (LMMs). The authors train an image captioning model consisting of a pretrained image encoder and language model and a connector model. To extract interpretable representations, the authors ... | Rebuttal 1:
Rebuttal: Thank you for the positive and insightful comments. Please find our response pointwise below:
**Qualitative analysis for feature superposition/specificity of concept vectors:** Thanks for the interesting suggestion. We conducted a preliminary qualitative study on some concept vectors in the dicti... | Summary: This paper proposes a new approach to understand multimodal concepts learned in LLMs with visual prefixes. To do so, the authors propose a dictionary learning-based approach that decomposes the representation of a word token in the product of two low-rank matrices via Semi-NMF, one representing the concepts an... | Rebuttal 1:
Rebuttal: We thank the reviewer for the interesting feedback and positive comments:
**Computational scaling for large number of tokens:** Our experiments use single GPU. The BERTScore evaluation does not scale well. It can take upto 3-4 hours to evaluate all baselines on some target tokens (COCO). The rep... | Summary: In this paper, the authors propose a framework for interpreting LMMs. Specifically, they introduce a dictionary learning-based approach applied to the representation of tokens. The elements of the learned dictionary correspond to the proposed concepts. These concepts are semantically well-grounded in both visi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We add our response for each point below:
**Notation system:** We will try our best to reorganize some notations for clarity. We remain open to incorporate any particular suggestions.
**Benefits of CAV-based approach for LMMs:** Concept based explainab... | Summary: The authors propose using dictionary learning to extract concepts from multimodal models and simultaneously ground them in the text and image latent space. They draw on prior work on multimodal neurons and concept activation vectors. The authors provide quantitative results using CLIPScore and BERTScore to mea... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We address their concerns pointwise below:
**Evaluates only on DePALM:** We have now also conducted experiments on LLaVA. We are able to extract meaningful multimodal concepts and achieve quantitatively consistent results as for DePALM. Further details re... | Rebuttal 1:
Rebuttal: We want to thank all the reviewers for their great interest and useful feedback. We address most reviewer comments individually. In this global post we would like to address two key concerns, each raised by at least two reviewers:
1. **New experiments on LLaVA (Reviewers Go37, XzMc, 17ab):** We ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: Authors propose to look at the representation of chosen concepts, in multimodal models. They test different automated methods to learn decompositions of a token representations (which they then linearise in a dictionary of concepts). They also provide quantitative and qualitative analysis of a few examples, sh... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments and intriguing questions. We respond to them pointwise below:
**CLIPScore and LMM with frozen CLIP encoder:** We believe the meaningful grounding is influenced more by the language model. We conducted experiments with two non CLIP visual encoders... | null | null | null | null | null | null |
Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning | Accept (poster) | Summary: This paper considers the problem of training neural operators to solve forward partial differential equation (PDE) problems in data-limited settings. The paper motivates this setting with the large data simulation cost incurred by these methods when generating a large training set of PDE solutions. The paper s... | Rebuttal 1:
Rebuttal: We truly thank the time and effort of reviewer u3i6 in reviewing our paper!
> **Q1**. Without accounting for this added cost of pre-training, the paper's claims about reducing data simulation costs feel vacuous.
Thanks for this great question!
1. We further provide extra experiments on joint uns... | Summary: This paper proposes a pre training strategy for operator learning. They introduce unsupervised training in the case of physical data. 2 architectures are studied: transformers and FNO. Finally, some experiments on different PDEs are conducted to highlight the properties of the model.
Strengths: The paper is w... | Rebuttal 1:
Rebuttal: We truly thank the time and effort of reviewer 6vSy in reviewing our paper!
> **Q1**. Masked auto-encoding has already been proposed in CV applications. The contribution however is unclear for me.
We would like to clarify and re-emphasize: The most important contribution of our paper is to defin... | Summary: This paper aims at improving the data efficiency of deep learning models for tackling Operator Learning. The paper focuses on two aspects: 1) They pretrain neural operators on data that do not assume labels, i.e. without the target function or the trajectory solution of states. To do so, they rely on a masked ... | Rebuttal 1:
Rebuttal: We truly thank the time and effort of reviewer oEEz in reviewing our paper!
> **Q1**. Overall the paper is quite difficult to read
Thanks for this suggestion! We will try to make our teaser figure (Figure 1) clearer and connect it more to the subsections in methods and experiments, so readers ca... | Summary: This paper presents an unsupervised pretraining approach for PDE solvers based on Meta Autoencoding and Super Resolution. The authors show that after pretraining, the model can achieve better accuracy than training a solver from scratch. This paper also presents an "in-context learning" strategy for inference ... | Rebuttal 1:
Rebuttal: We truly thank the time and effort of reviewer oUb1 in reviewing our paper!
Before providing detailed responses, we would like to make a **clarification**:
Since our paper focuses on **unsupervised pretraining** *but not* SciML foundation models (we *never* claimed our pretraining leads to such ... | Rebuttal 1:
Rebuttal: We deeply appreciate the feedback and suggestions from all four reviewers. We are pleased that **all four reviewers recognized** that our paper targets an **interesting and well-known challenge** in scientific machine learning (SciML).
We thank **all four reviewers** for acknowledging our main co... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problems | Accept (poster) | Summary: While there has been extensive research on L2D, general methods for designing such systems under various constraints (e.g., algorithmic fairness, expert intervention budget, defer of anomaly, etc.) remain largely unexplored. This paper utilizes $d$-dimensional generalization to the fundamental lemma of Neyman ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedbacks. We further address their questions in the following.
## Questions
**Q1** Dealing with overfitting is largely studied in the machine learning literature. In this work, we assume that the scores are estimated using the well-studied methods such a... | Summary: The paper studies multi-objective learn-to-defer problems, where the objectives include minimizing deferral loss and satisfying several constraints. It demonstrates that these problems are generally NP-hard and can be reduced to functional linear programming. Additionally, it shows that the problem can be furt... | Rebuttal 1:
Rebuttal: We thank the reviewer for reading the paper and their comments. We are respectively address the concerns of the reviewer.
1. **1.Why in Theorem 4.2, $f_{k, p}*(x)=\tau(\psi_1(x)-k\psi_0(x))$, while in Algorithm 1, its estimation version is $\hat{f}_{k,p}(x)=\tau(\hat{\psi}_0(x)-k\hat{\psi}_1(x))... | Summary: The paper introduces a unifying post-processing framework for multi-objective learn-to-defer problems, allowing the system to defer tasks to an expert under specified constraints. By generalizing the Neyman-Pearson lemma, the paper derives the Bayes optimal solution for this framework and develops an algorithm... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive and constructive comments on this paper. In the following, we respond their questions in order
**Q1** A: This work that we have further referenced in our manuscript, as we mentioned after (6), has three main differences with our work. *(i)* This work only co... | Summary: This paper aims to provide a provably consistent unified post-processing framework of learning to defer with constraints. The problem of constrained L2D is first reduced to a linear programming problem. Then the linear programming problem is further tackled with a generalized version of Neymar-Pearson lemma, w... | Rebuttal 1:
Rebuttal: We first thank the reviewer for their positive feedback to our submission. Here, we respond to their questions
1. **Q: It is mentioned that solving (2) is NP-hard. While the proof is quite clear, I wonder if such hardness is important. In my opinion, a common practice that can avoid directly solv... | Rebuttal 1:
Rebuttal: We first thank all reviewers for the time they have put in writing this set of constructive reviews. In particular, we are glad that the reviewers found our method "well-written", "thoroughly analyzed", "novel to the field of L2D", "enhancing the understanding of these problems", "can be extended ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LRM-Zero: Training Large Reconstruction Models with Synthesized Data | Accept (poster) | Summary: This paper proposes Zeroverse, a new dataset that is entirely synthesized for training large feed-forward 3D reconstruction models. Based on Zeroverse, LRM-Zero is trained with the network structure of GS-LRM. LRM-Zero achieves comparable performance as GS-LRM, which is trained on Objaverse. The idea of using ... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our writing clarity, the novelty and the value of our method, the generalization of Zeroverse for training both NeRF and 3DGS-based LRM models, and the effectiveness of our ablation study. We will respond to the reviewer’s comments below:
1. **Results on Omn... | Summary: This paper explores an unusual route of training a large-scale 3D reconstruction model using synthetic data. It demonstrates that high-quality reconstruction can be achieved solely with synthetic procedural data, bypassing the need for real, hand-crafted 3D models, which are challenging to collect. The paper, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating the novelty and potential value for future works, the clarify of our writing, the comprehensiveness of our experiments and related works. We respond to the questions of the reviewer below:
1. **Technical contributions**: Building upon the prior work Xu et al... | Summary: This paper proposes a pure synthetic training dataset named Zeroverse which is composed of synthetic data generated by simple shape primitives and textures without any real-world semantics. With the Zeroverse dataset, the authors trained a GS-LRM 3D construction model called LRM-Zero and showed that LRM-Zero a... | Rebuttal 1:
Rebuttal: We thank the reviewers for appreciating our writing quality, the value of the contribution of our work, and the comprehensiveness of our experiment results. We will address the questions of the reviewer below:
1. **Further improving LRM-Zero**: This is a good question. We have made many attempts ... | Summary: The paper explores the feasibility of using procedurally synthesized datasets of 3D shapes to optimize existing large reconstruction models (LRMs), that procure 3D shapes in the form of NeRFs. The data is constructed by 1) sampling several shapes from a set of parametric primitives (cubes, spheres, tori, etc.)... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating the value of our work to the community, the comprehensiveness of our ablation experiments, and our writing clarity. We reply to the questions from the reviewer as follows:
1. **Qualitative comparison results**: In Fig. 8 in our supplementary material, we hav... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful feedback and their recognition of our clear writing, comprehensive experiments, and the novelty and value of our proposed method (i.e. using synthesized data to train a LRM). We will address the missing citations mentioned by **3hvP** in the revision. Pl... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning | Accept (spotlight) | Summary: The article introduces an extensive empirical analysis of plasticity loss in on-policy reinforcement learning (RL), focusing on Proximal Policy Optimization (PPO). The main findings include that plasticity loss is also present in on-policy RL and that “regenerative” methods that regularly grow network paramete... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and provide helpful feedback.
Our intention with this work was to focus on the on-policy setting, given that the majority of the work in the area of plasticity loss has focused exclusively on the off-policy setting. Our decision not to include of... | Summary: This work studies the loss of plasticity phenomenon in the on-policy continual deep RL setting, where previous work has focused on studying and identifying mitigation strategies for the off-policy RL or supervised learning settings. They conduct experiments over a variety of settings (gridworld, CoinRun, and M... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback on our paper.
Both you and another reviewer have brought up the readability of our graphs. We are working to improve this for the camera-ready version. Reviewer NjbE suggested utilizing the “rliable” library to generate plots. We took their suggestion and gen... | Summary: This paper studies the problem of plasticity loss in on-policy deep RL. The study is quite wide as it covers many environments, types of non-stationarities, and solution methods. The first main result is the demonstration of plasticity loss in various settings. The second main result is the analysis of existin... | Rebuttal 1:
Rebuttal: We appreciate you taking the time to read and review our paper.
We used five replicates per experiment unless indicated otherwise, and the shaded regions of the graphs correspond to standard error. We will revise all figure captions to make both of these points explicit.
We used a learning rate... | null | null | Rebuttal 1:
Rebuttal: To all reviewers:
We thank the reviewers for their time and insights. Two reviewers have suggested clarity improvements to Figures 4 and 5. We have updated Figure 4 using the rliable library suggested by Reviewer NjbE (https://ibb.co/HxJcVmh). If reviewers agree that it is an improvement, we will... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Beyond Slow Signs in High-fidelity Model Extraction | Accept (poster) | Summary: This paper proposes a unified approach to model parameter extraction by combining two prior works and integrating efficiency optimization. The authors find out that the neuron wiggle sign extraction method proposed in the previous work addresses the bottleneck identified by Calini's paper. Furthermore, the aut... | Rebuttal 1:
Rebuttal: > There is no explicit description of the attack model/threat model used in this work
Currently, we have discussed the threat model in the background section, in part because it is the same as in the related work, namely, Carlini et al, Jagielski et al. Rolnick and Körding and Canales-Martinez et... | Summary: This paper explores advanced techniques for high-fidelity model extraction that go beyond simply observing "slow signs" like model outputs or gradients. The authors evaluate and enhance existing parameter extraction methods, particularly those developed by Carlini et al. and further improved by Canales-Martíne... | Rebuttal 1:
Rebuttal: > Scalability of approach to larger, more complex models is not fully explored.
With Table 2 we actually explore the largest models amongst all of the related work. Unfortunately the model extraction becomes very hard for deeper layers in for example an MNIST model with 8 hidden layers, so we had... | Summary: This paper proposes to perform model extraction attacks against deep neural networks. First, the authors combine two previous proposed methods into a uniformed code-base. Then, they optimize the sign extraction strategy to achieve a speed up in model extraction. Their proposed method can be used for larger mod... | Rebuttal 1:
Rebuttal: > Too much background in methodology, i.e. whole of Sec. 3.1
Beginning from line 201 “Confidence in Practice” is our contribution. We will mark it more explicitly to differentiate clearer between prior contribution and our contribution.
> Section 3.4 hard to follow
We are very sorry to hear tha... | Summary: This paper continues a line of work on cryptanalytically extracting network parameters from (input, logit) pairs. It includes a concise explanation of relevant prior work in the area, a codebase that unifies two key prior works in the area to enable standardized comparisons, and several improvements to these p... | Rebuttal 1:
Rebuttal: > Line 85: "In a study where the adversary is assumed to have complete access to both the training data and hyperparameters, 93.4% was the maximum fidelity reached by the replicated model." What dataset is this on? Is 93.4% lower than expected? By how much?
Jagielski et al. use the Fashion-MNIST ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning in Markov Games with Adaptive Adversaries: Policy Regret, Fundamental Barriers, and Efficient Algorithms | Accept (poster) | Summary: This paper provides upper and lower bound on the complexity of learning Markov games. The authors focus on the notion of "policy regret", which already exist in bandit and repeated games and adapt this notion to the general case of episodic Markov games.
The first results of the authors are negative results: ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback.
---
> "The paper is too dense, ... without looking at the appendix."
While two subroutines for Algorithm 3 were put in the appendix, these are fairly standard subroutines and a mere distraction. In fact, we moved it for the sake of better readab... | Summary: This paper studies learning in a dynamically evolving environment modeled as a Markov game (MG) and the adversarial is allowed to be adaptive. Authors focus on the policy regret rather than external regret commonly used by many existing work, and further investigate the fundamental limits on learning MG with d... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback.
---
> Question about "approximate consistency" $D$
We thank the reviewer for an interesting suggestion. Our approach estimates the version space of the response function of the adversary using the adversary’s actions. The MLE analysis (Lemma B.1... | Summary: This paper addresses the problem of designing optimal strategies in Markov Games against adaptive adversaries. Specifically, the paper proposes the notion of $\textit{policy regret}$ which admits the adversary's ability to adaptively change their policies according to the policies applied by the learner. This ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback.
---
> "Weakness 1."
There are three points we would like to make.
- (a) The assumption that the adversary’s behavior is consistent does not imply that it is sub-optimal. On the contrary, we assume that the adversary is all knowledgeable and ha... | Summary: The paper studies the learning problem in a Markov game against the adaptive adversary. The adversary's policy can depend on all the learner's past strategies. The paper first shows that if the adversary can be fully adaptive, then sublinear policy regret cannot be obtained for the learner. The paper then char... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback.
---
> If the adversary is $m$-memory bounded, could we regard the system state as the combination of $(s_t, \pi_t, \dots, \pi_{t-m+1})$ and then reduce everything to the $0$-memory bounded case?
Thank you for your interesting suggestion. If we au... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CLIPCEIL: Domain Generalization through CLIP via Channel rEfinement and Image-text aLignment | Accept (poster) | Summary: The paper tackles the issue of domain generalization for vision-language models like CLIP. The authors propose a new and simple method which is divided into multiple stages, to mitigate the performance gap for this problem setup. Their method achieves State of the art performance on the benchmarks evaluated.
... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's detailed comments and offer our responses below.
### Weakness
>**[W1]:** Present the comparisons on ImageNet
Thank you for your insightful suggestion. We conducted experiments on ImageNet (w/ 1000 classes) using the same setting as CoOp. Since the setting is the **s... | Summary: The paper addresses the challenge of domain generalization for CLIP. To tackle this, the authors introduce CLIPCEIL, a method that enhances CLIP's performance on unseen test datasets with domain shifts, which employs Channel rEfinement and Image-text aLignment techniques to refine visual feature channels, ensu... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's detailed comments and offer our responses below.
### Weakness
>**[w1]:** The adapter is designed for ViT, and is hard to use for ResNet backbone.
Thank you for your insightful comments. Our proposed method can also be **extended to the ResNet backbone**. The primary ... | Summary: The paper addresses Domain Generalization (DG) by leveraging the superior generalization abilities of CLIP. While most prior works that utilize CLIP focus solely on the adaptation of CLIP for the given downstream task, this work investigates the domain-specific properties of CLIP. Specifically, the authors dem... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's detailed comments and offer our responses below.
### Weakness
#### [Proposed Method]
>**[W1]:** The idea and motivation are the same as DomainDrop [14]. Some of the ideas in CLIPCEIL are similar to the following works, [R8-R11].
Thanks for your insightful comments. T... | Summary: The paper introduces a novel method called CLIPCEIL, designed to improve the performance of CLIP on unseen test datasets with domain shifts. The approach refines visual feature channels to maintain domain-invariant and class-relevant features using a lightweight adapter. This involves minimizing inter-domain v... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's careful comments and provide our responses below.
### Weakness
>**[W1]:** Overall novelty.
Thanks so much for your comments. First, we would like to emphasize the difference between **few-shot learning** and **domain generalization**. Although they are related, they... | Rebuttal 1:
Rebuttal: ## General Reply
We would like to express our sincere appreciation for all reviewers' invaluable feedback and comments. Below are the general replies to the common concerns and the summary of the additional conducted experiments.
First, we would like to clarify the difference between few-shot le... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
You Only Look Around: Learning Illumination-Invariant Feature for Low-light Object Detection | Accept (poster) | Summary: The paper focuses on the Low-light Object Detection task from the perspective of feature learning. In detail, the paper proposes an Illumination-Invariant Module to extract illumination-invariant features and a learning illumination-invariant paradigm. Experiments verify the effectiveness of the proposed metho... | Rebuttal 1:
Rebuttal: **We sincerely thank you for your insightful and positive comments.**
---
>**How does the method perform in comparison to recent related methods?**
We complement more detailed comparison experiments as presented in Table 1, which includes runtime, model size, and performance. Note that our YOLA... | Summary: This paper proposes YOLA, a framework for object detection in low-light conditions by leveraging illumination-invariant features. A novel Illumination-Invariant Module to extract illumination-invariant features for low-light image enhancement.
Strengths: Figures are helpful for understanding.
The proposed me... | Rebuttal 1:
Rebuttal: **We sincerely thank you for your constructive comments.**
---
>**In the pipeline, the work is basically low-light enhancement + detector. In evaluations, only object detection is evaluated, why not evaluate on low-light enhancement datasets/benchmarks?**
` First and foremost, we need to emphas... | Summary: In this paper, the authors propose an object detection method in low-light scenarios that is based on illumination-invariant feature learning. Additionally, the extraction of illumination-invariant features from low-light images, which can be easily integrated into existing object detection frameworks, The res... | Rebuttal 1:
Rebuttal: **We sincerely thank you for your insightful and constructive comments.**
>**As a plug and play module, I think the lighting invariant module should be integrated into more detectors to prove its effectiveness.**
We report more detectors as shown in Table 1. It contains the **Anchor-based** dete... | Summary: This paper proposes a plug-and-play module for extracting illumination-invariant features from low-light images. By integrating a zero-mean constraint within the module, a diverse set of kernels is effectively learned. These kernels excel at extracting illumination-invariant features, thereby enhancing detecti... | Rebuttal 1:
Rebuttal: **We would like to thank the reviewer for carefully reading our submission and providing many insightful comments.**
> **The authors assume uniform illumination between neighboring pixels to eliminate the influence of the positional term $m$ in Equation 1. However, images captured in real-world s... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Unsupervised Discovery of Formulas for Mathematical Constants | Accept (poster) | Summary: The authors propose an algorithm to filter, cluster and label polynomial continued fractions. It is based on several features linked to the asymptotic behaviour of their rational approximations. The authors detail how these are computed. They apply this algorithm to a large set of formulae ; they discuss the p... | Rebuttal 1:
Rebuttal: * **“The claim that "we connect the challenge of formula creation to modern approaches in AI for Science" seems a bit bold. To my understanding this work does not involve modern AI in the sense that it requires manual and careful feature extraction and that nothing is learnt.”**
In the revised ma... | Summary: The paper presents a classification of 1.5 million polynomial continued fractions (PCF), continued fractions having as coefficients the integer values of two polynomials, $A(n)$ and $B(n)$, with $A$ $B$ of degree two, with integer coefficients in $[-5,5]$. PCF are classified according to the asymptotic propert... | Rebuttal 1:
Rebuttal: * **“To demonstrate a possible link with Machine Learning, it would be useful that the authors discuss (and perhaps demonstrate) how their approach scales to large sets of PCF, by letting the coefficients and degrees of A and B grow larger.”**
We thank the referee for this suggestion.
We succes... | Summary: They generate continued fraction formulas and test if they evaluate to mathematical constants.
They introduce a distance metric to compare formulas.
They discover novel formulas for known constants.
Strengths: Mathematical constants are always used, so it is important to have formulas to calculate them
well... | Rebuttal 1:
Rebuttal: * **“It generates formula hypotheses, so we do not always know if the formulas are actually correct”**
* **“how many are proven to be correct and how many remain hypotheses?”**
We thank the referee for the constructive feedback.
*Please see the joint rebuttal.*
* **“Rather limited structure of t... | Summary: The paper addresses a long-standing challenge in number theory by proposing a new methodology for the categorization, characterization, and pattern identification of mathematical formulas, specifically Polynomial Continued Fraction (PCF) formulas. The authors introduce metrics based on the convergence dynamics... | Rebuttal 1:
Rebuttal: * **“While the addressed task and the methodology is unique, it may not be the best fit for a machine learning conference like NeurIPS. The focus on mathematical discovery might be better suited for a specialized conference or journal in mathematics”**
*Please see the joint rebuttal about this im... | Rebuttal 1:
Rebuttal: We would like to summarize and address the most important comments brought by more than one referee.
Regarding the link of our work to ML
---
* **“While the addressed task and the methodology is unique, it may not be the best fit for a machine learning conference like NeurIPS.”**
* **“The link w... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Intrinsic Robustness of Prophet Inequality to Strategic Reward Signaling | Accept (poster) | Summary: This paper studies the robustness of threshold algorithms when involving strategic manipulations in the classic prophet inequality problem. Specifically, the paper considers the scenario when each random reward of $N$ variables is associated with a strategic player, who can commit to a signaling scheme before ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the positive feedback! Please find below our response to your comment.
$(\alpha, \beta)$ **Pareto frontier**
We thank the reviewer for this very interesting comment. Characterizing the Pareto frontier of the approximation ratios requires substantial additional ... | Summary: The paper studies a variant of the classical prophet inequalities problem, where "boxes" are now strategic agents who can pool their realized value into bins and only reveal to the decision maker which bin it falls into. The expected value in that bin then plays the role of the realized value. The authors st... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the positive feedback! Please find below our response to each of your comments.
**The generality of threshold policies**
The reason that we restrict our attention to the threshold policies is that they are the policies that achieve the classic prophet inequali... | Summary: The paper studies a variant of the prophet inequality modeled as a game between the reward holders (Players $1, \ldots, n$) and the searcher. In this model, each reward $X_i$ is sampled from a distribution $H_i$ that is known to the searcher. However, each player $i$ can choose not to reveal $X_i$, and instead... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the feedback and valuable comments! We notice that there may exist some conceptual misunderstandings in the reviewer's comments about the searcher’s stopping policy and the reason for us to study threshold stopping policies. So we would like to first clarify the... | Summary: This paper considers a bayesian persuasion variant of the classical prophet inequality problem: an online decision-maker will face a sequence of independent positive random variables $(X_1,\dots,X_n)$, $X_i \sim F_i$ known, and must decide when to stop in order to maximize the expectation of the selected item.... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the positive feedback and valuable comments/suggestions! Please find our response to each of your questions below.
**Q1: Results for i.i.d case**
We would like to first clarify that, to the best of our knowledge, the existence of a static threshold policy that... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Implicit Regularization of Decentralized Gradient Descent for Sparse Regression | Accept (poster) | Summary: This paper proves the convergence of the decentralized gradient descent (DGD) algorithm under RIP condition when the initialization scale is small. The authors also propose a truncated version of the algorithm with a cheaper cost but comparable performance (in certain situations) to the original DGD.
Strength... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and effort in reviewing our manuscript and providing valuable feedback. We have addressed your comments and questions as detailed below.\
$\textbf{High-level explanation of promoting sparsity for centralized GD.}$ For gradient descent (GD), the diagonal linear rep... | Summary: This manuscript studies a decentralized optimization method for training linear sparse models using a network of agents that collect linear measurements. Unlike decentralized methods relying on L1 regularization, this approach leverages implicit regularization inherited in the gradient descent process. The aut... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback and for highlighting the areas that need further clarification.\
$\textbf{High-level explanation of promoting sparsity for centralized GD.}$ For gradient descent (GD), the diagonal linear reparameterization turns the additive updates into multiplicative updat... | Summary: This paper focuses on deriving the implicit regularization effects of decentralized gradient descent (DGD) for minimizing an objective function over undirected mesh networks. In particular, this paper establishes the fact that the solution returned by DGD with early stopping is statistically optimal under cert... | Rebuttal 1:
Rebuttal: Thank you for your detailed and insightful comments. We appreciate the opportunity to address your concerns.\
$\textbf{Experimental results for general architectures}.$\
We add two experiments to validate the implicit regularization of DGD on general overparameterized neural network architecture, ... | Summary: The paper shows that the implicit regularization enjoyed by a well-known reparameterizion of least squares extends to the decentralized setting. Convergence guarantees are provided, and it is also shown that communication can be limited by thresholding vectors before they are communicated to neighbors.
Stren... | Rebuttal 1:
Rebuttal: Thank Reviewer E5yh for your valuable comments. We appreciate the opportunity to clarify the contributions and address the concerns in your review.
$\textbf{(1) Contribution and significance}$ \
While implicit bias has been studied for centralized gradient methods for various models, the decent... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their careful review and insightful comments. We have addressed each of the questions raised by all reviewers in a point-by-point manner as detailed below. We hope our responses can address the concerns raised. Additionally, we have included supplementary ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
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